Digital Infrastructure, Intellectual Property, and the Resilience of China’s Urban Economy: Evaluating the Synergistic Effects of “Broadband China” and “Intellectual Property” Pilot Policies

Digital Infrastructure, Intellectual Property, and the Resilience of China’s Urban Economy: Evaluating the Synergistic Effects of “Broadband China” and “Intellectual Property” Pilot Policies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Digital Infrastructure, Intellectual Property, and the Resilience of China’s Urban Economy: Evaluating the Synergistic Effects of “Broadband China” and “Intellectual Property” Pilot Policies Qiujie He, Xiangling He, Guoqing Chen, Piyapatr Busababodhin, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6353700/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Urban economic resilience is both the foundation of a modern industrial system and an essential catalyst for superior development. This research investigates the effects of digital infrastructure and intellectual property development on urban economic resilience, using “Broadband China” and “Intellectual Property” pilot cities as case studies. A multi-period difference-in-differences model is utilized on panel statistics from 281 Chinese cities spanning 2007 to 2022 to assess policy impacts rigorously. The findings reveal that (1) The “Broadband China,” “Intellectual Property,” and dual pilot policies all significantly enhance urban economic resilience. However, the “Intellectual Property” pilot exerts a more pronounced influence than the “Broadband China” pilot, and the dual pilot policy outperforms single pilot policies. (2) Policy effectiveness is time-sensitive. Cities implementing “Intellectual Property” policies first, followed by “Broadband China,” experience greater benefits than those following the reverse sequence. (3) Mechanism tests show that both single and dual pilot programs enhance economic resilience through technological innovation, entrepreneurship incentives, and economic agglomeration. (4) Heterogeneity research reveals that policy implications are more substantial in resource-dependent cities, highly resilient urban clusters, and cities with high economic growth targets. These findings suggest the need to develop a dynamic policy adaptation mechanism, optimize the sequencing of institutional reforms, and implement differentiated strategies to achieve a resilient and stable evolution of urban economic systems. Business and commerce/Economics Social science/Social policy Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Urban economic resilience functions as a fundamental pillar for superior financial empowerment and a critical safeguard against systemic risks and external shocks, ensuring stability and sustainability [ 1 ]. Amid increasing global economic volatility, deepening transitions in growth drivers, and the rising frequency of “black swan” and “gray rhino” events, urban economies are facing intensified challenges, including geopolitical tensions, global supply chain restructuring, climate-induced disasters, cyclical economic crises, and trade conflicts [ 2 ]. These pressures have heightened demand contraction, supply disruptions, and weakened market expectations in China. In this context, urban economic resilience not only determines the capacity of a city to adjust and recuperate from emergencies but also performs an essential role in maintaining sustained economic expansion. Optimizing resource allocation and enhancing systemic risk resistance. It is a key variable in “overcoming crises, mitigating downturns, driving recovery, and reshaping growth.” Therefore, a systematic examination of the core factors influencing urban economic resilience and a comprehensive investigation into pathways to enhance it are essential. Strengthening economic resilience can effectively mitigate external shocks, reduce volatility’s impact on development quality, and establish a robust basis for economic revitalization, long-term prosperity, and high-quality development. Since the early 21st century, the rapid acceleration of urbanization has led to land resource misallocation, energy metabolism imbalance, and transportation network overload, triggering entropy effects in urban systems. These pressures have continuously lowered ecological resilience thresholds, pushing human settlements closer to critical safety margins [ 3 ]. In response, the 2002 UN Sustainable Development Summit introduced the “urban resilience” governance framework, defining it as a multidimensional system integrating public safety, social stability, and economic resilience through proactive planning, redundancy design, and disturbance response strategies [ 4 ]. International organizations such as the United Nations Office for Disaster Risk Reduction (UNDRR) and the Intergovernmental Panel on Climate Change (IPCC) rapidly embedded this concept into the Sustainable Development Goals (SDGs), transforming it into a governance toolkit for balancing the complex environment-economy-society nexus [ 5 ]. The worldwide economic depression of 2008 further exposed the vulnerabilities of conventional urban governance, prompting both academia and policymakers to rethink urban risk immunity systems and post-crisis recovery mechanisms [ 6 ]. As an institutional response, The Rockefeller Foundation initiated the 100 Resilient Cities Initiative in 2013, introducing an innovative risk management model that integrates infrastructure redundancy, social capital networks, and smart governance platforms [ 7 ]. In 2021, China’s 14th Five-Year Plan for New Urbanization prioritized urban resilience as one of its five core objectives, focusing on expanding digital infrastructure coverage and optimizing supply chain redundancy. Meanwhile, the UN-Habitat City Resilience Index (CRI) (2017–2023) refined the concept of economic resilience into three measurable dimensions: industrial chain flexibility, digital infrastructure penetration, and SME risk resistance, aligning governance strategies with SDG 11 and SDG 8. This shift marks a critical transition from theoretical construction to quantifiable urban resilience governance. New infrastructure, built upon advanced information technologies, integrates digital networks, convergence systems, and innovation platforms to drive digital transformation, industrial upgrading, and integrated innovation [ 8 ]. In the context of accelerating global digitalization, new infrastructure—spanning 5G, data centers, and industrial internet—has emerged as a critical public asset, reshaping national competitiveness and economic resilience [ 9 ]. Major economies are rapidly advancing their infrastructure strategies to strengthen their positions in the digital era [ 10 ]. The United States has reinforced computational networks through the CHIPS Act, while the European Union has focused on cross-border data flows. In contrast, China, leveraging its 60% global share of 5G base stations, has accelerated intelligent manufacturing and digital governance innovation. Studies suggest that new infrastructure mitigates resource misallocation in manufacturing, enhances total factor productivity (TFP) [ 9 ], and fosters data-driven innovation, improving resource-matching efficiency and advancing regional economic systems by size [ 11 ]. Information and communication technology (ICT) continues to evolve access to information, generating economic dividends and fostering regional economic prosperity [ 12 ][ 13 ]. In a profoundly networked, data-centric society, economic actors are undergoing a profound shift toward intelligent, networked collaboration, strengthening their ability to absorb external shocks [ 14 ]. New infrastructure, centered on information integration, network connectivity, and digital innovation, performs an indispensable part in strengthening economic resilience. Robust broadband infrastructure and internet accessibility have been found to improve supply chain stability and regional adaptability [ 15 ][ 16 ]. Additional research underscores the vitality of digital technologies in facilitating regional economic recovery and reinforcing systemic stability [ 17 ]. Moreover, the digital economy optimizes resource allocation, accelerates innovation, and strengthens economic resilience [ 18 ]. As the cornerstone of the internet-based economy, new infrastructure is crucial for building stable economic systems, refining policy frameworks, and advancing high-quality development, warranting further in-depth research. As a critical intangible asset, intellectual property (IP) is widely recognized as one of the most valuable components of modern economic systems [ 19 ]. According to endogenous growth theory, intellectual innovation functions as the principal catalyst for flourishing economies. However, due to its non-rivalrous and non-excludable nature, market failures often arise, diminishing motivations for innovators [ 20 ][ 21 ][ 22 ]. From an economic resilience perspective, a well-established IP protection framework fosters the growth of knowledge-intensive industries, enhances urban innovation capacity, and strengthens economic recovery in the face of external shocks. Existing research presents three main perspectives. The first argues that a strong IP system increases firms’ expected returns on innovation, incentivizes R&D investment, improves resource allocation efficiency, and fosters industrial clustering, ultimately creating a robust innovation ecosystem [ 23 ][ 24 ][ 25 ]. The second viewpoint suggests that excessive IP protection may lead to market monopolization, hinder knowledge diffusion, and restrict SMEs’ access to innovation, ultimately weakening overall innovation performance [ 26 ][ 27 ]. The third approach highlights a nonlinear relationship between IP protection and innovation, where moderate protection encourages technological advancement and industrial upgrading.In contrast, excessive protection results in resource misallocation, limits knowledge diffusion and reduces the adaptability and recovery capacity of urban economies [ 28 ][ 29 ][ 30 ]. Thus, a scientifically balanced IP protection policy is essential for strengthening urban economic resilience. It optimizes the innovation environment, facilitates industrial transformation, and enhances cities’ ability to withstand external disruptions, ensuring long-term financial stability and sustainable development. This study builds upon existing research to evaluate the implications of the “Broadband China” and “Intellectual Property” pilot programs, as well as their combined effects, on urban economic resilience, addressing a key research gap. The article makes three distinct contributions. First, it investigates how digital infrastructure and intellectual property policies jointly influence urban economic resilience, with a focus on their synergy, extending the research on economic resilience determinants and deepening the theoretical exploration of multi-policy effects. Second, it examines the mechanisms through which “Broadband China,” “Intellectual Property,” and dual pilot programs reinforce urban economic resilience, particularly through technological innovation, entrepreneurship incentives, and economic agglomeration. Third, it evaluates the heterogeneity of policy effects based on resource endowments, resilience levels, and economic growth targets. It provides theoretical and policy insights into optimizing digital infrastructure and intellectual property policy coordination. This is how the rest of an essay is organized. We presented the relevant literature review in the second part. Our theoretical theories are described in three parts. In the four parts, models, procedures, and data used in the empirical investigation are presented. We provide our estimated findings and thorough analysis in the five parts. We further explore the impact of mechanisms, heterogeneity, and effects of various policy enforcement sequences on the outcomes in the six parts. Finally, We wrap up the research and provide policy suggestions in the seven-part. Literature review Research on the Impact and Effect of the Construction of “Broadband China” Demonstration Cities and “Intellectual Property” Demonstration Cities In recent years, with the gradual implementation of a series of pilot policies, scholars have extensively studied the implications of the “Broadband China” and “Intellectual Property” pilot programs, leading to a growing body of literature. Existing studies mainly focus on two aspects: economic effects and environmental effects. First, regarding the financial impacts of the “Broadband China” pilot, digital technology penetration has dramatically boosted urban innovation capability [ 31 ] and factor allocation efficiency, particularly in optimizing human capital spatial agglomeration, driving financial deepening [ 32 ], and increasing rural household consumption [ 33 ]. At the same time, improvements in information efficiency have reshaped trade network structures, reduced institutional transaction costs [ 10 ], and generated sustainable development benefits, including enhanced household energy efficiency [ 34 ][ 35 ], accelerated enterprise digital transformation [ 36 ], and the formation of a comprehensive sustainable growth framework [ 37 ]. Second, regarding the environmental effects of the “Broadband China” pilot, digital technology penetration has driven the alteration of the energy framework, shifting household energy consumption toward cleaner sources [ 38 ] and optimizing carbon emissions performance [ 39 ][ 40 ]. Simultaneously, information flows have reshaped ecological governance models, resulting in enhancements in urban environmental sustainability [ 41 ] and promoting a switch to green total factor efficacy [ 42 ], thereby contributing to a low-carbon development paradigm. Third, with relation to the “Intellectual Property” pilot’s economic consequences, bolstering the safety of intellectual property has triggered a green technology diffusion effect, improving urban green innovation ecosystems [ 43 ], fostering corporate R&D collaboration and enhancing human capital quality [ 44 ], and thereby driving breakthroughs in corporate green innovation [ 45 ][ 46 ]. Simultaneously, budgetary rewards for funding scientific and technological advancement and improvements in factor allocation efficiency [ 47 ] have generated institutional dividends for total factor productivity growth, promoting a regional economic shift toward an innovation-driven paradigm [ 48 ]. Fourth, regarding the environmental effects of the “Intellectual Property” pilot, appropriate intellectual property protection establishes environmental entry barriers, effectively filtering out polluting enterprises [ 49 ] and triggering Schumpeterian impacts that drive the structural transformation of green total factor productivity [ 50 ]. Additionally, through the restructuring of industrial ecosystems, the policy facilitates low-carbon technology diffusion [ 51 ] and carbon intensity convergence (Han, 2024)[ 52 ], forming a sustainable development loop of “innovation incentives—structural optimization—emission control,” providing dual institutional and technological support for urban low-carbon transformation. Research on the Impact of Urban Economic Resilience The concept of resilience originated in physics and gradually evolved into a core analytical tool in risk governance. It is closely intertwined with concepts such as sensitivity and adaptive capacity, forming a complex theoretical network that supports the development of multi-dimensional adaptive system frameworks. Abson et al. l [ 53 ] pioneered a six-dimensional resilience model encompassing steady-state equilibrium, resource heterogeneity, efficiency optimization, structural flattening, risk buffering, and redundancy design. This theoretical advancement established a meta-framework for resilience research and provided essential analytical tools for interdisciplinary studies. As complexity science continues to evolve, resilience research has shifted towards finer spatial scales. Community resilience theory, for instance, defines both geographical and social boundaries to explain how social systems dynamically adapt to uncertainty through disturbance dissipation, self-organization, and stress response mechanisms [ 54 ]. Meanwhile, resilience has attracted extensive academic attention across disciplines including sociology, ecology, psychology, and catastrophe science, becoming the center of interdisciplinary study. The capacity to endure economic fluctuations is an economy to withstand and adapt to uncertainty while ensuring financial security, macroeconomic stability, and public welfare, making it a strategic national concern [ 55 ]. Given that municipal economies serve as the principal transporters of contemporary economic frameworks, in particular, urban economic durability is the capacity of a municipality to handle, adjust to, and change in response to exogenous shocks and financial volatility [ 56 ]. Since the introduction of urban economic resilience, empirical research on its determinants has deepened, with increasing attention to the effects of “Broadband China” pilot programs. Studies show that “Broadband China” implementation enhances economic resilience by optimizing factor allocation efficiency and upgrading industrial structures, forming a stable risk mitigation mechanism [ 57 ]. Additionally, it strengthens dynamic adaptability through the “technology innovation–industrial upgrading–entrepreneurship-driven” transmission chain [ 58 ]. The digital economy further improves economic resilience by acting as an intermediary for technological innovation [ 59 ]. However, studies on the implications of pilot endeavors for “Intellectual Property” on economic resilience remain in their early stages. Moreover, existing studies have yet to explore the interaction effects between the two policies systematically. Do these policies generate multiplicative or additive effects? More importantly, do these synergistic mechanisms exhibit significant spatial and temporal heterogeneity? Addressing these questions is crucial for clarifying policy overlaps, refining implementation pathways, and designing effective resilience enhancement strategies. This study aims to fill these gaps by providing theoretical insights and practical guidance for strengthening urban economic resilience. Research hypotheses The impact of the construction of “Broadband China” demonstration cities on urban economic resilience The neoclassical growth theory emphasizes technological progress as the core driver of economic expansion. In contrast, technological influences are greater internalized by endogenous development theory, making them important variables affecting marginal returns and highlighting the key role of knowledge spillovers in driving economic expansion. As a critical carrier of technological internalization, digital infrastructure restructures the resilience threshold of regional economic growth through the increasing marginal returns effect of knowledge spillovers [ 60 ]. Four dimensions may be applied to clarify how digital infrastructure strengthens urban economic resilience under the quasi-natural experiment known as “Broadband China.” First, it drives the intelligent upgrading of industries. Digital infrastructure optimizes production function parameters through real-time big data calculations, enhancing the dynamic adaptability of industry decision-making systems. This evolution of production models towards intelligence, precision, and flexibility builds an efficient risk response mechanism under external shocks, thus strengthening the robustness of the urban industrial structure [ 61 ]. Second, it stimulates innovation and entrepreneurship vitality. The deep penetration of digital technologies significantly reduces the sunk costs of entrepreneurial activities. It enhances factor flow efficiency through information sharing and market matching mechanisms, optimizing capital allocation methods. This helps cities maintain high market vitality and resource circulation efficiency in the face of economic uncertainty, thereby improving adaptability to risk shocks [ 62 ]. Third, it strengthens risk monitoring capabilities. The thorough incorporation of the Internet of Things, huge data, and artificial intelligence technologies builds a comprehensive dynamic monitoring network. It gets beyond the limitations of conventional risk identification in terms of time and location, creating a cross-validation mechanism with multi-source data, thereby establishing a risk mitigation and resilience enhancement feedback loop in cross-departmental collaborative governance. This improves a city’s ability to perceive, identify, predict, and intervene in sudden events [ 63 ]. Fourth, it enhances the clustering effect of factors. High-quality digital infrastructure construction not only improves a city’s livability and governance modernization level but also builds an innovation ecosystem driven by information flow. This enhances the city’s attractiveness to high-end human capital, financial capital, and advanced production factors, thereby strengthening the factor endowment base of UER and promoting stable growth and long-lasting, sustainable expansion of the city’s prosperity in complex environments [ 64 ]. Consequently, we put out the following theory: H1: The construction of “Broadband China” demonstration cities can significantly enhance the economic resilience of cities. The impact of the construction of “Intellectual Property” demonstration cities on urban economic resilience In a free market, the completeness and strength of intellectual property protection are typically fundamental to supporting innovation activities and maintaining fair competition [ 65 ]. The intellectual property city pilot program favors cooperation in the production of intellectual property, application, and protection by enhancing the value transformation of knowledge capital. Through institutional innovation, it overcomes the constraints of factor endowment, cultivates knowledge-intensive industry ecosystems, and attains enhancements in the total productivity of factors and the quality of economic development [ 66 ]. Intellectual property protection may bolster a municipality’s economic resiliency and adaptability by facilitating the aggregation of factors and resources. The establishment of “Intellectual Property” Experimental Municipalities helps accelerate the concentration of resources, gradually forming a stable “factor pool,” thereby enhancing a municipality’s economic flexibility and defense against outside influences. For traditional production factors, intellectual property pilot policies not only signal the enhancement of the innovation and entrepreneurial ecosystem but also help firms establish short-term monopolies in specific fields through market protection mechanisms, enhancing the city’s ability to attract physical capital. For new production factors, particularly data, although highly mobile, abuse, theft, and ownership issues often hinder the realization of data value. IP demonstration cities address this by building targeted regulatory systems to protect digital asset owners, strengthening the clustering effect of data elements, reducing fragmentation, and improving the economic benefits of data. This ensures that a city’s economic system can quickly recover and maintain stable growth in reaction to disruptions outside. In the face of external economic shocks, resource scarcity often becomes a key factor limiting the rapid repair and stable recovery of a city’s economic system. In contrast, the continuous innovation and diffusion of technology and knowledge are key drivers for economic restructuring. On the one hand, well-balanced intellectual property protection helps eliminate market failures in technology diffusion and knowledge spillovers, ensuring that innovation entities can maintain control over their economic interests at critical moments. This prevents the loss of market advantage due to a lack of patent protection or cost recovery barriers caused by “spillover effects” from technological imitation. On the other hand, bolstering the safeguarding of intellectual property effectively promotes technology diffusion and knowledge spillover, creating a “positive feedback” mechanism that drives industrial technological upgrades and enhances overall economic performance. Consequently, we put out the following theory: H2: The construction of “Intellectual Property” demonstration cities can significantly enhance the resilience of urban economy. The policy synergy effect of the dual pilot program The “Broadband China” and “Intellectual Property” pilot programs do not operate independently in enhancing urban economic resilience. Instead, they exhibit a strong “mutual empowerment and synergistic reinforcement” effect. The “Broadband China” initiative focuses on upgrading information infrastructure and driving enterprise digital transformation and industrial modernization [ 67 ]. This strengthens industrial restructuring and innovation-driven growth, promoting high-quality economic development. The “Broadband China Strategy and Implementation Plan” explicitly emphasizes the need for coordinated development between network infrastructure upgrades and industrial innovation to ensure alignment between broadband expansion and industry transformation demands. By improving information efficiency, digital infrastructure reduces search and logistics costs, enhances trade efficiency, and lowers trade barriers [ 10 ]. Moreover, it provides greater adaptability, recovery capacity, and structural resilience in response to global and regional economic shocks. The “Intellectual Property” pilot program, in contrast, focuses on building a comprehensive IP protection system to strengthen property rights incentives and optimize the innovation ecosystem. A robust IP framework serves as a stabilizer for technological innovation, encouraging firms to increase R&D investment. This accelerates industrial upgrading toward high-value-added, high-tech sectors, fostering advanced, intelligent, and green transformations. Under the dual forces of globalization and the digital economy, improving IP protection not only attracts high-end innovation resources but also enhances innovation factor mobility. It promotes deep integration between industry, academia, research, and application, reducing the time from laboratory breakthroughs to industrial implementation. This accelerates the commercialization and market adoption of technological innovations, further strengthening urban competitiveness in an innovation-driven economy. Consequently, we put out the following theory: H3: The construction of “Broadband China” demonstration cities and “Intellectual Property” demonstration cities has a mutually reinforcing policy synergy effect, manifested in the fact that the dual pilot policy has a greater impact on improving urban economic resilience than the single pilot policy. Mechanism effect According to the prevailing literature and the strategy ramifications of establishing “Broadband China” showcase cities and “Intellectual Property” demonstration cities, this paper looks more closely at the conveying channel of the dual pilot strategy on reinforcing the economic resilience of cities from the perspectives of innovation-driven effects, entrepreneurial incentive effects, and economic agglomeration effects (see Fig. 1). Mechanism effect According to the prevailing literature and the strategy ramifications of establishing “Broadband China” showcase cities and “Intellectual Property” demonstration cities, this paper looks more closely at the conveying channel of the dual pilot strategy on reinforcing the economic resilience of cities from the perspectives of innovation-driven effects, entrepreneurial incentive effects, and economic agglomeration effects (see Fig. 1). Innovation driven effect Innovation is the fundamental internal motivating factor behind the sustained growth and transformation of urban economies. Schumpeter’s theory of creative destruction reveals that technological innovation, through the restructuring of production factors and innovation in production functions, not only spurs the emergence of new industrial sectors but also establishes a dynamic competitive advantage in regional economies [ 68 ]. The “Broadband China” strategy and the dual pilot policies of the cities that serve as the national demonstration centers for intellectual property enhance urban economic resilience by optimizing the configuration of innovation factors, cultivating innovative talent, and fostering a creative environment. First, the configuration of innovation factors is the material foundation for building a resilient economy. As digital infrastructure flourishes, the paradigm of resource flow is altered, enabling the spatial reallocation of innovation resources. According to new structural economics, broadband networks and other infrastructures reduce information friction and geographical constraints, boosting the marginal productivity of technology and capital. This process eliminates the “digital divide” and generates a space compression effect, improving how the innovation factors are set up and overcoming the conventional center-periphery structure. In the integration of innovation and industrial chains, network externalities drive the formation of multi-centered innovation clusters, enhancing regional economic risk diversification. By optimizing resource allocation, urban economies reduce dependence on single industries, thus improving resilience to external shocks and strengthening adaptability and recovery capacity [ 69 ][ 70 ]. Second, the cultivation of innovative talent is the key driver of resilience evolution. Intellectual property demonstration policies balance patent quality signals with compensation for knowledge spillovers, effectively solving the “Arrow’s information paradox” and encouraging human capital to concentrate on research and development-intensive fields. In the digital economy era, platform-based organizations create reputation mechanisms through bilateral markets, enhancing the efficiency of allocating innovative talent. The “density economy” feature of talent, through knowledge recombination, generates a continuous flow of innovation, serving as a core buffer against economic fluctuations. Cities with more innovative cultures can recover more quickly, optimize management, and stimulate industrial evolution and modernization. This leads to more stable and sustainable development while also enhancing their adaptability and resilience [ 71 ][ 72 ][ 73 ]. Third, optimizing the innovation environment serves as the institutional guarantee for resilience. The “Broadband China” strategy reduces institutional transaction costs and reshapes the incentive structure of enterprise innovation behaviors. The development of intellectual property demonstration cities is an essential element of the evolution of property rights systems, effectively promoting patent applications [ 74 ]. This creates an “innovation institutional matrix” that establishes a dynamic balance between patent governance and technological standardization. Particularly in the digital economy, the development of data rights confirmation systems and computing infrastructure stimulates new types of innovation infrastructure, providing dynamic support for economic resilience. Consequently, we put out the following theory: H4: The construction of “Broadband China” demonstration cities, “Intellectual Property” demonstration cities, and the dual pilot policies can enhance the economic resilience of pilot cities by promoting innovation driven paths. Entrepreneurial incentive effect Schumpeter’s theory of innovation suggests that entrepreneurship is essentially an entrepreneurial response to market imbalances, accelerating industry evolution through the entry of new firms. In this process, the synergistic evolution of digital infrastructure and intellectual property systems provides dual driving forces for entrepreneurial incentives. This not only lowers barriers to entrepreneurship and optimizes the innovation ecosystem but also increases the effectiveness and caliber of entrepreneurship, thereby enhancing a municipality’s ability to withstand shocks from the outside world and respond economically [ 75 ]. First, reducing entrepreneurial costs. The widespread adoption of broadband networks and cloud service platforms has driven the shift from traditional, heavy-asset entrepreneurial models to more lightweight, digitalized models. This significantly lowers the capital threshold for startups, turning fixed costs into scalable digital service expenses. At the same time, the digitalization of government processes compresses administrative approval and market entry cycles, significantly reducing startup time costs. In terms of intellectual property, expedited review processes and enhanced infringement compensation mechanisms shorten technology confirmation cycles and increase the penalties for counterfeiting, effectively removing institutional barriers in the commercialization of innovation. These synergistic measures facilitate the efficient flow and concentration of innovation resources, fostering more flexible and efficient entrepreneurial entities and improving the city’s economic capacity to react swiftly to market swings and outside shocks. Second, optimizing the entrepreneurial environment. New collaboration models have emerged as a result of the extensive usage of digital infrastructure. Blockchain-based credit evaluation systems and data-sharing platforms reduce information asymmetry, enabling startups to connect with supply chains and market channels quickly. The development of intellectual property demonstration cities, by accelerating patent review, strengthening infringement penalties, and improving technology transfer markets, effectively shortens the commercialization cycle of technologies, ensures innovation returns, and bridges the gap between R&D investment and market returns. These collaborative actions not only improve the efficiency of innovation resource allocation but also reduce entrepreneurial risks and transaction costs, fostering diversified market entities with the ability to withstand volatility, thereby enhancing the city’s economic resilience and adaptability. Third, increasing entrepreneurial opportunity discovery. The improvement of digital infrastructure effectively meets entrepreneurs’ information needs, and the availability of sufficient information helps increase the likelihood of discovering entrepreneurial opportunities [ 76 ]. Big data technologies deeply analyze consumer behavior and industry trends, assisting entrepreneurs in precisely identifying market needs and forecasting technological evolution. Intelligent matching systems break down the barriers between technology supply and commercial application scenarios. At the same time, the data confirmation systems under intellectual property frameworks protect the core digital assets of enterprises, facilitating the efficient operation of technology transaction markets and enabling startups to access key innovation resources rapidly. These initiatives increase the efficiency with which entrepreneurs discover opportunities and convert them into tangible results. At the same time, the real-time information networks of digital platforms overcome the information lag in traditional markets, allowing urban economies to capture opportunities during technological transformations swiftly. Consequently, we put out the following theory: H5: The construction of “Broadband China” demonstration cities, “Intellectual Property” demonstration cities, and the dual pilot policies can enhance the economic resilience of pilot cities by promoting entrepreneurial incentive pathways Economic agglomeration effect The progression of digital infrastructure and innovation in intellectual property systems have reshaped industrial organizational forms, enhanced factor allocation efficiency, and accelerated knowledge spillover effects, creating multidimensional economic agglomeration advantages. This agglomeration effect enables a more efficient concentration of innovation resources [ 77 ], providing strong structural support for the resilience of urban economies. First, in terms of industrial agglomeration, broadband networks and industrial internet platforms break traditional geographical boundaries, enabling cross-regional production collaboration and real-time data interaction. Cloud computing transforms capital-intensive manufacturing processes into scalable, distributed digital service modules, further enhancing the flexibility and adaptability of industrial chains. Additionally, intellectual property demonstration policies, through the creation of patent pools and mutual recognition of technical standards, reduce technological collaboration barriers among firms and promote the formation of industry clusters characterized by flexible supply chains. This “digital connectivity - shared property rights” organizational model allows urban economies to respond quickly to market fluctuations and speeds up the formation and expansion of emerging fields by facilitating the emergence and propagation of innovative techniques and information, thereby strengthening urban economic competitiveness and innovation capacity [ 78 ]. Second, in terms of resource agglomeration, the resource agglomeration effect presents new characteristics through the synergy between digital platforms and intellectual property transactions. IoT technology enables full traceability and precise matching of production factors, while blockchain-based resource trading markets improve the cross-domain flow efficiency of advanced factors, including technology and resources. The innovation in intellectual property securitization addresses the liquidity dilemma of technological assets, transforming patent resources held by companies into tradable capital elements. The element circulation network created by digital infrastructure and the benefit-sharing mechanism ensured by the intellectual property system jointly drive resources toward high-value-added fields, fostering a diversified economic structure that is resilient to the risks of a single industry. Third, in terms of knowledge agglomeration, the dual promotion of data openness, sharing, and intellectual property protection has accelerated the formation of innovation networks. Big data platforms integrate multidimensional resources from industry, academia, and research, building cross-organizational digital collaboration spaces. Meanwhile, artificial intelligence technology structures and intelligently pushes tacit knowledge. Intellectual property systems solve the “free rider” problem in open innovation by creating models for measuring knowledge contributions and distributing benefits. These mechanisms facilitate data flow and confirm intellectual property, driving continuous breakthroughs in urban innovation ecosystems. They accelerate the rapid diffusion of technologies and further strengthen the iterative capabilities of industries. Consequently, we put out the following theory: H6: The construction of “Broadband China” demonstration cities, “Intellectual Property” demonstration cities, and the dual pilot policies can enhance the economic resilience of pilot cities by promoting economic agglomeration pathways. Model construction and variable description Model design A benchmark multi-period difference-in-differences (DID) model is described as follows to help for evaluating the effects of the dual pilot initiatives, the establishment of demonstration cities for “Broadband China” and “Intellectual Property” on the growth of economic resilience in pilot cities: \(\:{\text{U}\text{E}\text{R}}_{\text{i}\text{t}}={{\alpha\:}}_{0}+{{\alpha\:}}_{1}{\text{B}\text{C}\text{S}}_{\text{i}\text{t}}+{{\alpha\:}}_{\text{n}}{\text{C}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}}_{\text{i}\text{t}}+{{\delta\:}}_{\text{i}}+{{\mu\:}}_{\text{t}}+{{\epsilon\:}}_{\text{i}\text{t}}\) (1) \(\:{\text{U}\text{E}\text{R}}_{\text{i}\text{t}}={{\varnothing}}_{0}+{{\varnothing}}_{1}{\text{I}\text{P}\text{C}\text{S}}_{\text{i}\text{t}}+{{\varnothing}}_{\text{n}}{\text{C}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}}_{\text{i}\text{t}}+{{\delta\:}}_{\text{i}}+{{\mu\:}}_{\text{t}}+{{\epsilon\:}}_{\text{i}\text{t}}\) (2) \(\:{\text{U}\text{E}\text{R}}_{\text{i}\text{t}}={{\theta\:}}_{0}+{{\theta\:}}_{1}{\text{D}\text{P}\text{P}}_{\text{i}\text{t}}+{{\theta\:}}_{\text{n}}{\text{C}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}}_{\text{i}\text{t}}+{{\delta\:}}_{\text{i}}+{{\mu\:}}_{\text{t}}+{{\epsilon\:}}_{\text{i}\text{t}}\) (3) To better investigate the mechanism pathways of the construction of “Broadband China” demonstration cities, “Intellectual Property” demonstration cities, and the influence of dual pilot programs on urban economic resilience, this article sets the following mechanism effect model: \(\:{\text{M}}_{\text{i}\text{t}}={{\beta\:}}_{0}+{{{\beta\:}}_{1}{\text{X}}_{\text{i}\text{t}}+{\beta\:}}_{\text{n}}{\text{C}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}}_{\text{i}\text{t}}+{{\delta\:}}_{\text{i}}+{{\mu\:}}_{\text{t}}+{{\epsilon\:}}_{\text{i}\text{t}}\) (4) \(\:{\text{U}\text{E}\text{R}}_{\text{i}\text{t}}={\partial\:}_{0}+{{\partial\:}_{1}{\text{M}}_{\text{i}\text{t}}+\partial\:}_{\text{n}}{\text{C}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}}_{\text{i}\text{t}}+{{\delta\:}}_{\text{i}}+{{\mu\:}}_{\text{t}}+{{\epsilon\:}}_{\text{i}\text{t}}\) (5) \(\:{\text{U}\text{E}\text{R}}_{\text{i}\text{t}}={{\gamma\:}}_{0}+{{{\gamma\:}}_{1}{\text{X}}_{\text{i}\text{t}}+{{\gamma\:}}_{2}{\text{M}}_{\text{i}\text{t}}+{\gamma\:}}_{\text{n}}{\text{C}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}}_{\text{i}\text{t}}+{{\delta\:}}_{\text{i}}+{{\mu\:}}_{\text{t}}+{{\epsilon\:}}_{\text{i}\text{t}}\) (6) Inside the formula: i, t represent the city and year respectively; \(\:{\text{U}\text{E}\text{R}}_{\text{i}\text{t}}\) Representing a city’s economic resiliency, expressed as the level of economic resilience of city i in year t; the core explanatory variables are the construction of “Broadband China” demonstration cities, the construction of “Intellectual Property” demonstration cities, and the dual pilot policies ( \(\:{\text{B}\text{C}\text{S}}_{\text{i}\text{t}}\) , \(\:{\text{I}\text{P}\text{C}\text{S}}_{\text{i}\text{t}}\) , \(\:{\text{D}\text{P}\text{P}}_{\text{i}\text{t}}\) ), which are the multi period double difference sub items of whether city i is owned by the “Broadband China” demonstration city, whether it is owned by the “Intellectual Property” demonstration city, and whether it is a dual pilot city in year t, respectively; \(\:{\text{M}}_{\text{i}\text{t}}\) is the mediating variable (including technological innovation, entrepreneurial drive, and economic agglomeration); \(\:{\text{X}}_{\text{i}\text{t}}\) Representing the three core explanatory variables mentioned above ; \(\:{\text{C}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}}_{\text{i}\text{t}}\) Representing control variables; to eliminate the impact of individual urban characteristics and time trends on urban economic resilience, the model introduces individual urban effects and time effects, where \(\:{{\delta\:}}_{\text{i}}\) is the fixed effect of urban individuals, \(\:{{\mu\:}}_{\text{t}}\) is the fixed effect of time, and \(\:{{\epsilon\:}}_{\text{i}\text{t}}\) represents the random error term. Model design A benchmark multi-period difference-in-differences (DID) model is described as follows to help for evaluating the effects of the dual pilot initiatives, the establishment of demonstration cities for “Broadband China” and “Intellectual Property” on the growth of economic resilience in pilot cities: \(\:{\text{U}\text{E}\text{R}}_{\text{i}\text{t}}={{\alpha\:}}_{0}+{{\alpha\:}}_{1}{\text{B}\text{C}\text{S}}_{\text{i}\text{t}}+{{\alpha\:}}_{\text{n}}{\text{C}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}}_{\text{i}\text{t}}+{{\delta\:}}_{\text{i}}+{{\mu\:}}_{\text{t}}+{{\epsilon\:}}_{\text{i}\text{t}}\) (1) \(\:{\text{U}\text{E}\text{R}}_{\text{i}\text{t}}={{\varnothing}}_{0}+{{\varnothing}}_{1}{\text{I}\text{P}\text{C}\text{S}}_{\text{i}\text{t}}+{{\varnothing}}_{\text{n}}{\text{C}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}}_{\text{i}\text{t}}+{{\delta\:}}_{\text{i}}+{{\mu\:}}_{\text{t}}+{{\epsilon\:}}_{\text{i}\text{t}}\) (2) \(\:{\text{U}\text{E}\text{R}}_{\text{i}\text{t}}={{\theta\:}}_{0}+{{\theta\:}}_{1}{\text{D}\text{P}\text{P}}_{\text{i}\text{t}}+{{\theta\:}}_{\text{n}}{\text{C}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}}_{\text{i}\text{t}}+{{\delta\:}}_{\text{i}}+{{\mu\:}}_{\text{t}}+{{\epsilon\:}}_{\text{i}\text{t}}\) (3) To better investigate the mechanism pathways of the construction of “Broadband China” demonstration cities, “Intellectual Property” demonstration cities, and the influence of dual pilot programs on urban economic resilience, this article sets the following mechanism effect model: \(\:{\text{M}}_{\text{i}\text{t}}={{\beta\:}}_{0}+{{{\beta\:}}_{1}{\text{X}}_{\text{i}\text{t}}+{\beta\:}}_{\text{n}}{\text{C}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}}_{\text{i}\text{t}}+{{\delta\:}}_{\text{i}}+{{\mu\:}}_{\text{t}}+{{\epsilon\:}}_{\text{i}\text{t}}\) (4) \(\:{\text{U}\text{E}\text{R}}_{\text{i}\text{t}}={\partial\:}_{0}+{{\partial\:}_{1}{\text{M}}_{\text{i}\text{t}}+\partial\:}_{\text{n}}{\text{C}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}}_{\text{i}\text{t}}+{{\delta\:}}_{\text{i}}+{{\mu\:}}_{\text{t}}+{{\epsilon\:}}_{\text{i}\text{t}}\) (5) \(\:{\text{U}\text{E}\text{R}}_{\text{i}\text{t}}={{\gamma\:}}_{0}+{{{\gamma\:}}_{1}{\text{X}}_{\text{i}\text{t}}+{{\gamma\:}}_{2}{\text{M}}_{\text{i}\text{t}}+{\gamma\:}}_{\text{n}}{\text{C}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}}_{\text{i}\text{t}}+{{\delta\:}}_{\text{i}}+{{\mu\:}}_{\text{t}}+{{\epsilon\:}}_{\text{i}\text{t}}\) (6) Inside the formula: i, t represent the city and year respectively; \(\:{\text{U}\text{E}\text{R}}_{\text{i}\text{t}}\) Representing a city’s economic resiliency, expressed as the level of economic resilience of city i in year t; the core explanatory variables are the construction of “Broadband China” demonstration cities, the construction of “Intellectual Property” demonstration cities, and the dual pilot policies ( \(\:{\text{B}\text{C}\text{S}}_{\text{i}\text{t}}\) , \(\:{\text{I}\text{P}\text{C}\text{S}}_{\text{i}\text{t}}\) , \(\:{\text{D}\text{P}\text{P}}_{\text{i}\text{t}}\) ), which are the multi period double difference sub items of whether city i is owned by the “Broadband China” demonstration city, whether it is owned by the “Intellectual Property” demonstration city, and whether it is a dual pilot city in year t, respectively; \(\:{\text{M}}_{\text{i}\text{t}}\) is the mediating variable (including technological innovation, entrepreneurial drive, and economic agglomeration); \(\:{\text{X}}_{\text{i}\text{t}}\) Representing the three core explanatory variables mentioned above ; \(\:{\text{C}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}}_{\text{i}\text{t}}\) Representing control variables; to eliminate the impact of individual urban characteristics and time trends on urban economic resilience, the model introduces individual urban effects and time effects, where \(\:{{\delta\:}}_{\text{i}}\) is the fixed effect of urban individuals, \(\:{{\mu\:}}_{\text{t}}\) is the fixed effect of time, and \(\:{{\epsilon\:}}_{\text{i}\text{t}}\) represents the random error term. Variable selection Explained variables This research utilizes the entropy weight method and utilizes urban economic resilience as the dependent variable. It constructs an indicator system for urban economic resilience levels by selecting 14 indicators from three different aspects (see Table 1). Table 1 Measurement system of urban economic resilience indicators Indicator system Level 1 indicators Level 2 indicators Unit Direction Weight Urban economic resilience Resistance and recovery ability Per capita regional GDP Yuan + 0.0467 Urban residents’ disposable income Yuan + 0.0208 Rural residents’ savings Yuan + 0.0010 Urban registration population loss People + 0.1096 Export total/Regional GDP % + 0.0022 Adaptability and regulatory ability Local fiscal revenue expenditure ratio % + 0.0237 Social consumption retail total Yuan + 0.0959 Tertiary industry value/Regional GDP % + 0.0072 Year-end financial institution loans Yuan + 0.0232 Fixed asset investment Yuan + 0.0839 Transformation and development capability Number of special licenses Items + 0.1826 Number of students in universities per 10000 people People + 0.0864 Government science and technology output Yuan + 0.0812 Fiscal education output Yuan + 0.2357 Core explanatory variables The assignment methodologies for the three core explanatory variables in this study are as follows. First, in accordance with the “Broadband China” Strategy and Implementation Plan promulgated by the State Council in 2013, the National Development and Reform Commission (NDRC) and the Ministry of Industry and Information Technology (MIIT) published the lists of “Broadband China” demonstration cities between 2014 and 2016. As of the end of 2022, 105 out of the 271 sample cities were designated as demonstration cities, thus constituting the treatment sample, with the control sample consisting of the remaining cities. Following is the assignment rule: a city is assigned a value of 1 if it was designated as a demonstration city in a given year or subsequently; otherwise, it is assigned a value of 0. Second, the China National Intellectual Property Administration (CNIPA) established the “National Intellectual Property Pilot and Demonstration Cities (Districts) Evaluation Guidelines” in 2013. Toward the end of 2022, a total of 59 cities had been selected in six rounds of demonstration city evaluations, forming the treatment sample, while the control sample consisted of other cities. The assignment rule for this variable is: if a city was designated as a demonstration city in a given year or later, it is assigned a value of 1; otherwise, it is assigned a value of 0. Third, an overlap exists between the “Broadband China” and “Intellectual Property” demonstration cities, forming a subset of dual-pilot cities. By the end of 2022, 42 of the 271 sample cities were dual-pilot cities, which were categorized into the treatment sample, while the remaining cities constituted the control sample. The assignment rule for the dual-pilot variable is: if a city was simultaneously designated as both a “Broadband China” demonstration city and an “Intellectual Property” demonstration city in a given year or later, it is given the value of 1; otherwise, it is given the value 0. Control variables This study employs the following four control variables.Urban population density (Pop) is calculated as the natural logarithm of the ratio of the total land area of the city’s administrative territory to the total registered population at the end of the year. Degree of financial development (Fin) is determined by the proportion of the whole amount of deposits and loans held by financial organizations at the end of the year to the regional Gross Domestic Product (GDP). Upgrades to industrial structures (Str) is evaluated by a composite index that combines the value-added ratios of each sector to GDP, where the first sector’s value-added ratio to GDP is weighted by a factor of 1, the second sector’s ratio by a factor of 2, and the third sector’s ratio by a factor of 3. The urban income level (Income) is symbolised by the natural logarithm of the typical salary of workers in cities. Mechanism variables This study incorporates three mechanism variables, defined as follows. Technological innovation (TI) is quantified as the amount of the overall quantity of applications for patents to the inhabitants at year-end. Entrepreneurial drive (EI) is gauged by the percentage of the combined number of private sector and independent contractors to the total number of employed individuals, relative to the resident population. Economic agglomeration (EA) is symbolized by the proportion of regional Gross Domestic Product (GDP) to area of the administrative region’s land. Data sources According to the listings of “Intellectual Property” and “Broadband China” pilot cities published on the official websites of the Ministry of Industry and Information Technology and the National Intellectual Property Administration of China, there are 105 “Broadband China” pilot cities in the sample, with 36 located in the eastern region (34.29% of the sample), 38 in the central region (36.19%), and 31 in the western region (29.52%). There are 59 “Intellectual Property” pilot cities in the sample, with 35 in the eastern region (59.32%), 16 in the central region (27.12%), and 8 in the western region (13.56%). The sample also includes 42 dual-pilot cities, with 24 in the eastern region (57.15%), 14 in the central region (33.33%), and 4 in the western region (9.52%). A complete list of cities can be found in Fig. 2. Data from Tibet, Taiwan, Macau, and Hong Kong are not included for the reason of lacking information. The unprocessed data for the remaining variables is gathered from enterprise annual reports from the National Bureau of Statistics and different cities, as well as the China City Statistical Yearbook, China Energy Statistical Yearbook, China Industrial Statistical Yearbook, China Environmental Statistical Yearbook, and China Science and Technology Statistical Yearbook. Linear interpolations are employed to fill in the missing information. Table 2 offers descriptive statistics for the primary factors. Table 2 Descriptive statistics. Symbol Sample size Mean Standard deviation Minimum value Maximum value UER 4496 0.254 0.113 0.0374 0.815 BCS 4496 0.1866103 0.3896417 0 1 IPCS 4496 0.1072064 0.3094099 0 1 DPP 4496 0.0727 0.260 0 1 Fin 4496 0.453 0.224 0.0464 1.541 Pop 4496 0.0426 0.0331 0.000482 0.329 Str 4496 2.281 0.144 1.821 2.760 Income 4496 10.78 0.507 8.509 12.05 IT 4496 12.05 18.99 -0.480 181.4 EI 4496 0.245 0.153 -0.464 1.892 EA 4496 2.549 6.134 0.00753 138.6 Empirical analyses Benchmark regression The “Broadband China” and “Intellectual Property” pilot programs both have a noteworthy positive effect on the resiliency of the metropolitan economy, with a 1% degree of significance regression coefficients. This confirms that both policies effectively enhance economic resilience in pilot cities. A comparison of coefficient magnitudes shows that the “Intellectual Property” pilot program has a stronger effect. This is because it not only fosters innovation factor agglomeration and technology commercialization, directly enhancing urban innovation capacity and industrial competitiveness, but also optimizes the market environment, strengthens property rights protection, and improves long-term investment expectations. These mechanisms contribute to greater economic stability and adaptability. Compared to the pilots with a single strategy, the regression coefficient of the dual-pilot strategy is greater and also substantially positive at the 1% level. This suggests that the dual pilot approach works better in strengthening urban economic resilience. Thus, Hypotheses H1, H2, and H3 are supported. Table 3 Benchmark regression results. Variable UER UER UER BCS 0.0154 *** (3.65) IPCS 0.0320 *** (6.32) DPP 0.0344 *** (5.21) Pop 1.0369 *** 0.8880 *** 0.8913 *** (7.27) (7.42) (6.96) Fin 0.1350 *** 0.1418 *** 0.1420 *** (13.55) (14.93) (14.77) Str 0.1289 *** 0.1301 *** 0.1309 *** (8.04) (8.20) (8.15) Income 0.0229 *** 0.0240 *** 0.0242 *** (2.99) (3.30) (3.29) Observations 4496 4496 4496 Urban FE Yes Yes Yes Year FE Yes Yes Yes R 2 0.600 0.616 0.614 Note: * , ** , *** indicate 10 per cent, 5 per cent and 1 per cent significance levels, respectively, with standard errors in parentheses. As below. Parallel trend test To estimate the repercussions of policies, the difference-in-differences (DID) model is adopted in this investigation. A key assumption for DID estimation is the parallel trend condition, which requires that the treatment and control samples exhibit corresponding trends prior to the introduction of the policy. The test results are presented in Fig. 3, where panels (a), (b), and (c) correspond to the “Broadband China” pilot, the “Intellectual Property” pilot, and the dual pilot policy, respectively. The premise of parallel trends is supported by the lack of a discernible difference in urban economic resilience among the two sets of participants prior to the adoption of the program. After policy implementation, as compared to the control sample, the treatment sample exhibits extremely greater economic resilience. The outcomes above offer preliminary proof that the influence of all three strategies on urban economic resilience is extremely favorable. Robustness tests Endogeneity test To mitigate any endogeneity issues, the instrumental variable (IV) approach is used in this investigation. Specifically, terrain ruggedness affects digital infrastructure development, broadband access quality, and network signal strength while also influencing population density and economic activity. These factors, in turn, impact policy selection and implementation. Since terrain ruggedness is significantly correlated with the “Broadband China” and “Intellectual Property” pilot programs, as well as the dual pilot policy, and is an objectively determined geographical factor, it meets the homogeneity assumption. Therefore, this study constructs an interaction term between terrain ruggedness and policy variables as the IV and applies the two-stage least squares (2SLS) method. The first-stage regression shows a substantial positive association between the IV and the three policy variables. Regression analysis in the second step reveals that the fitted policy variables contribute significantly to the resiliency of the metropolitan economy, with coefficients that align with the basic framework. These findings confirm that the endogeneity issue is effectively mitigated, and the conclusions of the baseline model remain robust. Table 4 Results of instrumental variable method test. Variable 2SLS 2SLS 2SLS First phase Second phase First phase Second phase First phase Second phase BCS UER IPCS UER DPP UER IV 0.4105 *** 0.6035 *** 0.6502 *** (44.05) (39.99) (42.43) BCS 0.0086 ** (1.97) IPCS 0.0454 *** (8.55) DPP 0.0401 *** (6.31) Observations 4496 4496 4116 4116 4496 4496 Controlled variable Yes Yes Yes Yes Yes Yes Urban FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes R 2 0.453 0.759 0.454 0.775 0.426 0.778 Placebo test To validate that the effects being seen are not the result of chance, treatment groups and pilot policy implementation timeframes were assigned at random to perform a placebo test. There were 500 regressions conducted in all. Figure 4 exhibits what we find, where panels (a), (b), and (c) display the regression coefficient distribution for the “Broadband China” pilot, the “Intellectual Property” pilot, and the dual pilot policy, respectively. The placebo policy coefficients follow a normal distribution centered around zero and exhibit a notable deviation from the regression’s first findings. This confirms that arbitrary outside influences do not drive outcomes obtained from the baseline model but are indeed attributable to the execution of pilot programs. PSM-DID verification In order to match suitable control groups for dual-pilot cities, this research employs three techniques: kernel matching, radius matching, and closest neighbor matching. After matching, a t-test is performed to verify the balance of core city characteristics between the groups receiving treatment and those receiving control, ensuring no systematic differences between the samples. Table 5 exhibits the regression outcomes of the PSM-DID model. Table 5 Regression results of PSM-DID model. Variable Neighbor matching Radius matching Kernel matching BCS 0.0153 *** 0.0141 *** 0.0141 *** (3.64) (3.34) (3.34) IPCS 0.0320 *** 0.0327 *** 0.0328 *** (6.32) (5.67) (5.91) DPP 0.0349 *** 0.0343 *** 0.0343 *** (5.10) (4.67) (4.67) Observations 4494 4496 4455 4399 4263 4301 4399 4291 4301 Controlled variable Yes Yes Yes Yes Yes Yes Yes Yes Yes Urban FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes R 2 0.598 0.616 0.606 0.597 0.616 0.611 0.597 0.616 0.611 Other robustness tests First, other policy interferences are excluded. Considering that policies such as the Electronic Government Comprehensive Pilot (CEGPP) might influence urban economic resilience during the study, a comparable policy the baseline model for verifying contains a dummy variable. Table 6 exhibits the outcomes of the regression, column (1). Second, sample data screening is performed. To minimize possible bias and the effect of extremes on the baseline regression, the first and 99th percentiles of the statistics are used for trimming to reduce distortion and systematic bias in the outcomes of regression. Table 6, column (2), exhibits the regression findings. Third, key cities are excluded. Given that provincial capitals and sub-provincial cities may receive more policy benefits due to their special geographic, economic, and policy status, potentially causing heterogeneity in urban economic resilience, key cities are excluded to avoid interference with the analysis. Table 6, column (3), exhibits the regression findings. Fourth, owing to the consequences of the COVID-19 pandemic on the resilience of metropolitan economies in 2020, this study excludes the 2020 sample and redefines the time window for regression. Table 7, column (1), exhibits the regression findings. Fifth, a lag effect regression is conducted. Considering that there may be a delay in the influence of pilot initiatives on urban economic resilience, every explanatory variable is retested after a one-period lag. Table 7, column (2), exhibits the regression outcomes. The robustness tests verify that the “Broadband China” and “Intellectual Property” pilot programs, as well as the dual pilot policy, all contribute significantly to the resilience of the urban economy. Moreover, the dual pilot initiative has greater significance than just the single pilot strategy, and the “Intellectual Property” pilot program outperforms the “Broadband China” initiative. These outcomes demonstrate how solid and trustworthy the study’s conclusions are. Table 6 Other robustness tests. Variable (1) (2) (3) Exclude interference from other policies Exclude interference from other policies Exclude key cities BCS 0.0157 *** 0.0141 *** 0.0155 *** (3.7760) (3.29) (3.58) IPCS 0.0311 *** 0.0314 *** 0.0333 *** (6.0016) (5.89) (5.81) DPP 0.0338 *** 0.0321 *** 0.0358 *** (5.0669) (4.46) (4.63) CEGPP 0.0130 ** 0.0088 * 0.0106 ** (2.5596) (1.7511) (2.1110) Observations 4496 4496 4496 4116 4116 4116 4065 4065 4065 Controlled variable Yes Yes Yes Yes Yes Yes Yes Yes Yes Urban FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes R 2 0.604 0.618 0.616 0.614 0.632 0.626 0.641 0.655 0.652 Table 7 Other robustness tests. Variable (1) (2) Change time window Lag effect regression BCS 0.0149 *** 0.0153 *** (3.58) (3.63) IPCS 0.0323 *** 0.0344 *** (6.57) (5.99) DPP 0.0349 *** 0.0317 *** (5.41) (4.77) Observations 4215 4215 4215 4215 4215 4215 Controlled variable Yes Yes Yes Yes Yes Yes Urban FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes R 2 0.599 0.617 0.614 0.560 0.579 0.570 Re examination of the synergistic effect of the dual pilot program First, the net effects of of single pilot policies. To evaluate the separate effects of every pilot strategy, two separate analyses were conducted: First, The “Intellectual Property” pilot city sample was excluded. The treatment sample consisted of solely “Broadband China” trial cities, while cities that were neither “Broadband China” nor “Intellectual Property” pilot cities functioned as the control sample. This study sought to determine how the “Broadband China” project affected the economic resilience of cities. Second, The “Broadband China” pilot city sample was excluded. Cities that were only “Intellectual Property” pilot cities were set as the treatment sample, with the same control sample as in the first analysis. This allowed for an assessment of the independent effect of the “Intellectual Property” initiative on economic resilience. Based on Table 8, Column 1’s regression findings, both pilot programs considerably boost the strength of the metropolitan economy. However, the “Intellectual Property” pilot policy exhibits an increasingly powerful impact than the “Broadband China” initiative. Next, the net effect of the strategy of dual pilots. To evaluate the net impact of the strategy of dual pilots, non-pilot cities were excluded. Cities that only had one pilot program were set as the control group, while cities with both pilot policies were designated as the treatment group. This analysis aimed to assess the additional resilience gains when a city transitions switching from one to two pilot programs. At the 1% level, the regression statistics (Table 8, Column 2) demonstrate that the dual pilot policy’s coefficient is considerably favorable. This indicates that, compared to one-pilot initiatives, the dual-pilot policy has a greater influence on boosting the economic resilience of cities. Table 8 The synergistic effect test results of the dual pilot program. Variable (1) (2) Net effect of single pilot program The net effect of the dual pilot program BCS 0.0081 * 0.0081 * (1.91) (1.91) IPCS 0.0344 *** 0.0344 *** (3.48) (3.48) DPP 0.0245 *** 0.0245 *** (3.53) (3.53) Observations 3552 2816 1952 3552 2816 1952 Controlled variable Yes Yes Yes Yes Yes Yes Urban FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes R 2 0.625 0.617 0.630 0.625 0.617 0.630 Further analyses Mechanism testing Table 9 exhibits the outcomes of the innovation-driven mechanism test. From the perspective of the innovation-driven pathway, the “Broadband China” and “Intellectual Property” pilot programs, as well as the dual pilot policy, significantly promote technological innovation in pilot cities. In comparison to single pilot programs, the dual pilot strategy has a more substantial effect, and the “Intellectual Property” pilot program has a greater impact than the “Broadband China” initiative. Furthermore, innovation in technology drastically boosts the resilience of the urban economy. When the mechanism variable “technological innovation” is included in the baseline model, its regression coefficient remains significantly positive. The regression coefficients of policy variables also remain positive but decrease compared to the baseline model, indicating that technological innovation functions as a moderator. Additionally, the Sobel test yields Z-values of 2.789, 4.701, and 4.03, all exceeding the critical threshold of 0.97 at the 5% significance level. The Bootstrap test outcomes indicate 95% confidence intervals of [7.06e-06, 0.000039], [0.0028414, 0.0073088], and [0.0000384, 0.0001249], none of which include zero. These findings further confirm the significance of the innovation-driven mechanism. Thus, Hypothesis 4 is supported. Table 9 Test results of technological innovation mechanism. Variable TI UER BCS 17.5818 *** 0.0148 ** (7.54) (2.36) 0.0012 *** IPCS 18.6873 *** (11.12) (10.17) DPP 17.5818 *** 0.0148 ** (7.54) (2.36) TI 0.0012 *** 0.0011 *** 0.0011 *** 0.0011 *** (11.12) (9.43) (9.62) (9.43) Observations 4469 4496 4496 4496 4496 4496 4496 Controlled variable Yes Yes Yes Yes Yes Yes Yes Urban FE Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes R 2 0.611 0.644 0.611 0.649 0.652 0.651 0.652 In Table 10, the outcomes of the entrepreneurial incentive mechanism test are presented. From the perspective of the entrepreneurial incentive pathway, the “Broadband China” and “Intellectual Property” pilot programs, as well as the policy of dual pilots, considerably boost entrepreneurial incentives in pilot cities. Among them, the dual pilot policy has a stronger effect than single pilot policies, and the “Intellectual Property” pilot program has a greater impact than the “Broadband China” initiative. Moreover, increased entrepreneurial incentives significantly improve urban economic resilience. When the mechanism variable “entrepreneurial incentive” is included in the baseline model, its regression coefficient remains significantly positive. The policy variables also remain positive but decrease in magnitude, indicating that entrepreneurial incentives play a mediating role. Additionally, the Sobel test yields Z-values of 8.693, 7.461, and 11.84, all exceeding the critical threshold of 0.97 at the five percent threshold for significance. The Bootstrap test findings indicate 95% confidence intervals of [0.0001861, 0.0003179], [0.0116019, 0.0213764], and [0.0003827, 0.0009839], none of which include zero. These findings further confirm the significance of the entrepreneurial incentive mechanism. Thus, Hypothesis 5 is supported. Table 10 Test results of entrepreneurship driven mechanism. Variable EI UER BCS 0.0476 ** 0.0335 *** (2.15) (5.16) IPCS 0.0355 ** 0.0313 *** (2.17) (6.28) DPP 0.0476 ** 0.0335 *** (2.15) (5.16) EI 0.0250 ** 0.0180 * 0.0187 * 0.0180 * (2.13) (1.67) (1.73) (1.67) Observations 4496 4496 4496 4496 4496 4496 4496 Controlled variable Yes Yes Yes Yes Yes Yes Yes Urban FE Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes R 2 0.280 0.278 0.280 0.594 0.615 0.618 0.615 In Table 11, the outcomes of the economic agglomeration mechanism test are presented. From the perspective of the agglomeration-driven pathway, the “Broadband China” and “Intellectual Property” pilot programs, as well as the dual pilot policy, significantly promote economic agglomeration in pilot cities. In contrast with single pilot projects, the strategy of dual pilots has greater repercussions. And the “Intellectual Property” pilot program has a greater impact than the “Broadband China” initiative. Moreover, higher levels of economic agglomeration significantly enhance urban economic resilience. When the baseline model incorporates the mechanism variable “economic agglomeration,” its regression coefficient continues to maintain a substantially positive value. The policy variables also remain positive but decrease in magnitude, indicating that economic agglomeration plays a mediating role. Additionally, the Sobel test yields Z-values of 8.396, 8.071, and 11.19, all exceeding the critical threshold of 0.97 at the five percent threshold for significance. The Bootstrap test outcomes indicate 95% confidence intervals of [0.0001377, 0.0002462], [0.0109671, 0.021209], and [0.0004003, 0.0008316], none of which include zero. These findings further confirm the significance of the economic agglomeration mechanism. Thus, Hypothesis 6 is supported. Table 11 Test results of economic agglomeration mechanism. Variable EA UER BCS 2.8865 *** 0.0224 *** (6.20) (3.17) IPCS 2.5688 *** 0.0217 *** (6.88) (4.26) DPP 2.8865 *** 0.0224 *** (6.20) (3.17) EA 0.0053 *** 0.0042 *** 0.0040 *** 0.0042 *** (5.93) (4.43) (4.70) (4.43) Observations 4496 4496 4496 4496 4496 4496 4496 Controlled variable Yes Yes Yes Yes Yes Yes Yes Urban FE Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes R 2 0.815 0.816 0.815 0.621 0.629 0.631 0.629 Comparative analysis of different implementation sequences of the dual pilot policies In the 42 dual-pilot city samples examined in this study, 10 cities first became “Broadband China” demonstration cities and then “Intellectual Property” demonstration cities. In contrast, 27 cities first became “Intellectual Property” demonstration cities and then “Broadband China” demonstration cities. Five cities (Guiyang, Xiamen, Yichang, Zhuzhou, and Zibo) became cities with two pilots during the same period. After excluding these five cities, the analysis was conducted in the following two ways: First, using the single-pilot city sample as the control group, the dual-pilot city samples were categorized into two treatment groups: those that first became “Broadband China” demonstration cities and then “Intellectual Property” demonstration cities, and those that first became “Intellectual Property” demonstration cities and then “Broadband China” demonstration cities. The outcomes are presented in Table 12’s column (1). Second, using non-pilot city samples as the control group, the same two treatment groups were tested, and the results are shown in column (2) of Table 12. In comparison, the effect of first becoming an “Intellectual Property” demonstration city and then a “Broadband China” demonstration city on urban economic resilience improvement is more significant. This is because the construction of “Intellectual Property” Demonstration Cities, by enhancing innovation capabilities and intellectual property protection, lays a solid foundation for subsequent information infrastructure construction, enhances the long-term technological investment confidence of innovation entities, and thus improves the economy’s agility and reliability. Table 12 Comparative test results of differences in the implementation sequence of the dual pilot policies. Variable Sample of pilot cities(1) Sample of non pilot cities(2) “Broadband China” pilot preceding “Intellectual Property” pilot “Intellectual Property” pilot preceding “Broadband China” pilot “Broadband China” pilot preceding “Intellectual Property” pilot “Intellectual Property” pilot preceding “Broadband China” pilot DPP 0.0161 0.0276 *** 0.0302 *** 0.0425 *** (1.49) (2.97) (2.75) (4.79) Observations 1440 1712 2704 2976 Controlled variable Yes Yes Yes Yes Urban FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes R 2 0.607 0.622 0.624 0.625 Heterogeneity analysis Heterogeneity of urban resources Based on the official classification standard in the National Sustainable Development Plan for Resource-Based Cities (2013–2020), this study constructs a heterogeneity analysis model comparing resource-based and non-resource-based cities (see Table 13). The outcomes demonstrate that the treatment effects of the “Broadband China” and “Intellectual Property” pilot programs, as well as the policy of dual pilots, are statistically significant in both types of cities. However, the policy effects are notably stronger in resource-based cities. This discrepancy might be explained by the “Dutch disease” effect and the inflexible industrial frameworks of cities centered on resources, which often lead to path dependency in development. Policy interventions help overcome these constraints by breaking resource dependency, promoting industrial diversification, and improving the marginal substitutability of factor allocation. These mechanisms effectively address structural bottlenecks and enhance economic resilience. Conversely, non-resource-based cities face a dual challenge of industrial homogenization and underdeveloped innovation ecosystems. Their policy transmission mechanisms are constrained by market segmentation and institutional frictions, leading to diminishing marginal returns in technology diffusion. Table 13 Heterogeneity test results of urban resources. Variable Resource-based cities Non-resource-based cities Resource-based cities Non-resource-based cities Resource-based cities Non-resource-based cities BCS 0.0148 *** 0.0139 ** (2.66) (2.38) IPCS 0.0336 *** 0.0290 *** (2.82) (5.15) DPP 0.0393 *** 0.0297 *** (2.64) (3.98) Observations 1808 2688 1808 2688 1808 2688 Controlled variable Yes Yes Yes Yes Yes Yes Urban FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes R 2 0.626 0.606 0.629 0.623 0.630 0.619 Heterogeneity of urban resilience To determine the exact effect of policy implementation on the resiliency of the urban economy, this research constructs a heterogeneity analysis model comparing high-resilience and low-resilience urban clusters (see Table 14). The findings indicate that the results of therapy of the “Broadband China” and “Intellectual Property” pilot programs, as well as the dual pilot policy, are statistically significant in both types of cities. However, the policy effects are notably stronger in high-resilience clusters. The moderating role of initial urban conditions drives this gradient effect. High-resilience cities benefit from institutional redundancy, efficient factor allocation, and flexible industrial structures, allowing them to absorb policy benefits through Schumpeterian innovation mechanisms rapidly. In contrast, low-resilience cities face multiple barriers in policy transmission due to path dependency and deficiencies in their innovation ecosystems. These barriers manifest in three ways: factor misallocation reduces marginal returns, institutional frictions slow technology diffusion, and structural rigidity weakens adaptive adjustments. As a result, the effectiveness of policy interventions is diminished, further constraining efforts to enhance urban resilience. Table 14 Heterogeneity test results of urban resilience. Variable High resilience cluster Low resilience cluster High resilience cluster Low resilience cluster High resilience cluster Low resilience cluster BCS 0.0111 * 0.0029 (1.87) (0.83) IPCS 0.0275 *** 0.0224 *** (5.21) (10.88) DPP 0.0252 *** 0.0224 *** (3.56) (10.88) Observations 2248 2248 2248 2248 2248 2248 Controlled variable Yes Yes Yes Yes Yes Yes Urban FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes R 2 0.588 0.482 0.609 0.483 0.602 0.483 Heterogeneity of urban goals By constructing a measurement framework for economic growth target anchoring, this study applies Python-based text mining algorithms to analyze the Government Work Reports of Chinese cities. Using the median economic growth target as a benchmark, a heterogeneity analysis is conducted to compare cities with high and low growth targets (see Table 14). The findings demonstrate that the treatment effects of the “Broadband China” and “Intellectual Property” pilot programs, as well as the dual pilot policy, are statistically significant in both types of cities. However, the policy effects are notably stronger in municipalities with high-level targets for economic expansion. This disparity in policy effectiveness stems from differences in urban development endowments. High-growth target cities benefit from stronger institutional capacity and greater industrial flexibility, allowing them to amplify policy dividends. By aligning policy objectives with market incentives, these cities enhance factor allocation efficiency, accelerate technology diffusion, and develop modular production systems that mitigate risks. As a result, they drive industrial upgrading, optimize resource allocation, and reinforce economic resilience. In contrast, low-growth target cities face deeper structural constraints in policy transmission. The combined effects of weakening growth momentum and limited innovation capacity reduce their ability to absorb and apply new technologies. Institutional frictions and factor misallocation further reinforce path dependence, lowering the marginal benefits of policy interventions. These challenges hinder industrial upgrading and slow progress in strengthening urban economic resilience. Table 15 Heterogeneity test results of urban goals. Variable High goals Low goals High goals Low goals High goals Low goals BCS 0.0111 * 0.0029 (1.87) (0.83) IPCS 0.0275 *** 0.0224 *** (5.21) (10.88) DPP 0.0252 *** 0.0224 *** (3.56) (10.88) Observations 2248 22448 2248 2248 2248 2248 Controlled variable Yes Yes Yes Yes Yes Yes Fixed urban effects Yes Yes Yes Yes Yes Yes Fixed year effect Yes Yes Yes Yes Yes Yes R 2 0.588 0.482 0.609 0.483 0.602 0.483 Conclusions and recommendations Research conclusions The study yields the following key findings: (1) The “Broadband China” and “Intellectual Property” pilot programs, as well as the dual pilot initiative, all significantly enhance the resiliency of the metropolitan economy. Among them, the consequence of the “Intellectual Property” pilot program is stronger than that of the “Broadband China” program. Moreover, the dual pilot policy produces greater resilience gains than either single policy alone. (2) Policy effects exhibit temporal sensitivity. The sequence of implementation matters—cities that first adopt the “Intellectual Property” pilot followed by the “Broadband China” pilot experience greater policy benefits than those implementing the policies in the reverse order. (3) Both single and dual pilot policies improve economic resilience through three key channels: fostering technological innovation, stimulating entrepreneurship, and promoting economic agglomeration. (4) The effects of these policies are stronger in resource-based cities, highly resilient urban clusters, and cities with high economic growth targets. Policy recommendations First, fostering synergies between different policies is crucial to encouraging the integrated advancement of intellectual property protection and digital infrastructure. The current pilot programs are part of a top-down institutional innovation strategy authorized by the central government. On this basis, local governments should further strengthen policy implementation by expanding support for the “Broadband China” and “Intellectual Property” pilot cities. Enhancing both the coverage and depth of these policies will maximize their impact. Compared to single-policy interventions, the combined effects of dual pilot policies are often more significant. With proactive local government engagement, the strategic allocation and integration of policy tools can maximize comprehensive benefits across multiple levels. Therefore, local policymakers should emphasize flexibility and innovation in policy execution. By strategically sequencing and coordinating different initiatives, they can reinforce the synergy between these two pilot programs, ultimately improving urban economic resilience more effectively. Second, sustaining economic resilience requires strengthening the transmission mechanisms linking digital infrastructure and intellectual property protection. Breakthrough technological innovation should serve as the core driver, while entrepreneurial dynamics provide critical support. At the same time, the scale and knowledge spillover effects of high-quality economic agglomeration must be leveraged to enhance adaptability, shock resistance, and recovery capacity in the face of external challenges. On one hand, the spatial planning of digital infrastructure should be carefully coordinated. A well-structured policy support system—incorporating fiscal subsidies, tax incentives, and financial assistance—can enhance policy precision, accessibility, and effectiveness. Additionally, mechanisms for attracting high-end talent, optimizing innovation resource allocation, and promoting industrial chain collaboration should be strengthened. These efforts will help eliminate institutional barriers that hinder economic agglomeration effects, unlocking the positive externalities and knowledge diffusion benefits of industrial clusters, thereby reinforcing the foundation of financial resilience. On the other hand, a comprehensive, long-term innovation incentive system must be established. Targeted policy measures should motivate businesses to increase their R&D spending while boosting their potential for imaginative thinking. The integration of corporations, educational institutions, and research organizations should be deepened to facilitate cross-sector collaboration. By fostering interdisciplinary, cross-industry innovation networks, policymakers can accelerate breakthroughs in key technological areas, driving structural upgrades in economic resilience. Third, regional heterogeneity must be carefully considered when implementing pilot policies. Differences in resource endowments, economic development stages, and growth objectives require tailored strategies for integrating digital infrastructure and intellectual property protection. To optimize policy effectiveness, regional initiatives should be aligned with local development needs while remaining consistent with national strategic priorities. Strengthening coordination between differentiated policies will ensure they complement and reinforce each other, forming a coherent policy framework that supports both local and national development goals. Pilot programs should serve as catalysts for broader regional optimization, facilitating targeted interventions that drive systemic improvements. By adopting a phased, layered, and well-coordinated approach to regional policy implementation, governments can enhance policy precision, improve resource allocation efficiency, and quicken the formation of strategic emerging industries. Restrictive discussion This research offers insightful information for urban planning and policy decision-making but has certain limitations that warrant further exploration. First, the analysis of policy interactions remains insufficient. The study primarily examines the synergies between the “Broadband China” pilot cities, the “Intellectual Property” pilot cities, and the dual pilot policy, without considering their interactions with other initiatives such as the innovation-driven city pilot program and the digital economy development strategy. The total effect on the policy could be undervalued due to this omission. Second, the study does not incorporate firm-level factors. Urban economic resilience is shaped not only by macro-level policies but also by firms’ adaptive strategies in technological innovation, digital transformation, supply chain stability, and financial capacity. Future research could integrate firm-level data to examine how businesses enhance urban resilience through innovation investment, supply chain optimization, and capital allocation. Additionally, further analysis is needed to assess the transmission pathways for the dual pilot strategy on a microlevel. Declarations CRediT authorship contribution statement Q: Writing-review & editing, Conceptualisation, Project administration. X: Writing-original draft, Formal analysis, Data curation, Investigation, Writing-review & editing, Software. G: Software, Resources, Supervision. P: Validation, Methodology. L: Obtaining funds, Visualisation. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability Data will be made available on request. Acknowledgments This article was completed with the support of the Guangxi Philosophy and Social Science Program “Study on the Performance Enhancement of Ethnic Inter-embedded Community Governance in Guangxi under the Consciousness of Building Chinese National Community (24SHC002)”; Humanities and Social Science Fund of Ministry of Education of China “Research on the Practice Mode, Influencing Factors and Path of Party Building Leading Urban Community Governance Community (23YJC840032)”. References Wang Z, Wei W. Regional economic resilience in China: Measurement and determinants[J]. Regional studies, 2021, 55(7): 1228-1239. Fan J, Wang H, Zhang X. A General Equilibrium Analysis of Achieving the Goal of Stable Growth by China’s Market Expectations in the Context of the COVID-19 Pandemic[J]. Sustainability, 2022, 14(22): 15072. Allam Z, Jones D S. Pandemic stricken cities on lockdown. Where are our planning and design professionals [now, then and into the future]?[J]. Land use policy, 2020, 97: 104805. Limin B A I, Chunliang X, Xinghua F, et al. A comprehensive assessment of urban resilience and its spatial differentiation in China[J]. World Regional Studies, 2019, 28(6): 77. Berkes F. Understanding uncertainty and reducing vulnerability: lessons from resilience thinking[J]. Natural hazards, 2007, 41: 283-295. Birkmann J. Risk and vulnerability indicators at different scales: Applicability, usefulness and policy implications[J]. Environmental hazards, 2007, 7(1): 20-31. Collins S L, Carpenter S R, Swinton S M, et al. An integrated conceptual framework for long‐term social–ecological research[J]. Frontiers in Ecology and the Environment, 2011, 9(6): 351-357. Wen H, Liu Y, Zhou F. New-type infrastructure and urban economic resilience: Evidence from China[J]. International Review of Economics & Finance, 2024, 96: 103560. Wen H, Zhan J. New-type infrastructure and total factor productivity: evidence from listed manufacturing firms in China[J]. Economic Change and Restructuring, 2023, 56(6): 4465-4489. Zhou F, Wen H, Lee C C. Broadband infrastructure and export growth[J]. Telecommunications policy, 2022, 46(5): 102347. Tang J, Zhao X. Does the new digital infrastructure improve total factor productivity?[J]. Bulletin of Economic Research, 2023, 75(4): 895-916. He C, Zhou C, Wen H. Improving the consumer welfare of rural residents through public support policies: A study on old revolutionary areas in China[J]. Socio-Economic Planning Sciences, 2024, 91: 101767. Nair M, Pradhan R P, Arvin M B. Endogenous dynamics between R&D, ICT and economic growth: Empirical evidence from the OECD countries[J]. Technology in Society, 2020, 62: 101315. Ren S, Hao Y, Xu L, et al. Digitalization and energy: How does internet development affect China’s energy consumption?[J]. Energy Economics, 2021, 98: 105220. Abrardi L, Sabatino L. Ultra-broadband investment and economic resilience: Evidence from the Covid-19 pandemic[J]. Telecommunications Policy, 2023, 47(2): 102480. Papaioannou S K. ICT and economic resilience: Evidence from the COVID-19 pandemic[J]. Economic Modelling, 2023, 128: 106500. Tian Y, Guo L. Digital development and the improvement of urban economic resilience: Evidence from China[J]. Heliyon, 2023, 9(10). Badri F, Amrina D H. Strengthening the MSME digital economy as Indonesia’s national economic resilience[C]//Journal of International Conference Proceedings. 2023, 6(6): 274-285. Song Y, Xiu Y, Zhao M, et al. Intellectual property protection and enterprise innovation: Evidence from China[J]. Finance Research Letters, 2024, 62: 105253. Gould D M, Gruben W C. The role of intellectual property rights in economic growth[J]. Journal of development economics, 1996, 48(2): 323-350. Pack, H., 1994. Endogenous growth theory: intellectual appeal and empirical shortcomings. J. Econ. perspectives 8 (1), 55–72. Romer P M. Endogenous technological change[J]. Journal of political Economy, 1990, 98(5, Part 2): S71-S102. Bican P M, Guderian C C, Ringbeck A. Managing knowledge in open innovation processes: an intellectual property perspective[J]. Journal of Knowledge Management, 2017, 21(6): 1384-1405. Candelin-Palmqvist H, Sandberg B, Mylly U M. Intellectual property rights in innovation management research: A review[J]. Technovation, 2012, 32(9-10): 502-512. Grimaldi M, Greco M, Cricelli L. A framework of intellectual property protection strategies and open innovation[J]. Journal of Business Research, 2021, 123: 156-164. Chirico F, Criaco G, Baù M, et al. To patent or not to patent: That is the question. Intellectual property protection in family firms[J]. Entrepreneurship Theory and Practice, 2020, 44(2): 339-367. Hwang H, Wu J Z, Yu E S H. Innovation, imitation and intellectual property rights in developing countries[J]. Review of development economics, 2016, 20(1): 138-151. Qian Y. Do national patent laws stimulate domestic innovation in a global patenting environment? A cross-country analysis of pharmaceutical patent protection, 1978–2002[J]. The Review of Economics and Statistics, 2007, 89(3): 436-453. Furukawa Y. Intellectual property protection and innovation: An inverted-U relationship[J]. Economics Letters, 2010, 109(2): 99-101. Ezzeddine S, Hammami M S. RETRACTED: Nonlinear Effects of Intellectual Property Rights on Technological Innovation[J]. Journal of Economic Integration, 2018, 33(2): 1337-1362. Wang C, Wang L. Does broadband infrastructure promote urban innovation? Evidence from “Broadband China” demonstration policy[J]. Structural Change and Economic Dynamics, 2024, 69: 349-362. Luo Q, Hu H, Feng D, et al. How does broadband infrastructure promote entrepreneurship in China: Evidence from a quasi-natural experiment[J]. Telecommunications Policy, 2022, 46(10): 102440. Wan J, Nie C, Zhang F. Does broadband infrastructure really affect consumption of rural households?–A quasi-natural experiment evidence from China[J]. China Agricultural Economic Review, 2021, 13(4): 832-850. Yan H, Yi X, Jiang J, et al. Can information technology construction alleviate household energy poverty? Empirical evidence from the “broadband China” Pilot Policy[J]. Energy Policy, 2024, 185: 113966. Zhang C, Weng X, Guo Y. Digital infrastructure construction and household energy efficiency: Based on a quasi-natural experiment in China[J]. Science of The Total Environment, 2024, 911: 168544. Wang C, Zhang M. The road to change: Broadband China strategy and enterprise digitization[J]. Plos one, 2022, 17(5): e0269133. Song Y, Huang H, Li Y, et al. Towards inclusive green growth in China: Synergistic roles and mechanisms of new infrastructure construction[J]. Journal of Environmental Management, 2024, 353: 120281. He Z, Zhang Y, Wang X. Has the Development of Broadband Infrastructure Improved Household Energy Consumption in Rural China?[J]. Sustainability (2071-1050), 2024, 16(19). Peng H R, Ling K, Zhang Y J. The carbon emission reduction effect of digital infrastructure development: Evidence from the broadband China policy[J]. Journal of Cleaner Production, 2024, 434: 140060. Li B, Liu J, Liu Q, et al. The effects of broadband infrastructure on carbon emission efficiency of resource-based cities in China: a quasi-natural experiment from the “Broadband China” pilot policy[J]. International journal of environmental research and public health, 2022, 19(11): 6734. Zhong X, Liu G, Chen P, et al. The impact of internet development on urban eco-efficiencY—a quasi-natural experiment of “broadband China” pilot policy[J]. International Journal of Environmental Research and Public Health, 2022, 19(3): 1363. Wen H, Liang W, Lee C C. Urban broadband infrastructure and green total-factor energy efficiency in China[J]. Utilities Policy, 2022, 79: 101414. Yu H, Jiexin T, Zhenzhen C, et al. Influence of national intellectual property demonstration enterprise policy on urban green innovation: Evidence from China[J]. Environment, Development and Sustainability, 2023: 1-20. Chen D, Chen S. Patent Protection Policy and Firms’ Green Technology Innovation: Mediating Roles of Open Innovation and Human Capital[J]. Sustainability, 2024, 16(5): 2217. Gao X, Zhao Y. Does intellectual property protection promote green innovation in firms? A perspective on R&D spillovers and financing constraints[J]. Plos one, 2023, 18(11): e0288315. Liu Y, Chen L, Luo H, et al. The impact of intellectual property rights protection on green innovation: A quasi-natural experiment based on the pilot policy of the Chinese intellectual property court[J]. Math. Biosci. Eng, 2024, 21: 2587-2607. Li F. Comprehensive intellectual property ability and firm productivity: A quasi-natural experiment based on national intellectual property demonstration advantage firm policy in China[J]. Plos one, 2024, 19(4): e0302693. Zhu Y, Sun M. The enabling effect of intellectual property strategy on total factor productivity of enterprises: evidence from China’s intellectual property model cities[J]. Sustainability, 2022, 15(1): 549. Zhou D, Guo J. Can intellectual property demonstration cities influence the location choice of polluting enterprises? Evidence from China[J]. Environment, Development and Sustainability, 2024, 26(6): 14163-14187. Mao K, Failler P. Does stronger protection of intellectual property improve sustainable development? Evidence from city data in China[J]. Sustainability, 2022, 14(21): 14369. Yao L, Li A, Wang S. The impact of intellectual property demonstration policies on carbon emission efficiency[J]. Scientific Reports, 2024, 14(1): 15336. Han H. Can intellectual property rights pilots reduce carbon emissions? Evidence from China[J]. Frontiers in Environmental Science, 2024, 12: 1336803. Abson D J, Dougill A J, Stringer L C. Using principal component analysis for information-rich socio-ecological vulnerability mapping in Southern Africa[J]. Applied Geography, 2012, 35(1-2): 515-524. Adamowicz M. The potential for innovative and smart rural development in the peripheral regions of Eastern Poland[J]. Agriculture, 2021, 11(3): 188. Li R, Xu M, Zhou H. Impact of high-speed rail operation on urban economic resilience: evidence from local and spillover perspectives in China[J]. Cities, 2023, 141: 104498. Simmie J, Martin R. The economic resilience of regions: towards an evolutionary approach[J]. Cambridge journal of regions, economy and society, 2010, 3(1): 27-43. Zhang J, Yang Z, He B. Does digital infrastructure improve urban economic resilience? Evidence from the Yangtze River Economic Belt in China[J]. Sustainability, 2023, 15(19): 14289. Jiang W, Wang K L, Miao Z. Can telecommunications infrastructure enhance urban resilience? Empirical evidence from a differences-in-differences approach in China[J]. Environment, Development and Sustainability, 2023: 1-32. Shi Y, Zhang T, Jiang Y. Digital economy, technological innovation and urban resilience[J]. Sustainability, 2023, 15(12): 9250. Romer P M. Endogenous technological change[J]. Journal of political Economy, 1990, 98(5, Part 2): S71-S102. Slot M, Fraikin M, Damgrave R, et al. Digital infrastructures as the basis for implementing digital twinning[J]. Procedia CIRP, 2022, 109: 568-573. Schade P, Schuhmacher M C. Digital infrastructure and entrepreneurial action-formation: A multilevel study[J]. Journal of Business Venturing, 2022, 37(5): 106232. Allam Z, Jones D S. Future (post-COVID) digital, smart and sustainable cities in the wake of 6G: Digital twins, immersive realities and new urban economies[J]. Land use policy, 2021, 101: 105201. Kovacs-Györi A, Ristea A, Havas C, et al. Opportunities and challenges of geospatial analysis for promoting urban livability in the era of big data and machine learning[J]. ISPRS International Journal of Geo-Information, 2020, 9(12): 752. Le H T T, Luong T T D, Nguyen T T T, et al. Determinants of intellectual property rights protection in Asian developing countries[J]. Journal of the Knowledge Economy, 2023, 14(4): 4780-4806. Chen Q, Xia X, Hui Y, et al. Urban Intellectual Property Strategy and University Innovation: A Quasi-Natural Experiment Based on the Intellectual Property Model City of China[J]. Systems, 2024, 12(1): 21. Jia X, Xie B, Wang X. The impact of network infrastructure on enterprise digital transformation——A quasi-natural experiment from the “broadband China” Strategy[J]. Applied Economics, 2024, 56(12): 1363-1380. Martin R, Sunley P, Tyler P. Local growth evolutions: recession, resilience and recovery[J]. Cambridge Journal of Regions, Economy and Society, 2015, 8(2): 141-148. Martin R. Regional economic resilience, hysteresis and recessionary shocks[J]. Journal of economic geography, 2012, 12(1): 1-32. Romão J. Tourism, smart specialisation, growth, and resilience[J]. Annals of Tourism Research, 2020, 84: 102995. Martin R, Sunley P, Gardiner B, et al. How regions react to recessions: Resilience and the role of economic structure[J]. Regional studies, 2016, 50(4): 561-585. Boschma R. Towards an evolutionary perspective on regional resilience[J]. Regional studies, 2015, 49(5): 733-751. Bristow G, Healy A. Innovation and regional economic resilience: an exploratory analysis[J]. The annals of regional science, 2018, 60(2): 265-284. Lemley M A, Shapiro C. Probabilistic patents[J]. Journal of Economic Perspectives, 2005, 19(2): 75-98. Pradhan R P, Arvin M B, Nair M, et al. The dynamics among entrepreneurship, innovation, and economic growth in the Eurozone countries[J]. Journal of Policy Modeling, 2020, 42(5): 1106-1122. Shane S, Venkataraman S. The promise of entrepreneurship as a field of research[J]. Academy of management review, 2000, 25(1): 217-226. Wen J, Okolo C V, Ugwuoke I C, et al. Research on influencing factors of renewable energy, energy efficiency, on technological innovation. Does trade, investment and human capital development matter?[J]. Energy Policy, 2022, 160: 112718. Duranton G, Puga D. Diversity and specialisation in cities: why, where and when does it matter?[J]. Urban studies, 2000, 37(3): 533-555. Additional Declarations No competing interests reported. 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Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIie3RsWrDMBCAYQWF6yLsjicanFcQGEQfx8KQrdCpKBBIIMEe2pC1j+GpZIwReLrQ1aPzCNmyhITOLZG7ZdA33490HGNBcIfgYXPs0EISc153mZ35k0jsUvVMUSpLyFVHjT9JMNM4LRJTfQstDyve42Nil6uW9KByQluzABaX75lnl0V9+LQTLh28tWY7Ykj7yvNKvUyRGogc/2oNAVP44kkwh6dzcRHMMf1qCt4nmQDKAvDRDTXrlwjiCgmUXEKOGTXCu8u4/Bj8nHK+iV19PNlZEpfr28kv4n/jQRAEwZ+umYRI4Sqxq6QAAAAASUVORK5CYII=","orcid":"","institution":"Chongqing University","correspondingAuthor":true,"prefix":"","firstName":"Wenjing","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-04-01 13:38:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6353700/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6353700/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86663359,"identity":"0ba9b0c5-b77c-4088-b500-188cf39e9e44","added_by":"auto","created_at":"2025-07-14 10:49:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":29209,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMechanism diagram of the impact of the dual pilot policy on urban economic resilience.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6353700/v1/b81b9e68b9fdadd9b0ef75a6.png"},{"id":86663361,"identity":"d9d91734-0918-4b61-88e8-cff0e2c8ff23","added_by":"auto","created_at":"2025-07-14 10:49:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":256426,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution map of “Broadband China” demonstration cities and “Intellectual Property” demonstration cities in China.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6353700/v1/1135a677d9ed6f70c00ec87e.png"},{"id":86663364,"identity":"08cd0e5c-ea58-4a18-bf3c-0735f6e6f2cf","added_by":"auto","created_at":"2025-07-14 10:49:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":83210,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eParallel trend test chart.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6353700/v1/80798d13cc49bf92b6ffb325.png"},{"id":86665074,"identity":"e98fea3f-2040-4b72-b3dd-a43b7318e558","added_by":"auto","created_at":"2025-07-14 10:57:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":158786,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePlacebo test chart.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6353700/v1/cf112e10d13c37411d7f4211.png"},{"id":86667103,"identity":"48518723-1077-4a04-bbf7-9d7a2c5c210d","added_by":"auto","created_at":"2025-07-14 11:13:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3049113,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6353700/v1/38c68e95-4e5e-40e6-a722-41be7fd366bd.pdf"},{"id":86665656,"identity":"55ebc5e2-2e0c-469d-8729-8a6c96180f96","added_by":"auto","created_at":"2025-07-14 11:05:47","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":3617586,"visible":true,"origin":"","legend":"","description":"","filename":"dataandcode.zip","url":"https://assets-eu.researchsquare.com/files/rs-6353700/v1/957c307f74941b03b4663064.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Digital Infrastructure, Intellectual Property, and the Resilience of China’s Urban Economy: Evaluating the Synergistic Effects of “Broadband China” and “Intellectual Property” Pilot Policies","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUrban economic resilience functions as a fundamental pillar for superior financial empowerment and a critical safeguard against systemic risks and external shocks, ensuring stability and sustainability [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Amid increasing global economic volatility, deepening transitions in growth drivers, and the rising frequency of \u0026ldquo;black swan\u0026rdquo; and \u0026ldquo;gray rhino\u0026rdquo; events, urban economies are facing intensified challenges, including geopolitical tensions, global supply chain restructuring, climate-induced disasters, cyclical economic crises, and trade conflicts [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These pressures have heightened demand contraction, supply disruptions, and weakened market expectations in China. In this context, urban economic resilience not only determines the capacity of a city to adjust and recuperate from emergencies but also performs an essential role in maintaining sustained economic expansion. Optimizing resource allocation and enhancing systemic risk resistance. It is a key variable in \u0026ldquo;overcoming crises, mitigating downturns, driving recovery, and reshaping growth.\u0026rdquo; Therefore, a systematic examination of the core factors influencing urban economic resilience and a comprehensive investigation into pathways to enhance it are essential. Strengthening economic resilience can effectively mitigate external shocks, reduce volatility\u0026rsquo;s impact on development quality, and establish a robust basis for economic revitalization, long-term prosperity, and high-quality development.\u003c/p\u003e\u003cp\u003eSince the early 21st century, the rapid acceleration of urbanization has led to land resource misallocation, energy metabolism imbalance, and transportation network overload, triggering entropy effects in urban systems. These pressures have continuously lowered ecological resilience thresholds, pushing human settlements closer to critical safety margins [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In response, the 2002 UN Sustainable Development Summit introduced the \u0026ldquo;urban resilience\u0026rdquo; governance framework, defining it as a multidimensional system integrating public safety, social stability, and economic resilience through proactive planning, redundancy design, and disturbance response strategies [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. International organizations such as the United Nations Office for Disaster Risk Reduction (UNDRR) and the Intergovernmental Panel on Climate Change (IPCC) rapidly embedded this concept into the Sustainable Development Goals (SDGs), transforming it into a governance toolkit for balancing the complex environment-economy-society nexus [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The worldwide economic depression of 2008 further exposed the vulnerabilities of conventional urban governance, prompting both academia and policymakers to rethink urban risk immunity systems and post-crisis recovery mechanisms [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. As an institutional response, The Rockefeller Foundation initiated the 100 Resilient Cities Initiative in 2013, introducing an innovative risk management model that integrates infrastructure redundancy, social capital networks, and smart governance platforms [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In 2021, China\u0026rsquo;s 14th Five-Year Plan for New Urbanization prioritized urban resilience as one of its five core objectives, focusing on expanding digital infrastructure coverage and optimizing supply chain redundancy. Meanwhile, the UN-Habitat City Resilience Index (CRI) (2017\u0026ndash;2023) refined the concept of economic resilience into three measurable dimensions: industrial chain flexibility, digital infrastructure penetration, and SME risk resistance, aligning governance strategies with SDG 11 and SDG 8. This shift marks a critical transition from theoretical construction to quantifiable urban resilience governance.\u003c/p\u003e\u003cp\u003eNew infrastructure, built upon advanced information technologies, integrates digital networks, convergence systems, and innovation platforms to drive digital transformation, industrial upgrading, and integrated innovation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In the context of accelerating global digitalization, new infrastructure\u0026mdash;spanning 5G, data centers, and industrial internet\u0026mdash;has emerged as a critical public asset, reshaping national competitiveness and economic resilience [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Major economies are rapidly advancing their infrastructure strategies to strengthen their positions in the digital era [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The United States has reinforced computational networks through the CHIPS Act, while the European Union has focused on cross-border data flows. In contrast, China, leveraging its 60% global share of 5G base stations, has accelerated intelligent manufacturing and digital governance innovation. Studies suggest that new infrastructure mitigates resource misallocation in manufacturing, enhances total factor productivity (TFP) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and fosters data-driven innovation, improving resource-matching efficiency and advancing regional economic systems by size [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Information and communication technology (ICT) continues to evolve access to information, generating economic dividends and fostering regional economic prosperity [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e][\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In a profoundly networked, data-centric society, economic actors are undergoing a profound shift toward intelligent, networked collaboration, strengthening their ability to absorb external shocks [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. New infrastructure, centered on information integration, network connectivity, and digital innovation, performs an indispensable part in strengthening economic resilience. Robust broadband infrastructure and internet accessibility have been found to improve supply chain stability and regional adaptability [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e][\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Additional research underscores the vitality of digital technologies in facilitating regional economic recovery and reinforcing systemic stability [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Moreover, the digital economy optimizes resource allocation, accelerates innovation, and strengthens economic resilience [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. As the cornerstone of the internet-based economy, new infrastructure is crucial for building stable economic systems, refining policy frameworks, and advancing high-quality development, warranting further in-depth research.\u003c/p\u003e\u003cp\u003eAs a critical intangible asset, intellectual property (IP) is widely recognized as one of the most valuable components of modern economic systems [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. According to endogenous growth theory, intellectual innovation functions as the principal catalyst for flourishing economies. However, due to its non-rivalrous and non-excludable nature, market failures often arise, diminishing motivations for innovators [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e][\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e][\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. From an economic resilience perspective, a well-established IP protection framework fosters the growth of knowledge-intensive industries, enhances urban innovation capacity, and strengthens economic recovery in the face of external shocks. Existing research presents three main perspectives. The first argues that a strong IP system increases firms\u0026rsquo; expected returns on innovation, incentivizes R\u0026amp;D investment, improves resource allocation efficiency, and fosters industrial clustering, ultimately creating a robust innovation ecosystem [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e][\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e][\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The second viewpoint suggests that excessive IP protection may lead to market monopolization, hinder knowledge diffusion, and restrict SMEs\u0026rsquo; access to innovation, ultimately weakening overall innovation performance [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e][\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The third approach highlights a nonlinear relationship between IP protection and innovation, where moderate protection encourages technological advancement and industrial upgrading.In contrast, excessive protection results in resource misallocation, limits knowledge diffusion and reduces the adaptability and recovery capacity of urban economies [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e][\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e][\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Thus, a scientifically balanced IP protection policy is essential for strengthening urban economic resilience. It optimizes the innovation environment, facilitates industrial transformation, and enhances cities\u0026rsquo; ability to withstand external disruptions, ensuring long-term financial stability and sustainable development.\u003c/p\u003e\u003cp\u003eThis study builds upon existing research to evaluate the implications of the \u0026ldquo;Broadband China\u0026rdquo; and \u0026ldquo;Intellectual Property\u0026rdquo; pilot programs, as well as their combined effects, on urban economic resilience, addressing a key research gap. The article makes three distinct contributions. First, it investigates how digital infrastructure and intellectual property policies jointly influence urban economic resilience, with a focus on their synergy, extending the research on economic resilience determinants and deepening the theoretical exploration of multi-policy effects. Second, it examines the mechanisms through which \u0026ldquo;Broadband China,\u0026rdquo; \u0026ldquo;Intellectual Property,\u0026rdquo; and dual pilot programs reinforce urban economic resilience, particularly through technological innovation, entrepreneurship incentives, and economic agglomeration. Third, it evaluates the heterogeneity of policy effects based on resource endowments, resilience levels, and economic growth targets. It provides theoretical and policy insights into optimizing digital infrastructure and intellectual property policy coordination.\u003c/p\u003e\u003cp\u003eThis is how the rest of an essay is organized. We presented the relevant literature review in the second part. Our theoretical theories are described in three parts. In the four parts, models, procedures, and data used in the empirical investigation are presented. We provide our estimated findings and thorough analysis in the five parts. We further explore the impact of mechanisms, heterogeneity, and effects of various policy enforcement sequences on the outcomes in the six parts. Finally, We wrap up the research and provide policy suggestions in the seven-part.\u003c/p\u003e"},{"header":"Literature review","content":"\u003cp\u003e\u003cb\u003eResearch on the Impact and Effect of the Construction of “Broadband China” Demonstration Cities and “Intellectual Property” Demonstration Cities\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn recent years, with the gradual implementation of a series of pilot policies, scholars have extensively studied the implications of the “Broadband China” and “Intellectual Property” pilot programs, leading to a growing body of literature. Existing studies mainly focus on two aspects: economic effects and environmental effects. First, regarding the financial impacts of the “Broadband China” pilot, digital technology penetration has dramatically boosted urban innovation capability [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and factor allocation efficiency, particularly in optimizing human capital spatial agglomeration, driving financial deepening [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and increasing rural household consumption [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. At the same time, improvements in information efficiency have reshaped trade network structures, reduced institutional transaction costs [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and generated sustainable development benefits, including enhanced household energy efficiency [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e][\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], accelerated enterprise digital transformation [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and the formation of a comprehensive sustainable growth framework [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Second, regarding the environmental effects of the “Broadband China” pilot, digital technology penetration has driven the alteration of the energy framework, shifting household energy consumption toward cleaner sources [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and optimizing carbon emissions performance [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e][\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Simultaneously, information flows have reshaped ecological governance models, resulting in enhancements in urban environmental sustainability [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] and promoting a switch to green total factor efficacy [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], thereby contributing to a low-carbon development paradigm. Third, with relation to the “Intellectual Property” pilot’s economic consequences, bolstering the safety of intellectual property has triggered a green technology diffusion effect, improving urban green innovation ecosystems [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], fostering corporate R\u0026amp;D collaboration and enhancing human capital quality [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], and thereby driving breakthroughs in corporate green innovation [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e][\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Simultaneously, budgetary rewards for funding scientific and technological advancement and improvements in factor allocation efficiency [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] have generated institutional dividends for total factor productivity growth, promoting a regional economic shift toward an innovation-driven paradigm [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Fourth, regarding the environmental effects of the “Intellectual Property” pilot, appropriate intellectual property protection establishes environmental entry barriers, effectively filtering out polluting enterprises [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] and triggering Schumpeterian impacts that drive the structural transformation of green total factor productivity [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Additionally, through the restructuring of industrial ecosystems, the policy facilitates low-carbon technology diffusion [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] and carbon intensity convergence (Han, 2024)[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], forming a sustainable development loop of “innovation incentives—structural optimization—emission control,” providing dual institutional and technological support for urban low-carbon transformation.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eResearch on the Impact of Urban Economic Resilience\u003c/h2\u003e\u003cp\u003eThe concept of resilience originated in physics and gradually evolved into a core analytical tool in risk governance. It is closely intertwined with concepts such as sensitivity and adaptive capacity, forming a complex theoretical network that supports the development of multi-dimensional adaptive system frameworks. Abson et al. l [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] pioneered a six-dimensional resilience model encompassing steady-state equilibrium, resource heterogeneity, efficiency optimization, structural flattening, risk buffering, and redundancy design. This theoretical advancement established a meta-framework for resilience research and provided essential analytical tools for interdisciplinary studies. As complexity science continues to evolve, resilience research has shifted towards finer spatial scales. Community resilience theory, for instance, defines both geographical and social boundaries to explain how social systems dynamically adapt to uncertainty through disturbance dissipation, self-organization, and stress response mechanisms [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMeanwhile, resilience has attracted extensive academic attention across disciplines including sociology, ecology, psychology, and catastrophe science, becoming the center of interdisciplinary study. The capacity to endure economic fluctuations is an economy to withstand and adapt to uncertainty while ensuring financial security, macroeconomic stability, and public welfare, making it a strategic national concern [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Given that municipal economies serve as the principal transporters of contemporary economic frameworks, in particular, urban economic durability is the capacity of a municipality to handle, adjust to, and change in response to exogenous shocks and financial volatility [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSince the introduction of urban economic resilience, empirical research on its determinants has deepened, with increasing attention to the effects of “Broadband China” pilot programs. Studies show that “Broadband China” implementation enhances economic resilience by optimizing factor allocation efficiency and upgrading industrial structures, forming a stable risk mitigation mechanism [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Additionally, it strengthens dynamic adaptability through the “technology innovation–industrial upgrading–entrepreneurship-driven” transmission chain [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The digital economy further improves economic resilience by acting as an intermediary for technological innovation [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. However, studies on the implications of pilot endeavors for “Intellectual Property” on economic resilience remain in their early stages. Moreover, existing studies have yet to explore the interaction effects between the two policies systematically. Do these policies generate multiplicative or additive effects? More importantly, do these synergistic mechanisms exhibit significant spatial and temporal heterogeneity? Addressing these questions is crucial for clarifying policy overlaps, refining implementation pathways, and designing effective resilience enhancement strategies. This study aims to fill these gaps by providing theoretical insights and practical guidance for strengthening urban economic resilience.\u003c/p\u003e\u003c/div\u003e\n\n\n\n\n\n\n\n\n\n"},{"header":"Research hypotheses","content":"\u003ch2\u003eThe impact of the construction of “Broadband China” demonstration cities on urban economic resilience\u003c/h2\u003e\u003cp\u003eThe neoclassical growth theory emphasizes technological progress as the core driver of economic expansion. In contrast, technological influences are greater internalized by endogenous development theory, making them important variables affecting marginal returns and highlighting the key role of knowledge spillovers in driving economic expansion. As a critical carrier of technological internalization, digital infrastructure restructures the resilience threshold of regional economic growth through the increasing marginal returns effect of knowledge spillovers [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Four dimensions may be applied to clarify how digital infrastructure strengthens urban economic resilience under the quasi-natural experiment known as “Broadband China.” First, it drives the intelligent upgrading of industries. Digital infrastructure optimizes production function parameters through real-time big data calculations, enhancing the dynamic adaptability of industry decision-making systems. This evolution of production models towards intelligence, precision, and flexibility builds an efficient risk response mechanism under external shocks, thus strengthening the robustness of the urban industrial structure [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Second, it stimulates innovation and entrepreneurship vitality. The deep penetration of digital technologies significantly reduces the sunk costs of entrepreneurial activities. It enhances factor flow efficiency through information sharing and market matching mechanisms, optimizing capital allocation methods. This helps cities maintain high market vitality and resource circulation efficiency in the face of economic uncertainty, thereby improving adaptability to risk shocks [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Third, it strengthens risk monitoring capabilities. The thorough incorporation of the Internet of Things, huge data, and artificial intelligence technologies builds a comprehensive dynamic monitoring network. It gets beyond the limitations of conventional risk identification in terms of time and location, creating a cross-validation mechanism with multi-source data, thereby establishing a risk mitigation and resilience enhancement feedback loop in cross-departmental collaborative governance. This improves a city’s ability to perceive, identify, predict, and intervene in sudden events [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Fourth, it enhances the clustering effect of factors. High-quality digital infrastructure construction not only improves a city’s livability and governance modernization level but also builds an innovation ecosystem driven by information flow. This enhances the city’s attractiveness to high-end human capital, financial capital, and advanced production factors, thereby strengthening the factor endowment base of UER and promoting stable growth and long-lasting, sustainable expansion of the city’s prosperity in complex environments [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Consequently, we put out the following theory:\u003c/p\u003e\u003cp\u003eH1: The construction of “Broadband China” demonstration cities can significantly enhance the economic resilience of cities.\u003c/p\u003e\u003ch3\u003eThe impact of the construction of “Intellectual Property” demonstration cities on urban economic resilience\u003c/h3\u003e\u003cp\u003eIn a free market, the completeness and strength of intellectual property protection are typically fundamental to supporting innovation activities and maintaining fair competition [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. The intellectual property city pilot program favors cooperation in the production of intellectual property, application, and protection by enhancing the value transformation of knowledge capital. Through institutional innovation, it overcomes the constraints of factor endowment, cultivates knowledge-intensive industry ecosystems, and attains enhancements in the total productivity of factors and the quality of economic development [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Intellectual property protection may bolster a municipality’s economic resiliency and adaptability by facilitating the aggregation of factors and resources. The establishment of “Intellectual Property” Experimental Municipalities helps accelerate the concentration of resources, gradually forming a stable “factor pool,” thereby enhancing a municipality’s economic flexibility and defense against outside influences. For traditional production factors, intellectual property pilot policies not only signal the enhancement of the innovation and entrepreneurial ecosystem but also help firms establish short-term monopolies in specific fields through market protection mechanisms, enhancing the city’s ability to attract physical capital. For new production factors, particularly data, although highly mobile, abuse, theft, and ownership issues often hinder the realization of data value. IP demonstration cities address this by building targeted regulatory systems to protect digital asset owners, strengthening the clustering effect of data elements, reducing fragmentation, and improving the economic benefits of data. This ensures that a city’s economic system can quickly recover and maintain stable growth in reaction to disruptions outside. In the face of external economic shocks, resource scarcity often becomes a key factor limiting the rapid repair and stable recovery of a city’s economic system. In contrast, the continuous innovation and diffusion of technology and knowledge are key drivers for economic restructuring. On the one hand, well-balanced intellectual property protection helps eliminate market failures in technology diffusion and knowledge spillovers, ensuring that innovation entities can maintain control over their economic interests at critical moments. This prevents the loss of market advantage due to a lack of patent protection or cost recovery barriers caused by “spillover effects” from technological imitation. On the other hand, bolstering the safeguarding of intellectual property effectively promotes technology diffusion and knowledge spillover, creating a “positive feedback” mechanism that drives industrial technological upgrades and enhances overall economic performance. Consequently, we put out the following theory:\u003c/p\u003e\u003cp\u003eH2: The construction of “Intellectual Property” demonstration cities can significantly enhance the resilience of urban economy.\u003c/p\u003e\u003ch3\u003eThe policy synergy effect of the dual pilot program\u003c/h3\u003e\u003cp\u003eThe “Broadband China” and “Intellectual Property” pilot programs do not operate independently in enhancing urban economic resilience. Instead, they exhibit a strong “mutual empowerment and synergistic reinforcement” effect. The “Broadband China” initiative focuses on upgrading information infrastructure and driving enterprise digital transformation and industrial modernization [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. This strengthens industrial restructuring and innovation-driven growth, promoting high-quality economic development. The “Broadband China Strategy and Implementation Plan” explicitly emphasizes the need for coordinated development between network infrastructure upgrades and industrial innovation to ensure alignment between broadband expansion and industry transformation demands. By improving information efficiency, digital infrastructure reduces search and logistics costs, enhances trade efficiency, and lowers trade barriers [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Moreover, it provides greater adaptability, recovery capacity, and structural resilience in response to global and regional economic shocks. The “Intellectual Property” pilot program, in contrast, focuses on building a comprehensive IP protection system to strengthen property rights incentives and optimize the innovation ecosystem. A robust IP framework serves as a stabilizer for technological innovation, encouraging firms to increase R\u0026amp;D investment. This accelerates industrial upgrading toward high-value-added, high-tech sectors, fostering advanced, intelligent, and green transformations. Under the dual forces of globalization and the digital economy, improving IP protection not only attracts high-end innovation resources but also enhances innovation factor mobility. It promotes deep integration between industry, academia, research, and application, reducing the time from laboratory breakthroughs to industrial implementation. This accelerates the commercialization and market adoption of technological innovations, further strengthening urban competitiveness in an innovation-driven economy. Consequently, we put out the following theory:\u003c/p\u003e\u003cp\u003eH3: The construction of “Broadband China” demonstration cities and “Intellectual Property” demonstration cities has a mutually reinforcing policy synergy effect, manifested in the fact that the dual pilot policy has a greater impact on improving urban economic resilience than the single pilot policy.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eMechanism effect\u003c/h2\u003e\u003cp\u003eAccording to the prevailing literature and the strategy ramifications of establishing “Broadband China” showcase cities and “Intellectual Property” demonstration cities, this paper looks more closely at the conveying channel of the dual pilot strategy on reinforcing the economic resilience of cities from the perspectives of innovation-driven effects, entrepreneurial incentive effects, and economic agglomeration effects (see Fig.\u0026nbsp;1).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003ch2\u003eMechanism effect\u003c/h2\u003e\u003cp\u003eAccording to the prevailing literature and the strategy ramifications of establishing “Broadband China” showcase cities and “Intellectual Property” demonstration cities, this paper looks more closely at the conveying channel of the dual pilot strategy on reinforcing the economic resilience of cities from the perspectives of innovation-driven effects, entrepreneurial incentive effects, and economic agglomeration effects (see Fig.\u0026nbsp;1).\u003c/p\u003e\u003ch3\u003eInnovation driven effect\u003c/h3\u003e\u003cp\u003eInnovation is the fundamental internal motivating factor behind the sustained growth and transformation of urban economies. Schumpeter’s theory of creative destruction reveals that technological innovation, through the restructuring of production factors and innovation in production functions, not only spurs the emergence of new industrial sectors but also establishes a dynamic competitive advantage in regional economies [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. The “Broadband China” strategy and the dual pilot policies of the cities that serve as the national demonstration centers for intellectual property enhance urban economic resilience by optimizing the configuration of innovation factors, cultivating innovative talent, and fostering a creative environment. First, the configuration of innovation factors is the material foundation for building a resilient economy. As digital infrastructure flourishes, the paradigm of resource flow is altered, enabling the spatial reallocation of innovation resources. According to new structural economics, broadband networks and other infrastructures reduce information friction and geographical constraints, boosting the marginal productivity of technology and capital. This process eliminates the “digital divide” and generates a space compression effect, improving how the innovation factors are set up and overcoming the conventional center-periphery structure. In the integration of innovation and industrial chains, network externalities drive the formation of multi-centered innovation clusters, enhancing regional economic risk diversification. By optimizing resource allocation, urban economies reduce dependence on single industries, thus improving resilience to external shocks and strengthening adaptability and recovery capacity [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e][\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Second, the cultivation of innovative talent is the key driver of resilience evolution. Intellectual property demonstration policies balance patent quality signals with compensation for knowledge spillovers, effectively solving the “Arrow’s information paradox” and encouraging human capital to concentrate on research and development-intensive fields. In the digital economy era, platform-based organizations create reputation mechanisms through bilateral markets, enhancing the efficiency of allocating innovative talent. The “density economy” feature of talent, through knowledge recombination, generates a continuous flow of innovation, serving as a core buffer against economic fluctuations. Cities with more innovative cultures can recover more quickly, optimize management, and stimulate industrial evolution and modernization. This leads to more stable and sustainable development while also enhancing their adaptability and resilience [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e][\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e][\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Third, optimizing the innovation environment serves as the institutional guarantee for resilience. The “Broadband China” strategy reduces institutional transaction costs and reshapes the incentive structure of enterprise innovation behaviors. The development of intellectual property demonstration cities is an essential element of the evolution of property rights systems, effectively promoting patent applications [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. This creates an “innovation institutional matrix” that establishes a dynamic balance between patent governance and technological standardization. Particularly in the digital economy, the development of data rights confirmation systems and computing infrastructure stimulates new types of innovation infrastructure, providing dynamic support for economic resilience. Consequently, we put out the following theory:\u003c/p\u003e\u003cp\u003eH4: The construction of “Broadband China” demonstration cities, “Intellectual Property” demonstration cities, and the dual pilot policies can enhance the economic resilience of pilot cities by promoting innovation driven paths.\u003c/p\u003e\u003ch3\u003eEntrepreneurial incentive effect\u003c/h3\u003e\u003cp\u003eSchumpeter’s theory of innovation suggests that entrepreneurship is essentially an entrepreneurial response to market imbalances, accelerating industry evolution through the entry of new firms. In this process, the synergistic evolution of digital infrastructure and intellectual property systems provides dual driving forces for entrepreneurial incentives. This not only lowers barriers to entrepreneurship and optimizes the innovation ecosystem but also increases the effectiveness and caliber of entrepreneurship, thereby enhancing a municipality’s ability to withstand shocks from the outside world and respond economically [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. First, reducing entrepreneurial costs. The widespread adoption of broadband networks and cloud service platforms has driven the shift from traditional, heavy-asset entrepreneurial models to more lightweight, digitalized models. This significantly lowers the capital threshold for startups, turning fixed costs into scalable digital service expenses. At the same time, the digitalization of government processes compresses administrative approval and market entry cycles, significantly reducing startup time costs. In terms of intellectual property, expedited review processes and enhanced infringement compensation mechanisms shorten technology confirmation cycles and increase the penalties for counterfeiting, effectively removing institutional barriers in the commercialization of innovation. These synergistic measures facilitate the efficient flow and concentration of innovation resources, fostering more flexible and efficient entrepreneurial entities and improving the city’s economic capacity to react swiftly to market swings and outside shocks. Second, optimizing the entrepreneurial environment. New collaboration models have emerged as a result of the extensive usage of digital infrastructure. Blockchain-based credit evaluation systems and data-sharing platforms reduce information asymmetry, enabling startups to connect with supply chains and market channels quickly. The development of intellectual property demonstration cities, by accelerating patent review, strengthening infringement penalties, and improving technology transfer markets, effectively shortens the commercialization cycle of technologies, ensures innovation returns, and bridges the gap between R\u0026amp;D investment and market returns. These collaborative actions not only improve the efficiency of innovation resource allocation but also reduce entrepreneurial risks and transaction costs, fostering diversified market entities with the ability to withstand volatility, thereby enhancing the city’s economic resilience and adaptability. Third, increasing entrepreneurial opportunity discovery. The improvement of digital infrastructure effectively meets entrepreneurs’ information needs, and the availability of sufficient information helps increase the likelihood of discovering entrepreneurial opportunities [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Big data technologies deeply analyze consumer behavior and industry trends, assisting entrepreneurs in precisely identifying market needs and forecasting technological evolution. Intelligent matching systems break down the barriers between technology supply and commercial application scenarios. At the same time, the data confirmation systems under intellectual property frameworks protect the core digital assets of enterprises, facilitating the efficient operation of technology transaction markets and enabling startups to access key innovation resources rapidly. These initiatives increase the efficiency with which entrepreneurs discover opportunities and convert them into tangible results. At the same time, the real-time information networks of digital platforms overcome the information lag in traditional markets, allowing urban economies to capture opportunities during technological transformations swiftly. Consequently, we put out the following theory:\u003c/p\u003e\u003cp\u003eH5: The construction of “Broadband China” demonstration cities, “Intellectual Property” demonstration cities, and the dual pilot policies can enhance the economic resilience of pilot cities by promoting entrepreneurial incentive pathways\u003c/p\u003e\u003ch2\u003eEconomic agglomeration effect\u003c/h2\u003e\u003cp\u003eThe progression of digital infrastructure and innovation in intellectual property systems have reshaped industrial organizational forms, enhanced factor allocation efficiency, and accelerated knowledge spillover effects, creating multidimensional economic agglomeration advantages. This agglomeration effect enables a more efficient concentration of innovation resources [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e], providing strong structural support for the resilience of urban economies. First, in terms of industrial agglomeration, broadband networks and industrial internet platforms break traditional geographical boundaries, enabling cross-regional production collaboration and real-time data interaction. Cloud computing transforms capital-intensive manufacturing processes into scalable, distributed digital service modules, further enhancing the flexibility and adaptability of industrial chains. Additionally, intellectual property demonstration policies, through the creation of patent pools and mutual recognition of technical standards, reduce technological collaboration barriers among firms and promote the formation of industry clusters characterized by flexible supply chains. This “digital connectivity - shared property rights” organizational model allows urban economies to respond quickly to market fluctuations and speeds up the formation and expansion of emerging fields by facilitating the emergence and propagation of innovative techniques and information, thereby strengthening urban economic competitiveness and innovation capacity [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Second, in terms of resource agglomeration, the resource agglomeration effect presents new characteristics through the synergy between digital platforms and intellectual property transactions. IoT technology enables full traceability and precise matching of production factors, while blockchain-based resource trading markets improve the cross-domain flow efficiency of advanced factors, including technology and resources. The innovation in intellectual property securitization addresses the liquidity dilemma of technological assets, transforming patent resources held by companies into tradable capital elements. The element circulation network created by digital infrastructure and the benefit-sharing mechanism ensured by the intellectual property system jointly drive resources toward high-value-added fields, fostering a diversified economic structure that is resilient to the risks of a single industry. Third, in terms of knowledge agglomeration, the dual promotion of data openness, sharing, and intellectual property protection has accelerated the formation of innovation networks. Big data platforms integrate multidimensional resources from industry, academia, and research, building cross-organizational digital collaboration spaces. Meanwhile, artificial intelligence technology structures and intelligently pushes tacit knowledge. Intellectual property systems solve the “free rider” problem in open innovation by creating models for measuring knowledge contributions and distributing benefits. These mechanisms facilitate data flow and confirm intellectual property, driving continuous breakthroughs in urban innovation ecosystems. They accelerate the rapid diffusion of technologies and further strengthen the iterative capabilities of industries. Consequently, we put out the following theory:\u003c/p\u003e\u003cp\u003eH6: The construction of “Broadband China” demonstration cities, “Intellectual Property” demonstration cities, and the dual pilot policies can enhance the economic resilience of pilot cities by promoting economic agglomeration pathways.\u003c/p\u003e"},{"header":"Model construction and variable description","content":"\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003eModel design\u003c/h2\u003e\u003cp\u003eA benchmark multi-period difference-in-differences (DID) model is described as follows to help for evaluating the effects of the dual pilot initiatives, the establishment of demonstration cities for “Broadband China” and “Intellectual Property” on the growth of economic resilience in pilot cities:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{U}\\text{E}\\text{R}}_{\\text{i}\\text{t}}={{\\alpha\\:}}_{0}+{{\\alpha\\:}}_{1}{\\text{B}\\text{C}\\text{S}}_{\\text{i}\\text{t}}+{{\\alpha\\:}}_{\\text{n}}{\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}}_{\\text{i}\\text{t}}+{{\\delta\\:}}_{\\text{i}}+{{\\mu\\:}}_{\\text{t}}+{{\\epsilon\\:}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{U}\\text{E}\\text{R}}_{\\text{i}\\text{t}}={{\\varnothing}}_{0}+{{\\varnothing}}_{1}{\\text{I}\\text{P}\\text{C}\\text{S}}_{\\text{i}\\text{t}}+{{\\varnothing}}_{\\text{n}}{\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}}_{\\text{i}\\text{t}}+{{\\delta\\:}}_{\\text{i}}+{{\\mu\\:}}_{\\text{t}}+{{\\epsilon\\:}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{U}\\text{E}\\text{R}}_{\\text{i}\\text{t}}={{\\theta\\:}}_{0}+{{\\theta\\:}}_{1}{\\text{D}\\text{P}\\text{P}}_{\\text{i}\\text{t}}+{{\\theta\\:}}_{\\text{n}}{\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}}_{\\text{i}\\text{t}}+{{\\delta\\:}}_{\\text{i}}+{{\\mu\\:}}_{\\text{t}}+{{\\epsilon\\:}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo better investigate the mechanism pathways of the construction of “Broadband China” demonstration cities, “Intellectual Property” demonstration cities, and the influence of dual pilot programs on urban economic resilience, this article sets the following mechanism effect model:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{M}}_{\\text{i}\\text{t}}={{\\beta\\:}}_{0}+{{{\\beta\\:}}_{1}{\\text{X}}_{\\text{i}\\text{t}}+{\\beta\\:}}_{\\text{n}}{\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}}_{\\text{i}\\text{t}}+{{\\delta\\:}}_{\\text{i}}+{{\\mu\\:}}_{\\text{t}}+{{\\epsilon\\:}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{U}\\text{E}\\text{R}}_{\\text{i}\\text{t}}={\\partial\\:}_{0}+{{\\partial\\:}_{1}{\\text{M}}_{\\text{i}\\text{t}}+\\partial\\:}_{\\text{n}}{\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}}_{\\text{i}\\text{t}}+{{\\delta\\:}}_{\\text{i}}+{{\\mu\\:}}_{\\text{t}}+{{\\epsilon\\:}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{U}\\text{E}\\text{R}}_{\\text{i}\\text{t}}={{\\gamma\\:}}_{0}+{{{\\gamma\\:}}_{1}{\\text{X}}_{\\text{i}\\text{t}}+{{\\gamma\\:}}_{2}{\\text{M}}_{\\text{i}\\text{t}}+{\\gamma\\:}}_{\\text{n}}{\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}}_{\\text{i}\\text{t}}+{{\\delta\\:}}_{\\text{i}}+{{\\mu\\:}}_{\\text{t}}+{{\\epsilon\\:}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eInside the formula: i, t represent the city and year respectively; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{U}\\text{E}\\text{R}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e Representing a city’s economic resiliency, expressed as the level of economic resilience of city i in year t; the core explanatory variables are the construction of “Broadband China” demonstration cities, the construction of “Intellectual Property” demonstration cities, and the dual pilot policies (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{B}\\text{C}\\text{S}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{I}\\text{P}\\text{C}\\text{S}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{D}\\text{P}\\text{P}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e), which are the multi period double difference sub items of whether city i is owned by the “Broadband China” demonstration city, whether it is owned by the “Intellectual Property” demonstration city, and whether it is a dual pilot city in year t, respectively; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{M}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e is the mediating variable (including technological innovation, entrepreneurial drive, and economic agglomeration); \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{X}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e Representing the three core explanatory variables mentioned above ; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e Representing control variables; to eliminate the impact of individual urban characteristics and time trends on urban economic resilience, the model introduces individual urban effects and time effects, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\delta\\:}}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e is the fixed effect of urban individuals, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\mu\\:}}_{\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e is the fixed effect of time, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\epsilon\\:}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e represents the random error term.\u003c/p\u003e\u003c/div\u003e\u003ch2\u003eModel design\u003c/h2\u003e\u003cp\u003eA benchmark multi-period difference-in-differences (DID) model is described as follows to help for evaluating the effects of the dual pilot initiatives, the establishment of demonstration cities for “Broadband China” and “Intellectual Property” on the growth of economic resilience in pilot cities:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{U}\\text{E}\\text{R}}_{\\text{i}\\text{t}}={{\\alpha\\:}}_{0}+{{\\alpha\\:}}_{1}{\\text{B}\\text{C}\\text{S}}_{\\text{i}\\text{t}}+{{\\alpha\\:}}_{\\text{n}}{\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}}_{\\text{i}\\text{t}}+{{\\delta\\:}}_{\\text{i}}+{{\\mu\\:}}_{\\text{t}}+{{\\epsilon\\:}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{U}\\text{E}\\text{R}}_{\\text{i}\\text{t}}={{\\varnothing}}_{0}+{{\\varnothing}}_{1}{\\text{I}\\text{P}\\text{C}\\text{S}}_{\\text{i}\\text{t}}+{{\\varnothing}}_{\\text{n}}{\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}}_{\\text{i}\\text{t}}+{{\\delta\\:}}_{\\text{i}}+{{\\mu\\:}}_{\\text{t}}+{{\\epsilon\\:}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{U}\\text{E}\\text{R}}_{\\text{i}\\text{t}}={{\\theta\\:}}_{0}+{{\\theta\\:}}_{1}{\\text{D}\\text{P}\\text{P}}_{\\text{i}\\text{t}}+{{\\theta\\:}}_{\\text{n}}{\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}}_{\\text{i}\\text{t}}+{{\\delta\\:}}_{\\text{i}}+{{\\mu\\:}}_{\\text{t}}+{{\\epsilon\\:}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo better investigate the mechanism pathways of the construction of “Broadband China” demonstration cities, “Intellectual Property” demonstration cities, and the influence of dual pilot programs on urban economic resilience, this article sets the following mechanism effect model:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{M}}_{\\text{i}\\text{t}}={{\\beta\\:}}_{0}+{{{\\beta\\:}}_{1}{\\text{X}}_{\\text{i}\\text{t}}+{\\beta\\:}}_{\\text{n}}{\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}}_{\\text{i}\\text{t}}+{{\\delta\\:}}_{\\text{i}}+{{\\mu\\:}}_{\\text{t}}+{{\\epsilon\\:}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{U}\\text{E}\\text{R}}_{\\text{i}\\text{t}}={\\partial\\:}_{0}+{{\\partial\\:}_{1}{\\text{M}}_{\\text{i}\\text{t}}+\\partial\\:}_{\\text{n}}{\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}}_{\\text{i}\\text{t}}+{{\\delta\\:}}_{\\text{i}}+{{\\mu\\:}}_{\\text{t}}+{{\\epsilon\\:}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{U}\\text{E}\\text{R}}_{\\text{i}\\text{t}}={{\\gamma\\:}}_{0}+{{{\\gamma\\:}}_{1}{\\text{X}}_{\\text{i}\\text{t}}+{{\\gamma\\:}}_{2}{\\text{M}}_{\\text{i}\\text{t}}+{\\gamma\\:}}_{\\text{n}}{\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}}_{\\text{i}\\text{t}}+{{\\delta\\:}}_{\\text{i}}+{{\\mu\\:}}_{\\text{t}}+{{\\epsilon\\:}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eInside the formula: i, t represent the city and year respectively; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{U}\\text{E}\\text{R}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e Representing a city’s economic resiliency, expressed as the level of economic resilience of city i in year t; the core explanatory variables are the construction of “Broadband China” demonstration cities, the construction of “Intellectual Property” demonstration cities, and the dual pilot policies (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{B}\\text{C}\\text{S}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{I}\\text{P}\\text{C}\\text{S}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{D}\\text{P}\\text{P}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e), which are the multi period double difference sub items of whether city i is owned by the “Broadband China” demonstration city, whether it is owned by the “Intellectual Property” demonstration city, and whether it is a dual pilot city in year t, respectively; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{M}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e is the mediating variable (including technological innovation, entrepreneurial drive, and economic agglomeration); \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{X}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e Representing the three core explanatory variables mentioned above ; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e Representing control variables; to eliminate the impact of individual urban characteristics and time trends on urban economic resilience, the model introduces individual urban effects and time effects, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\delta\\:}}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e is the fixed effect of urban individuals, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\mu\\:}}_{\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e is the fixed effect of time, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\epsilon\\:}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e represents the random error term.\u003c/p\u003e\u003ch2\u003eVariable selection\u003c/h2\u003e\u003ch2\u003eExplained variables\u003c/h2\u003e\u003cp\u003eThis research utilizes the entropy weight method and utilizes urban economic resilience as the dependent variable. It constructs an indicator system for urban economic resilience levels by selecting 14 indicators from three different aspects (see Table\u0026nbsp;1).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMeasurement system of urban economic resilience indicators\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndicator system\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLevel 1 indicators\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLevel 2 indicators\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnit\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDirection\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWeight\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"13\" rowspan=\"14\"\u003e\u003cp\u003eUrban economic resilience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eResistance and recovery ability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePer capita regional GDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYuan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0467\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUrban residents’ disposable income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYuan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0208\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRural residents’ savings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYuan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUrban registration population loss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePeople\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.1096\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExport total/Regional GDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eAdaptability and regulatory ability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLocal fiscal revenue expenditure ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0237\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSocial consumption retail total\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYuan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0959\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTertiary industry value/Regional GDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0072\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYear-end financial institution loans\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYuan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0232\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFixed asset investment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYuan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0839\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eTransformation and development capability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of special licenses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eItems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.1826\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of students in universities per 10000 people\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePeople\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0864\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGovernment science and technology output\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYuan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0812\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFiscal education output\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYuan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.2357\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eCore explanatory variables\u003c/h2\u003e\u003cp\u003eThe assignment methodologies for the three core explanatory variables in this study are as follows. First, in accordance with the “Broadband China” Strategy and Implementation Plan promulgated by the State Council in 2013, the National Development and Reform Commission (NDRC) and the Ministry of Industry and Information Technology (MIIT) published the lists of “Broadband China” demonstration cities between 2014 and 2016. As of the end of 2022, 105 out of the 271 sample cities were designated as demonstration cities, thus constituting the treatment sample, with the control sample consisting of the remaining cities. Following is the assignment rule: a city is assigned a value of 1 if it was designated as a demonstration city in a given year or subsequently; otherwise, it is assigned a value of 0. Second, the China National Intellectual Property Administration (CNIPA) established the “National Intellectual Property Pilot and Demonstration Cities (Districts) Evaluation Guidelines” in 2013. Toward the end of 2022, a total of 59 cities had been selected in six rounds of demonstration city evaluations, forming the treatment sample, while the control sample consisted of other cities. The assignment rule for this variable is: if a city was designated as a demonstration city in a given year or later, it is assigned a value of 1; otherwise, it is assigned a value of 0. Third, an overlap exists between the “Broadband China” and “Intellectual Property” demonstration cities, forming a subset of dual-pilot cities. By the end of 2022, 42 of the 271 sample cities were dual-pilot cities, which were categorized into the treatment sample, while the remaining cities constituted the control sample. The assignment rule for the dual-pilot variable is: if a city was simultaneously designated as both a “Broadband China” demonstration city and an “Intellectual Property” demonstration city in a given year or later, it is given the value of 1; otherwise, it is given the value 0.\u003c/p\u003e\u003ch2\u003eControl variables\u003c/h2\u003e\u003cp\u003eThis study employs the following four control variables.Urban population density (Pop) is calculated as the natural logarithm of the ratio of the total land area of the city’s administrative territory to the total registered population at the end of the year. Degree of financial development (Fin) is determined by the proportion of the whole amount of deposits and loans held by financial organizations at the end of the year to the regional Gross Domestic Product (GDP). Upgrades to industrial structures (Str) is evaluated by a composite index that combines the value-added ratios of each sector to GDP, where the first sector’s value-added ratio to GDP is weighted by a factor of 1, the second sector’s ratio by a factor of 2, and the third sector’s ratio by a factor of 3. The urban income level (Income) is symbolised by the natural logarithm of the typical salary of workers in cities.\u003c/p\u003e\u003ch2\u003eMechanism variables\u003c/h2\u003e\u003cp\u003eThis study incorporates three mechanism variables, defined as follows. Technological innovation (TI) is quantified as the amount of the overall quantity of applications for patents to the inhabitants at year-end. Entrepreneurial drive (EI) is gauged by the percentage of the combined number of private sector and independent contractors to the total number of employed individuals, relative to the resident population. Economic agglomeration (EA) is symbolized by the proportion of regional Gross Domestic Product (GDP) to area of the administrative region’s land.\u003c/p\u003e\u003ch2\u003eData sources\u003c/h2\u003e\u003cp\u003eAccording to the listings of “Intellectual Property” and “Broadband China” pilot cities published on the official websites of the Ministry of Industry and Information Technology and the National Intellectual Property Administration of China, there are 105 “Broadband China” pilot cities in the sample, with 36 located in the eastern region (34.29% of the sample), 38 in the central region (36.19%), and 31 in the western region (29.52%). There are 59 “Intellectual Property” pilot cities in the sample, with 35 in the eastern region (59.32%), 16 in the central region (27.12%), and 8 in the western region (13.56%). The sample also includes 42 dual-pilot cities, with 24 in the eastern region (57.15%), 14 in the central region (33.33%), and 4 in the western region (9.52%). A complete list of cities can be found in Fig.\u0026nbsp;2. Data from Tibet, Taiwan, Macau, and Hong Kong are not included for the reason of lacking information. The unprocessed data for the remaining variables is gathered from enterprise annual reports from the National Bureau of Statistics and different cities, as well as the China City Statistical Yearbook, China Energy Statistical Yearbook, China Industrial Statistical Yearbook, China Environmental Statistical Yearbook, and China Science and Technology Statistical Yearbook. Linear interpolations are employed to fill in the missing information. Table\u0026nbsp;2 offers descriptive statistics for the primary factors.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSymbol\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSample size\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStandard deviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMinimum value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMaximum value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUER\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.815\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1866103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.3896417\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIPCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1072064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.3094099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.541\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePop\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0331\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.329\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.760\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.507\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.480\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e181.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.892\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00753\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e138.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eEmpirical analyses\u003c/h2\u003e\u003ch2\u003eBenchmark regression\u003c/h2\u003e\u003cp\u003eThe “Broadband China” and “Intellectual Property” pilot programs both have a noteworthy positive effect on the resiliency of the metropolitan economy, with a 1% degree of significance regression coefficients. This confirms that both policies effectively enhance economic resilience in pilot cities. A comparison of coefficient magnitudes shows that the “Intellectual Property” pilot program has a stronger effect. This is because it not only fosters innovation factor agglomeration and technology commercialization, directly enhancing urban innovation capacity and industrial competitiveness, but also optimizes the market environment, strengthens property rights protection, and improves long-term investment expectations. These mechanisms contribute to greater economic stability and adaptability. Compared to the pilots with a single strategy, the regression coefficient of the dual-pilot strategy is greater and also substantially positive at the 1% level. This suggests that the dual pilot approach works better in strengthening urban economic resilience. Thus, Hypotheses H1, H2, and H3 are supported.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBenchmark regression results.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUER\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUER\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUER\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0154\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIPCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0320\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(6.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0344\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(5.21)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePop\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0369\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8880\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8913\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(7.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(7.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(6.96)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1350\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1418\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1420\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(13.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(14.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(14.77)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1289\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1301\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1309\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(8.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(8.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(8.15)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0229\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0240\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0242\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(3.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3.29)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.614\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNote:\u003csup\u003e*\u003c/sup\u003e, \u003csup\u003e**\u003c/sup\u003e, \u003csup\u003e***\u003c/sup\u003e indicate 10 per cent, 5 per cent and 1 per cent significance levels, respectively, with standard errors in parentheses. As below.\u003c/p\u003e\u003ch2\u003eParallel trend test\u003c/h2\u003e\u003cp\u003eTo estimate the repercussions of policies, the difference-in-differences (DID) model is adopted in this investigation. A key assumption for DID estimation is the parallel trend condition, which requires that the treatment and control samples exhibit corresponding trends prior to the introduction of the policy. The test results are presented in Fig.\u0026nbsp;3, where panels (a), (b), and (c) correspond to the “Broadband China” pilot, the “Intellectual Property” pilot, and the dual pilot policy, respectively. The premise of parallel trends is supported by the lack of a discernible difference in urban economic resilience among the two sets of participants prior to the adoption of the program. After policy implementation, as compared to the control sample, the treatment sample exhibits extremely greater economic resilience. The outcomes above offer preliminary proof that the influence of all three strategies on urban economic resilience is extremely favorable.\u003c/p\u003e\u003ch2\u003eRobustness tests\u003c/h2\u003e\u003ch2\u003eEndogeneity test\u003c/h2\u003e\u003cp\u003eTo mitigate any endogeneity issues, the instrumental variable (IV) approach is used in this investigation. Specifically, terrain ruggedness affects digital infrastructure development, broadband access quality, and network signal strength while also influencing population density and economic activity. These factors, in turn, impact policy selection and implementation. Since terrain ruggedness is significantly correlated with the “Broadband China” and “Intellectual Property” pilot programs, as well as the dual pilot policy, and is an objectively determined geographical factor, it meets the homogeneity assumption. Therefore, this study constructs an interaction term between terrain ruggedness and policy variables as the IV and applies the two-stage least squares (2SLS) method. The first-stage regression shows a substantial positive association between the IV and the three policy variables. Regression analysis in the second step reveals that the fitted policy variables contribute significantly to the resiliency of the metropolitan economy, with coefficients that align with the basic framework. These findings confirm that the endogeneity issue is effectively mitigated, and the conclusions of the baseline model remain robust.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of instrumental variable method test.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e2SLS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e2SLS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e2SLS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFirst phase\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSecond phase\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFirst phase\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSecond phase\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFirst phase\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSecond phase\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUER\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIPCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUER\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eUER\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.4105\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6035\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.6502\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(44.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(39.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(42.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0086\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIPCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0454\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(8.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0401\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(6.31)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControlled variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.454\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.778\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003ePlacebo test\u003c/h2\u003e\u003cp\u003eTo validate that the effects being seen are not the result of chance, treatment groups and pilot policy implementation timeframes were assigned at random to perform a placebo test. There were 500 regressions conducted in all. Figure\u0026nbsp;4 exhibits what we find, where panels (a), (b), and (c) display the regression coefficient distribution for the “Broadband China” pilot, the “Intellectual Property” pilot, and the dual pilot policy, respectively. The placebo policy coefficients follow a normal distribution centered around zero and exhibit a notable deviation from the regression’s first findings. This confirms that arbitrary outside influences do not drive outcomes obtained from the baseline model but are indeed attributable to the execution of pilot programs.\u003c/p\u003e\u003ch2\u003ePSM-DID verification\u003c/h2\u003e\u003cp\u003eIn order to match suitable control groups for dual-pilot cities, this research employs three techniques: kernel matching, radius matching, and closest neighbor matching. After matching, a t-test is performed to verify the balance of core city characteristics between the groups receiving treatment and those receiving control, ensuring no systematic differences between the samples. Table\u0026nbsp;5 exhibits the regression outcomes of the PSM-DID model.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRegression results of PSM-DID model.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eNeighbor matching\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eRadius matching\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eKernel matching\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0153\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0141\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0141\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(3.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(3.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIPCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0320\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0327\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.0328\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(6.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(5.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0349\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0343\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.0343\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(5.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(4.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(4.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e4301\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControlled variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.611\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.611\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eOther robustness tests\u003c/h2\u003e\u003cp\u003eFirst, other policy interferences are excluded. Considering that policies such as the Electronic Government Comprehensive Pilot (CEGPP) might influence urban economic resilience during the study, a comparable policy the baseline model for verifying contains a dummy variable. Table\u0026nbsp;6 exhibits the outcomes of the regression, column (1). Second, sample data screening is performed. To minimize possible bias and the effect of extremes on the baseline regression, the first and 99th percentiles of the statistics are used for trimming to reduce distortion and systematic bias in the outcomes of regression. Table\u0026nbsp;6, column (2), exhibits the regression findings. Third, key cities are excluded. Given that provincial capitals and sub-provincial cities may receive more policy benefits due to their special geographic, economic, and policy status, potentially causing heterogeneity in urban economic resilience, key cities are excluded to avoid interference with the analysis. Table\u0026nbsp;6, column (3), exhibits the regression findings. Fourth, owing to the consequences of the COVID-19 pandemic on the resilience of metropolitan economies in 2020, this study excludes the 2020 sample and redefines the time window for regression. Table\u0026nbsp;7, column (1), exhibits the regression findings. Fifth, a lag effect regression is conducted. Considering that there may be a delay in the influence of pilot initiatives on urban economic resilience, every explanatory variable is retested after a one-period lag. Table\u0026nbsp;7, column (2), exhibits the regression outcomes. The robustness tests verify that the “Broadband China” and “Intellectual Property” pilot programs, as well as the dual pilot policy, all contribute significantly to the resilience of the urban economy. Moreover, the dual pilot initiative has greater significance than just the single pilot strategy, and the “Intellectual Property” pilot program outperforms the “Broadband China” initiative. These outcomes demonstrate how solid and trustworthy the study’s conclusions are.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOther robustness tests.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eExclude interference from other policies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eExclude interference from other policies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eExclude key cities\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0157\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0141\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0155\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3.7760)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(3.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(3.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIPCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0311\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0314\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.0333\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(6.0016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(5.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0338\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0321\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.0358\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(5.0669)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(4.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(4.63)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCEGPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0130\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0088\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0106\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.5596)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.7511)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2.1110)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e4065\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControlled variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.604\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.626\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.641\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.655\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.652\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOther robustness tests.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eChange time window\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eLag effect regression\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0149\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0153\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(3.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIPCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0323\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0344\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(6.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0349\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0317\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(5.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(4.77)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4215\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControlled variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.570\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eRe examination of the synergistic effect of the dual pilot program\u003c/h2\u003e\u003cp\u003eFirst, the net effects of of single pilot policies. To evaluate the separate effects of every pilot strategy, two separate analyses were conducted: First, The “Intellectual Property” pilot city sample was excluded. The treatment sample consisted of solely “Broadband China” trial cities, while cities that were neither “Broadband China” nor “Intellectual Property” pilot cities functioned as the control sample. This study sought to determine how the “Broadband China” project affected the economic resilience of cities. Second, The “Broadband China” pilot city sample was excluded. Cities that were only “Intellectual Property” pilot cities were set as the treatment sample, with the same control sample as in the first analysis. This allowed for an assessment of the independent effect of the “Intellectual Property” initiative on economic resilience. Based on Table\u0026nbsp;8, Column 1’s regression findings, both pilot programs considerably boost the strength of the metropolitan economy. However, the “Intellectual Property” pilot policy exhibits an increasingly powerful impact than the “Broadband China” initiative.\u003c/p\u003e\u003cp\u003eNext, the net effect of the strategy of dual pilots. To evaluate the net impact of the strategy of dual pilots, non-pilot cities were excluded. Cities that only had one pilot program were set as the control group, while cities with both pilot policies were designated as the treatment group. This analysis aimed to assess the additional resilience gains when a city transitions switching from one to two pilot programs. At the 1% level, the regression statistics (Table\u0026nbsp;8, Column 2) demonstrate that the dual pilot policy’s coefficient is considerably favorable. This indicates that, compared to one-pilot initiatives, the dual-pilot policy has a greater influence on boosting the economic resilience of cities.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe synergistic effect test results of the dual pilot program.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eNet effect of single pilot program\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eThe net effect of the dual pilot program\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0081\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0081\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(1.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIPCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0344\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0344\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(3.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(3.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0245\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0245\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(3.53)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3552\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1952\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3552\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1952\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControlled variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.630\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.630\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"Further analyses","content":"\u003cdiv id=\"Sec36\" class=\"Section2\"\u003e\u003ch2\u003eMechanism testing\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;9 exhibits the outcomes of the innovation-driven mechanism test. From the perspective of the innovation-driven pathway, the \u0026ldquo;Broadband China\u0026rdquo; and \u0026ldquo;Intellectual Property\u0026rdquo; pilot programs, as well as the dual pilot policy, significantly promote technological innovation in pilot cities. In comparison to single pilot programs, the dual pilot strategy has a more substantial effect, and the \u0026ldquo;Intellectual Property\u0026rdquo; pilot program has a greater impact than the \u0026ldquo;Broadband China\u0026rdquo; initiative. Furthermore, innovation in technology drastically boosts the resilience of the urban economy. When the mechanism variable \u0026ldquo;technological innovation\u0026rdquo; is included in the baseline model, its regression coefficient remains significantly positive. The regression coefficients of policy variables also remain positive but decrease compared to the baseline model, indicating that technological innovation functions as a moderator. Additionally, the Sobel test yields Z-values of 2.789, 4.701, and 4.03, all exceeding the critical threshold of 0.97 at the 5% significance level. The Bootstrap test outcomes indicate 95% confidence intervals of [7.06e-06, 0.000039], [0.0028414, 0.0073088], and [0.0000384, 0.0001249], none of which include zero. These findings further confirm the significance of the innovation-driven mechanism. Thus, Hypothesis 4 is supported.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTest results of technological innovation mechanism.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eTI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u003cp\u003eUER\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.5818\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0148\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(7.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(2.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0012\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIPCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.6873\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(11.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(10.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.5818\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0148\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(7.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(2.36)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0012\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0011\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0011\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0011\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(11.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(9.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(9.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(9.43)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4469\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControlled variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.611\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.644\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.611\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.652\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.651\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.652\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn Table\u0026nbsp;10, the outcomes of the entrepreneurial incentive mechanism test are presented. From the perspective of the entrepreneurial incentive pathway, the \u0026ldquo;Broadband China\u0026rdquo; and \u0026ldquo;Intellectual Property\u0026rdquo; pilot programs, as well as the policy of dual pilots, considerably boost entrepreneurial incentives in pilot cities. Among them, the dual pilot policy has a stronger effect than single pilot policies, and the \u0026ldquo;Intellectual Property\u0026rdquo; pilot program has a greater impact than the \u0026ldquo;Broadband China\u0026rdquo; initiative. Moreover, increased entrepreneurial incentives significantly improve urban economic resilience. When the mechanism variable \u0026ldquo;entrepreneurial incentive\u0026rdquo; is included in the baseline model, its regression coefficient remains significantly positive. The policy variables also remain positive but decrease in magnitude, indicating that entrepreneurial incentives play a mediating role. Additionally, the Sobel test yields Z-values of 8.693, 7.461, and 11.84, all exceeding the critical threshold of 0.97 at the five percent threshold for significance. The Bootstrap test findings indicate 95% confidence intervals of [0.0001861, 0.0003179], [0.0116019, 0.0213764], and [0.0003827, 0.0009839], none of which include zero. These findings further confirm the significance of the entrepreneurial incentive mechanism. Thus, Hypothesis 5 is supported.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTest results of entrepreneurship driven mechanism.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eEI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u003cp\u003eUER\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0476\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0335\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIPCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0355\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0313\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(6.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0476\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0335\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(5.16)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0250\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0180\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0187\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0180\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(2.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(1.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(1.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControlled variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.615\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.615\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn Table\u0026nbsp;11, the outcomes of the economic agglomeration mechanism test are presented. From the perspective of the agglomeration-driven pathway, the \u0026ldquo;Broadband China\u0026rdquo; and \u0026ldquo;Intellectual Property\u0026rdquo; pilot programs, as well as the dual pilot policy, significantly promote economic agglomeration in pilot cities. In contrast with single pilot projects, the strategy of dual pilots has greater repercussions. And the \u0026ldquo;Intellectual Property\u0026rdquo; pilot program has a greater impact than the \u0026ldquo;Broadband China\u0026rdquo; initiative. Moreover, higher levels of economic agglomeration significantly enhance urban economic resilience. When the baseline model incorporates the mechanism variable \u0026ldquo;economic agglomeration,\u0026rdquo; its regression coefficient continues to maintain a substantially positive value. The policy variables also remain positive but decrease in magnitude, indicating that economic agglomeration plays a mediating role. Additionally, the Sobel test yields Z-values of 8.396, 8.071, and 11.19, all exceeding the critical threshold of 0.97 at the five percent threshold for significance. The Bootstrap test outcomes indicate 95% confidence intervals of [0.0001377, 0.0002462], [0.0109671, 0.021209], and [0.0004003, 0.0008316], none of which include zero. These findings further confirm the significance of the economic agglomeration mechanism. Thus, Hypothesis 6 is supported.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTest results of economic agglomeration mechanism.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eEA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u003cp\u003eUER\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.8865\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0224\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(6.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(3.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIPCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.5688\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0217\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(6.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(4.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.8865\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0224\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(6.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(3.17)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0053\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0042\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0040\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0042\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(5.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(4.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(4.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(4.43)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4496\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControlled variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.631\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.629\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec37\" class=\"Section2\"\u003e\u003ch2\u003eComparative analysis of different implementation sequences of the dual pilot policies\u003c/h2\u003e\u003cp\u003eIn the 42 dual-pilot city samples examined in this study, 10 cities first became \u0026ldquo;Broadband China\u0026rdquo; demonstration cities and then \u0026ldquo;Intellectual Property\u0026rdquo; demonstration cities. In contrast, 27 cities first became \u0026ldquo;Intellectual Property\u0026rdquo; demonstration cities and then \u0026ldquo;Broadband China\u0026rdquo; demonstration cities. Five cities (Guiyang, Xiamen, Yichang, Zhuzhou, and Zibo) became cities with two pilots during the same period. After excluding these five cities, the analysis was conducted in the following two ways: First, using the single-pilot city sample as the control group, the dual-pilot city samples were categorized into two treatment groups: those that first became \u0026ldquo;Broadband China\u0026rdquo; demonstration cities and then \u0026ldquo;Intellectual Property\u0026rdquo; demonstration cities, and those that first became \u0026ldquo;Intellectual Property\u0026rdquo; demonstration cities and then \u0026ldquo;Broadband China\u0026rdquo; demonstration cities. The outcomes are presented in Table\u0026nbsp;12\u0026rsquo;s column (1). Second, using non-pilot city samples as the control group, the same two treatment groups were tested, and the results are shown in column (2) of Table\u0026nbsp;12. In comparison, the effect of first becoming an \u0026ldquo;Intellectual Property\u0026rdquo; demonstration city and then a \u0026ldquo;Broadband China\u0026rdquo; demonstration city on urban economic resilience improvement is more significant. This is because the construction of \u0026ldquo;Intellectual Property\u0026rdquo; Demonstration Cities, by enhancing innovation capabilities and intellectual property protection, lays a solid foundation for subsequent information infrastructure construction, enhances the long-term technological investment confidence of innovation entities, and thus improves the economy\u0026rsquo;s agility and reliability.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparative test results of differences in the implementation sequence of the dual pilot policies.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eSample of pilot cities(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eSample of non pilot cities(2)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ldquo;Broadband China\u0026rdquo; pilot preceding \u0026ldquo;Intellectual Property\u0026rdquo; pilot\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ldquo;Intellectual Property\u0026rdquo; pilot preceding \u0026ldquo;Broadband China\u0026rdquo; pilot\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ldquo;Broadband China\u0026rdquo; pilot preceding \u0026ldquo;Intellectual Property\u0026rdquo; pilot\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ldquo;Intellectual Property\u0026rdquo; pilot preceding \u0026ldquo;Broadband China\u0026rdquo; pilot\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0276\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0302\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0425\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4.79)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1440\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2704\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2976\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControlled variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.622\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.624\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.625\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec38\" class=\"Section3\"\u003e\u003ch2\u003eHeterogeneity analysis\u003c/h2\u003e\u003c/div\u003e\u003cdiv id=\"Sec39\" class=\"Section3\"\u003e\u003ch2\u003eHeterogeneity of urban resources\u003c/h2\u003e\u003cp\u003eBased on the official classification standard in the National Sustainable Development Plan for Resource-Based Cities (2013\u0026ndash;2020), this study constructs a heterogeneity analysis model comparing resource-based and non-resource-based cities (see Table\u0026nbsp;13). The outcomes demonstrate that the treatment effects of the \u0026ldquo;Broadband China\u0026rdquo; and \u0026ldquo;Intellectual Property\u0026rdquo; pilot programs, as well as the policy of dual pilots, are statistically significant in both types of cities. However, the policy effects are notably stronger in resource-based cities. This discrepancy might be explained by the \u0026ldquo;Dutch disease\u0026rdquo; effect and the inflexible industrial frameworks of cities centered on resources, which often lead to path dependency in development. Policy interventions help overcome these constraints by breaking resource dependency, promoting industrial diversification, and improving the marginal substitutability of factor allocation. These mechanisms effectively address structural bottlenecks and enhance economic resilience. Conversely, non-resource-based cities face a dual challenge of industrial homogenization and underdeveloped innovation ecosystems. Their policy transmission mechanisms are constrained by market segmentation and institutional frictions, leading to diminishing marginal returns in technology diffusion.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab13\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHeterogeneity test results of urban resources.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResource-based cities\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-resource-based cities\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eResource-based cities\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNon-resource-based cities\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eResource-based cities\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNon-resource-based cities\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0148\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0139\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIPCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0336\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0290\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(5.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0393\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0297\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(2.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(3.98)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1808\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2688\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1808\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2688\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1808\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2688\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControlled variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.626\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.623\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.630\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.619\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec40\" class=\"Section3\"\u003e\u003ch2\u003eHeterogeneity of urban resilience\u003c/h2\u003e\u003cp\u003eTo determine the exact effect of policy implementation on the resiliency of the urban economy, this research constructs a heterogeneity analysis model comparing high-resilience and low-resilience urban clusters (see Table\u0026nbsp;14). The findings indicate that the results of therapy of the \u0026ldquo;Broadband China\u0026rdquo; and \u0026ldquo;Intellectual Property\u0026rdquo; pilot programs, as well as the dual pilot policy, are statistically significant in both types of cities. However, the policy effects are notably stronger in high-resilience clusters. The moderating role of initial urban conditions drives this gradient effect. High-resilience cities benefit from institutional redundancy, efficient factor allocation, and flexible industrial structures, allowing them to absorb policy benefits through Schumpeterian innovation mechanisms rapidly. In contrast, low-resilience cities face multiple barriers in policy transmission due to path dependency and deficiencies in their innovation ecosystems. These barriers manifest in three ways: factor misallocation reduces marginal returns, institutional frictions slow technology diffusion, and structural rigidity weakens adaptive adjustments. As a result, the effectiveness of policy interventions is diminished, further constraining efforts to enhance urban resilience.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab14\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 14\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHeterogeneity test results of urban resilience.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh resilience cluster\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow resilience cluster\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh resilience cluster\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLow resilience cluster\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHigh resilience cluster\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLow resilience cluster\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0111\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIPCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0275\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0224\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(5.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(10.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0252\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0224\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(3.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(10.88)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2248\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControlled variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.588\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.483\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.483\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eHeterogeneity of urban goals\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBy constructing a measurement framework for economic growth target anchoring, this study applies Python-based text mining algorithms to analyze the Government Work Reports of Chinese cities. Using the median economic growth target as a benchmark, a heterogeneity analysis is conducted to compare cities with high and low growth targets (see Table\u0026nbsp;14). The findings demonstrate that the treatment effects of the \u0026ldquo;Broadband China\u0026rdquo; and \u0026ldquo;Intellectual Property\u0026rdquo; pilot programs, as well as the dual pilot policy, are statistically significant in both types of cities. However, the policy effects are notably stronger in municipalities with high-level targets for economic expansion. This disparity in policy effectiveness stems from differences in urban development endowments. High-growth target cities benefit from stronger institutional capacity and greater industrial flexibility, allowing them to amplify policy dividends. By aligning policy objectives with market incentives, these cities enhance factor allocation efficiency, accelerate technology diffusion, and develop modular production systems that mitigate risks. As a result, they drive industrial upgrading, optimize resource allocation, and reinforce economic resilience. In contrast, low-growth target cities face deeper structural constraints in policy transmission. The combined effects of weakening growth momentum and limited innovation capacity reduce their ability to absorb and apply new technologies. Institutional frictions and factor misallocation further reinforce path dependence, lowering the marginal benefits of policy interventions. These challenges hinder industrial upgrading and slow progress in strengthening urban economic resilience.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab15\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 15\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHeterogeneity test results of urban goals.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh goals\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow goals\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh goals\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLow goals\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHigh goals\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLow goals\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0111\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIPCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0275\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0224\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(5.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(10.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0252\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0224\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(3.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(10.88)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2248\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControlled variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFixed urban effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFixed year effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.588\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.483\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.483\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Conclusions and recommendations","content":"\u003cp\u003e\u003cb\u003eResearch conclusions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study yields the following key findings: (1) The \u0026ldquo;Broadband China\u0026rdquo; and \u0026ldquo;Intellectual Property\u0026rdquo; pilot programs, as well as the dual pilot initiative, all significantly enhance the resiliency of the metropolitan economy. Among them, the consequence of the \u0026ldquo;Intellectual Property\u0026rdquo; pilot program is stronger than that of the \u0026ldquo;Broadband China\u0026rdquo; program. Moreover, the dual pilot policy produces greater resilience gains than either single policy alone. (2) Policy effects exhibit temporal sensitivity. The sequence of implementation matters\u0026mdash;cities that first adopt the \u0026ldquo;Intellectual Property\u0026rdquo; pilot followed by the \u0026ldquo;Broadband China\u0026rdquo; pilot experience greater policy benefits than those implementing the policies in the reverse order. (3) Both single and dual pilot policies improve economic resilience through three key channels: fostering technological innovation, stimulating entrepreneurship, and promoting economic agglomeration. (4) The effects of these policies are stronger in resource-based cities, highly resilient urban clusters, and cities with high economic growth targets.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePolicy recommendations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFirst, fostering synergies between different policies is crucial to encouraging the integrated advancement of intellectual property protection and digital infrastructure. The current pilot programs are part of a top-down institutional innovation strategy authorized by the central government. On this basis, local governments should further strengthen policy implementation by expanding support for the \u0026ldquo;Broadband China\u0026rdquo; and \u0026ldquo;Intellectual Property\u0026rdquo; pilot cities. Enhancing both the coverage and depth of these policies will maximize their impact. Compared to single-policy interventions, the combined effects of dual pilot policies are often more significant. With proactive local government engagement, the strategic allocation and integration of policy tools can maximize comprehensive benefits across multiple levels. Therefore, local policymakers should emphasize flexibility and innovation in policy execution. By strategically sequencing and coordinating different initiatives, they can reinforce the synergy between these two pilot programs, ultimately improving urban economic resilience more effectively.\u003c/p\u003e\u003cp\u003eSecond, sustaining economic resilience requires strengthening the transmission mechanisms linking digital infrastructure and intellectual property protection. Breakthrough technological innovation should serve as the core driver, while entrepreneurial dynamics provide critical support. At the same time, the scale and knowledge spillover effects of high-quality economic agglomeration must be leveraged to enhance adaptability, shock resistance, and recovery capacity in the face of external challenges. On one hand, the spatial planning of digital infrastructure should be carefully coordinated. A well-structured policy support system\u0026mdash;incorporating fiscal subsidies, tax incentives, and financial assistance\u0026mdash;can enhance policy precision, accessibility, and effectiveness.\u003c/p\u003e\u003cp\u003eAdditionally, mechanisms for attracting high-end talent, optimizing innovation resource allocation, and promoting industrial chain collaboration should be strengthened. These efforts will help eliminate institutional barriers that hinder economic agglomeration effects, unlocking the positive externalities and knowledge diffusion benefits of industrial clusters, thereby reinforcing the foundation of financial resilience. On the other hand, a comprehensive, long-term innovation incentive system must be established. Targeted policy measures should motivate businesses to increase their R\u0026amp;D spending while boosting their potential for imaginative thinking. The integration of corporations, educational institutions, and research organizations should be deepened to facilitate cross-sector collaboration. By fostering interdisciplinary, cross-industry innovation networks, policymakers can accelerate breakthroughs in key technological areas, driving structural upgrades in economic resilience.\u003c/p\u003e\u003cp\u003eThird, regional heterogeneity must be carefully considered when implementing pilot policies. Differences in resource endowments, economic development stages, and growth objectives require tailored strategies for integrating digital infrastructure and intellectual property protection. To optimize policy effectiveness, regional initiatives should be aligned with local development needs while remaining consistent with national strategic priorities. Strengthening coordination between differentiated policies will ensure they complement and reinforce each other, forming a coherent policy framework that supports both local and national development goals. Pilot programs should serve as catalysts for broader regional optimization, facilitating targeted interventions that drive systemic improvements. By adopting a phased, layered, and well-coordinated approach to regional policy implementation, governments can enhance policy precision, improve resource allocation efficiency, and quicken the formation of strategic emerging industries.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRestrictive discussion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis research offers insightful information for urban planning and policy decision-making but has certain limitations that warrant further exploration. First, the analysis of policy interactions remains insufficient. The study primarily examines the synergies between the \u0026ldquo;Broadband China\u0026rdquo; pilot cities, the \u0026ldquo;Intellectual Property\u0026rdquo; pilot cities, and the dual pilot policy, without considering their interactions with other initiatives such as the innovation-driven city pilot program and the digital economy development strategy. The total effect on the policy could be undervalued due to this omission. Second, the study does not incorporate firm-level factors. Urban economic resilience is shaped not only by macro-level policies but also by firms\u0026rsquo; adaptive strategies in technological innovation, digital transformation, supply chain stability, and financial capacity. Future research could integrate firm-level data to examine how businesses enhance urban resilience through innovation investment, supply chain optimization, and capital allocation. Additionally, further analysis is needed to assess the transmission pathways for the dual pilot strategy on a microlevel.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQ: Writing-review \u0026amp; editing, Conceptualisation, Project administration. X: Writing-original draft, Formal analysis, Data curation, Investigation, Writing-review \u0026amp; editing, Software. G: Software, Resources, Supervision. P: Validation, Methodology. L: Obtaining funds, Visualisation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article was completed with the support of the Guangxi Philosophy and Social Science Program “Study on the Performance Enhancement of Ethnic Inter-embedded Community Governance in Guangxi under the Consciousness of Building Chinese National Community (24SHC002)”; Humanities and Social Science Fund of Ministry of Education of China “Research on the Practice Mode, Influencing Factors and Path of Party Building Leading Urban Community Governance Community (23YJC840032)”.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWang Z, Wei W. Regional economic resilience in China: Measurement and determinants[J]. Regional studies, 2021, 55(7): 1228-1239.\u003c/li\u003e\n\u003cli\u003eFan J, Wang H, Zhang X. A General Equilibrium Analysis of Achieving the Goal of Stable Growth by China\u0026rsquo;s Market Expectations in the Context of the COVID-19 Pandemic[J]. Sustainability, 2022, 14(22): 15072.\u003c/li\u003e\n\u003cli\u003eAllam Z, Jones D S. Pandemic stricken cities on lockdown. Where are our planning and design professionals [now, then and into the future]?[J]. Land use policy, 2020, 97: 104805.\u003c/li\u003e\n\u003cli\u003eLimin B A I, Chunliang X, Xinghua F, et al. A comprehensive assessment of urban resilience and its spatial differentiation in China[J]. World Regional Studies, 2019, 28(6): 77.\u003c/li\u003e\n\u003cli\u003eBerkes F. Understanding uncertainty and reducing vulnerability: lessons from resilience thinking[J]. Natural hazards, 2007, 41: 283-295.\u003c/li\u003e\n\u003cli\u003eBirkmann J. Risk and vulnerability indicators at different scales: Applicability, usefulness and policy implications[J]. Environmental hazards, 2007, 7(1): 20-31.\u003c/li\u003e\n\u003cli\u003eCollins S L, Carpenter S R, Swinton S M, et al. An integrated conceptual framework for long‐term social\u0026ndash;ecological research[J]. Frontiers in Ecology and the Environment, 2011, 9(6): 351-357.\u003c/li\u003e\n\u003cli\u003eWen H, Liu Y, Zhou F. New-type infrastructure and urban economic resilience: Evidence from China[J]. International Review of Economics \u0026amp; Finance, 2024, 96: 103560.\u003c/li\u003e\n\u003cli\u003eWen H, Zhan J. New-type infrastructure and total factor productivity: evidence from listed manufacturing firms in China[J]. Economic Change and Restructuring, 2023, 56(6): 4465-4489.\u003c/li\u003e\n\u003cli\u003eZhou F, Wen H, Lee C C. Broadband infrastructure and export growth[J]. Telecommunications policy, 2022, 46(5): 102347.\u003c/li\u003e\n\u003cli\u003eTang J, Zhao X. Does the new digital infrastructure improve total factor productivity?[J]. Bulletin of Economic Research, 2023, 75(4): 895-916.\u003c/li\u003e\n\u003cli\u003eHe C, Zhou C, Wen H. Improving the consumer welfare of rural residents through public support policies: A study on old revolutionary areas in China[J]. Socio-Economic Planning Sciences, 2024, 91: 101767.\u003c/li\u003e\n\u003cli\u003eNair M, Pradhan R P, Arvin M B. Endogenous dynamics between R\u0026amp;D, ICT and economic growth: Empirical evidence from the OECD countries[J]. Technology in Society, 2020, 62: 101315.\u003c/li\u003e\n\u003cli\u003eRen S, Hao Y, Xu L, et al. Digitalization and energy: How does internet development affect China\u0026rsquo;s energy consumption?[J]. Energy Economics, 2021, 98: 105220.\u003c/li\u003e\n\u003cli\u003eAbrardi L, Sabatino L. Ultra-broadband investment and economic resilience: Evidence from the Covid-19 pandemic[J]. Telecommunications Policy, 2023, 47(2): 102480.\u003c/li\u003e\n\u003cli\u003ePapaioannou S K. ICT and economic resilience: Evidence from the COVID-19 pandemic[J]. Economic Modelling, 2023, 128: 106500.\u003c/li\u003e\n\u003cli\u003eTian Y, Guo L. Digital development and the improvement of urban economic resilience: Evidence from China[J]. Heliyon, 2023, 9(10).\u003c/li\u003e\n\u003cli\u003eBadri F, Amrina D H. Strengthening the MSME digital economy as Indonesia\u0026rsquo;s national economic resilience[C]//Journal of International Conference Proceedings. 2023, 6(6): 274-285.\u003c/li\u003e\n\u003cli\u003eSong Y, Xiu Y, Zhao M, et al. Intellectual property protection and enterprise innovation: Evidence from China[J]. Finance Research Letters, 2024, 62: 105253.\u003c/li\u003e\n\u003cli\u003eGould D M, Gruben W C. The role of intellectual property rights in economic growth[J]. Journal of development economics, 1996, 48(2): 323-350.\u003c/li\u003e\n\u003cli\u003ePack, H., 1994. Endogenous growth theory: intellectual appeal and empirical shortcomings. J. Econ. perspectives 8 (1), 55\u0026ndash;72.\u003c/li\u003e\n\u003cli\u003eRomer P M. Endogenous technological change[J]. Journal of political Economy, 1990, 98(5, Part 2): S71-S102.\u003c/li\u003e\n\u003cli\u003eBican P M, Guderian C C, Ringbeck A. Managing knowledge in open innovation processes: an intellectual property perspective[J]. Journal of Knowledge Management, 2017, 21(6): 1384-1405.\u003c/li\u003e\n\u003cli\u003eCandelin-Palmqvist H, Sandberg B, Mylly U M. Intellectual property rights in innovation management research: A review[J]. Technovation, 2012, 32(9-10): 502-512.\u003c/li\u003e\n\u003cli\u003eGrimaldi M, Greco M, Cricelli L. A framework of intellectual property protection strategies and open innovation[J]. Journal of Business Research, 2021, 123: 156-164.\u003c/li\u003e\n\u003cli\u003eChirico F, Criaco G, Ba\u0026ugrave; M, et al. To patent or not to patent: That is the question. Intellectual property protection in family firms[J]. Entrepreneurship Theory and Practice, 2020, 44(2): 339-367.\u003c/li\u003e\n\u003cli\u003eHwang H, Wu J Z, Yu E S H. Innovation, imitation and intellectual property rights in developing countries[J]. Review of development economics, 2016, 20(1): 138-151.\u003c/li\u003e\n\u003cli\u003eQian Y. Do national patent laws stimulate domestic innovation in a global patenting environment? A cross-country analysis of pharmaceutical patent protection, 1978\u0026ndash;2002[J]. The Review of Economics and Statistics, 2007, 89(3): 436-453.\u003c/li\u003e\n\u003cli\u003eFurukawa Y. Intellectual property protection and innovation: An inverted-U relationship[J]. Economics Letters, 2010, 109(2): 99-101.\u003c/li\u003e\n\u003cli\u003eEzzeddine S, Hammami M S. RETRACTED: Nonlinear Effects of Intellectual Property Rights on Technological Innovation[J]. Journal of Economic Integration, 2018, 33(2): 1337-1362.\u003c/li\u003e\n\u003cli\u003eWang C, Wang L. Does broadband infrastructure promote urban innovation? Evidence from \u0026ldquo;Broadband China\u0026rdquo; demonstration policy[J]. Structural Change and Economic Dynamics, 2024, 69: 349-362.\u003c/li\u003e\n\u003cli\u003eLuo Q, Hu H, Feng D, et al. How does broadband infrastructure promote entrepreneurship in China: Evidence from a quasi-natural experiment[J]. Telecommunications Policy, 2022, 46(10): 102440.\u003c/li\u003e\n\u003cli\u003eWan J, Nie C, Zhang F. Does broadband infrastructure really affect consumption of rural households?\u0026ndash;A quasi-natural experiment evidence from China[J]. China Agricultural Economic Review, 2021, 13(4): 832-850.\u003c/li\u003e\n\u003cli\u003eYan H, Yi X, Jiang J, et al. Can information technology construction alleviate household energy poverty? Empirical evidence from the \u0026ldquo;broadband China\u0026rdquo; Pilot Policy[J]. Energy Policy, 2024, 185: 113966.\u003c/li\u003e\n\u003cli\u003eZhang C, Weng X, Guo Y. Digital infrastructure construction and household energy efficiency: Based on a quasi-natural experiment in China[J]. Science of The Total Environment, 2024, 911: 168544.\u003c/li\u003e\n\u003cli\u003eWang C, Zhang M. The road to change: Broadband China strategy and enterprise digitization[J]. Plos one, 2022, 17(5): e0269133.\u003c/li\u003e\n\u003cli\u003eSong Y, Huang H, Li Y, et al. Towards inclusive green growth in China: Synergistic roles and mechanisms of new infrastructure construction[J]. Journal of Environmental Management, 2024, 353: 120281.\u003c/li\u003e\n\u003cli\u003eHe Z, Zhang Y, Wang X. Has the Development of Broadband Infrastructure Improved Household Energy Consumption in Rural China?[J]. Sustainability (2071-1050), 2024, 16(19).\u003c/li\u003e\n\u003cli\u003ePeng H R, Ling K, Zhang Y J. The carbon emission reduction effect of digital infrastructure development: Evidence from the broadband China policy[J]. Journal of Cleaner Production, 2024, 434: 140060.\u003c/li\u003e\n\u003cli\u003eLi B, Liu J, Liu Q, et al. The effects of broadband infrastructure on carbon emission efficiency of resource-based cities in China: a quasi-natural experiment from the \u0026ldquo;Broadband China\u0026rdquo; pilot policy[J]. International journal of environmental research and public health, 2022, 19(11): 6734.\u003c/li\u003e\n\u003cli\u003eZhong X, Liu G, Chen P, et al. The impact of internet development on urban eco-efficiencY\u0026mdash;a quasi-natural experiment of \u0026ldquo;broadband China\u0026rdquo; pilot policy[J]. International Journal of Environmental Research and Public Health, 2022, 19(3): 1363.\u003c/li\u003e\n\u003cli\u003eWen H, Liang W, Lee C C. Urban broadband infrastructure and green total-factor energy efficiency in China[J]. Utilities Policy, 2022, 79: 101414.\u003c/li\u003e\n\u003cli\u003eYu H, Jiexin T, Zhenzhen C, et al. Influence of national intellectual property demonstration enterprise policy on urban green innovation: Evidence from China[J]. Environment, Development and Sustainability, 2023: 1-20.\u003c/li\u003e\n\u003cli\u003eChen D, Chen S. Patent Protection Policy and Firms\u0026rsquo; Green Technology Innovation: Mediating Roles of Open Innovation and Human Capital[J]. Sustainability, 2024, 16(5): 2217.\u003c/li\u003e\n\u003cli\u003eGao X, Zhao Y. Does intellectual property protection promote green innovation in firms? A perspective on R\u0026amp;D spillovers and financing constraints[J]. Plos one, 2023, 18(11): e0288315.\u003c/li\u003e\n\u003cli\u003eLiu Y, Chen L, Luo H, et al. The impact of intellectual property rights protection on green innovation: A quasi-natural experiment based on the pilot policy of the Chinese intellectual property court[J]. Math. Biosci. Eng, 2024, 21: 2587-2607.\u003c/li\u003e\n\u003cli\u003eLi F. Comprehensive intellectual property ability and firm productivity: A quasi-natural experiment based on national intellectual property demonstration advantage firm policy in China[J]. Plos one, 2024, 19(4): e0302693.\u003c/li\u003e\n\u003cli\u003eZhu Y, Sun M. The enabling effect of intellectual property strategy on total factor productivity of enterprises: evidence from China\u0026rsquo;s intellectual property model cities[J]. Sustainability, 2022, 15(1): 549.\u003c/li\u003e\n\u003cli\u003eZhou D, Guo J. Can intellectual property demonstration cities influence the location choice of polluting enterprises? Evidence from China[J]. Environment, Development and Sustainability, 2024, 26(6): 14163-14187.\u003c/li\u003e\n\u003cli\u003eMao K, Failler P. Does stronger protection of intellectual property improve sustainable development? Evidence from city data in China[J]. Sustainability, 2022, 14(21): 14369.\u003c/li\u003e\n\u003cli\u003eYao L, Li A, Wang S. The impact of intellectual property demonstration policies on carbon emission efficiency[J]. Scientific Reports, 2024, 14(1): 15336.\u003c/li\u003e\n\u003cli\u003eHan H. Can intellectual property rights pilots reduce carbon emissions? Evidence from China[J]. Frontiers in Environmental Science, 2024, 12: 1336803.\u003c/li\u003e\n\u003cli\u003eAbson D J, Dougill A J, Stringer L C. Using principal component analysis for information-rich socio-ecological vulnerability mapping in Southern Africa[J]. Applied Geography, 2012, 35(1-2): 515-524.\u003c/li\u003e\n\u003cli\u003eAdamowicz M. The potential for innovative and smart rural development in the peripheral regions of Eastern Poland[J]. Agriculture, 2021, 11(3): 188.\u003c/li\u003e\n\u003cli\u003eLi R, Xu M, Zhou H. Impact of high-speed rail operation on urban economic resilience: evidence from local and spillover perspectives in China[J]. Cities, 2023, 141: 104498.\u003c/li\u003e\n\u003cli\u003eSimmie J, Martin R. The economic resilience of regions: towards an evolutionary approach[J]. Cambridge journal of regions, economy and society, 2010, 3(1): 27-43.\u003c/li\u003e\n\u003cli\u003eZhang J, Yang Z, He B. Does digital infrastructure improve urban economic resilience? Evidence from the Yangtze River Economic Belt in China[J]. Sustainability, 2023, 15(19): 14289.\u003c/li\u003e\n\u003cli\u003eJiang W, Wang K L, Miao Z. Can telecommunications infrastructure enhance urban resilience? Empirical evidence from a differences-in-differences approach in China[J]. Environment, Development and Sustainability, 2023: 1-32.\u003c/li\u003e\n\u003cli\u003eShi Y, Zhang T, Jiang Y. Digital economy, technological innovation and urban resilience[J]. Sustainability, 2023, 15(12): 9250.\u003c/li\u003e\n\u003cli\u003eRomer P M. Endogenous technological change[J]. Journal of political Economy, 1990, 98(5, Part 2): S71-S102.\u003c/li\u003e\n\u003cli\u003eSlot M, Fraikin M, Damgrave R, et al. Digital infrastructures as the basis for implementing digital twinning[J]. Procedia CIRP, 2022, 109: 568-573.\u003c/li\u003e\n\u003cli\u003eSchade P, Schuhmacher M C. Digital infrastructure and entrepreneurial action-formation: A multilevel study[J]. Journal of Business Venturing, 2022, 37(5): 106232.\u003c/li\u003e\n\u003cli\u003eAllam Z, Jones D S. Future (post-COVID) digital, smart and sustainable cities in the wake of 6G: Digital twins, immersive realities and new urban economies[J]. Land use policy, 2021, 101: 105201.\u003c/li\u003e\n\u003cli\u003eKovacs-Gy\u0026ouml;ri A, Ristea A, Havas C, et al. Opportunities and challenges of geospatial analysis for promoting urban livability in the era of big data and machine learning[J]. ISPRS International Journal of Geo-Information, 2020, 9(12): 752.\u003c/li\u003e\n\u003cli\u003eLe H T T, Luong T T D, Nguyen T T T, et al. Determinants of intellectual property rights protection in Asian developing countries[J]. Journal of the Knowledge Economy, 2023, 14(4): 4780-4806.\u003c/li\u003e\n\u003cli\u003eChen Q, Xia X, Hui Y, et al. Urban Intellectual Property Strategy and University Innovation: A Quasi-Natural Experiment Based on the Intellectual Property Model City of China[J]. Systems, 2024, 12(1): 21.\u003c/li\u003e\n\u003cli\u003eJia X, Xie B, Wang X. The impact of network infrastructure on enterprise digital transformation\u0026mdash;\u0026mdash;A quasi-natural experiment from the \u0026ldquo;broadband China\u0026rdquo; Strategy[J]. Applied Economics, 2024, 56(12): 1363-1380.\u003c/li\u003e\n\u003cli\u003eMartin R, Sunley P, Tyler P. Local growth evolutions: recession, resilience and recovery[J]. Cambridge Journal of Regions, Economy and Society, 2015, 8(2): 141-148.\u003c/li\u003e\n\u003cli\u003eMartin R. Regional economic resilience, hysteresis and recessionary shocks[J]. Journal of economic geography, 2012, 12(1): 1-32.\u003c/li\u003e\n\u003cli\u003eRom\u0026atilde;o J. Tourism, smart specialisation, growth, and resilience[J]. Annals of Tourism Research, 2020, 84: 102995.\u003c/li\u003e\n\u003cli\u003eMartin R, Sunley P, Gardiner B, et al. How regions react to recessions: Resilience and the role of economic structure[J]. Regional studies, 2016, 50(4): 561-585.\u003c/li\u003e\n\u003cli\u003eBoschma R. Towards an evolutionary perspective on regional resilience[J]. Regional studies, 2015, 49(5): 733-751.\u003c/li\u003e\n\u003cli\u003eBristow G, Healy A. Innovation and regional economic resilience: an exploratory analysis[J]. The annals of regional science, 2018, 60(2): 265-284.\u003c/li\u003e\n\u003cli\u003eLemley M A, Shapiro C. Probabilistic patents[J]. Journal of Economic Perspectives, 2005, 19(2): 75-98.\u003c/li\u003e\n\u003cli\u003ePradhan R P, Arvin M B, Nair M, et al. The dynamics among entrepreneurship, innovation, and economic growth in the Eurozone countries[J]. Journal of Policy Modeling, 2020, 42(5): 1106-1122.\u003c/li\u003e\n\u003cli\u003eShane S, Venkataraman S. The promise of entrepreneurship as a field of research[J]. Academy of management review, 2000, 25(1): 217-226.\u003c/li\u003e\n\u003cli\u003eWen J, Okolo C V, Ugwuoke I C, et al. Research on influencing factors of renewable energy, energy efficiency, on technological innovation. Does trade, investment and human capital development matter?[J]. Energy Policy, 2022, 160: 112718.\u003c/li\u003e\n\u003cli\u003eDuranton G, Puga D. Diversity and specialisation in cities: why, where and when does it matter?[J]. Urban studies, 2000, 37(3): 533-555.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6353700/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6353700/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrban economic resilience is both the foundation of a modern industrial system and an essential catalyst for superior development. This research investigates the effects of digital infrastructure and intellectual property development on urban economic resilience, using \u0026ldquo;Broadband China\u0026rdquo; and \u0026ldquo;Intellectual Property\u0026rdquo; pilot cities as case studies. A multi-period difference-in-differences model is utilized on panel statistics from 281 Chinese cities spanning 2007 to 2022 to assess policy impacts rigorously. The findings reveal that (1) The \u0026ldquo;Broadband China,\u0026rdquo; \u0026ldquo;Intellectual Property,\u0026rdquo; and dual pilot policies all significantly enhance urban economic resilience. However, the \u0026ldquo;Intellectual Property\u0026rdquo; pilot exerts a more pronounced influence than the \u0026ldquo;Broadband China\u0026rdquo; pilot, and the dual pilot policy outperforms single pilot policies. (2) Policy effectiveness is time-sensitive. Cities implementing \u0026ldquo;Intellectual Property\u0026rdquo; policies first, followed by \u0026ldquo;Broadband China,\u0026rdquo; experience greater benefits than those following the reverse sequence. (3) Mechanism tests show that both single and dual pilot programs enhance economic resilience through technological innovation, entrepreneurship incentives, and economic agglomeration. (4) Heterogeneity research reveals that policy implications are more substantial in resource-dependent cities, highly resilient urban clusters, and cities with high economic growth targets. These findings suggest the need to develop a dynamic policy adaptation mechanism, optimize the sequencing of institutional reforms, and implement differentiated strategies to achieve a resilient and stable evolution of urban economic systems.\u003c/p\u003e","manuscriptTitle":"Digital Infrastructure, Intellectual Property, and the Resilience of China’s Urban Economy: Evaluating the Synergistic Effects of “Broadband China” and “Intellectual Property” Pilot Policies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 10:49:43","doi":"10.21203/rs.3.rs-6353700/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-19T17:05:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-06T13:42:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326439835177733473437568316568662188410","date":"2025-08-23T10:17:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-20T13:23:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-14T22:27:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"284221002524152478483789470617991755764","date":"2025-07-14T13:01:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"156759549932900871556387738617822223782","date":"2025-07-11T16:55:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-09T12:49:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-11T17:22:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-03T11:26:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-21T11:34:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-04-01T13:35:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"643e5bf7-57e7-4f92-a2eb-11f6e8ccab6d","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":51354409,"name":"Business and commerce/Economics"},{"id":51354410,"name":"Social science/Social policy"}],"tags":[],"updatedAt":"2026-05-25T03:53:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-14 10:49:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6353700","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6353700","identity":"rs-6353700","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}