The Impact of Digital Economy Policy on the Entrepreneurial Vitality of the Logistics Industry: Empirical Evidence from China

The Impact of Digital Economy Policy on the Entrepreneurial Vitality of the Logistics Industry: Empirical Evidence from China | 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 Research Article The Impact of Digital Economy Policy on the Entrepreneurial Vitality of the Logistics Industry: Empirical Evidence from China Xiaohong Ren, Jiayun Nie, Jia Shen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7194873/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Dec, 2025 Read the published version in Applied Spatial Analysis and Policy → Version 1 posted You are reading this latest preprint version Abstract Digital economy policy, functioning as the bedrock upon which the digital economy thrives, holds a seminal role in igniting the entrepreneurial vitality within the logistics industry. This study utilizes panel data from 277 cities in China spanning the period 2009–2022 to investigate the impact of digital economy policy on the entrepreneurial vitality of the logistics industry. The results unequivocally demonstrate that digital economy policy significantly elevates the entrepreneurial vitality of the logistics industry. Mechanism analysis elucidates that digital economy policy achieves this through three fundamental avenues: fostering the development of digital infrastructure, amplifying digital innovation vitality, and nurturing a talent pool. Moreover, heterogeneity analysis reveals a more pronounced positive influence of digital economy policy in advanced cities and e-commerce demonstration cities. Digital Economy Policy Digital Economy Entrepreneurial Vitality of the Logistics Industry Digital Infrastructure Digital innovation vitality Figures Figure 1 1. Introduction With the rapid development of new-generation digital technologies such as big data, cloud computing, artificial intelligence, and the Internet of Things, the digital economy is emerging as a new engine driving high-quality economic growth (Ma and Zhu, 2022). In 2024, the scale of China's digital economy surged to RMB 63.8 trillion. Digital economy policy (DEP), defined as the strategic policies formulated by governments to advance the digital economy (Shahbaz et al., 2022 ), provides a robust foundation for the prosperity of the digital economy (Dana et al., 2022 ). Currently, igniting domestic economic vitality is pivotal to achieving high-quality economic development. As the core force of the market economy, enterprises serve as the primary drivers of economic progress, and fully stimulating entrepreneurial enthusiasm across society can provide potent impetus for economic growth (Fernandes et al., 2022 ; Munyo and Veiga, 2024 ). The logistics industry, as a crucial supporting sector of the national economy, holds paramount importance in terms of its entrepreneurial vitality, playing a significant role in enhancing economic efficiency and promoting industrial upgrading. Consequently, can DEP stimulate the entrepreneurial vitality of the logistics industry (EVLI)? And what are the mechanisms behind this? Delving into these questions not only uncovers the specific influence pathways of DEP on the EVLI but also offers theoretical support and practical guidance for governments to formulate more precise and effective policies, thereby further providing invaluable policy references and practical guidance for the high-quality development of the logistics industry. As a pivotal tool for driving national digital transformation, DEP encompasses a broad scope, not only involving narrow sense specialized digital economy policies (DEPs) but also encompassing policies that facilitate digital transformation, regulation, and governance in transportation, healthcare, environmental protection, and government services. These policies play a crucial role in addressing the challenges encountered in the development of the digital economy, as emphasized by Li et al. ( 2020 ). When formulating DEPs, the importance of infrastructure, human resources, and regulations cannot be overlooked, as suggested by Liu ( 2022 ). Scientifically sound DEPs are of vital significance to the digital transformation of national development (Foster and Azmeh, 2020 ). They not only significantly promote the digital transformation of enterprises (Xiao et al., 2024 ) but also facilitate the attraction of more innovative resources into the field of digital economy development. These resources, including financial support and talent support, collectively enhance the region's technological research and development capabilities and the ability to convert research outcomes, thereby shaping new industrial formats and models and empowering the transformation and upgrading of traditional industries (Zhou et al., 2024 ). Furthermore, DEP plays a significant role in local governments' digital management and the enhancement of science and innovation services (Cegarra-Navarro et al., 2012 ). By optimizing resource allocation, it provides necessary resource support for ecological and environmental innovation (Wang et al., 2022 ) and facilitates the penetration of digital technology into micro-industries and enterprises, addressing agency problems and improving decision-making effectiveness. This penetration and effectiveness enhancement may drive the transformation of heavily polluting industries from resource-intensive to green and environmentally friendly development (Domazet et al., 2018 ; Litvinenko, 2020 ). Furthermore, some scholars propose that "dual transformation" policies—consisting of digital transformation and energy transformation—are important drivers for enhancing energy efficiency and reducing carbon emissions (Benedetti et al., 2023 ). By integrating enterprises' environmental capabilities into their digital frameworks, DEP fosters green innovation within enterprises (Li et al., 2021 ) and significantly promotes industrial green innovation (Dou and Gao, 2022 ), with particularly notable positive impacts on heavily polluting industries (X. Wang et al., 2024 ). Meanwhile, the appropriate provision of DEPs further enhances the green transformation of manufacturing by promoting the deepening of division of labor, optimizing the allocation of innovative factors, and facilitating the diversification and aggregation of high-end producer services (Si et al., 2024 ). In summary, DEP plays a crucial role in driving national digital transformation, promoting industrial innovation and upgrading, enhancing local government management effectiveness, and advancing green and sustainable development. Entrepreneurial vitality refers to the extent of the growth of emerging enterprises within a specific industry or region (Barreneche García, 2014 ). Regional entrepreneurial activities not only mitigate the issue of slowing economic growth but also effectively propel industrial upgrading and innovative development (Mi et al., 2024 ). Existing literature has explored the factors influencing entrepreneurial vitality from multiple perspectives. At the individual level, entrepreneurial traits (Djankov et al., 2006 ), social capital (LaFave and Thomas, 2016 ), and risk appetite (Bianchi and Bobba, 2013 ) significantly impact individual entrepreneurial behavior. At the macro level, factors such as city size (Barreneche García, 2014 ), institutional environment (Lu and Tao, 2010 ), business environment (Peng et al., 2022 ), digital economy (B. Wang et al., 2024 ), digital infrastructure (Hasbi, 2020 ), financial sector development (Kerr and Nanda, 2009 ), and population aging (Feng and Li, 2023 ) all influence entrepreneurial activities. Furthermore, government intervention emerges as a pivotal factor affecting entrepreneurship (Romer, 1986 ). Entrepreneurs discern governmental policy measures to assess the implications of institutional and policy shifts on entrepreneurial feasibility and returns, thereby influencing their decisions to embark on entrepreneurial ventures (Baumol, 1996 ). An intensification of administrative approval burdens exerts a negative influence on entrepreneurial activities (Zhang et al., 2016 ). Consequently, existing research underscores the role of government as a facilitator, advocating for the reduction of administrative barriers, optimization of governmental functions, and the fostering of a conducive business environment for new firm entries, thereby minimizing institutional transaction costs for entrepreneurial innovation and stimulating the intrinsic motivation of entrepreneurs (Lim et al., 2010 ). While the impact of DEP on entrepreneurial vitality has yet to be the focal point of academic discourse, its significance as a cornerstone for the development of the digital economy cannot be overlooked. Most current research concentrates on the catalytic role of the digital economy and digital technologies in enhancing entrepreneurial vitality, with inadequate exploration into the specific mechanisms through which DEPs influence entrepreneurial vitality. The digital economy, by fortifying digital infrastructure (Ma and Zhu, 2022), significantly enhances information circulation, providing entrepreneurs with abundant informational resources to facilitate more informed decision-making (Lee et al., 2021 ). This economic paradigm not only propels the digitization of industries (Zhang et al., 2022 ) but also establishes interactive platforms, laying the groundwork for entrepreneurship (Tian et al., 2022 ). With the thriving development of industrial internet, industrial digitization substantially elevates the digitization of production processes, popularizes digital R&D tools, augments intelligent decision-making capabilities, and effectively reduces corporate costs (Klarin and Suseno, 2021 ). At the digital governance level, online government service platforms markedly enhance government service efficiency, alleviate corporate burdens (Xiang et al., 2022 ), optimize the business environment, and facilitate the emergence of new enterprises (B. Wang et al., 2024 ). Simultaneously, the rapid development of digital finance alleviates financing challenges for enterprises and promotes the equalization of entrepreneurial opportunities (Dabbous and Tarhini, 2021 ). The extensive integration and evolution of digital technologies are leading profound transformations in business models, opening up new avenues for entrepreneurs (AFAWUBO and NOGLO, 2022 ; Kraus et al., 2021 ). Entrepreneurs can identify potential business opportunities and innovative business models from digital platforms (Ross and Blumenstein, 2015 ). Digitization enables entrepreneurs to keenly perceive market changes and swiftly respond to them (Troise et al., 2022 ). Additionally, digital transformation reduces the costs and barriers to entrepreneurship (Niebel, 2018 ), broadens market boundaries (Hong et al., 2024 ), fosters international collaboration and exchange (Reuber and Fischer, 2011 ), transcends cultural, organizational, and institutional boundaries (Bouncken and Barwinski, 2021 ), and stimulates the innovative vitality of entrepreneurs (Elia et al., 2020 ; Dabbous et al., 2023 ). Notably, AI-driven technological transformations have also emerged as a significant factor influencing new entrepreneurial activities (Davidsson et al., 2020 ). Cities with strong innovative capabilities, leveraging abundant innovative resources and technological advantages, attract more highly skilled talent and resources, further enhancing their entrepreneurial vitality (Peng and Tao, 2022 ), particularly within the entrepreneurial vitality of logistics industry. This paper conducts an in-depth empirical analysis of the impact of DEPs on the EVLI and its underlying mechanisms, utilizing panel data from 277 cities in China spanning from 2009 to 2022. The potential marginal contributions of this study are manifested in several aspects: Firstly, while existing research predominantly focuses on the influence of the digital economy on entrepreneurial vitality in general, there is a notable scarcity of studies examining the specific impact of DEPs on the EVLI, let alone from a policy perspective. This paper unveils the profound implications of DEPs on the EVLI at the policy level, thereby enriching the literature on the relationship between DEPs and entrepreneurial vitality and providing a theoretical basis for policymakers to make informed decisions. Additionally, by thoroughly analyzing the relationship between DEPs and the EVLI, this study offers insights into the trend of logistics entrepreneurship under policy guidance, providing a forward-looking perspective for industry planning. Secondly, in terms of measuring DEPs, this paper innovatively employs data from the Peking University Law Database, constructing a proxy variable for DEPs at the city level by counting the number of DEP documents issued by each city during the study period. This methodology not only ensures the authority and accuracy of the data but also provides a feasible path for quantitatively analyzing DEPs. Compared to previous studies relying on qualitative descriptions or limited case analyses, this paper achieves significant innovation in data acquisition and processing. Lastly, in the empirical analysis section, this paper delves into the mechanisms through which DEPs influence the EVLI from three dimensions: promoting digital infrastructure construction, enhancing digital innovation vitality, and providing talent support. Furthermore, it analyzes the heterogeneous characteristics of this impact. Such a multidimensional exploration of mechanisms aids in comprehending the intrinsic link between DEPs and the EVLI more holistically, while the heterogeneity analysis provides an empirical foundation and decision-making basis for different cities to formulate differentiated DEPs tailored to their unique characteristics. 2. Theoretical analysis and research hypothesis DEPs play a pivotal role in promoting the EVLI. Firstly, these policies provide clear strategic guidance and policy support for the logistics sector, lowering market entry barriers for entrepreneurs and stimulating entrepreneurial enthusiasm within the industry. These policies encompass various aspects of digital transformation and, through the improvement of regulatory and governance policies, create a fair and transparent market environment for entrepreneurs, thereby enhancing their confidence in starting businesses. Secondly, DEPs facilitate the development and utilization of data resources, providing abundant information resources for logistics entrepreneurs. In the digital economy era, data has become a crucial asset for logistics enterprises (Y. Song et al., 2021 ). By guiding the rational development and utilization of data resources, DEPs offer entrepreneurs precise market insights and decision-making support (Lee et al., 2021 ), aiding them in identifying potential business opportunities (Ross and Blumenstein, 2015 ) and optimizing entrepreneurial strategies. Furthermore, DEPs propel the digital transformation of logistics enterprises (Xiao et al., 2024 ), enhancing the innovative capabilities and operational efficiency of startups. By incentivizing digital innovation and promoting deep integration between digital and real-world elements, these policies facilitate the digital upgrading of logistics enterprises in areas such as research and design, production processing, and product technology, realizing optimal resource allocation and efficient utilization. This not only reduces operational costs for startups (Chen, 2020 ) but also enhances their market competitiveness, injecting new vitality into the sustainable development of the logistics industry. Lastly, DEPs foster innovation and development in green and environmentally friendly logistics. By optimizing resource allocation (Zhang and Yu, 2024 ) and promoting green transformation in heavily polluting industries (Domazet et al., 2018 ; Litvinenko, 2020 ), these policies provide logistics entrepreneurs with green and environmentally friendly entrepreneurial directions. This not only helps reduce carbon emissions and environmental pollution in the logistics industry but also promotes structural upgrading (Liu et al., 2024 ), creating broader market spaces and development opportunities for entrepreneurs. Therefore, DEPs play a crucial role in enhancing the EVLI, providing entrepreneurs with policy support, information resources, innovative capabilities, and green development directions. This paper will examine the mechanisms through which DEPs influence the EVLI from three dimensions: promoting digital infrastructure construction, enhancing digital innovation vitality, and providing talent support. A diagram illustrating the mechanism analysis is shown in Fig. 1 . DEPs play a vital role in promoting digital infrastructure construction, which in turn has a profound impact on enhancing the EVLI. Firstly, by increasing investment and construction efforts in digital infrastructure (Ma and Zhu, 2022), DEPs provide efficient and intelligent information technology support for the logistics sector. These infrastructures, including high-speed networks, big data centers, and cloud computing platforms, constitute the cornerstone of the digital transformation of the logistics industry. With the empowerment of digital technology, logistics enterprises can achieve real-time monitoring, intelligent analysis, and rapid response to logistics information, significantly improving operational efficiency and service quality (Manresa et al., 2024 ). Secondly, the improvement of digital infrastructure provides a more convenient and efficient entrepreneurial environment for logistics entrepreneurs. Relying on these infrastructures, entrepreneurs can quickly build their logistics platforms and service systems, lowering the thresholds and costs of starting a business. Meanwhile, the widespread application of digital technology has given rise to numerous new logistics business models and commercial patterns, providing entrepreneurs with broader entrepreneurial spaces and development opportunities (Kraus et al., 2021 ). In addition, DEPs promote the intelligent, automated, and green development of logistics enterprises by guiding and supporting the deep integration of digital technology and the logistics industry. This deep integration not only enhances the core competitiveness of logistics enterprises but also promotes the transformation, upgrading, and sustainable development of the entire logistics industry. Therefore, by promoting digital infrastructure construction, DEPs provide logistics entrepreneurs with powerful technical support and an entrepreneurial environment, stimulating the EVLI. This vitality is not only reflected in the number and scale of logistics enterprises but also in the innovative capabilities and service levels of the logistics industry. In the future, with the continuous deepening of DEPs and the continuous improvement of digital infrastructure, the EVLI will be further unleashed and enhanced. The DEP plays a pivotal role in enhancing the EVLI, with its core lying in stimulating digital innovation dynamism. Through an array of measures such as financial support, tax incentives, and innovation stimuli, the DEP fosters an environment conducive to the research and application of digital technologies. These policies encourage enterprises to augment investments in cutting-edge technological domains, including artificial intelligence, big data, and the Internet of Things (IoT), thereby driving continuous innovation and development in digital technologies. With the relentless evolution of digital technologies, the logistics industry has embraced unprecedented opportunities for growth. Digital innovation provides the logistics sector with more efficient and intelligent solutions. Leveraging big data analytics, enterprises can accurately predict logistics demands and optimize inventory management. Through IoT technology, real-time tracking and intelligent scheduling of goods become feasible. Meanwhile, the application of artificial intelligence significantly enhances the automation and intelligence levels of logistics services. The thriving digital economy has also given rise to novel entrepreneurial opportunities within the logistics industry. On the one hand, traditional logistics enterprises can undergo digital transformation to improve service quality and operational efficiency, thereby bolstering their market competitiveness. On the other hand, emerging technology companies can harness digital technologies to develop innovative logistics products and services, catering to the diversified needs of the market (Y. Song et al., 2021 ). These entrepreneurial activities not only propel the rapid development of the logistics industry but also foster the prosperity of related industrial chains. Consequently, the DEP injects new impetus into the development of the logistics industry by enhancing digital innovation dynamism. It unleashes the entrepreneurial potential of logistics enterprises, advances the intelligence and efficiency of logistics services, and offers vast development space and infinite possibilities for entrepreneurial activities in the logistics industry. Furthermore, the DEP provides robust talent support for the development of the logistics industry by formulating and implementing a series of talent cultivation, recruitment, and incentive measures. These policies encourage universities and vocational training institutions to strengthen education and training in the fields of the digital economy and logistics, nurturing interdisciplinary talents with expertise in both digital technology and logistics management. As the logistics industry undergoes digital transformation, the demand for talent is evolving. Traditional logistics talents have become insufficient to meet the needs of modern logistics, whereas talents with digital thinking, data analysis skills, and technological application abilities are in high demand. By guiding and supporting talent cultivation, the DEP ensures the continuous emergence of these new talents, providing a steady stream of intellectual support for entrepreneurial activities in the logistics industry. Additionally, the DEP attracts domestic and foreign talents to the logistics industry by optimizing talent mobility mechanisms. By offering competitive salaries, comprehensive social security, and promising career prospects, these policies ignite the innovative vitality and entrepreneurial passion of talents. In entrepreneurial activities within the logistics industry, these talents play a crucial role in driving innovation and upgrades in logistics services. The enhancement of talent support not only boosts the EVLI (Peng and Tao, 2022 ) but also facilitates the transformation and upgrading of the entire sector (Li et al., 2024 ). Hence, the DEP infuses new momentum into the EVLI through the provision of talent support. Based on the aforementioned analysis, this paper proposes the following hypotheses: Hypothesis 1 The DEP can enhance the EVLI. Hypothesis 2 The DEP can enhance the EVLI by promoting digital infrastructure construction. Hypothesis 3 The DEP can enhance the EVLI by boosting digital innovation dynamism. Hypothesis 4 The DEP can enhance the EVLI by providing talent support. 3. Methodology 3.1 Description of variables 3.1.1 Dependent variable In this study, the EVLI (EVLI) serves as the dependent variable. It is measured by the proportion of newly registered logistics enterprises in a given year to the total number of registered enterprises in each city. 3.1.2 Core explanatory variable To quantify the intensity of DEPs, our core explanatory variable focuses on the number of relevant policies issued by each city in a given year. Initially, we constructed a series of keywords closely associated with the core elements of the digital economy, including 5G, big data, blockchain, artificial intelligence, Internet of Things, cloud computing, as well as smart transportation, smart energy, smart agriculture, and smart healthcare. Additionally, the directly indicative terms "digitalization" and "digital economy" were included. Policies whose titles contained these keywords were deemed relevant to the digital economy. Subsequently, utilizing the Peking University Legal Information Database, we systematically collected and organized various digital economy-related policy documents issued by city governments within the study period, encompassing local working documents, normative documents, judicial files, government regulations, special economic zone regulations, and municipal regulations of districts. 3.1.3 Control variables Our control variables encompass the following aspects: economic development level (LGDP), which, through its enhancement, stimulates an increase in logistics demand, creating a broader stage and more favorable conditions for the logistics industry. The natural logarithm of regional gross domestic product is adopted as the quantitative indicator. Population size (PEO), with an increase in population fostering expanded consumer demand, subsequently stimulating logistics demand and presenting more opportunities for entrepreneurial activities in the logistics sector. Population natural growth rate is used as the measure. The scale of employment in the logistics industry (EMP), its expansion indicating a robust talent pool, facilitates the formation of entrepreneurial teams and the exploration of innovative potential. This is assessed by the proportion of logistics industry employees to the total workforce. Fixed assets (FIX), by enhancing logistics efficiency and operational capacity, bolster enterprises' resilience to market risks, influencing the entrepreneurial vitality and market competitiveness of the logistics industry. Per capita fixed asset investment is utilized for evaluation. The level of scientific expenditure (TEC), through its increase, promotes technological innovation and enhances R&D capabilities, providing technical support and driving force for logistics entrepreneurship. The natural logarithm of scientific expenditure is employed for quantification. The level of educational expenditure (EDU), its augmentation accelerating talent cultivation and quality improvement, furnishes a high-quality talent base for logistics entrepreneurship. The logarithm of educational expenditure serves as the metric. Unemployment rate (UNEMP), as a vital indicator reflecting labor market supply and demand conditions, impacts talent acquisition and cost control in logistics entrepreneurship. It is measured by the proportion of registered unemployed individuals in urban areas to the total urban population. Social consumption level (CONS), its elevation signifying robust logistics demand, presents more market opportunities for logistics entrepreneurship. The ratio of total retail sales of consumer goods to regional gross domestic product is adopted as the assessment criterion. 3.2 Baseline model To examine the impact of DEPs on the EVLI, we constructed the following empirical model: Where EVLI it represents the entrepreneurial vitality of the logistics industry in city i in year t; DEP denotes digital economy policy; Control encompasses city-level control variables; α and β represent the regression coefficients of the respective variables; µ i captures the fixed effects at the city level, υ t denotes the fixed effects corresponding to the year; ε it captures the random disturbance term. 3.3 Data sources and descriptive statistics Considering the impact of the 2008 global financial crisis, our study employs panel data from 277 cities in China spanning the period from 2009 to 2022. Data on newly registered enterprises are sourced from the China Business Registration Database, DEP data are derived from the Peking University Legal Information Database, and city-level data are obtained from various issues of the China City Statistical Yearbook. To account for the influence of outliers, all continuous variables underwent a 1% winsorization process (Ni et al., 2023 ). Descriptive statistics for each variable are presented in Supplementary Table 1. Table 1 Descriptive statistics for the variables Variables Observations Mean SD Min Max EVLI 3878 0.155 0.224 0.004 1.326 DEP 3878 0.963 3.000 0.000 21.000 LGDP 3878 16.592 0.946 14.578 19.132 PEO 3878 5.576 5.109 -7.200 20.480 EMP 3878 0.134 1.047 -1.897 3.550 FIX 3878 5.113 4.561 0.335 23.879 TEC 3878 10.379 1.450 7.322 14.519 EDU 3878 13.092 0.822 11.167 15.495 UNEMP 3878 0.006 0.004 0.001 0.024 CONS 3878 0.377 0.105 0.130 0.690 4. Empirical analysis 4.1 Baseline regression Table 2 presents the analytical results of the benchmark regression, incorporating city fixed effects and year fixed effects as control factors. Specifically, Column (1) independently examines the impact of DEP on the EVLI, with standard errors clustered at the city level. In Column (2), we further elevate the clustering level of standard errors to the city-year dimension to capture temporal variations in greater detail. Subsequently, Column (3) builds upon Column (1) by introducing a series of control variables to enhance the robustness of the model. Similarly, Column (4) extends Column (2) with the inclusion of these control variables. The regression analysis consistently reveals positive coefficients for DEP at the 1% statistical significance level, robustly demonstrating that DEP effectively fosters the EVLI. Hence, Hypothesis 1 is validated. Table 2 Baseline regression Variables (1) (2) (3) (4) DEP 0.0092*** 0.0092*** 0.0082*** 0.0082*** (0.0026) (0.0015) (0.0026) (0.0015) Control No No Yes Yes City FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes Observations 3878 3878 3878 3878 R 2 0.616 0.616 0.624 0.624 Note: Standard errors in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01 4.2 Endogeneity test To address potential endogeneity concerns, this study employs the instrumental variable (IV) approach, selecting the per capita postal and telecommunications volume (PT) in 1984 and the number of fixed-line telephones per hundred people (LI) in 1984 as IVs, and utilizes two-stage least squares (2SLS) estimation. In terms of relevance, the postal and telecommunications volume in 1984 reflects the scale and penetration of urban communication infrastructure at that time, laying the groundwork for subsequent Internet and digital economy development. As technology advances and the times evolve, these early infrastructures have influenced the application and evolution of Internet technologies in later stages. The number of fixed-line telephones per hundred people in 1984 measures the initial level of urban communication infrastructure, indicating the foundational conditions for the nascent digital economy. Although fixed-line telephones have transitioned to mobile and Internet communications, the early communication infrastructure has paved the way for subsequent digital economy development. In formulating current DEP, the development of a city's digital infrastructure may be considered a crucial reference factor. Regarding exogeneity, the IVs chosen in this study are historical data from 1984, making it unlikely for them to exert a direct influence on the current EVLI. Thus, the two IVs meet the exogeneity condition. This study incorporates the interaction terms between the selected IVs and time dummy variables as the IVs for DEP and proceeds with the endogeneity test. The IV regression results are presented in Table 3 . The results indicate that both IVs are highly positively correlated with DEP and that there are no issues of under identification or weak identification. This suggests that, after addressing endogeneity concerns, the conclusion that DEP enhances the EVLI remains valid. Table 3 Endogeneity test Variables (1) (2) The First Stage The Second Stage DEP 0.0266 *** (0. 0072) PT 0.0257 *** (0.0083) LI 0.1238 *** (0.0347) Kleibergen-Paap LM 56.101 *** Cragg-Donald Wald F 115.412 Kleibergen-Paap Wald F 47.321 Stock-Yogo 10% 19.93 Control Yes Yes City FE Yes Yes Year FE Yes Yes Observations 3094 3094 R 2 0.1977 Note: Standard errors in parentheses, *** p < 0.01 4.3 Robustness test 4.3.1 Replacing the core explanatory variable In the baseline regression, this study measures the intensity of DEP using the number of DEP announcements made by each city in a given year. However, we acknowledge that DEP announced in previous years may continue to exert an influence in subsequent years. Therefore, we reconstruct the DEP variable by aggregating the annual announcements over time. Column (1) in Table 4 presents the regression results after replacing the core explanatory variable. The results indicate that the conclusions remain robust after substituting the core explanatory variable. 4.3.2 Excluding special samples Municipalities directly under the central government and other provincial capitals in China possess unique political and economic statuses. Such advantages may, to some extent, overestimate the promotional effect of DEP on the entrepreneurial vitality of logistics industry. The regression results excluding municipalities directly under the central government are shown in Column (2) of Table 4 , while those excluding provincial capitals are presented in Column (3) of Table 4 . The regression outcomes demonstrate that the previous conclusions retain their robustness after excluding these special samples. 4.3.3 Consideration of policy lag effect The manifestation of policy effects is often a gradual process, particularly in the initial stages following policy announcement, when its potential influence may not have been fully realized, exhibiting a notable lag feature. Addressing this lag effect, we incorporate a one-period lag of DEP and present the regression results in Column (4) of Table 4 . The results reveal that even after considering the lag effect, the previous conclusions maintain their robustness. Table 4 Robustness test Variables (1) (3) (4) (5) Replace the core explanatory variable Exclude municipalities Excluding capital cities Lag processing DEP 0.0014*** 0.0088*** 0.0071*** 0.0071*** (0.0004) (0.0016) (0.0023) (0.0017) Control Yes Yes Yes Yes City FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes Observations 3878 3822 3458 3601 R 2 0.622 0.612 0.591 0.633 Note: Standard errors in parentheses, *** p < 0.01 4.4 Mechanism analysis 4.4.1 Digital infrastructure DEP not only propels the development of digital infrastructure but also provides robust technical support and an entrepreneurial environment for logistics entrepreneurs, thereby significantly enhancing the entrepreneurial vitality of logistics industry. To quantify the level of digital infrastructure development, this study selects six indicators: optical fiber density, number of Internet broadband access ports per capita, proportion of employees in the information transmission and other related industries, telecommunications business revenue, mobile phone penetration rate, and Internet penetration rate. The entropy weight method is employed to calculate the digital infrastructure index for each city. Column (1) of Table 5 presents the regression results of DEP on digital infrastructure, with the DEP coefficient being significantly positive, confirming the substantial driving force of DEP on digital infrastructure development and thus validating Hypothesis 2 . 4.4.2 Digital innovation vitality By fostering a favorable environment and providing clear guidance, DEP effectively incentivizes enterprises to increase R&D investment and drive technological innovation, thereby significantly enhancing digital innovation vitality, which in turn benefits the entrepreneurial environment of logistics industry. To quantify this effect, this study distinguishes between digital invention patents and utility model patents based on the International Patent Classification and adopts the number of digital patent applications per 10,000 population in a city as an indicator to measure urban digital innovation vitality. As shown in Column (2) of Table 5 , the regression analysis results of DEP on digital innovation vitality indicate a significantly positive regression coefficient, directly proving the substantial enhancement effect of DEP on urban digital innovation vitality. To further explore the specific impact of DEP on logistics industry, we meticulously classify digital patent fields closely related to logistics industry according to the Classification of National Economic Industries, including industrial robot manufacturing, service and consumer robot manufacturing, Internet technology innovation platforms, Internet data services, Internet of Things technology services, and intelligent in-vehicle equipment manufacturing. Reference is made to the assessment method for digital innovation vitality to construct evaluation indicators for digital innovation vitality in logistics industry. The regression results in Column (3) of Table 5 show that the regression coefficient of DEP on digital innovation vitality in logistics industry is also significantly positive, robustly validating the positive role of DEP in significantly enhancing urban digital innovation vitality in logistics industry and providing solid empirical evidence for assessing the innovative incentive effect of DEP on entrepreneurial vitality of logistics industry. Accordingly, Hypothesis 3 is validated. 4.4.3 Talent support effect DEP optimizes education and training systems and promotes the development of highly skilled talent, providing solid talent support for the entrepreneurial vitality of logistics industry. To quantify this effect, this paper initially adopts the number of ordinary undergraduate and college students per 100 people as a measure of urban human capital. Column (4) of Table 5 presents the regression analysis results of DEP on human capital, revealing that DEP significantly enhances the level of human capital. Furthermore, this paper introduces the proportion of employees in the information transmission, computer services, and software industry to total employment as an indicator to assess urban digital talent reserves. The regression results reported in Column (5) of Table 5 show that DEP has a positive impact on enhancing urban digital talent. This finding confirms the effectiveness of DEP in elevating the level of urban digital talent, thereby providing indispensable talent support for the entrepreneurial vitality of logistics industry. Accordingly, Hypothesis 4 is validated. Table 5 Mechanism test Variables (1) (2) (3) (4) (5) Digital infrastructure Digital innovation vitality Digital innovation vitality of logistics industry Human capital Digital talent DEP 0.0024*** 2.1418*** 0.4372*** 0.0168** 0.0023*** (0.0008) (0.2198) (0.0461) (0.0071) (0.0003) Control Yes Yes Yes Yes Yes City FE Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Observations 3808 3806 3805 3878 3878 R 2 0.540 0.895 0.864 0.965 0.862 Note: Standard errors in parentheses, * p < 0.1, *** p < 0.01 4.5 Heterogeneity analysis 4.5.1 City hierarchy To delve into the city-hierarchy disparities in the impact of DEPs on the EVLI, this study categorizes sample cities into tier-one cities (including first-tier, new first-tier, second-tier, and some third-tier cities) and non-tier-one cities, based on the classification criteria outlined in the "2021 City Commercial Charm Rankings." Subsequently, a dummy variable CC for city hierarchy is introduced, where CC is assigned a value of 1 for tier-one cities and 0 otherwise. Furthermore, the interaction term between DEP (representing DEP) and CC is incorporated into the baseline regression model for analysis. The regression results, detailed in Column (1) of Supplementary table 1, reveal that the coefficient of the interaction term is significantly positive, while the coefficient of DEP is significantly negative. This indicates that DEPs significantly enhance the EVLI in tier-one cities but may exert a certain inhibitory effect in non-tier-one cities. The robust enhancement of entrepreneurial vitality in the logistics industry within tier-one cities, driven by DEPs, is primarily attributed to their well-developed infrastructure, abundant resource endowments, and favorable policy environments. Specifically, tier-one cities boast relatively comprehensive infrastructure in transportation, communications, and other sectors, providing a solid foundation for the efficient operation of the logistics industry. Additionally, these cities possess more mature market mechanisms and comprehensive talent cultivation systems, offering vast market opportunities and talent support for logistics entrepreneurs. In contrast, non-tier-one cities, characterized by lagging infrastructure, limited resources, and inadequate policy support, may experience constrained effects of DEPs in stimulating entrepreneurial vitality in the logistics industry, or even adverse impacts in some instances. 4.5.1 E-commerce development China's e-commerce industry has undergone rapid development, playing a pivotal role in advancing digital economy and logistics. To further promote the healthy development of e-commerce, the National Development and Reform Commission, Ministry of Commerce, and other relevant departments jointly initiated the "National E-commerce Demonstration City" program in 2011 and have progressively announced a list of 70 national e-commerce demonstration cities. These demonstration cities possess first-mover advantages in the digital economy and logistics sectors, with optimized policy implementation and resource allocation. Typically, they boast more sophisticated logistics infrastructure and advanced information technology application levels, enabling them to better embrace and apply DEPs (Yang et al., 2024 ). Furthermore, by leveraging the driving force of the digital economy, e-commerce demonstration cities further ignite the EVLI. To validate this effect, a dummy variable EC is constructed, assigning a value of 1 to cities designated as e-commerce demonstration cities and 0 otherwise. Subsequently, the interaction term between DEP and EC is included in the baseline regression model. The regression results, presented in Column (2) of Supplementary table 1, show that the coefficient of the interaction term is significantly positive, while the coefficient of DEP is significantly negative. This suggests that DEPs significantly promote the EVLI in e-commerce demonstration cities but may exert a negative influence on non-demonstration cities. In electronic commerce demonstration cities, DEPs have the capacity to augment the EVLI. This assertion is substantiated by the fact that these cities commonly possess sophisticated infrastructure, ample resources, and a conducive policy environment. Demonstration cities often exhibit a more mature market landscape and a comprehensive talent nurturing framework, thereby providing substantial support for logistics entrepreneurship. Moreover, these cities typically amplify investments in logistics infrastructure, encompassing the development of modern logistics parks and distribution hubs, which significantly enhances transparency and traceability in logistics operations. Simultaneously, demonstration cities employ policy guidance to catalyze the digital transformation of logistics enterprises, ultimately leading to improved service quality and operational efficiency. Conversely, in non-electronic commerce demonstration cities, DEPs may struggle to fully harness their potential or even impede the EVLI, owing to inadequate infrastructure, resource constraints, and insufficient policy support. These cities may lack crucial digital infrastructure, such as high-speed internet connectivity and intelligent warehousing facilities, which limits the innovative capacity and development of logistics enterprises. Furthermore, non-demonstration cities may suffer from the absence of robust policy frameworks and capital infusions, rendering logistics enterprises vulnerable to substantial challenges during their digital transformation endeavors. 5. Conclusion Stimulating the EVLI serves as a pivotal avenue for fostering economic transformation, upgrading, and high-quality development. In this endeavor, the policy support of the digital economy plays an indispensable role. This study empirically examines the impact, mechanism, and heterogeneous effects of DEPs on the EVLI, utilizing panel data from 277 cities in China spanning the period from 2009 to 2022. The findings reveal that DEPs can augment the EVLI. Mechanism tests demonstrate that DEPs elevate the EVLI through three crucial pathways: fostering digital infrastructure development, enhancing digital innovation vitality, and providing talent support. Heterogeneity analysis underscores that DEPs bolster the EVLI in advanced cities and electronic commerce demonstration cities, whereas they exert an inhibitory effect on the EVLI in general cities and non-electronic commerce demonstration cities. While this study has attained notable results in exploring the impact of DEPs on the EVLI, it also acknowledges inherent limitations and simplifications. Specifically, biases may exist in the proxy variables employed to measure DEPs and the EVLI. Furthermore, despite incorporating a multitude of control variables, the study may not have comprehensively captured all potential influencing factors, potentially compromising the accuracy of the regression results. Moreover, given its reliance on Chinese city panel data, the applicability of this study may be confined to domestic cities, with insufficient exploration of foreign urban development contexts. To address these limitations, future research can delve deeper in the following respects: Firstly, optimize measurement indicators and methodologies to enhance the precision and comprehensiveness of assessing DEPs and the EVLI. Secondly, broaden the research scope by incorporating foreign cities into the analytical framework to validate the universality of the study's findings. Thirdly, further expand the analysis of mechanisms, exploring additional potential intermediary variables or pathways, such as how DEPs indirectly augment the EVLI through optimizing resource allocation and fostering industrial upgrading. Lastly, this study is grounded in static panel data, neglecting temporal dynamics. Future research can adopt dynamic models and forward-looking analytical methods to study the evolving impacts of DEPs across different time scales. By constructing dynamic system models, deeply analyze the intricate interactions among DEPs, government policies, corporate behavior, and environmental quality, and predict the long-term ramifications of DEPs on the EVLI. This will provide theoretical underpinning for formulating more scientific and sustainable logistics industry development strategies. Declarations Funding General Project of the National Social Science Foundation, grant/award number: 21BJY223; General Project of the Chongqing Natural Science Foundation, grant/award number: CSTB2023NSCQ-MSX0046; and Major Project of Chongqing Social Science Planning “Construction of Western Land-Sea New Passage”, grant/award number: 2023ZDLH06. Data availability statement Data available on request from the authors. Declaration of interest There is no conflict of interest. Ethic statement Not applicable. References AFAWUBO, K., NOGLO, Y.A., 2022. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7194873","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":495416327,"identity":"752c9100-b494-400c-8943-7f238bf19296","order_by":0,"name":"Xiaohong Ren","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYJACZoYKCWZ+ZuaDD0jQcsaCXbKdLdmAeC2MbRX8Bud5zASIUm7e3ntMuuCMhLTxYQYzBoYam2iCWmTOnEs2nlEhYWx2mCHtAcOxtNwGQlokJHIMH/OckUgGajluwNhwmAgt8m8MDvO2SdRvbmZskyBOiwSP4WOgFmYDZmY2IrXw5BgbAx3GLHGYjdkggSi/sJ8xk+apqGPm7z//8cGHGhvCWlBBAmnKR8EoGAWjYBTgAgDJBDWmngfIXwAAAABJRU5ErkJggg==","orcid":"","institution":"Chongqing Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Xiaohong","middleName":"","lastName":"Ren","suffix":""},{"id":495416328,"identity":"57c87a00-e73f-4524-8830-23966f60b923","order_by":1,"name":"Jiayun Nie","email":"","orcid":"","institution":"Chongqing Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Jiayun","middleName":"","lastName":"Nie","suffix":""},{"id":495416329,"identity":"54a7e07c-de00-4e9b-8d0f-8decf9166f9f","order_by":2,"name":"Jia Shen","email":"","orcid":"","institution":"Chongqing Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Shen","suffix":""}],"badges":[],"createdAt":"2025-07-23 09:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7194873/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7194873/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12061-025-09761-4","type":"published","date":"2025-12-04T15:56:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88350791,"identity":"785ddf61-6161-4524-959e-c7baf5fc5edb","added_by":"auto","created_at":"2025-08-05 14:19:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":80310,"visible":true,"origin":"","legend":"\u003cp\u003eMechanism analysis\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7194873/v1/37b2a5a51a08bb0a5ea440b2.png"},{"id":97723748,"identity":"c4d7b7ce-650f-4edf-ba07-ba089b3dd0ac","added_by":"auto","created_at":"2025-12-08 16:01:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1107558,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7194873/v1/c55cdc30-d2ff-4a0a-985c-e0442a8cb601.pdf"},{"id":88350790,"identity":"bac8c4a1-571d-4176-9727-75ceb3da2172","added_by":"auto","created_at":"2025-08-05 14:19:16","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15564,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7194873/v1/7b2337fc088cc0184670a7bd.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Impact of Digital Economy Policy on the Entrepreneurial Vitality of the Logistics Industry: Empirical Evidence from China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWith the rapid development of new-generation digital technologies such as big data, cloud computing, artificial intelligence, and the Internet of Things, the digital economy is emerging as a new engine driving high-quality economic growth (Ma and Zhu, 2022). In 2024, the scale of China's digital economy surged to RMB 63.8 trillion. Digital economy policy (DEP), defined as the strategic policies formulated by governments to advance the digital economy (Shahbaz et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), provides a robust foundation for the prosperity of the digital economy (Dana et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Currently, igniting domestic economic vitality is pivotal to achieving high-quality economic development. As the core force of the market economy, enterprises serve as the primary drivers of economic progress, and fully stimulating entrepreneurial enthusiasm across society can provide potent impetus for economic growth (Fernandes et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Munyo and Veiga, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The logistics industry, as a crucial supporting sector of the national economy, holds paramount importance in terms of its entrepreneurial vitality, playing a significant role in enhancing economic efficiency and promoting industrial upgrading. Consequently, can DEP stimulate the entrepreneurial vitality of the logistics industry (EVLI)? And what are the mechanisms behind this? Delving into these questions not only uncovers the specific influence pathways of DEP on the EVLI but also offers theoretical support and practical guidance for governments to formulate more precise and effective policies, thereby further providing invaluable policy references and practical guidance for the high-quality development of the logistics industry.\u003c/p\u003e\u003cp\u003eAs a pivotal tool for driving national digital transformation, DEP encompasses a broad scope, not only involving narrow sense specialized digital economy policies (DEPs) but also encompassing policies that facilitate digital transformation, regulation, and governance in transportation, healthcare, environmental protection, and government services. These policies play a crucial role in addressing the challenges encountered in the development of the digital economy, as emphasized by Li et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). When formulating DEPs, the importance of infrastructure, human resources, and regulations cannot be overlooked, as suggested by Liu (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Scientifically sound DEPs are of vital significance to the digital transformation of national development (Foster and Azmeh, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). They not only significantly promote the digital transformation of enterprises (Xiao et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) but also facilitate the attraction of more innovative resources into the field of digital economy development. These resources, including financial support and talent support, collectively enhance the region's technological research and development capabilities and the ability to convert research outcomes, thereby shaping new industrial formats and models and empowering the transformation and upgrading of traditional industries (Zhou et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, DEP plays a significant role in local governments' digital management and the enhancement of science and innovation services (Cegarra-Navarro et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). By optimizing resource allocation, it provides necessary resource support for ecological and environmental innovation (Wang et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and facilitates the penetration of digital technology into micro-industries and enterprises, addressing agency problems and improving decision-making effectiveness. This penetration and effectiveness enhancement may drive the transformation of heavily polluting industries from resource-intensive to green and environmentally friendly development (Domazet et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Litvinenko, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, some scholars propose that \"dual transformation\" policies\u0026mdash;consisting of digital transformation and energy transformation\u0026mdash;are important drivers for enhancing energy efficiency and reducing carbon emissions (Benedetti et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). By integrating enterprises' environmental capabilities into their digital frameworks, DEP fosters green innovation within enterprises (Li et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and significantly promotes industrial green innovation (Dou and Gao, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), with particularly notable positive impacts on heavily polluting industries (X. Wang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Meanwhile, the appropriate provision of DEPs further enhances the green transformation of manufacturing by promoting the deepening of division of labor, optimizing the allocation of innovative factors, and facilitating the diversification and aggregation of high-end producer services (Si et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In summary, DEP plays a crucial role in driving national digital transformation, promoting industrial innovation and upgrading, enhancing local government management effectiveness, and advancing green and sustainable development.\u003c/p\u003e\u003cp\u003eEntrepreneurial vitality refers to the extent of the growth of emerging enterprises within a specific industry or region (Barreneche Garc\u0026iacute;a, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Regional entrepreneurial activities not only mitigate the issue of slowing economic growth but also effectively propel industrial upgrading and innovative development (Mi et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Existing literature has explored the factors influencing entrepreneurial vitality from multiple perspectives. At the individual level, entrepreneurial traits (Djankov et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), social capital (LaFave and Thomas, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and risk appetite (Bianchi and Bobba, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) significantly impact individual entrepreneurial behavior. At the macro level, factors such as city size (Barreneche Garc\u0026iacute;a, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), institutional environment (Lu and Tao, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), business environment (Peng et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), digital economy (B. Wang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), digital infrastructure (Hasbi, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), financial sector development (Kerr and Nanda, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and population aging (Feng and Li, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) all influence entrepreneurial activities. Furthermore, government intervention emerges as a pivotal factor affecting entrepreneurship (Romer, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). Entrepreneurs discern governmental policy measures to assess the implications of institutional and policy shifts on entrepreneurial feasibility and returns, thereby influencing their decisions to embark on entrepreneurial ventures (Baumol, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). An intensification of administrative approval burdens exerts a negative influence on entrepreneurial activities (Zhang et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Consequently, existing research underscores the role of government as a facilitator, advocating for the reduction of administrative barriers, optimization of governmental functions, and the fostering of a conducive business environment for new firm entries, thereby minimizing institutional transaction costs for entrepreneurial innovation and stimulating the intrinsic motivation of entrepreneurs (Lim et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile the impact of DEP on entrepreneurial vitality has yet to be the focal point of academic discourse, its significance as a cornerstone for the development of the digital economy cannot be overlooked. Most current research concentrates on the catalytic role of the digital economy and digital technologies in enhancing entrepreneurial vitality, with inadequate exploration into the specific mechanisms through which DEPs influence entrepreneurial vitality. The digital economy, by fortifying digital infrastructure (Ma and Zhu, 2022), significantly enhances information circulation, providing entrepreneurs with abundant informational resources to facilitate more informed decision-making (Lee et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This economic paradigm not only propels the digitization of industries (Zhang et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) but also establishes interactive platforms, laying the groundwork for entrepreneurship (Tian et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). With the thriving development of industrial internet, industrial digitization substantially elevates the digitization of production processes, popularizes digital R\u0026amp;D tools, augments intelligent decision-making capabilities, and effectively reduces corporate costs (Klarin and Suseno, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). At the digital governance level, online government service platforms markedly enhance government service efficiency, alleviate corporate burdens (Xiang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), optimize the business environment, and facilitate the emergence of new enterprises (B. Wang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Simultaneously, the rapid development of digital finance alleviates financing challenges for enterprises and promotes the equalization of entrepreneurial opportunities (Dabbous and Tarhini, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The extensive integration and evolution of digital technologies are leading profound transformations in business models, opening up new avenues for entrepreneurs (AFAWUBO and NOGLO, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kraus et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Entrepreneurs can identify potential business opportunities and innovative business models from digital platforms (Ross and Blumenstein, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Digitization enables entrepreneurs to keenly perceive market changes and swiftly respond to them (Troise et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, digital transformation reduces the costs and barriers to entrepreneurship (Niebel, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), broadens market boundaries (Hong et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), fosters international collaboration and exchange (Reuber and Fischer, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), transcends cultural, organizational, and institutional boundaries (Bouncken and Barwinski, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and stimulates the innovative vitality of entrepreneurs (Elia et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Dabbous et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Notably, AI-driven technological transformations have also emerged as a significant factor influencing new entrepreneurial activities (Davidsson et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Cities with strong innovative capabilities, leveraging abundant innovative resources and technological advantages, attract more highly skilled talent and resources, further enhancing their entrepreneurial vitality (Peng and Tao, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), particularly within the entrepreneurial vitality of logistics industry.\u003c/p\u003e\u003cp\u003eThis paper conducts an in-depth empirical analysis of the impact of DEPs on the EVLI and its underlying mechanisms, utilizing panel data from 277 cities in China spanning from 2009 to 2022. The potential marginal contributions of this study are manifested in several aspects: Firstly, while existing research predominantly focuses on the influence of the digital economy on entrepreneurial vitality in general, there is a notable scarcity of studies examining the specific impact of DEPs on the EVLI, let alone from a policy perspective. This paper unveils the profound implications of DEPs on the EVLI at the policy level, thereby enriching the literature on the relationship between DEPs and entrepreneurial vitality and providing a theoretical basis for policymakers to make informed decisions. Additionally, by thoroughly analyzing the relationship between DEPs and the EVLI, this study offers insights into the trend of logistics entrepreneurship under policy guidance, providing a forward-looking perspective for industry planning. Secondly, in terms of measuring DEPs, this paper innovatively employs data from the Peking University Law Database, constructing a proxy variable for DEPs at the city level by counting the number of DEP documents issued by each city during the study period. This methodology not only ensures the authority and accuracy of the data but also provides a feasible path for quantitatively analyzing DEPs. Compared to previous studies relying on qualitative descriptions or limited case analyses, this paper achieves significant innovation in data acquisition and processing. Lastly, in the empirical analysis section, this paper delves into the mechanisms through which DEPs influence the EVLI from three dimensions: promoting digital infrastructure construction, enhancing digital innovation vitality, and providing talent support. Furthermore, it analyzes the heterogeneous characteristics of this impact. Such a multidimensional exploration of mechanisms aids in comprehending the intrinsic link between DEPs and the EVLI more holistically, while the heterogeneity analysis provides an empirical foundation and decision-making basis for different cities to formulate differentiated DEPs tailored to their unique characteristics.\u003c/p\u003e"},{"header":"2. Theoretical analysis and research hypothesis","content":"\u003cp\u003eDEPs play a pivotal role in promoting the EVLI. Firstly, these policies provide clear strategic guidance and policy support for the logistics sector, lowering market entry barriers for entrepreneurs and stimulating entrepreneurial enthusiasm within the industry. These policies encompass various aspects of digital transformation and, through the improvement of regulatory and governance policies, create a fair and transparent market environment for entrepreneurs, thereby enhancing their confidence in starting businesses. Secondly, DEPs facilitate the development and utilization of data resources, providing abundant information resources for logistics entrepreneurs. In the digital economy era, data has become a crucial asset for logistics enterprises (Y. Song et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). By guiding the rational development and utilization of data resources, DEPs offer entrepreneurs precise market insights and decision-making support (Lee et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), aiding them in identifying potential business opportunities (Ross and Blumenstein, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and optimizing entrepreneurial strategies. Furthermore, DEPs propel the digital transformation of logistics enterprises (Xiao et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), enhancing the innovative capabilities and operational efficiency of startups. By incentivizing digital innovation and promoting deep integration between digital and real-world elements, these policies facilitate the digital upgrading of logistics enterprises in areas such as research and design, production processing, and product technology, realizing optimal resource allocation and efficient utilization. This not only reduces operational costs for startups (Chen, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) but also enhances their market competitiveness, injecting new vitality into the sustainable development of the logistics industry. Lastly, DEPs foster innovation and development in green and environmentally friendly logistics. By optimizing resource allocation (Zhang and Yu, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and promoting green transformation in heavily polluting industries (Domazet et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Litvinenko, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), these policies provide logistics entrepreneurs with green and environmentally friendly entrepreneurial directions. This not only helps reduce carbon emissions and environmental pollution in the logistics industry but also promotes structural upgrading (Liu et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), creating broader market spaces and development opportunities for entrepreneurs. Therefore, DEPs play a crucial role in enhancing the EVLI, providing entrepreneurs with policy support, information resources, innovative capabilities, and green development directions.\u003c/p\u003e\u003cp\u003eThis paper will examine the mechanisms through which DEPs influence the EVLI from three dimensions: promoting digital infrastructure construction, enhancing digital innovation vitality, and providing talent support. A diagram illustrating the mechanism analysis is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDEPs play a vital role in promoting digital infrastructure construction, which in turn has a profound impact on enhancing the EVLI. Firstly, by increasing investment and construction efforts in digital infrastructure (Ma and Zhu, 2022), DEPs provide efficient and intelligent information technology support for the logistics sector. These infrastructures, including high-speed networks, big data centers, and cloud computing platforms, constitute the cornerstone of the digital transformation of the logistics industry. With the empowerment of digital technology, logistics enterprises can achieve real-time monitoring, intelligent analysis, and rapid response to logistics information, significantly improving operational efficiency and service quality (Manresa et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Secondly, the improvement of digital infrastructure provides a more convenient and efficient entrepreneurial environment for logistics entrepreneurs. Relying on these infrastructures, entrepreneurs can quickly build their logistics platforms and service systems, lowering the thresholds and costs of starting a business. Meanwhile, the widespread application of digital technology has given rise to numerous new logistics business models and commercial patterns, providing entrepreneurs with broader entrepreneurial spaces and development opportunities (Kraus et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, DEPs promote the intelligent, automated, and green development of logistics enterprises by guiding and supporting the deep integration of digital technology and the logistics industry. This deep integration not only enhances the core competitiveness of logistics enterprises but also promotes the transformation, upgrading, and sustainable development of the entire logistics industry. Therefore, by promoting digital infrastructure construction, DEPs provide logistics entrepreneurs with powerful technical support and an entrepreneurial environment, stimulating the EVLI. This vitality is not only reflected in the number and scale of logistics enterprises but also in the innovative capabilities and service levels of the logistics industry. In the future, with the continuous deepening of DEPs and the continuous improvement of digital infrastructure, the EVLI will be further unleashed and enhanced.\u003c/p\u003e\u003cp\u003eThe DEP plays a pivotal role in enhancing the EVLI, with its core lying in stimulating digital innovation dynamism. Through an array of measures such as financial support, tax incentives, and innovation stimuli, the DEP fosters an environment conducive to the research and application of digital technologies. These policies encourage enterprises to augment investments in cutting-edge technological domains, including artificial intelligence, big data, and the Internet of Things (IoT), thereby driving continuous innovation and development in digital technologies. With the relentless evolution of digital technologies, the logistics industry has embraced unprecedented opportunities for growth. Digital innovation provides the logistics sector with more efficient and intelligent solutions. Leveraging big data analytics, enterprises can accurately predict logistics demands and optimize inventory management. Through IoT technology, real-time tracking and intelligent scheduling of goods become feasible. Meanwhile, the application of artificial intelligence significantly enhances the automation and intelligence levels of logistics services. The thriving digital economy has also given rise to novel entrepreneurial opportunities within the logistics industry. On the one hand, traditional logistics enterprises can undergo digital transformation to improve service quality and operational efficiency, thereby bolstering their market competitiveness. On the other hand, emerging technology companies can harness digital technologies to develop innovative logistics products and services, catering to the diversified needs of the market (Y. Song et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These entrepreneurial activities not only propel the rapid development of the logistics industry but also foster the prosperity of related industrial chains. Consequently, the DEP injects new impetus into the development of the logistics industry by enhancing digital innovation dynamism. It unleashes the entrepreneurial potential of logistics enterprises, advances the intelligence and efficiency of logistics services, and offers vast development space and infinite possibilities for entrepreneurial activities in the logistics industry.\u003c/p\u003e\u003cp\u003eFurthermore, the DEP provides robust talent support for the development of the logistics industry by formulating and implementing a series of talent cultivation, recruitment, and incentive measures. These policies encourage universities and vocational training institutions to strengthen education and training in the fields of the digital economy and logistics, nurturing interdisciplinary talents with expertise in both digital technology and logistics management. As the logistics industry undergoes digital transformation, the demand for talent is evolving. Traditional logistics talents have become insufficient to meet the needs of modern logistics, whereas talents with digital thinking, data analysis skills, and technological application abilities are in high demand. By guiding and supporting talent cultivation, the DEP ensures the continuous emergence of these new talents, providing a steady stream of intellectual support for entrepreneurial activities in the logistics industry. Additionally, the DEP attracts domestic and foreign talents to the logistics industry by optimizing talent mobility mechanisms. By offering competitive salaries, comprehensive social security, and promising career prospects, these policies ignite the innovative vitality and entrepreneurial passion of talents. In entrepreneurial activities within the logistics industry, these talents play a crucial role in driving innovation and upgrades in logistics services. The enhancement of talent support not only boosts the EVLI (Peng and Tao, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) but also facilitates the transformation and upgrading of the entire sector (Li et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Hence, the DEP infuses new momentum into the EVLI through the provision of talent support.\u003c/p\u003e\u003cp\u003eBased on the aforementioned analysis, this paper proposes the following hypotheses:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 1\u003c/strong\u003e\u003cp\u003eThe DEP can enhance the EVLI.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 2\u003c/strong\u003e\u003cp\u003eThe DEP can enhance the EVLI by promoting digital infrastructure construction.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 3\u003c/strong\u003e\u003cp\u003eThe DEP can enhance the EVLI by boosting digital innovation dynamism.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 4\u003c/strong\u003e\u003cp\u003eThe DEP can enhance the EVLI by providing talent support.\u003c/p\u003e\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Description of variables\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1 Dependent variable\u003c/h2\u003e\u003cp\u003eIn this study, the EVLI (EVLI) serves as the dependent variable. It is measured by the proportion of newly registered logistics enterprises in a given year to the total number of registered enterprises in each city.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2 Core explanatory variable\u003c/h2\u003e\u003cp\u003eTo quantify the intensity of DEPs, our core explanatory variable focuses on the number of relevant policies issued by each city in a given year. Initially, we constructed a series of keywords closely associated with the core elements of the digital economy, including 5G, big data, blockchain, artificial intelligence, Internet of Things, cloud computing, as well as smart transportation, smart energy, smart agriculture, and smart healthcare. Additionally, the directly indicative terms \"digitalization\" and \"digital economy\" were included. Policies whose titles contained these keywords were deemed relevant to the digital economy. Subsequently, utilizing the Peking University Legal Information Database, we systematically collected and organized various digital economy-related policy documents issued by city governments within the study period, encompassing local working documents, normative documents, judicial files, government regulations, special economic zone regulations, and municipal regulations of districts.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e3.1.3 Control variables\u003c/h2\u003e\u003cp\u003eOur control variables encompass the following aspects: economic development level (LGDP), which, through its enhancement, stimulates an increase in logistics demand, creating a broader stage and more favorable conditions for the logistics industry. The natural logarithm of regional gross domestic product is adopted as the quantitative indicator. Population size (PEO), with an increase in population fostering expanded consumer demand, subsequently stimulating logistics demand and presenting more opportunities for entrepreneurial activities in the logistics sector. Population natural growth rate is used as the measure. The scale of employment in the logistics industry (EMP), its expansion indicating a robust talent pool, facilitates the formation of entrepreneurial teams and the exploration of innovative potential. This is assessed by the proportion of logistics industry employees to the total workforce. Fixed assets (FIX), by enhancing logistics efficiency and operational capacity, bolster enterprises' resilience to market risks, influencing the entrepreneurial vitality and market competitiveness of the logistics industry. Per capita fixed asset investment is utilized for evaluation. The level of scientific expenditure (TEC), through its increase, promotes technological innovation and enhances R\u0026amp;D capabilities, providing technical support and driving force for logistics entrepreneurship. The natural logarithm of scientific expenditure is employed for quantification. The level of educational expenditure (EDU), its augmentation accelerating talent cultivation and quality improvement, furnishes a high-quality talent base for logistics entrepreneurship. The logarithm of educational expenditure serves as the metric. Unemployment rate (UNEMP), as a vital indicator reflecting labor market supply and demand conditions, impacts talent acquisition and cost control in logistics entrepreneurship. It is measured by the proportion of registered unemployed individuals in urban areas to the total urban population. Social consumption level (CONS), its elevation signifying robust logistics demand, presents more market opportunities for logistics entrepreneurship. The ratio of total retail sales of consumer goods to regional gross domestic product is adopted as the assessment criterion.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Baseline model\u003c/h2\u003e\u003cp\u003eTo examine the impact of DEPs on the EVLI, we constructed the following empirical model:\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\n\u003cp\u003eWhere EVLI\u003csub\u003eit\u003c/sub\u003e represents the entrepreneurial vitality of the logistics industry in city i in year t; DEP denotes digital economy policy; Control encompasses city-level control variables; α and β represent the regression coefficients of the respective variables; \u0026micro;\u003csub\u003ei\u003c/sub\u003e captures the fixed effects at the city level, υ\u003csub\u003et\u003c/sub\u003e denotes the fixed effects corresponding to the year; ε\u003csub\u003eit\u003c/sub\u003e captures the random disturbance term.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Data sources and descriptive statistics\u003c/h2\u003e\u003cp\u003eConsidering the impact of the 2008 global financial crisis, our study employs panel data from 277 cities in China spanning the period from 2009 to 2022. Data on newly registered enterprises are sourced from the China Business Registration Database, DEP data are derived from the Peking University Legal Information Database, and city-level data are obtained from various issues of the China City Statistical Yearbook. To account for the influence of outliers, all continuous variables underwent a 1% winsorization process (Ni et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Descriptive statistics for each variable are presented in Supplementary Table\u0026nbsp;1.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\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\u003eDescriptive statistics for the variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObservations\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\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEVLI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.326\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDEP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.963\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e21.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14.578\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e19.132\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePEO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-7.200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e20.480\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEMP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.550\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFIX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e23.879\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14.519\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEDU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15.495\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUNEMP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCONS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.377\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.690\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"},{"header":"4. Empirical analysis","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Baseline regression\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the analytical results of the benchmark regression, incorporating city fixed effects and year fixed effects as control factors. Specifically, Column (1) independently examines the impact of DEP on the EVLI, with standard errors clustered at the city level. In Column (2), we further elevate the clustering level of standard errors to the city-year dimension to capture temporal variations in greater detail. Subsequently, Column (3) builds upon Column (1) by introducing a series of control variables to enhance the robustness of the model. Similarly, Column (4) extends Column (2) with the inclusion of these control variables. The regression analysis consistently reveals positive coefficients for DEP at the 1% statistical significance level, robustly demonstrating that DEP effectively fosters the EVLI. Hence, Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e is validated.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\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\u003eBaseline regression\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\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\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\u003eDEP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0092***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0092***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0082***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0082***\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(0.0026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0015)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0015)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\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\u003eCity 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\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3878\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\u003e0.616\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\u003e0.624\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.624\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Standard errors in parentheses, \u003csup\u003e*\u003c/sup\u003e p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, \u003csup\u003e**\u003c/sup\u003e p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e***\u003c/sup\u003e p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Endogeneity test\u003c/h2\u003e\u003cp\u003eTo address potential endogeneity concerns, this study employs the instrumental variable (IV) approach, selecting the per capita postal and telecommunications volume (PT) in 1984 and the number of fixed-line telephones per hundred people (LI) in 1984 as IVs, and utilizes two-stage least squares (2SLS) estimation. In terms of relevance, the postal and telecommunications volume in 1984 reflects the scale and penetration of urban communication infrastructure at that time, laying the groundwork for subsequent Internet and digital economy development. As technology advances and the times evolve, these early infrastructures have influenced the application and evolution of Internet technologies in later stages. The number of fixed-line telephones per hundred people in 1984 measures the initial level of urban communication infrastructure, indicating the foundational conditions for the nascent digital economy. Although fixed-line telephones have transitioned to mobile and Internet communications, the early communication infrastructure has paved the way for subsequent digital economy development. In formulating current DEP, the development of a city's digital infrastructure may be considered a crucial reference factor. Regarding exogeneity, the IVs chosen in this study are historical data from 1984, making it unlikely for them to exert a direct influence on the current EVLI. Thus, the two IVs meet the exogeneity condition. This study incorporates the interaction terms between the selected IVs and time dummy variables as the IVs for DEP and proceeds with the endogeneity test. The IV regression results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The results indicate that both IVs are highly positively correlated with DEP and that there are no issues of under identification or weak identification. This suggests that, after addressing endogeneity concerns, the conclusion that DEP enhances the EVLI remains valid.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\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\u003eEndogeneity test\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe First Stage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe Second Stage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDEP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0266\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\u003cp\u003e(0. 0072)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0257\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\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(0.0083)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1238\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\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(0.0347)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKleibergen-Paap LM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56.101\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCragg-Donald Wald F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e115.412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKleibergen-Paap Wald F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47.321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStock-Yogo 10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCity 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\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\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\u003e3094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3094\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\u003e0.1977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: Standard errors in parentheses, \u003csup\u003e***\u003c/sup\u003e p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Robustness test\u003c/h2\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e4.3.1 Replacing the core explanatory variable\u003c/h2\u003e\u003cp\u003eIn the baseline regression, this study measures the intensity of DEP using the number of DEP announcements made by each city in a given year. However, we acknowledge that DEP announced in previous years may continue to exert an influence in subsequent years. Therefore, we reconstruct the DEP variable by aggregating the annual announcements over time. Column (1) in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the regression results after replacing the core explanatory variable. The results indicate that the conclusions remain robust after substituting the core explanatory variable.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e4.3.2 Excluding special samples\u003c/h2\u003e\u003cp\u003eMunicipalities directly under the central government and other provincial capitals in China possess unique political and economic statuses. Such advantages may, to some extent, overestimate the promotional effect of DEP on the entrepreneurial vitality of logistics industry. The regression results excluding municipalities directly under the central government are shown in Column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, while those excluding provincial capitals are presented in Column (3) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The regression outcomes demonstrate that the previous conclusions retain their robustness after excluding these special samples.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e4.3.3 Consideration of policy lag effect\u003c/h2\u003e\u003cp\u003eThe manifestation of policy effects is often a gradual process, particularly in the initial stages following policy announcement, when its potential influence may not have been fully realized, exhibiting a notable lag feature. Addressing this lag effect, we incorporate a one-period lag of DEP and present the regression results in Column (4) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The results reveal that even after considering the lag effect, the previous conclusions maintain their robustness.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\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\u003eRobustness test\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\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReplace the core explanatory variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExclude municipalities\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExcluding capital cities\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLag processing\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDEP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0014***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0088***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0071***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0071***\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(0.0004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0017)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl\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\u003eCity 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\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3601\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\u003e0.622\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.591\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.633\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Standard errors in parentheses, \u003csup\u003e***\u003c/sup\u003e p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Mechanism analysis\u003c/h2\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e4.4.1 Digital infrastructure\u003c/h2\u003e\u003cp\u003eDEP not only propels the development of digital infrastructure but also provides robust technical support and an entrepreneurial environment for logistics entrepreneurs, thereby significantly enhancing the entrepreneurial vitality of logistics industry. To quantify the level of digital infrastructure development, this study selects six indicators: optical fiber density, number of Internet broadband access ports per capita, proportion of employees in the information transmission and other related industries, telecommunications business revenue, mobile phone penetration rate, and Internet penetration rate. The entropy weight method is employed to calculate the digital infrastructure index for each city. Column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the regression results of DEP on digital infrastructure, with the DEP coefficient being significantly positive, confirming the substantial driving force of DEP on digital infrastructure development and thus validating Hypothesis \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e4.4.2 Digital innovation vitality\u003c/h2\u003e\u003cp\u003eBy fostering a favorable environment and providing clear guidance, DEP effectively incentivizes enterprises to increase R\u0026amp;D investment and drive technological innovation, thereby significantly enhancing digital innovation vitality, which in turn benefits the entrepreneurial environment of logistics industry. To quantify this effect, this study distinguishes between digital invention patents and utility model patents based on the International Patent Classification and adopts the number of digital patent applications per 10,000 population in a city as an indicator to measure urban digital innovation vitality. As shown in Column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the regression analysis results of DEP on digital innovation vitality indicate a significantly positive regression coefficient, directly proving the substantial enhancement effect of DEP on urban digital innovation vitality. To further explore the specific impact of DEP on logistics industry, we meticulously classify digital patent fields closely related to logistics industry according to the Classification of National Economic Industries, including industrial robot manufacturing, service and consumer robot manufacturing, Internet technology innovation platforms, Internet data services, Internet of Things technology services, and intelligent in-vehicle equipment manufacturing. Reference is made to the assessment method for digital innovation vitality to construct evaluation indicators for digital innovation vitality in logistics industry. The regression results in Column (3) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e show that the regression coefficient of DEP on digital innovation vitality in logistics industry is also significantly positive, robustly validating the positive role of DEP in significantly enhancing urban digital innovation vitality in logistics industry and providing solid empirical evidence for assessing the innovative incentive effect of DEP on entrepreneurial vitality of logistics industry. Accordingly, Hypothesis \u003cspan refid=\"FPar3\" class=\"InternalRef\"\u003e3\u003c/span\u003e is validated.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e4.4.3 Talent support effect\u003c/h2\u003e\u003cp\u003eDEP optimizes education and training systems and promotes the development of highly skilled talent, providing solid talent support for the entrepreneurial vitality of logistics industry. To quantify this effect, this paper initially adopts the number of ordinary undergraduate and college students per 100 people as a measure of urban human capital. Column (4) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the regression analysis results of DEP on human capital, revealing that DEP significantly enhances the level of human capital. Furthermore, this paper introduces the proportion of employees in the information transmission, computer services, and software industry to total employment as an indicator to assess urban digital talent reserves. The regression results reported in Column (5) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e show that DEP has a positive impact on enhancing urban digital talent. This finding confirms the effectiveness of DEP in elevating the level of urban digital talent, thereby providing indispensable talent support for the entrepreneurial vitality of logistics industry. Accordingly, Hypothesis \u003cspan refid=\"FPar4\" class=\"InternalRef\"\u003e4\u003c/span\u003e is validated.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\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\u003eMechanism test\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDigital infrastructure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDigital innovation vitality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDigital innovation vitality of logistics industry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHuman capital\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDigital talent\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDEP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0024***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.1418***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.4372***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0168**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0023***\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(0.0008)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.2198)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0461)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0071)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0003)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCity 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\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\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\u003e3808\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3806\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3805\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3878\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\u003e0.540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.895\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.965\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.862\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: Standard errors in parentheses, \u003csup\u003e*\u003c/sup\u003e p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, \u003csup\u003e***\u003c/sup\u003e p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Heterogeneity analysis\u003c/h2\u003e\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003ch2\u003e4.5.1 City hierarchy\u003c/h2\u003e\u003cp\u003eTo delve into the city-hierarchy disparities in the impact of DEPs on the EVLI, this study categorizes sample cities into tier-one cities (including first-tier, new first-tier, second-tier, and some third-tier cities) and non-tier-one cities, based on the classification criteria outlined in the \"2021 City Commercial Charm Rankings.\" Subsequently, a dummy variable CC for city hierarchy is introduced, where CC is assigned a value of 1 for tier-one cities and 0 otherwise. Furthermore, the interaction term between DEP (representing DEP) and CC is incorporated into the baseline regression model for analysis. The regression results, detailed in Column (1) of Supplementary table 1, reveal that the coefficient of the interaction term is significantly positive, while the coefficient of DEP is significantly negative. This indicates that DEPs significantly enhance the EVLI in tier-one cities but may exert a certain inhibitory effect in non-tier-one cities.\u003c/p\u003e\u003cp\u003eThe robust enhancement of entrepreneurial vitality in the logistics industry within tier-one cities, driven by DEPs, is primarily attributed to their well-developed infrastructure, abundant resource endowments, and favorable policy environments. Specifically, tier-one cities boast relatively comprehensive infrastructure in transportation, communications, and other sectors, providing a solid foundation for the efficient operation of the logistics industry. Additionally, these cities possess more mature market mechanisms and comprehensive talent cultivation systems, offering vast market opportunities and talent support for logistics entrepreneurs. In contrast, non-tier-one cities, characterized by lagging infrastructure, limited resources, and inadequate policy support, may experience constrained effects of DEPs in stimulating entrepreneurial vitality in the logistics industry, or even adverse impacts in some instances.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003e4.5.1 E-commerce development\u003c/h2\u003e\u003cp\u003eChina's e-commerce industry has undergone rapid development, playing a pivotal role in advancing digital economy and logistics. To further promote the healthy development of e-commerce, the National Development and Reform Commission, Ministry of Commerce, and other relevant departments jointly initiated the \"National E-commerce Demonstration City\" program in 2011 and have progressively announced a list of 70 national e-commerce demonstration cities. These demonstration cities possess first-mover advantages in the digital economy and logistics sectors, with optimized policy implementation and resource allocation. Typically, they boast more sophisticated logistics infrastructure and advanced information technology application levels, enabling them to better embrace and apply DEPs (Yang et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, by leveraging the driving force of the digital economy, e-commerce demonstration cities further ignite the EVLI. To validate this effect, a dummy variable EC is constructed, assigning a value of 1 to cities designated as e-commerce demonstration cities and 0 otherwise. Subsequently, the interaction term between DEP and EC is included in the baseline regression model. The regression results, presented in Column (2) of Supplementary table 1, show that the coefficient of the interaction term is significantly positive, while the coefficient of DEP is significantly negative. This suggests that DEPs significantly promote the EVLI in e-commerce demonstration cities but may exert a negative influence on non-demonstration cities.\u003c/p\u003e\u003cp\u003eIn electronic commerce demonstration cities, DEPs have the capacity to augment the EVLI. This assertion is substantiated by the fact that these cities commonly possess sophisticated infrastructure, ample resources, and a conducive policy environment. Demonstration cities often exhibit a more mature market landscape and a comprehensive talent nurturing framework, thereby providing substantial support for logistics entrepreneurship. Moreover, these cities typically amplify investments in logistics infrastructure, encompassing the development of modern logistics parks and distribution hubs, which significantly enhances transparency and traceability in logistics operations. Simultaneously, demonstration cities employ policy guidance to catalyze the digital transformation of logistics enterprises, ultimately leading to improved service quality and operational efficiency. Conversely, in non-electronic commerce demonstration cities, DEPs may struggle to fully harness their potential or even impede the EVLI, owing to inadequate infrastructure, resource constraints, and insufficient policy support. These cities may lack crucial digital infrastructure, such as high-speed internet connectivity and intelligent warehousing facilities, which limits the innovative capacity and development of logistics enterprises. Furthermore, non-demonstration cities may suffer from the absence of robust policy frameworks and capital infusions, rendering logistics enterprises vulnerable to substantial challenges during their digital transformation endeavors.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eStimulating the EVLI serves as a pivotal avenue for fostering economic transformation, upgrading, and high-quality development. In this endeavor, the policy support of the digital economy plays an indispensable role. This study empirically examines the impact, mechanism, and heterogeneous effects of DEPs on the EVLI, utilizing panel data from 277 cities in China spanning the period from 2009 to 2022. The findings reveal that DEPs can augment the EVLI. Mechanism tests demonstrate that DEPs elevate the EVLI through three crucial pathways: fostering digital infrastructure development, enhancing digital innovation vitality, and providing talent support. Heterogeneity analysis underscores that DEPs bolster the EVLI in advanced cities and electronic commerce demonstration cities, whereas they exert an inhibitory effect on the EVLI in general cities and non-electronic commerce demonstration cities.\u003c/p\u003e\u003cp\u003eWhile this study has attained notable results in exploring the impact of DEPs on the EVLI, it also acknowledges inherent limitations and simplifications. Specifically, biases may exist in the proxy variables employed to measure DEPs and the EVLI. Furthermore, despite incorporating a multitude of control variables, the study may not have comprehensively captured all potential influencing factors, potentially compromising the accuracy of the regression results. Moreover, given its reliance on Chinese city panel data, the applicability of this study may be confined to domestic cities, with insufficient exploration of foreign urban development contexts.\u003c/p\u003e\u003cp\u003eTo address these limitations, future research can delve deeper in the following respects: Firstly, optimize measurement indicators and methodologies to enhance the precision and comprehensiveness of assessing DEPs and the EVLI. Secondly, broaden the research scope by incorporating foreign cities into the analytical framework to validate the universality of the study's findings. Thirdly, further expand the analysis of mechanisms, exploring additional potential intermediary variables or pathways, such as how DEPs indirectly augment the EVLI through optimizing resource allocation and fostering industrial upgrading. Lastly, this study is grounded in static panel data, neglecting temporal dynamics. Future research can adopt dynamic models and forward-looking analytical methods to study the evolving impacts of DEPs across different time scales. By constructing dynamic system models, deeply analyze the intricate interactions among DEPs, government policies, corporate behavior, and environmental quality, and predict the long-term ramifications of DEPs on the EVLI. This will provide theoretical underpinning for formulating more scientific and sustainable logistics industry development strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGeneral Project of the National Social Science Foundation, grant/award number: 21BJY223; General Project of the Chongqing Natural Science Foundation, grant/award number: CSTB2023NSCQ-MSX0046; and Major Project of Chongqing Social Science Planning \u0026ldquo;Construction of Western Land-Sea New Passage\u0026rdquo;, grant/award number: 2023ZDLH06.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData available on request from the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthic statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAFAWUBO, K., NOGLO, Y.A., 2022. 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Land 13. https://doi.org/10.3390/land13070960\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Digital Economy Policy, Digital Economy, Entrepreneurial Vitality of the Logistics Industry, Digital Infrastructure, Digital innovation vitality","lastPublishedDoi":"10.21203/rs.3.rs-7194873/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7194873/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDigital economy policy, functioning as the bedrock upon which the digital economy thrives, holds a seminal role in igniting the entrepreneurial vitality within the logistics industry. This study utilizes panel data from 277 cities in China spanning the period 2009\u0026ndash;2022 to investigate the impact of digital economy policy on the entrepreneurial vitality of the logistics industry. The results unequivocally demonstrate that digital economy policy significantly elevates the entrepreneurial vitality of the logistics industry. Mechanism analysis elucidates that digital economy policy achieves this through three fundamental avenues: fostering the development of digital infrastructure, amplifying digital innovation vitality, and nurturing a talent pool. Moreover, heterogeneity analysis reveals a more pronounced positive influence of digital economy policy in advanced cities and e-commerce demonstration cities.\u003c/p\u003e","manuscriptTitle":"The Impact of Digital Economy Policy on the Entrepreneurial Vitality of the Logistics Industry: Empirical Evidence from China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-05 14:19:12","doi":"10.21203/rs.3.rs-7194873/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3721f424-da54-40a9-8ac8-4097f8945363","owner":[],"postedDate":"August 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T15:58:55+00:00","versionOfRecord":{"articleIdentity":"rs-7194873","link":"https://doi.org/10.1007/s12061-025-09761-4","journal":{"identity":"applied-spatial-analysis-and-policy","isVorOnly":false,"title":"Applied Spatial Analysis and Policy"},"publishedOn":"2025-12-04 15:56:54","publishedOnDateReadable":"December 4th, 2025"},"versionCreatedAt":"2025-08-05 14:19:12","video":"","vorDoi":"10.1007/s12061-025-09761-4","vorDoiUrl":"https://doi.org/10.1007/s12061-025-09761-4","workflowStages":[]},"version":"v1","identity":"rs-7194873","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7194873","identity":"rs-7194873","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}