Unlocking Pathways for Urban Inclusive Green Growth: Talent Agglomeration —A New Perspective on High-End Human Capital Agglomeration

Unlocking Pathways for Urban Inclusive Green Growth: Talent Agglomeration —A New Perspective on High-End Human Capital Agglomeration | 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 Unlocking Pathways for Urban Inclusive Green Growth: Talent Agglomeration —A New Perspective on High-End Human Capital Agglomeration Chenyang Guo, Lan Fang, Lan Yang, Jiexiao Ge This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8023565/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Amid slowing global economic growth, the “demographic dividend” has diminishing returns, making it essential to leverage the “talent dividend” to achieve urban inclusive green growth ( UIGG ). Using panel data from 270 prefecture-level cities in China (2003–2019), this study examines the impact and mechanisms of talent agglomeration ( Talagg ) on UIGG . The findings show that: (1) Talagg significantly and directly promotes UIGG . This finding still holds after a number of robustness tests. (2) Heterogeneity studies show that the effect varies obviously by geographic location, city type, and development level. (3) Mechanism analyses identify two key pathways through which Talagg improves UIGG : green technology innovation and entrepreneurial activity. (4) Further analysis highlights synergistic effects between Talagg and industrial agglomeration, as well as labor marketization, which plays a crucial moderating role in enhancing UIGG . This study provides a new insight for tackling environmental challenges and social inequality in cities, offering policy implications for China and other emerging economies seeking to transition from traditional development models. Urban Inclusive Green Growth Talent Agglomeration High-end Human Capital Sustainable Development Figures Figure 1 Figure 2 1. Introduction About 80% of global GDP and 70% of energy consumption emissions originate from economic activity in cities (Shan et al., 2021 ). Cities, as the core carrier and spatial support of human socio-economic activity, are both economic growth engines and environmentally sensitive zones. Nowadays, the imbalance between the economic and the environment is the most significant issue to urban growth (Wu et al., 2025 ). China has achieved remarkable economic performance since the 1980s, thanks to a rudimentary development model based on resources and factors (Wang M et al., 2024 ). China's GDP in 2024 surpassed 134.9084 trillion yuan, more than 366 times greater that of 1978 (0.3679). However, the rapid economic growth has resulted in a number of environmental and social challenges. On the one hand, the extensive growth model, which is characterized by high pollution and low efficiency, has led to problems involving excessive energy consumption, serious environmental damage, and low economic effectiveness (Gu et al., 2021 ; Xu et al., 2025 ), seriously constraining the sustainable development of cities. On the other hand, development possibilities and economic gains have not been dispersed equally. Social phenomena like the urban-rural dual structure, the ever-widening wealth gap (Kebede et al., 2023 ), unequal employment opportunities, and deteriorating public healthcare conditions (Long and Ji, 2019 ; Sun et al., 2020 ), are becoming more severe and impeding inclusive economic growth. These issues aren't just limited to poor countries. Many advanced nations also face that. Wealth disparity, social injustice, and environmental degradation, which are ''non-inclusive'' and ''non-green'', are substantially harming the sustainable and healthy growth of the global economy (Gu et al., 2021 ). The United Nations' (UN) New Urban Agenda (2016) and the 2030 Agenda for Sustainable Development Goals (SDG 11) clearly articulate the transformational requirements of "making cities and human settlements inclusive, safe, resilient and sustainable", resulting in widespread adoption of the development concept of "Inclusive Green Growth (IGG)". IGG aims to achieve sustainable growth through the three-dimensional synergy of economy, society, and environment (World Bank, 2012 ), attempting to resolve the fundamental contradiction between efficiency and equity, growth and sustainability in traditional urbanization. IGG's core essence is that, while pursuing economic growth, it prioritizes equal involvement of all economic individuals in the process, sharing the fruits of economic growth, and narrowing the wealth gap. It also integrates green development into economic decision-making, with an emphasis on improving resource efficiency, ecological environmental protection, and the reducing pollutant emissions. This development model can not only address the issue of "non-green" in the economic growth of industrialized countries, but it may also alleviate the "non-inclusive" social disputes in developing countries (Yang et al., 2024 ; Kamguia et al., 2025 ). IGG has emerged as a new paradigm for achieving the ultimate goal of human social development. Furthermore, with the structural slowing of economic growth, China's urban labor factor supply has reached an inflection point. China is currently undergoing a significant transformation from a populous country to a human capital powerhouse (Cai et al., 2025 ). In the knowledge economy, talent resources are strategic assets for which every country or region competes (Gu et al., 2024 ). The spatial distribution of talent as a key production element, known as talent agglomeration ( Talagg ), is reconstructing the power mechanism of urban development. Talagg can contribute new ideas, superior technology, and a wealth of social capital to the city's industrial upgrading and economic transformation, providing a continual stream of vitality. This provides abundant human capital and intellectual support for the city's high-quality development while also significantly increasing productivity and innovation (Peri et al., 2015 ), promoting diversified urban development, reducing the reliance on a single market, and improving economic stability and inclusiveness. Therefore, against such a backdrop, it is essential to investigate the causal relationship and internal mechanism between Talagg and IGG. This has important implications for the healthy and long-term growth of the urban economy in China and other countries across the world. The marginal contributions of this paper are twofold. First, it reveals the "black box" mechanism that links high-quality human capital and sustainable development, theoretically enriching the related research domains of human capital, social welfare and green development. It not only gives new empirical evidence for the current urban transformation, but also points out the direction for the future growth of global cities. Second, in terms of experience, it broadens the research perspective of IGG. Unlike most of the previous studies that focused on population size, this paper takes a population quality perspective, combines labor structure and agglomeration theories, and comprehensively and systematically examines the effects, mechanisms, and differences in the impact of Talagg on the coordinated development of urban economy-society-environment. This allow us to develop policy recommendations that are both practical and feasible for decision-makers. The remainder of the paper is organized as follows. The next section provides a theoretical analysis and research hypothesis, while Section 3 describes the data and methodology. We present our empirical regression results in Section 4 , and shed light on mechanism section in Section 5 . We also conducted extended analysis in Section 6 . The last section is the conclusion and suggestions. 2. Theoretical Analysis and Research Hypothesis 2.1 Talagg on Urban Inclusive Green Growth ( UIGG ) Lucas ( 2015 ) argues that human capital accumulation is the source of economic growth. Talent is the primary source of human capital involved in innovation research. And it also plays an important role in urban green development. Porter's theory of competitive advantage further demonstrates that senior factors centered on talents can promote regional development more than primary factors centered on natural resources. Therefore, Talagg is an essential driver to promote UIGG . First of all, Talagg can enhance the impetus of urban economic growth (Farndale et al., 2022 ). From the opinion of agglomeration, Talagg and economic growth are parallel processes. Agglomeration can boost both social productivity and regional economic growth (Davis et al., 2014; Fontagné and Santoni, 2019 ). Talent can offer cities with abundant human capital and intellectual support. The concentration of talent can augment the learning effect in the operation of the production processes of local enterprises, optimize the management mode, lower running costs, and improve production efficiency (Zhou et al., 2018 ). It is also conducive to the realization of technological updating and information sharing within cities, promoting the specialized division of labor in production and improving the efficiency of resource factor allocation, thereby promoting the efficient and sustainable growth of the urban economy (Hsieh et al., 2019 ). Secondly, Talagg contributes to greater urban inclusivity. Talagg can quickly attract and drive all kinds of resource factors and related enterprises to the city. It can promote the transformation of R&D projects, accelerate the output and market application of innovation results (McGuirk et al., 2015 ), and narrow the development gaps within the city. And it can also help to diversify markets and enhance economic stability. Furthermore, talents’ advanced management concepts can also assist the local governments in raising the decision-making level, completing the governance system, improving the governance efficiency and reducing management costs, thereby significantly improving urban governance levels (Ren Y et al., 2024 ) and strengthening urban resilience to external risks and inclusive development. In addition, Talagg helps hasten the transformation of urban greening. Talagg plays a positive role in accelerating the improvement of the key production technology, the transformation and upgrading of traditional industries (Zhang et al., 2024 ), green technical progress and environmental quality (Gharbi et al., 2025 ). By influencing consumption preferences, investment ideas, employment methods, and environmental protection awareness, talent guides urban residents' lifestyles towards greening and decarbonization (Xu and Huang, 2021 ). And this forces industrial companies to conducting green transformation, and thus reducing urban dependence on high-pollution, high-energy-consumption industries. As a result, an industrial "green atmosphere" is created to raise the efficiency of green economy, which in turn promotes the process of urban green transformation. So, this paper proposes the following hypothesis 1. H1: Talagg can promote UIGG . 2.2 Mechanisms Analysis (1) Green Technological Innovation ( Greeinn ) Innovation is an inexhaustible driving force for economic and social progress. The first and most important factor in promoting technical innovation is talent (Yang and Pan, 2020 ). According to the Marshall's externality theory, agglomeration economies can foster innovation mainly due to the positive externality of agglomeration (Ren M et al., 2024 ). Talagg enhances the interactive exchange and experience sharing among inventive individuals (Atkin et al., 2022 ), since knowledge and skills are transferred with talent mobility (Chemmanur et al., 2018 ; Teslenko et al., 2021 ). Besides, it also reduces the acquisition costs and risk of failure of technical breakthroughs (Moretti, 2021 ), allowing for the continuation of inventive activities. Green technologies are becoming an increasingly important emphasis in innovation programs. Greeinn is the primary driver of urban sustainable development (Liu et al., 2023 ), as well as a critical instrument for mitigating global warming and responding with natural catastrophes. Talagg promotes urban Greeinn by increasing innovation inputs and outputs. On the one hand, Talagg supports green technological upgrading through agglomeration of innovative input resources. It is capable of meeting the requirement for high-quality labor factor inputs in urban sustainable development. While it also brings about the agglomeration of knowledge, technology, capital and other innovation input resources in the city, which in turn stimulates the government and relevant enterprises to increase funding for green innovation R&D projects (Zheng et al., 2021 ; Cai et al., 2025 ). And this provides policy support and financial guarantees for the green transformation of cities. Talagg , on the other hand, boosts green development efficiency by accelerating innovation output. It forms effective knowledge networks and specialized social circles through close cooperation, which can efficiently coordinate internal and external resources of cities, quickly solve technical problems (Squicciarini and Voigtländer, 2015 ), capture green development approaches in a timely manner (Fang and Wolski, 2021 ), speed up the R&D and output of green innovations (Kerr et al., 2017 ; Tang et al., 2024 ), and enhance the decarbonization of urban production and greening of life (Shan et al., 2021 ). This facilitates the achievement of regional sustainable development goals. In summary, this paper considers the following hypotheses. H2a: Talagg can impel UIGG through Greeinn. (2) Entrepreneurial Activity ( Startup ) Urban public service systems (like makerspaces, Startup cafes, etc.) provide comprehensive social venues and platforms for talent brainstorming and technology exchanges. It not only improves the communication between talents, but also nourishes a vast number of potential entrepreneurial opportunities (Garay et al., 2017 ). A favorable entrepreneurial environment can enrich local entrepreneurial abilities (Glaeser et al., 2015 ), strengthen the city's attraction to persons with entrepreneurial aspirations, and thus increase the number of local entrepreneurs and entrepreneurial activities (Jian et al., 2024 ). Firstly, Talagg has the potential to lower the likelihood of adverse selection. It offers entrepreneurial firms with the required skilled workers while reducing recruitment and training costs. And it can also address the technical barriers and budgetary challenges. In other words, it lowers the cost of entrepreneurship on the premise of ensuring the necessary human and material inputs, and lays a good foundation for the growth of entrepreneurial enterprises (Rong et al., 2024 ). This will attract more people into entrepreneurial activity. High-quality social groupings among talent provide Startup with difficult-to-access business information and channels. They can make it easier for entrepreneurs to find partners, investors and customers, as well as reduce the Startup cycle and risk. Thus, more entrepreneurs are drawn to entrepreneurial activity (Gennaioli et al., 2013 ). Secondly, entrepreneurial actions can optimize the allocation of market resources, and guide the development of green industries. Talagg accelerates economic development while intensifying competition and cooperation among cities (Yang and Pan, 2020 ). Cooperation across various firms and individuals, while sharing resources and risks, is also continuously increase competitiveness (Combes et al., 2012 ). Entrepreneurial enterprises, in particular, use the market competition mechanism to allocate resources toward more promising green and emerging industries. Entrepreneurs take advantage of factor resource utilization to strengthen the green orientation of products, reduce the dependence of the economic on high-pollution and high-energy-consumption models, and then support urban green growth. Therefore, Talagg can boost the increase of urban entrepreneurial activities and improve the environmental performance of enterprises (Ruthensteiner and Leitner, 2025). In summary, we present the following hypothesis. H2b: Talagg can promote UIGG via Startup . 2.3 Moderating Effect (1) "Soft" Environment : Talagg , industry agglomeration ( Indagg ) and UIGG Talagg's function in urban development necessitates the use of specific platforms and carriers, which Indagg fulfills perfectly. Indagg is a key feature of modern economic growth and an external representation of productivity agglomeration. The concentration of the same or related industries in a particular region will inevitably lead to the gathering of talents in the region. Similarly, the concentrated flow of talents will also promote enterprise clustering (Jian et al., 2024 ). Using wage signals, Indagg regulates talent supply and demand of and attracts high-quality and high-skilled workforce. As a result, it encourages more regional R&D and innovation activities, enhances the total factor productivity, and eventually contributes to regional economic growth. First, Indagg creates numbers job opportunities and attracts professionals from related fields to congregate. At the same time, Indagg brings more regular formal or informal communication within the same industry. Different ideas of highly qualified labor collide with each other, and the same technical skills of highly skilled personnel are optimized and developed (Squicciarini and Voigtländer, 2015 ; Moretti, 2021 ). The "Local Buzzing" effect facilitates the rapid diffusion and sharing of knowledge and technology (Bathelt et al., 2004 ), stimulates the innovation potential of talent, nurtures urban social networks and innovation ecosystems, and improves the collaboration patterns and innovation efficiency among industries (Huggins and Thompson, 2023 ), thereby enhancing urban development potential. Secondly, Indagg can broaden Talagg's impact on green innovation, and support the quick application and popularization of green technologies. Talagg plays a significant role in enhancing the knowledge-intensive business service (KIBS). The "Sticky Knowledge" generated by KIBS Indagg dynamically matches with the highly skilled labor force, accelerating the transformation of talent resources to talent capital while improving the green technological innovation (Zhang and Guo, 2025 ). Enterprises that take the lead in adopting new green technologies can gain an early advantage, attract the concentration of industry-related talents, expertise, technology and other resources based on the competitive advantages of the industry. This could increase sector specialization, lower the cost and risk of factor search (Wang M et al., 2024 ), and attract related companies to cluster and share the professional service network and ancillary facilities. Finally, it promotes energy conservation and economic efficiency in urban (Giuliano et al., 2019 ). Furthermore, compared to other locations, the environmental criteria implemented in industrial clusters will be much stricter. According to the Porter Hypothesis, strict environmental regulations can compel talent to research and develop clean technologies (Ouyang et al., 2020 ; Wang H et al., 2024 ), strengthen the incentives of green innovation for firms, and pique talent enthusiasm of green innovation (Moretti, 2021 ), thereby accelerating inclusive growth. Thus, we put forward the following hypothesis. H3a: Indagg has a substantial moderating effect in the process of Talagg affecting UIGG . (2) "Hard" Systems : Talagg , labor marketization ( Labmark ) and UIGG Promoting the free movement of labor and the optimal allocation of human resources is a powerful support for the regional economy's sustainable development. As China's most important population mobility management system, the reform of the household registration system can dramatically unleash the vitality of the labor market, which has a significant impact on the mobility of talent and industrial innovation (Cai et al., 2025 ). And it contributes significantly to the high-quality development of the urban economy (Sequeira et al., 2020 ). The current labor market segmentation and distortion problems in China are more serious. One of the reasons for this is the household registration constraint (Brandt et al., 2013 ). For foreigners, the various types of social welfare and guarantees (political, employment, education, social security, etc.) attached to the household registration system raise the bar for settling down in cities greatly. Such institutional restraints may make it difficult for talents to move reasonably in accordance with the market economy rules. The factor pricing mechanism cannot be freely determined by market supply and demand, resulting in distortion of talent factor prices, which in turn causes regional talent resource mismatch (Yang and Pan, 2020 ; Guo et al., 2024 ). The reform of the household registration system can effectively tackle the problem of inefficient allocation in the labor market, thereby increasing the degree of Labmark . The free flow of talents can not only improve the quality of labor market matching, but also give full play to human capital externalities and skill complementarities, enhance urban innovation efficiency (Cai et al., 2025 ; Zhang and Guo, 2025 ), and eventually strengthen the momentum of urban economic (Tombe and Zhu, 2019 ). In addition, well-developed public services are more attractive to competent workers. Equal public basic services, especially for the migrant population, can not only reduces the loss of labor efficiency wages and societal welfare (Diamond, 2016 ), but also increase a sense of belonging to the city. That will have a direct impact on urban modernization and harmonious development. In summary, we believe that the reform of the household registration system has a favorable impact on the efficiency of human capital allocation, urban economic vitality and the sense of identification of foreign talents. Therefore, we suggests the following hypotheses. H3b: The reform of the household registration system can play a positive moderating function in the process of Talagg affecting UIGG . Based on the above theoretical analysis, we draw the logic mechanism diagram (Fig. 1 ). 3. Estimation Strategy and Datas 3.1 Baseline Model In order to study the impact of Talagg on UIGG in Chinese, the following baseline regression model is constructed: $$\:{\text{UIGG}}_{\text{it}}\text{=}{\text{α}}_{\text{0}}\text{+}{\text{α}}_{\text{1}}{\text{Tal}\text{agg}}_{\text{it}}\text{+}{{\text{α}}_{\text{2}}\text{Control}}_{\text{it}}\text{+CityFE+YearFE+}{\text{ε}}_{\text{it}}\text{}$$ 1 Eq. ( 1 ) where i is the city, t is the year, and α 0 is the constant term. α 1 is the estimated coefficient we interest. UIGG it is the level of IGG in city i in year t . Talagg it is the level of Talagg in city i in year t . Conrtol it is a collection of control variables. CityFE is the city fixed effect, YearFE is the year fixed effect, and ε it is the random disturbance term. The coefficient α 1 measures the average difference in the impact of Talagg on UIGG . 3.2 Variables (1) Explained variable: UIGG Table 1 UIGG Indicator System Primary Indicators Secondary Indicators Tertiary Indicators Attribution Economic Development Economic Output Income Level Consumption Level Real GDP per capita GDP growth rate % The ratio of urban and rural per capita income % The ratio of urban and rural per capita consumption % + + - - Social Inclusion Educational Resources Healthcare Resources Social Welfare Employment Level Teacher-Student ratio of primary and secondary school % Number of physicians per 10,000 people The coverage rate of pension insurance % The coverage rate of medical insurance % The coverage rate of unemployment insurance % Urban registered unemployment rate % + + + + + - Green Livability Living Environment Park green space per capita km 2 Green covered area ha + + Pollution Control Industrial wastewater discharge ton Industrial SO 2 emissions ton Industrial fume (dust) emissions ton Comprehensive utilization rate of industrial solid waste % Harmless treatment rate of domestic garbage % - - - + + Note: The above data are all from EPS. UIGG is a comprehensive concept. Referring to Sun et al. ( 2020 ) and Wang D et al. (2022), we construct a UIGG index system based on three dimensions: economic development, social inclusion, and green livability in Table 1 , and measure it adopting the entropy method. Table 2 Control Variables Variables Indicator Source Structure Tertiary GDP/GDP % EPS Invest The actual use of foreign investment 10 billion EPS Internet The number of international Internet users/Household population % EPS Property (Industrial output value of Hong Kong, Macao and Taiwan Enterprises + Industrial output value of foreign enterprises)/Total industrial output value % CEIC Perpost Postal and Telecommunication business revenue/Permanent population CEIC CSMAR Perroad Real urban road area /urban population EPS Science The science and technology expenditure/The general budget expenditure % CEIC Perbook Total public library book collection/Permanent population EPS CEIC Security Social security and employment expenditure/The government budget expenditure % CEIC (2) Explanatory Variable: Talagg The stability of the quantity of talents is a strong support for urban development. Compared with academic qualifications, it is more realistic to judge talent by occupation. Given the availability of city-level data, 1 this paper draws on the ideas of Moretti ( 2021 ) and Bai et al. ( 2022 ). Talagg is represented by the proportion of employees in scientific research, technical services and geological exploration, as well as information transmission, computer services and software industries to the total number of employees. In comparison to other industries, these industries have a comparatively high knowledge reserve and skill level among their workforce. So it is appropriate and practicable to utilize this indication to calculate Talagg . (3) Control Variables We referred to existing studies and selected the following control variables. The specific definition of variables and data sources are shown in Table 2 . (4) Data Description Combining the comparability and availability of data, and excluding cities with serious missing data, this paper finally compiles and obtains balanced panel data for 270 cities from 2003–2019. The sample data are mainly from the EPS, CEIC and CSMAR databases, and part of the data are from the China City Statistical Yearbook and the China Urban Construction Statistical Yearbook . In addition, considering the effect of extreme values and heteroskedasticity, we applied the following to the data treatments: ①All variables involving values are deflated using 2003 as the base year. ②All variables, excluding ratios, are logarithmically treated to reduce the problem of heteroskedasticity. ③baseline variables are supplemented with linear interpolation and moving average methods, and 1% two-way deflator is adopted. Table 3 shows the descriptive statistics of the baseline datas in this paper. Table 3 Descriptive Statistics Variables Mean Max Min p50 p25 p75 UIGG 0.0336 0.1477 0.0100 0.0288 0.0223 0.0386 Talagg 0.0277 0.1518 0.0068 0.0236 0.0175 0.0316 Structure 0.3927 0.7288 0.1699 0.3804 0.3306 0.4469 Invest 0.2684 2.3110 0.0000 0.1028 0.0302 0.3412 Internet 0.1471 0.9794 0.0020 0.1026 0.0426 0.1973 Property 0.1455 0.8398 0.0019 0.0853 0.0417 0.2039 Science 0.0124 0.1183 0.0004 0.0078 0.0037 0.0157 Security 0.1129 0.3259 0.0051 0.1131 0.0780 0.1435 Perpost 0.0807 0.7839 0.0064 0.0670 0.0422 0.0966 Perroad 0.1540 0.6108 0.0246 0.1412 0.1007 0.1928 Perbook 0.3485 1.4402 0.0244 0.2822 0.1860 0.4416 4. Result and Discussion 4.1 Baseline Regression Before the baseline regression, we first conducted VIF tests on the baseline variables to avoid multicollinearity issues. The results show that VIF values do not exceed 5 for all baseline variables. Subsequently, adopting high-dimensional fixed-effects model, we proceed stepwise regression to examine the direct effect of Talagg on UIGG . Table 4 shows the results of the baseline regression. Table 4 Baseline Results (1) (2) (3) (4) (5) (6) Talagg 0.1250 *** 0.1072 *** 0.1022 *** 0.0988 *** 0.0973 *** 0.0981 *** (0.0136) (0.0127) (0.0126) (0.0120) (0.0118) (0.0119) Structure 0.0093 *** 0.0084 *** 0.0077 *** 0.0078 *** 0.0079 *** (0.0021) (0.0022) (0.0020) (0.0020) (0.0020) Invest 0.0050 *** 0.0048 *** 0.0041 *** 0.0041 *** 0.0042 *** (0.0007) (0.0006) (0.0007) (0.0007) (0.0007) Internet 0.0095 *** 0.0079 *** 0.0077 *** 0.0071 *** (0.0014) (0.0014) (0.0013) (0.0013) Property 0.0018 0.0025 0.0031 * 0.0029 (0.0019) (0.0019) (0.0018) (0.0018) Science 0.0735 *** 0.0685 *** 0.0607 *** (0.0129) (0.0127) (0.0123) Security 0.0126 ** 0.0126 ** 0.0120 ** (0.0049) (0.0049) (0.0049) Perpost 0.0051 *** 0.0049 *** (0.0021) (0.0020) Perroad 0.0058 *** 0.0048 ** (0.0020) (0.0019) Perbook 0.0047 *** (0.0007) City-FE Y Y Y Y Y Y Year-FE Y Y Y Y Y Y N 4590 4590 4590 4590 4590 4590 R 2 0.9361 0.9379 0.9391 0.9400 0.9402 0.9409 adj. R 2 0.9319 0.9337 0.9350 0.9359 0.9362 0.9369 Note: Standard errors in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01 As can be seen from columns (1) ~ (6) of Table 4 , the estimated coefficients of Talagg are all significantly positive at the 1% level, which strongly suggests that an increase in Talagg can significantly and positively contribute to UIGG . As analyzed in section 2 , Talagg can enhance urban development dynamics, guide the green upgrading of industries, promote urban diversification, and enhance urban governance capacity, and thus promoting UIGG . Our conclusions are similar to Cai et al. ( 2023 ). In the control variables, except for Property , all the other variables have a significant positive impact on UIGG . Specifically, the estimated coefficient of Structure is significantly positive at the 1% statistical level. This indicates that the optimization and upgrading of the industrial structure has a significant improvement to enhancing UIGG . The rationalization and diversification of industrial structure plays an important role in the high-quality advancement of the urban economy, and has a significant effect in providing jobs, alleviating the pressure of industrial pollution, and boosting the economic growth rate, etc. The coefficient of Invest is significantly positive at the 1% statistical level. This indicates that foreign capital utilization is beneficial to UIGG .This is in line with the findings of Ofori et al. ( 2023 ). To some extent, the level of foreign capital utilization also represents the efficiency of local foreign trade and capital utilization. The higher the level of foreign capital utilization, the better the city's trade flow, ability to attract capital and degree of openness and inclusiveness. This has a positive effect on the inclusive development of the city. The estimated coefficient of Internet is significantly positive at 1% statistical level. This indicates that the Internet penetration can significantly boost UIGG . The Internet, as an important manifestation of the information age, has greatly accelerated the speed of information dissemination among market subjects and increased market activity. The Internet has greatly facilitated the virtual economy such as online transactions and cross-border trade. The popularization of network technology ensures the digital power of urban economic growth. The coefficient of Science is obviously positive at the 1% statistical level. This indicates that financial science and technology support can significantly improve UIGG . There is no doubt that science and technology are the primarily driving forces for development. The more the local government's financial expenditure on science and technology investment, the more the city's scientific and technological productivity is guaranteed, and thus the better the city's economic growth performance. Moreover, abundant funds for science and technology can also improve industrial upgrading and green innovation. The coefficient of Security is significantly positive at the 5% statistical level. This indicates that social security is conducive to UIGG . The aging of China's population is becoming more prominent. Coupled with the continuous decline in the birth rate in recent years, the burden of supporting the labor population has also risen significantly. With the gradual improvement of the social security mechanism, the government's increasing expenditure on social security has greatly reduced the cost of supporting the young and middle-aged labor force, and enhanced the sense of identity of the foreigner to the city. The coefficient of Perpost is distinctly positive at the 1% statistical level. This manifests that the level of informatization could drive UIGG . The more developed a city's information industry is, the higher the degree of acceptance of new things, and thus the more vitality economic growth and social inclusion. Moreover, the high frequency of information exchange and dissemination also contributes to the upgrading and updating of the green technology. The estimated coefficient of Perroad is significantly positive at the statistical level of 5%. This means that infrastructure can significantly facilitate UIGG . The level of infrastructure plays a fundamental role in the development of a city. It not only reflects the city's productivity, but also promotes social harmony and meets citizens' satisfaction. Complete infrastructure can also effectively support the application and promotion of green technology, thus realizing sustainable urban development. The estimated coefficient of Perbook is significantly positive at the 1% statistical level. This indicates that cultural capital has a significant positive impact on advancing UIGG . Cultural capital provides a social foundation for inclusive growth by enhancing residents' sense of cultural identity and belonging, promoting communication and understanding among different groups, and reducing social conflicts. Besides, cultural capital also spurs the blossom of creative industries, creates high value-added employment opportunities, and promotes economic diversification through cultural tourism development. 4.2 Endogeneity This paper uses the instrumental variable (IV) way to test the possible endogeneity among variables. Referring to Lin and Tan (2019) and Cai et al. ( 2023 ), we choose terrain undulation as IV and use the 2SLS method to verify. In order to fulfill the requirement of panel data with variable instrumental variables, the product of terrain undulation ( Terrain ) and the number of permanent residents ( Population ) is selected as the instrumental variable of Talagg . The IV is somewhat reasonable. On the one hand, for the general labor force, the more complex the topographic relief, the greater the cost of migration. However, the migration costs incurred by the cross-regional flow of talents can almost be negligible. This is because not only do they have a certain economic base, but also the local government will give all kinds of relocation subsidies to the talents. From this, it can be inferred that the IV may have a positive correlation with the explanatory variables, which satisfies the characteristic of correlation with endogenous variables. On the other hand, as an exogenous geographical variable, urban terrain undulation is an objective geographic feature that is almost impossible to change (Chen and Kung, 2016 ). It cannot directly affect UIGG , which satisfies the exogeneity requirement. Table 5 IV Estimates Results Variables (1) (2) First Second Terrain*Population 0.6450 *** (0.0129) Talagg 0.1826 *** (0.0163) Controls Y Y City-FE Y Y Year-FE Y Y Anderson canon. corr. LM statistic 1542.03 1542.028 Cragg-Donald Wald F statistic 2489.66 2489.664 N 4304 4304 R 2 - 0.514 Number of City 269 269 Note: Same as Table 4 . Table 5 shows the results of 2SLS estimation. All estimated coefficients are all significantly positive at the 1% level. From the first stage results, it can be seen that there is a significant positive correlation between IV ( Terrain*Population ) and Talagg , which is in line with the previous analysis and confirms the relevance. And the LM value of the non-identifiable test and the F value of the weak instrumental variable test are much larger than the empirical critical value, rejecting the original hypothesis. The second stage results show a substantial and positive correlation between Talent and UIGG . This is consistent with the baseline results. The above analysis shows that our main conclusions still hold after the introduction of IV to mitigate the impact of potential endogeneity. 4.3 Robustness Hypothesis , that Talagg can significantly boost UIGG , is proved in the baseline test. In order to strengthen the robustness of this conclusion, we conduct a series of robustness tests as follows, namely, replacing the explanatory variables, adding control variables, changing the clustering robust standard errors, lagging the dependent variable by one period, and eliminating the interference of regional heterogeneity and geographical characteristics. The details are as follows: (1) Replace the explanatory variable In order to prove the robustness of the baseline results, we first replace the ratio of employees in two industries to the total employees with the ratio of employees in six industries 2 to the total employees, and then recalculating Talent and running the regression. The results are shown in column (1) of Table 6 . Table 6 Robustness Tests (1) (2) (3) (4) (5) (6) (7) Talagg 0.0155 *** 0.0963 *** 0.0981 *** 0.0852 *** 0.0981 *** 0.0981 *** 0.0948 *** (0.0027) (0.0114) (0.0119) (0.0118) (0.0121) (0.0119) (0.0118) jd*year 0.1047 *** (0.0252) wd*year 0.0034 (0.0271) Controls Y Y Y Y Y Y Y City-FE Y Y Y Y Y Y Y Year-FE Y Y Y Y Y Y Y City*Year N N Y N N N N N 4590 4590 4590 4320 4590 4590 4590 R 2 0.9400 0.9421 0.9409 0.9383 0.9409 0.9409 0.9412 adj. R 2 0.9359 0.9381 0.9369 0.9338 0.9369 0.9369 0.9372 Note: Same as Table 4 . (2) Add control variables To avoid the issue of estimation bias caused by omitted variables, more city-level control variables are added for testing. The additional control variables are as follows: Financial expenditure on education/Local fiscal General Budget Expenditures ( Education , Source: CEIC); Total bank loans/Total bank deposits ( Finance , Source: CEIC); Fiscal expenditures/GDP ( Govern , Source: CEIC); And per capita mobile phone users ( Mobile , Source: EPS). The problem of omitted variables is reduced by controlling for more individual city characteristics. Results are reported in column (2) of Table 6 . (3) Cluster interaction effects Consider that within-group correlations (individual and time-varying characteristics) may have an impact on the estimation results. We perform robustness tests by changing the clustered robust standard errors. Specifically, the clustering criteria are refixed at the city*year level in regressions to control for time-varying area-level characteristic factors. The results are reported in column (3) of Table 6 . (4) Explained variable lagged one period It takes time for Talagg to promote urban development. And thus there may be a time lag effect on UIGG . At the same time, the data may have short-term fluctuations. Using lagged data can help smooth out the impact of short-term fluctuations on the regression results. Therefore, to improve the accuracy of the baseline estimation, the explained variable is used for regression again with a lag of one period. The results are shown in column (4) of Table 6 . (5) Bootstrap repeated sampling The core of Bootstrap is to generate a large number of simulated samples by repeated sampling of the original samples with put-backs, and then estimate the distributional properties of the statistics. Its rationality stems from the direct simulation of sampling variation, which can effectively verify the stability of empirical results. In this paper, the baseline result is re-estimated by repeating the sampling 500 times. The results are shown in column (5) of Table 6 . (6) Excluding regional variability Due to special national conditions, the developed degree in eastern China has long been better than that in central and western. In order to exclude regional differences that may affect the results of the baseline estimation, we introduce the interaction term between Talagg and regional dummy variables (1 for eastern regions and 0 for non-eastern regions) on the basis of the baseline model. The results are presented in column (6) of Table 6 . (7) Excluding the interference of geographic features China is a vast country with complex and variable geographic features. This may have a certain influence on talent mobility. For rule out the possibility that the geographic feature may cause the inaccuracy of the estimated result, we introduce the interaction term between latitude and longitude and year for each city and estimate again. The results are shown in column (7) of Table 6 . To sum up, after the above robustness tests, all of Talagg ’s estimated coefficients on UIGG pass the significance test at the 1% statistical level. All results are highly consistent with the baseline estimates, which greatly enhances the credibility of our basic conclusions. 4.4 Heterogeneity Considering the differences in the individual characteristics of each city, we examined the heterogeneity effect of Talagg on UIGG from four aspects, namely, city type, city positioning, geographic location and quantile regression, respectively. The results are shown in Appendix Table 4. To visually present the heterogeneous results, we plotted Fig. 2 . (1) City Location In the early stage of reform and opening up, China's eastern coastal provinces leveraged their natural geographical and transportation advantages to become pioneer regions, with economic development levels far surpassing those of central and western provinces. To reduce regional disparities, the Chinese government successively launched the "Western Development" and "Central Revival" regional development strategies. This paper categorizes cities into east, middle, and west regions based on the standards of the National Bureau of Statistics of China 3 , and conducts empirical tests. As shown in Panel A of Fig. 2 , the effect of Talagg on UIGG is significantly positive in the east-middle region, but not significant in the west region. This fully demonstrates that the impact of Talagg on UIGG exhibits significant geographical location heterogeneity. The influence of Talagg on east-middle cities is more positive and significant. The underlying reason may align with the polarization theory, where developed region continuously absorbs high-quality factor resources, and the accumulation of factor endowment disparities exacerbates income inequality. Compared to the east-middle region of China, the west region has relatively underdeveloped economic conditions and lagging social security systems, making it less attractive to talent. Most highly skilled talent tends to flow toward economically developed cities with higher welfare levels, resulting in a more obvious effect of Talagg on UIGG in east-middle cities. (2) City Type The effect of Talagg on UIGG may be affected by city class. Central cities have advantages in terms of market size, resource allocation and policy support, which are conducive to the full utilization of talent resources. Moreover, in reality, compared with ordinary cities, highly skilled talents generally tend to move to the central cities agglomeration. Therefore, according to the city type, this paper sets the municipalities, provincial capitals and sub-provincial cities in the data sample as center cities, and the rest of the cities as ordinary cities. As can be seen from Panel A of Fig. 2 ., the promotion of Talagg to UIGG passes the significance test in the center cities, while it did not pass in ordinary cities. This indicates that the effect of Talagg on UIGG varies significantly among cities of different administrative levels. By virtue of the huge market advantage, complete public service system, and innate policy resources, central cities provide sufficient space for talents to gather and play. These are not available in ordinary cities. (3) Quantile Regression The results of the baseline regression confirm the uplifting effect of Talagg on UIGG . But it is impossible to know whether there is a difference in its impact on cities with different IGG levels. Therefore, this paper employs Quantile Regression to explore the differences in the impact of Talagg on different UIGG levels. Panel quantile regression can better handle heterogeneity across individuals, be more robust to outliers and non-normally distributed data, and thus be able to provide differentiated policy recommendations and decision support for different groups (Long et al., 2023 ). As an extension of linear regression, quantile regression allows the estimated coefficients to vary with the quantile points of the explanatory variables, thus revealing the heterogeneous effects of the explanatory variables on different levels of the explained variables. Specifically, the 25% and 75% UIGG quantile points are selected for quantile regression analysis in this paper. The test results are shown in Panel D of Fig. 2 . For the cities with lower UIGG group (25%), Talent has a significant positive contribution. For the cities with higher UIGG group (75%), the impact effect of Talent is positive but fails the significance test. This suggests that there is a significant difference in the effect of Talagg in cities with different UIGG levels. This is also consistent with the marginal utility theory of agglomeration factors. When the city's IGG level is low, Talagg can provide sufficient intellectual support and guidance suggestions for urban sustainable development, thus significantly enhancing urban inclusiveness and greenness. When the city's IGG level is already at a higher level, the improvement effect of Talagg may not be so obvious. In other words, as the UIGG level increases, the effect of Talagg will gradually weaken. 5. Mechanisms The baseline results show that Talagg can provide high-quality human capital for urban sustainable development and promote UIGG . According to the theoretical analysis in section 2.2 , we will launch the verification of H2a and H2b in this section. 5.1 Mediating Variables (1) Green Innovation ( Greinn ) Innovation is the first driving force of development. The improvement of urban innovation capability can inevitably impel the high-quality development of the regional economy. Most scholars use patent applications to measure the level of regional technological innovation, which has a certain degree of rationality. However, in this paper, we believe that compared with the quantity of patent applications, the number of patents obtained can more effectively reflect the regional innovation ability. Therefore, we choose the proportion of green patents obtained to the total number of patents obtained to measure the level of urban Greinn . The larger its value, the higher the ability of urban green innovation. (2) Entrepreneurial Activity ( Startup ) Startup reflects the overall intensity of entrepreneurial activities in a specific region. In addition to start-up capital, entrepreneurship requires excellent professional skills, which is the characteristic of highly-skilled talents. The entrepreneurial atmosphere of a city can directly reflect its economic vitality and social inclusion. Therefore, we refer to the research method of Bai et al. ( 2022 ). We choose to use the quantity of newly created enterprises per 100 people in a city to measure Startup . This approach avoids to a certain extent the problem of measurement bias due to the heterogeneity of enterprise sizes in the region, and is able to portray the Startup of cities more accurately. 5.2 Model Setting and Results Referring to Guo et al. ( 2024 ), we construct the following model to test the mechanisms of the role of talent agglomeration on UIGG . $$\:{\text{M}}_{\text{it}}\text{=}{\text{γ}}_{\text{0}}\text{+}{\text{γ}}_{\text{1}}{\text{Tal}\text{agg}}_{\text{it}}\text{+}{\text{∑}\text{Control}}_{\text{it}}\text{+}{\text{μ}}_{\text{it}}\text{}$$ 2 In the above equation, M it is the mechanism variable, including Greinn and Startup . The rest of the variables are defined the same as in Eq. ( 1 ). The estimation results of mechanisms test are shown in Table 7 , Column (1) ~(2) are green innovations. Columns (3)~(4) are entrepreneurial activity. The regression results for both are significantly positive at the 1% statistical level. It confirms that Talagg can indeed promote green innovation technology, increase entrepreneurial activity, and thus enhance UIGG . H2a and H2b of this paper are confirmed. Table 7 Mechanisms Results Greinn Startup (1) (2) (3) (4) Talagg 0.1921 *** 0.1904 *** 7.8111 *** 6.3439 *** (0.0460) (0.0488) (0.9627) (0.8131) N 4556 4556 4573 4573 R 2 0.4790 0.4821 0.7866 0.8057 adj. R 2 0.4444 0.4465 0.7725 0.7924 Note: Same as Table 4 . 6. Expanded Analysis In order to reveal more comprehensively and deeply the influence of Talagg on UIGG , referring to the existing studies, we select the two variables of Industrial Agglomeration ( Indagg ) and Labor Marketization ( Labmark , i.e., household registration system) as moderating variables. Incorporating them into the baseline model with the interaction term of Talagg to further conduct the empirical test. 6.1 Moderate Variables (1) Indagg Indagg measures the degree of concentration of an industry in a specific geographical area. Referring to the research of Wang Z et al. ( 2025 ), this paper adopts location entropy (the geographic concentration of industrial) to indicate the degree of Indagg , which is calculated by the following formula: In Eq. (3), i denotes the city, t denotes the year, and n denotes the number of cities. Ind it denotes the regional Secondary Industry GDP of city i in year t . Area it denotes the administrative land area of city i in year t . Indagg it denotes the level of Indagg of city i in year t . The larger the value, the higher Indagg of the city. (2) Labmark Although China has continued to promote labor market reform since the reform and opening up, effectively reducing the barriers to labor mobility. The household registration restriction is still an important obstacle to the free flow of labor in China. Relaxing the household registration system can effectively alleviate the problems of poor labor mobility and inefficient talent allocation, and improve the degree of Labmark . In this paper, we use the household registration policies of each city to test how marketized Talagg affects UIGG . We use the settlement index calculated by Zhang et al. ( 2018 ). The household registration data covers 120 major cities in China and is basically representative of the urban household registration characteristics in China. We only use the indices in this dataset that is directly related to talent introduction index. There are three calculation methods for this index: projection method ( Household_pp ), equal weight method ( Household_ew ), and entropy value method ( Household_en ). Since the index contains data from two periods, our paper only uses the index from 2014–2016 to characterize the urban Labmark level. 6.2 Empirical Regression Referring to Shehzad et al. ( 2023 ), the interaction terms of Talagg and Indagg and Talagg and Labmark were respectively included in Eq. ( 1 ) for regression, and the moderating effects of Indagg and Labmark were tested. The test results of the moderating effects are reported in Table 6 . As shown in column (1) of Table 8 , the coefficients of Talagg*Indagg are all significantly positive at the 1% statistical level. It fully explains that the conjugated effect of Indagg and Talagg can promote UIGG . In other words, Indagg can play a positive moderating function in the process of Talagg on UIGG . Table 8 Moderate Results Indagg Labmark (1) (2) (3) (4) Talagg*Indagg 0.0242*** (0.0026) Talagg*Labmark_pp 0.2561 *** (0.0423) Talagg*Labmark_ew 0.4250 *** (0.0690) Talagg*Labmark_en 0.4009 *** (0.0648) Controls Y Y Y Y City-FE Y Y Y Y Year-FE Y Y Y Y N 4590 1921 1921 1921 R 2 0.9438 0.9484 0.9484 0.9480 adj. R 2 0.9399 0.9444 0.9444 0.9444 Note: Same as Table 4 . In columns (2) ~ (4) of Table 8 , the coefficients of all Talagg*Labmark are obviously positive at the 1% statistical level. This fully indicates that the policy synergy effect of household registration system reform and talent introduction can enhance UIGG . The increase of Labmark level can play a significant moderating effect in the process of Talagg on UIGG . In summary, the synergistic effect of Indagg and Talagg , as well as the policy cooperation effect of household registration system reform and Talagg , making the urban economic development more inclusive and greenning. This finding also supports the hypotheses H3a and H3b. 7. Conclusions and Implications 7.1 Main Conclusions Talent, as the first resource for economic and social growth, can provide the necessary intellectual support required for the long-term development of global cities. The agglomeration of highly skilled labor can also fuel urban economic growth, increase social diversity, and encourage the green transformation of industry. This is also the new direction outlined in the UN SDG declaration for future urban development. In this paper, we first construct an UIGG indicator system containing 17 indicators in three dimensions (economic development, social inclusion and green livability), and then use the entropy method to measure UIGG . Then we organize and match other explanatory variables, and finally obtain a balanced panel data set for 270 prefecture-level and above cities in China during 2003–2019. We examine the impact of Talagg on UIGG in China and its internal mechanisms from both theoretical and empirical perspectives. The findings show that, first, Talagg can significantly enhance UIGG . This basic conclusion still holds strongly after a variety of robustness tests. Second, the heterogeneity test finds that the improvement effect of Talagg on UIGG is stronger in central cities, east-middle cities, and cities with low IGG levels. Third, the mechanisms test shows that Talagg promotes the inclusive and greening characteristics of urban economic development by facilitating green technological innovation and increasing entrepreneurial activity. Fourth, the expanded analysis shows that both the horizontal business environment and vertical institutional management have an impact on urban development. Specifically, the conjugate effect of Talagg and Indagg , as well as the policy synergy between Talagg and household registration reform, can better boost UIGG . 7.2 Policy Implications Drawing on the preceding empirical findings and theoretical frameworks, this paper proposes the following key priorities to advance UIGG . Firstly, strengthening the talent recruitment policy for UIGG's intellectual foundation. Aligned with UN SDGs, global cities should deepen consensus on regional inclusive green development and leverage Talagg's intellectual support. Governments need to build an inclusive talent system–removing barriers between universities, research institutes, and enterprises–to align local talent supply with UIGG needs. Increased investment in education and vocational training will enhance workforce quality, fostering talents for urban diversification and green transition. Secondly, tailoring policies to regional heterogeneity. Adopt differentiated talent strategies. The core cities should link high-end talents with international innovation. The remote or inland cities may use flexible models (e.g., remote collaboration) to ease geographic constraints on green economic development. The developed or industrialized cities could transfer green technologies and management expertise to underdeveloped cities via industrial gradients. And for low-IGG cities, upgrading public services and infrastructure will boost talent attraction and narrow regional gaps. Thirdly, deepening intermediary mechanisms for Talagg - Greinn and Talagg - Startup links. Local relevant official departments should encourage green technical innovation by increasing subsidies for talent-led R&D, protecting innovators' incomes, and setting up venture funds to cut innovation costs. At the same time, they could optimize the entrepreneurial ecosystem via tax cuts, simplified approval, and shorter financing chains to enhance talent's entrepreneurial willingness. Fourthly, improving synergies with different policies. On the one hand, urban decision-makers should be committed to use industrial platforms to attract talents, pursue ''dual-wheel drive'' (fostering emerging clusters while upgrading traditional industries), and leverage Indagg 's externality to improve resource allocation. On the other hand, population management authorities should endeavor to relax high-skilled talent settlement rules, optimize cross-regional social security mutual recognition and skill assessment, and base public services on resident population. This promotes service equality, boosts talent belonging, and enhances urban inclusiveness. Declarations Author contributions G.C. and F.L. wrote the main manuscript text; G.C. and Y.L. prepared Tables 1–6; G.J. prepared Figs. 1 and Figs. 2. All authors reviewed the manuscript. Funding The National Social Science Foundation of China, FJYB036. Data availability The research data for this article is sourced from the EPS database (https://www.epsnet.com.cn), CEIC database (https://www.ceicdata.com) and Guotai An database (https://data.csmar.com). All of these are publicly available databases. Clinical trial number Not applicable. Ethics approval and consent to participate Not applicable. Consent for publication All authors have read and approved the final version of the manuscript. They consent to its submission for publication and confirm that the work is original, has not been published previously, and is not currently under consideration for publication elsewhere. The authors agree to be accountable for all aspects of the work and ensure that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Competing interests The authors declare no competing interests. Authors Information Chenyang Guo a , Lan Fang a,b,* , Lan Yang c , Jiexiao Ge d a Northwest Institute of Historical Environment and Socio-Economic Development, Shaanxi Normal University, Xi'an, Shaanxi, 710119, China b Leibniz Institute of Agricultural Development in Transition Economies (IAMO), Theodor-Lieser-Str. 2, 06120 Halle (Saale), Germany c School of Economics, Zhongnan University of Economics and Law, Wuhan, 430073, China d School of Economics, Beijing Institute of Technology, Beijing, 102481, China First author: Chenyang Guo E-mail address: [email protected] * Corresponding author: Lan Fang F-mail address: [email protected] (L. Fang) Co-author: Lan Yang E-mail address: [email protected] Co-author: Jiexiao Ge E-mail address: [email protected] References Atkin, D., Chen, M. K., Popov, A., (2022). The Returns to Face-to-face Interactions: Knowledge Spillovers in Silicon Valley. National Bureau of Economic Research . https://doi.org/10.3386/w30147. Bai, J., Zhang, Y., Bian, Y., (2022). Does Innovation-driven Policy Increase Entrepreneurial Activity in Cities--Evidence from the National Innovative City Pilot Policy. China Industrial Economics , (6), 61-78. (In Chinese). https://doi.org/10.19581/j.cnki.ciejournal.2022.06.016. Bathelt, H., Malmberg, A., Maskell, P., (2004). Clusters and knowledge: local buzz, global pipelines and the process of knowledge creation. Progress in Human Geography , 28(1), 31-56. https://doi.org/10.1191/0309132504ph469oa. Brandt, L., Tombe, T., Zhu, X., (2013). Factor market distortions across time, space and sectors in China. Review of Economic Dynamics , 16(1), 39-58. https://doi.org/10.1016/j.red.2012.10.002. Cai, Q., Liu, H., Pan, Y., Zou, J., (2025). The innovation effects of local talent competition: The case of China's municipal "Vying for Talent" policies. Habitat International , 164, 103523. https://doi.org/10.1016/j.habitatint.2025.103523. Cai, Q., Wang, S., Liu, H., Shu, S., (2023). Does the Population Agglomeration of Urban Agglomerations Promote Local Firms' Innovation? China Industrial Economics , (3), 152-170. (In Chinese). https://doi.org/10.19581/j.cnki.ciejournal.2023.03.006. Chemmanur, T. J., Kong, L., Krishnan, K., Yu, Q., (2018). Top Management Human Capital, Inventor Mobility, and Corporate Innovation. Journal of Financial and Quantitative Analysis , 54(6), 2383-2422. https://doi.org/10.1017/S0022109018001497. Chen, T., Kung, J. K. S., (2016). Do land revenue windfalls create a political resource curse? Evidence from China. Journal of Development Economics , 123, 86-106. https://doi.org/10.1016/j.jdeveco.2016.08.005. Combes, P. P., Duranton, G., Gobillon, L., Puga, D., Roux, S., (2012). The Productivity Advantages of Large Cities: Distinguishing Agglomeration From Firm Selection. Econometrica , 80(6), 2543-2594. https://doi.org/10.3982/ECTA8442. Davis, M. A., FISHER, J. D. M., WHITED, T. M., (2014). Macroeconomic Implications of Agglomeration. Econometrica , 82(2), 731-764. https://doi.org/10.3982/ECTA9029. Diamond, R., (2016). The Determinants and Welfare Implications of US Workers' Diverging Location Choices by Skill: 1980–2000. American Economic Review , 106(3), 479-524. https://doi.org/10.1257/aer.20131706. Fang, Z., Wolski, M., (2021). Human capital, energy and economic growth in China: evidence from multivariate nonlinear Granger causality tests. Empirical Economics , 60(2), 607-632. https://doi.org/10.1007/s00181-019-01781-7. Farndale, E., Beamond, M., Corbett-Etchevers, I., Xu, S., (2022). Accessing host country national talent in emerging economies: A resource perspective review and future research agenda. Journal of World Business , 57(1), 101256. https://doi.org/10.1016/j.jwb.2021.101256. Fontagné, L., Santoni, G., (2019). Agglomeration economies and firm-level labor misallocation. Journal of Economic Geography , 19(1), 251-272. https://doi.org/10.1093/jeg/lby007. Garay, L., Font, X., Pereira-Moliner, J., (2017). Understanding sustainability behaviour: The relationship between information acquisition, proactivity and performance. Tourism Management , 60, 418-429. https://doi.org/10.1016/j.tourman.2016.12.017. Gennaioli, N., La Porta, R., Lopez-de-Silanes, F., Shleifer, A., (2013). Human Capital and Regional Development. The Quarterly Journal of Economics , 128(1), 105-164. https://doi.org/10.1093/qje/qjs050. Gharbi, S., Zarrad, O., Kalai, M., & Helali, K., (2025). Examining the symmetric and the asymmetric impacts of green innovation and digitalization on the European Union’s green economic growth. Discover Sustainability , 6(1), https://doi.org/929.10.1007/s43621-025-01531-z. Giuliano, G., Kang, S., Yuan, Q., (2019). Agglomeration economies and evolving urban form. The Annals of Regional Science , 63(3), 377-398. https://doi.org/10.1007/s00168-019-00957-4. Glaeser, E. L., Kerr, S. P., Kerr, W. R., (2015). Entrepreneurship and urban growth: An empirical assessment with historical mines. The Review of Economics and Statistics , 97(2), 498-520. https://doi.org/10.1162/REST_a_00456. Gu, H., Wang, J., Ling, Y., (2024). Economic geography of talent migration and agglomeration in China: A dual-driver framework. China Economic Review , 86, 102180. https://doi.org/10.1016/j.chieco.2024.102180. Gu, K., Dong, F., Sun, H., Zhou, Y., (2021). How economic policy uncertainty processes impact on inclusive green growth in emerging industrialized countries: A case study of China. Journal of Cleaner Production , 322, 128963. https://doi.org/10.1016/j.jclepro.2021.128963. Guo, M., Luo, D., Liu, C., (2024). City civilization, employment creation and talent agglomeration: Empirical evidence from the "National Civilized City" policy in China. China Economic Review , 87, 102215. https://doi.org/10.1016/j.chieco.2024.102215. Hsieh, C. T., Hurst, E., Jones, C. I., Klenow, P. J., (2019). The Allocation of Talent and U.S. Economic Growth. Econometrica , 87(5), 1439-1474. https://doi.org/10.3982/ECTA11427. Huggins, R., Thompson, P., (2023). Human agency, network dynamics and regional development: the behavioural principles of new path creation. Regional Studies , 57(8), 1469-1481. https://doi.org//10.1080/00343404.2022.2060958. Jian, X., Du, D., Liang, D., (2024). Scale or effectiveness? The nonlinear impact of talent agglomeration on high-quality economic development in China. Heliyon , 10(9). https://doi.org/10.1016/j.heliyon.2024.e30121. Kamguia, B., Tadadjeu, S., Ndoya, H., Djeunankan, R., (2025). Assessing the nexus between industrialization and inclusive green growth in Africa. The critical role of energy efficiency. Ecological Economics , 233,108610. https://doi.org/10.1016/j.ecolecon.2025.108601. Kebede, J., Naranpanawa, A., Selvanathan, S., (2023). Financial inclusion and income inequality nexus: A case of Africa. Economic Analysis and Policy , 77, 539-557. https://doi.org/10.1016/j.eap.2022.12.006. Kerr, S. P., Kerr, W., Özden, Ç., Parsons, C., (2017). High-Skilled Migration and Agglomeration. Annual Review of Economics , 9(1), 201-234. https://doi.org/10.1146/annurev-economics-063016-103705. Liu, X., Liu, F., Ren, X., (2023). Firms' digitalization in manufacturing and the structure and direction of green innovation. Journal of Environmental Management , 335, 117525. https://doi.org/10.1016/j.jenvman.2023.117525. Long, H., Feng, G. F., Gong, Q., Chang, C. P., (2023). ESG performance and green innovation: An investigation based on quantile regression. Business Strategy and the Environment , 32(7), 5102-5118. https://doi.org/10.1002/bse.3410. Long, X., Ji, X., (2019). Economic Growth Quality, Environmental Sustainability, and Social Welfare in China - Provincial Assessment Based on Genuine Progress Indicator (GPI). Ecological Economics , 159, 157-176. https://doi.org/10.1016/j.ecolecon.2019.01.002. Lucas, R. E., (2015). Human Capital and Growth. American Economic Review , 105(5), 85-88.20. https://doi.org//10.1257/aer.p20151065. McGuirk, H., Lenihan, H., Hart, M., (2015). Measuring the impact of innovative human capital on small firms' propensity to innovate. Research Policy , 44(4), 965-976. https://doi.org/10.1016/j.respol.2014.11.008. Moretti, E., (2021). The Effect of High-Tech Clusters on the Productivity of Top Inventors. American Economic Review , 111(10), 3328-3375. https://doi.org/10.1257/aer.20191277. Ofori, I. K., Gbolonyo, E. Y., Ojong, N., (2023). Foreign direct investment and inclusive green growth in Africa: Energy efficiency contingencies and thresholds. Energy Economics , 117, 106414. https://doi.org/10.1016/j.eneco.2022.106414. Ouyang, X., Li, Q., Du, K., (2020). How does environmental regulation promote technological innovations in the industrial sector? Evidence from Chinese provincial panel data. Energy Policy , 139, 111310. https://doi.org/10.1016/j.enpol.2020.111310. Peri, G., Shih, K., Sparber, C., (2015). STEM workers, H-1B visas, and productivity in US cities. Journal of Labor Economics , 33(S1), S225-S255. https://doi.org/10.1086/679061. Ren, M., Zhou, T., Wang, C., (2024). New energy vehicle innovation network, innovation resources agglomeration externalities and energy efficiency: Navigating industry chain innovation. Technological Forecasting and Social Change , 200, 123114. https://doi.org/10.1016/j.techfore.2023.123114. Ren, Y., He, X., Jiang, Q., Zhang, F., Zhang, B., (2024). Advancing high-quality development in China: Unraveling the dynamics, disparities, and determinants of inclusive green growth at the prefecture level. Ecological Indicators , 169, 112898. https://doi.org/10.1016/j.ecolind.2024.112898. Rong, C., Cristia, J. F. E., Marian, M. L., Alzuman, A., Comite, U., (2024). Does green entrepreneurial orientation impact entrepreneurial success through green innovation capability in the manufacturing and services sector of emerging economies? International Entrepreneurship and Management Journal , 21(1), 51. https://doi.org/10.1007/s11365-024-01059-0. Sequeira, S., Nunn, N., Qian, N., (2020). Immigrants and the Making of America. The Review of Economic Studies , 87(1), 382-419. https://doi.org/10.1093/restud/rdz003. Shan, S., Yılmaz, G. S., Waqas, K. H., Gheorghita, D., (2021). Role of green technology innovation and renewable energy in carbon neutrality: A sustainable investigation from Turkey. Journal of Environmental Management , 294, 113004. https://doi.org/10.1016/j.jenvman.2021.113004. Shan, Y., Fang, S., Cai, B., Zhou, Y., Li, D., Feng, K., Hubacek, K., (2021). Chinese cities exhibit varying degrees of decoupling of economic growth and CO2 emissions between 2005 and 2015. One Earth , 4(1), 124-134. https://doi.org/10.1016/j.oneear.2020.12.004. Shehzad, M. U., Zhang, J., Latif, K. F., Jamil, K., Waseel, A. H., (2023). Do green entrepreneurial orientation and green knowledge management matter in the pursuit of ambidextrous green innovation: A moderated mediation model. Journal of Cleaner Production , 388, 135971. https://doi.org/10.1016/j.jclepro.2023.135971. Song, Y., Huang, H., Li, Y., Xia, J., (2024). Towards inclusive green growth in China: Synergistic roles and mechanisms of new infrastructure construction. Journal of Environmental Management , 353, 120281. https://doi.org/10.1016/j.jenvman.2024.120281. Squicciarini, M. P., Voigtländer, N., (2015). Human Capital and Industrialization: Evidence from the Age of Enlightenment. The Quarterly Journal of Economics , 130(4), 1825-1883. https://doi.org/10.1093/qje/qjv025. Sun, Y., Ding, W., Yang, Z., Yang, G., Du, J., (2020). Measuring China's regional inclusive green growth. Science of the Total Environment , 713, 136367. https://doi.org/10.1016/j.scitotenv.2019.136367. Tang, K., Wang, Y. Y., Wang, H. J., (2024). The impact of innovation capability on green development in China's urban agglomerations. Technological Forecasting and Social Change , 200, 123128. https://doi.org/10.1016/j.techfore.2023.123128. Teslenko, V., Melnikov, R., Bazin, D., (2021). Evaluation of the impact of human capital on innovation activity in Russian regions. Regional Studies, Regional Science , 8(1), 109-126. https://doi.org/10.1080/21681376.2021.1900744. Tombe, T., Zhu, X., (2019). Trade, Migration, and Productivity: A Quantitative Analysis of China. American Economic Review , 109(5), 1843-1872. https://doi.org/10.1257/aer.20150811. Wang, D., Hou, Y., Li, X., Xu, Y., (2022). Developing a functional index to dynamically examine the spatio-temporal disparities of China's inclusive green growth. Ecological Indicators , 139, 108861. https://doi.org/10.1016/j.ecolind.2022.108861. Wang, H., Deng, W., Zhang, Z., Li, M., (2024). Does government's environmental attention improve urban energy efficiency? International Review of Financial Analysis , 91, 103046. https://doi.org/10.1016/j.irfa.2023.103046. Wang, M., Wu, Y., Zhang, X., Lei, L., (2024). How does industrial agglomeration affect internal structures of green economy in China? An analysis based on a three-hierarchy meta-frontier DEA and systematic GMM approach. Technological Forecasting and Social Change , 206, 123560. https://doi.org/10.1016/j.techfore.2024.123560. Wang, Z., Yan, E., Zhang, Z., Li, C., Gong, Q., (2025). The impact and mechanism of government environmental policy innovation on industrial agglomeration: An empirical study based on a Quasi-natural experiment of the river chief system policy in the Yangtze River economic belt. Economic Analysis and Policy , 86, 893-911. https://doi.org/10.1016/j.eap.2025.04.014. World Bank, (2012). Inclusive Green Growth: The Pathway to Sustainable Development. Wu, J., Segerson, K., Langpap, C., (2025). What drives the long-term relationship between economic development and environmental quality? The role of spatial dispersion vs. agglomeration. Journal of Environmental Economics and Management , 131, 103126. https://doi.org/10.1016/j.jeem.2025.103126. Xu, A., Dai, Y., Hu, Z., Qiu, K., (2025). Can green finance policy promote inclusive green growth? Based on the quasi-natural experiment of China's green finance reform and innovation pilot zone. International Review of Economics & Finance , 100, 104090. https://doi.org/10.1016/j.iref.2025.104090. Xu, J. H., Huang, Y. C., (2021). Can the technological talents agglomeration promote regional green development? Modern Economic Research , (12), 116-125. (In Chinese). https://doi.org/10.13891/j.cnki.mer.2021.12.013. Yang, J., Wang, Y., Tang, C., Zhang, Z., (2024). Can digitalization reduce industrial pollution? Roles of environmental investment and green innovation. Environmental Research , 240, 117442. https://doi.org/10.1016/j.envres.2023.117442. Yang, Z., Pan, Y., (2020). Human capital, housing prices, and regional economic development: Will "vying for talent" through policy succeed? Cities , 98, 102577. https://doi.org/10.1016/j.cities.2019.102577. Zhang, H., Guo, R., (2025). How does the spatial agglomeration of human capital affect strategic innovation in China? Journal of Asian Economics , 96, 101868. https://doi.org/10.1016/j.asieco.2024.101868. Zhang, J., Wang, R., Lu, C., (2018). A quantitative analysis of Hukou reform in Chinese cities: 2000–2016. Growth and Change , 50(1), 201-221. https://doi.org/10.1111/grow.12284. Zhang, R., Ying, W., Wu, K., Sun, H., (2024). The impact of innovative human capital agglomeration on urban green development efficiency: Based on panel data of 278 Cities in China. Sustainable Cities and Society , 111, 105566. https://doi.org/10.1016/j.scs.2024.105566. Zheng, L., Ulrich, K., Sendra-García, J., (2021). Qualitative comparative analysis: Configurational paths to innovation performance. Journal of Business Research , 128, 83-93. https://doi.org/10.1016/j.jbusres.2021.01.044. Zhou, Y., Guo, Y., Liu, Y., (2018). High-level talent flow and its influence on regional unbalanced development in China. Applied Geography , 91, 89-98. https://doi.org/10.1016/j.apgeog.2017.12.023. Footnotes Note: Since the statistical number of personnel in these two types of industries in the statistical yearbook is only published until 2019, the data span is set to 2003–2019 in this paper to ensure the consistency of the panel data. Information transmission, computer services and software industry; Scientific research, technical services and geological exploration industry; Financial industry; Leasing and business services industry; Education industry; Culture, sports and entertainment industry. East: Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan. Middle: Shanxi, Jilin, Heilongjiang, Henan, Hubei, Hunan, Anhui, Jiangxi. West: Inner Mongolia, Chongqing, Sichuan, Guangxi, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang, Tibet. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8023565","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":545683254,"identity":"7cf5dcc4-6712-4431-a47e-b9570114540e","order_by":0,"name":"Chenyang Guo","email":"","orcid":"","institution":"Northwest Institute of Historical Environment and Socio-Economic Development, Shaanxi Normal University","correspondingAuthor":false,"prefix":"","firstName":"Chenyang","middleName":"","lastName":"Guo","suffix":""},{"id":545683256,"identity":"d97fa1d8-4267-49fb-99f1-78aba09f4f13","order_by":1,"name":"Lan Fang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYBAC9gYg8QDMZGN8kFBRQ1gLzwEgkQDRwmzw4Mwx0rSwST5sYSZCC/vZwy8Sau4k9ksfS6tIbGBj4G/vTsCvhScvzSLh2LPEmX1px24k7pBhkDhzdgNeLfYMOWYGCWyHczecYW+7kXiGjcFAIhe/Fh7+N0At/yBaChLbmInQIpFj/CCxDaSF7RgDkVremDEk9h2un9nDliyRcOYYD0G/8PDnGH/48O2wMT8Pm+HHHxU1cvztvfi1AAGbBIoZhJSDAPMHYlSNglEwCkbBCAYA9ihLUu1hOL0AAAAASUVORK5CYII=","orcid":"","institution":"Northwest Institute of Historical Environment and Socio-Economic Development, Shaanxi Normal University","correspondingAuthor":true,"prefix":"","firstName":"Lan","middleName":"","lastName":"Fang","suffix":""},{"id":545683257,"identity":"b8517138-54eb-4f4e-935a-8a8038ee8925","order_by":2,"name":"Lan Yang","email":"","orcid":"","institution":"Zhongnan University of Economics and Law","correspondingAuthor":false,"prefix":"","firstName":"Lan","middleName":"","lastName":"Yang","suffix":""},{"id":545683260,"identity":"648383b9-b67f-4906-8e56-6559b80b307c","order_by":3,"name":"Jiexiao Ge","email":"","orcid":"","institution":"Beijing Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jiexiao","middleName":"","lastName":"Ge","suffix":""}],"badges":[],"createdAt":"2025-11-04 02:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8023565/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8023565/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96249907,"identity":"d3e181ed-b022-40df-88bc-0c04791e568f","added_by":"auto","created_at":"2025-11-19 07:36:43","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":196090,"visible":true,"origin":"","legend":"","description":"","filename":"UnlockingPathwaysforUrbanInclusiveGreenGrowthTalentAgglomerationANewPerspectiveonHighEndHumanCapitalAgglomeration.docx","url":"https://assets-eu.researchsquare.com/files/rs-8023565/v1/510bfbf3d0fdb39f218f832f.docx"},{"id":96248317,"identity":"60c3d6bc-02c3-4cd1-b8bf-1ba74cb90566","added_by":"auto","created_at":"2025-11-19 07:28:19","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5515,"visible":true,"origin":"","legend":"","description":"","filename":"ee6d3c4295ba4b198328fbb7b33f7a59.json","url":"https://assets-eu.researchsquare.com/files/rs-8023565/v1/7a5b6a719d177b41c6a4f09d.json"},{"id":96084578,"identity":"e3a43e50-7279-4da6-b503-b23ef130a57e","added_by":"auto","created_at":"2025-11-17 12:15:52","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":221057,"visible":true,"origin":"","legend":"","description":"","filename":"ee6d3c4295ba4b198328fbb7b33f7a591enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8023565/v1/d361c836a649d63878b92804.xml"},{"id":96084572,"identity":"04ca5d0f-04cf-4e79-9e48-7ccf2740f1e8","added_by":"auto","created_at":"2025-11-17 12:15:52","extension":"emf","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":82136,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.emf","url":"https://assets-eu.researchsquare.com/files/rs-8023565/v1/3c45a451c70023e28dc1504f.emf"},{"id":96084580,"identity":"f737e749-cae9-4615-8873-058dd4bde828","added_by":"auto","created_at":"2025-11-17 12:15:52","extension":"jpeg","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":240673,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8023565/v1/95e8c862c8d15c4fd2be5821.jpeg"},{"id":96249523,"identity":"ddf5b244-6913-4bf9-a7c8-c6e8c13453c8","added_by":"auto","created_at":"2025-11-19 07:33:50","extension":"wmf","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2610,"visible":true,"origin":"","legend":"","description":"","filename":"image3.wmf","url":"https://assets-eu.researchsquare.com/files/rs-8023565/v1/5406bb2594f9ccc2c5209c5a.wmf"},{"id":96248220,"identity":"bc1e9e49-d24e-4dda-9f68-950ffc573341","added_by":"auto","created_at":"2025-11-19 07:28:11","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":20824,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8023565/v1/e2978e3aab63e42c42b367cb.png"},{"id":96247356,"identity":"4fb24914-8b7d-44ca-b70d-5fa31ecbdad6","added_by":"auto","created_at":"2025-11-19 07:27:25","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":46601,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8023565/v1/be42a1d9876e2bed7aacaf5e.png"},{"id":96249792,"identity":"f4dd6614-289a-4f78-b2e4-e230350671b6","added_by":"auto","created_at":"2025-11-19 07:36:16","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2003,"visible":true,"origin":"","legend":"","description":"","filename":"Onlineimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8023565/v1/5479802a33d810d7acc8fc8e.png"},{"id":96084582,"identity":"2e725738-b72a-4bf6-8201-6f530aedce3a","added_by":"auto","created_at":"2025-11-17 12:15:52","extension":"xml","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":221269,"visible":true,"origin":"","legend":"","description":"","filename":"ee6d3c4295ba4b198328fbb7b33f7a591structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8023565/v1/041a86926f89dadc79166832.xml"},{"id":96084581,"identity":"e6f1d28d-a800-4f69-b5cb-8f414b3672f9","added_by":"auto","created_at":"2025-11-17 12:15:52","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":236779,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8023565/v1/cb06b255338d2426afc077f3.html"},{"id":96084570,"identity":"427bbd5f-abcb-461a-80f4-1efbc7315de1","added_by":"auto","created_at":"2025-11-17 12:15:52","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":103151,"visible":true,"origin":"","legend":"\u003cp\u003eLogic diagram of \u003cem\u003eTalagg\u003c/em\u003e and \u003cem\u003eUIGG\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8023565/v1/511f381cdcd9ec33d880ffed.jpg"},{"id":96084571,"identity":"65cfcc07-b889-4640-88ca-32ebe9e502a6","added_by":"auto","created_at":"2025-11-17 12:15:52","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":86381,"visible":true,"origin":"","legend":"\u003cp\u003eThe heterogeneity effect of \u003cem\u003eTalagg\u003c/em\u003e on \u003cem\u003eUIGG\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8023565/v1/574380cebf03d6d6e08224ab.jpg"},{"id":97370068,"identity":"071ecd22-5f1c-4325-9e18-1d2651e0dc76","added_by":"auto","created_at":"2025-12-03 16:26:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1684169,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8023565/v1/27f679e6-4a2b-40ec-af5b-0fde3489b3a6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unlocking Pathways for Urban Inclusive Green Growth: Talent Agglomeration —A New Perspective on High-End Human Capital Agglomeration","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAbout 80% of global GDP and 70% of energy consumption emissions originate from economic activity in cities (Shan et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Cities, as the core carrier and spatial support of human socio-economic activity, are both economic growth engines and environmentally sensitive zones. Nowadays, the imbalance between the economic and the environment is the most significant issue to urban growth (Wu et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). China has achieved remarkable economic performance since the 1980s, thanks to a rudimentary development model based on resources and factors (Wang M et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). China's GDP in 2024 surpassed 134.9084 trillion yuan, more than 366 times greater that of 1978 (0.3679). However, the rapid economic growth has resulted in a number of environmental and social challenges. On the one hand, the extensive growth model, which is characterized by high pollution and low efficiency, has led to problems involving excessive energy consumption, serious environmental damage, and low economic effectiveness (Gu et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), seriously constraining the sustainable development of cities. On the other hand, development possibilities and economic gains have not been dispersed equally. Social phenomena like the urban-rural dual structure, the ever-widening wealth gap (Kebede et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), unequal employment opportunities, and deteriorating public healthcare conditions (Long and Ji, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), are becoming more severe and impeding inclusive economic growth. These issues aren't just limited to poor countries. Many advanced nations also face that. Wealth disparity, social injustice, and environmental degradation, which are ''non-inclusive'' and ''non-green'', are substantially harming the sustainable and healthy growth of the global economy (Gu et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe United Nations' (UN) \u003cem\u003eNew Urban Agenda\u003c/em\u003e (2016) and \u003cem\u003ethe 2030 Agenda for Sustainable Development Goals\u003c/em\u003e (SDG 11) clearly articulate the transformational requirements of \"making cities and human settlements inclusive, safe, resilient and sustainable\", resulting in widespread adoption of the development concept of \"Inclusive Green Growth (IGG)\". IGG aims to achieve sustainable growth through the three-dimensional synergy of economy, society, and environment (World Bank, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), attempting to resolve the fundamental contradiction between efficiency and equity, growth and sustainability in traditional urbanization. IGG's core essence is that, while pursuing economic growth, it prioritizes equal involvement of all economic individuals in the process, sharing the fruits of economic growth, and narrowing the wealth gap. It also integrates green development into economic decision-making, with an emphasis on improving resource efficiency, ecological environmental protection, and the reducing pollutant emissions. This development model can not only address the issue of \"non-green\" in the economic growth of industrialized countries, but it may also alleviate the \"non-inclusive\" social disputes in developing countries (Yang et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kamguia et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). IGG has emerged as a new paradigm for achieving the ultimate goal of human social development.\u003c/p\u003e\u003cp\u003eFurthermore, with the structural slowing of economic growth, China's urban labor factor supply has reached an inflection point. China is currently undergoing a significant transformation from a populous country to a human capital powerhouse (Cai et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In the knowledge economy, talent resources are strategic assets for which every country or region competes (Gu et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The spatial distribution of talent as a key production element, known as talent agglomeration (\u003cem\u003eTalagg\u003c/em\u003e), is reconstructing the power mechanism of urban development. \u003cem\u003eTalagg\u003c/em\u003e can contribute new ideas, superior technology, and a wealth of social capital to the city's industrial upgrading and economic transformation, providing a continual stream of vitality. This provides abundant human capital and intellectual support for the city's high-quality development while also significantly increasing productivity and innovation (Peri et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), promoting diversified urban development, reducing the reliance on a single market, and improving economic stability and inclusiveness.\u003c/p\u003e\u003cp\u003eTherefore, against such a backdrop, it is essential to investigate the causal relationship and internal mechanism between \u003cem\u003eTalagg\u003c/em\u003e and IGG. This has important implications for the healthy and long-term growth of the urban economy in China and other countries across the world.\u003c/p\u003e\u003cp\u003eThe marginal contributions of this paper are twofold. First, it reveals the \"black box\" mechanism that links high-quality human capital and sustainable development, theoretically enriching the related research domains of human capital, social welfare and green development. It not only gives new empirical evidence for the current urban transformation, but also points out the direction for the future growth of global cities. Second, in terms of experience, it broadens the research perspective of IGG. Unlike most of the previous studies that focused on population size, this paper takes a population quality perspective, combines labor structure and agglomeration theories, and comprehensively and systematically examines the effects, mechanisms, and differences in the impact of \u003cem\u003eTalagg\u003c/em\u003e on the coordinated development of urban economy-society-environment. This allow us to develop policy recommendations that are both practical and feasible for decision-makers.\u003c/p\u003e\u003cp\u003eThe remainder of the paper is organized as follows. The next section provides a theoretical analysis and research hypothesis, while Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e3\u003c/span\u003e describes the data and methodology. We present our empirical regression results in Section \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and shed light on mechanism section in Section \u003cspan refid=\"Sec14\" class=\"InternalRef\"\u003e5\u003c/span\u003e. We also conducted extended analysis in Section \u003cspan refid=\"Sec17\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The last section is the conclusion and suggestions.\u003c/p\u003e"},{"header":"2. Theoretical Analysis and Research Hypothesis","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 \u003cem\u003eTalagg\u003c/em\u003e on Urban Inclusive Green Growth (\u003cem\u003eUIGG\u003c/em\u003e)\u003c/h2\u003e\u003cp\u003eLucas (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) argues that human capital accumulation is the source of economic growth. Talent is the primary source of human capital involved in innovation research. And it also plays an important role in urban green development. Porter's theory of competitive advantage further demonstrates that senior factors centered on talents can promote regional development more than primary factors centered on natural resources. Therefore, \u003cem\u003eTalagg\u003c/em\u003e is an essential driver to promote \u003cem\u003eUIGG\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eFirst of all, \u003cem\u003eTalagg\u003c/em\u003e can enhance the impetus of urban economic growth (Farndale et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). From the opinion of agglomeration, \u003cem\u003eTalagg\u003c/em\u003e and economic growth are parallel processes. Agglomeration can boost both social productivity and regional economic growth (Davis et al., 2014; Fontagn\u0026eacute; and Santoni, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Talent can offer cities with abundant human capital and intellectual support. The concentration of talent can augment the learning effect in the operation of the production processes of local enterprises, optimize the management mode, lower running costs, and improve production efficiency (Zhou et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It is also conducive to the realization of technological updating and information sharing within cities, promoting the specialized division of labor in production and improving the efficiency of resource factor allocation, thereby promoting the efficient and sustainable growth of the urban economy (Hsieh et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSecondly, \u003cem\u003eTalagg\u003c/em\u003e contributes to greater urban inclusivity. \u003cem\u003eTalagg\u003c/em\u003e can quickly attract and drive all kinds of resource factors and related enterprises to the city. It can promote the transformation of R\u0026amp;D projects, accelerate the output and market application of innovation results (McGuirk et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and narrow the development gaps within the city. And it can also help to diversify markets and enhance economic stability. Furthermore, talents\u0026rsquo; advanced management concepts can also assist the local governments in raising the decision-making level, completing the governance system, improving the governance efficiency and reducing management costs, thereby significantly improving urban governance levels (Ren Y et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and strengthening urban resilience to external risks and inclusive development.\u003c/p\u003e\u003cp\u003eIn addition, \u003cem\u003eTalagg\u003c/em\u003e helps hasten the transformation of urban greening. \u003cem\u003eTalagg\u003c/em\u003e plays a positive role in accelerating the improvement of the key production technology, the transformation and upgrading of traditional industries (Zhang et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), green technical progress and environmental quality (Gharbi et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). By influencing consumption preferences, investment ideas, employment methods, and environmental protection awareness, talent guides urban residents' lifestyles towards greening and decarbonization (Xu and Huang, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). And this forces industrial companies to conducting green transformation, and thus reducing urban dependence on high-pollution, high-energy-consumption industries. As a result, an industrial \"green atmosphere\" is created to raise the efficiency of green economy, which in turn promotes the process of urban green transformation. So, this paper proposes the following hypothesis 1.\u003c/p\u003e\u003cp\u003eH1: \u003cem\u003eTalagg\u003c/em\u003e can promote \u003cem\u003eUIGG\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Mechanisms Analysis\u003c/h2\u003e\u003cp\u003e(1) Green Technological Innovation (\u003cem\u003eGreeinn\u003c/em\u003e)\u003c/p\u003e\u003cp\u003eInnovation is an inexhaustible driving force for economic and social progress. The first and most important factor in promoting technical innovation is talent (Yang and Pan, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). According to the Marshall's externality theory, agglomeration economies can foster innovation mainly due to the positive externality of agglomeration (Ren M et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). \u003cem\u003eTalagg\u003c/em\u003e enhances the interactive exchange and experience sharing among inventive individuals (Atkin et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), since knowledge and skills are transferred with talent mobility (Chemmanur et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Teslenko et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Besides, it also reduces the acquisition costs and risk of failure of technical breakthroughs (Moretti, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), allowing for the continuation of inventive activities. Green technologies are becoming an increasingly important emphasis in innovation programs. \u003cem\u003eGreeinn\u003c/em\u003e is the primary driver of urban sustainable development (Liu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), as well as a critical instrument for mitigating global warming and responding with natural catastrophes.\u003c/p\u003e\u003cp\u003e\u003cem\u003eTalagg\u003c/em\u003e promotes urban \u003cem\u003eGreeinn\u003c/em\u003e by increasing innovation inputs and outputs. On the one hand, \u003cem\u003eTalagg\u003c/em\u003e supports green technological upgrading through agglomeration of innovative input resources. It is capable of meeting the requirement for high-quality labor factor inputs in urban sustainable development. While it also brings about the agglomeration of knowledge, technology, capital and other innovation input resources in the city, which in turn stimulates the government and relevant enterprises to increase funding for green innovation R\u0026amp;D projects (Zheng et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Cai et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). And this provides policy support and financial guarantees for the green transformation of cities. \u003cem\u003eTalagg\u003c/em\u003e, on the other hand, boosts green development efficiency by accelerating innovation output. It forms effective knowledge networks and specialized social circles through close cooperation, which can efficiently coordinate internal and external resources of cities, quickly solve technical problems (Squicciarini and Voigtl\u0026auml;nder, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), capture green development approaches in a timely manner (Fang and Wolski, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), speed up the R\u0026amp;D and output of green innovations (Kerr et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tang et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and enhance the decarbonization of urban production and greening of life (Shan et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This facilitates the achievement of regional sustainable development goals. In summary, this paper considers the following hypotheses.\u003c/p\u003e\u003cp\u003eH2a: \u003cem\u003eTalagg\u003c/em\u003e can impel \u003cem\u003eUIGG\u003c/em\u003e through Greeinn.\u003c/p\u003e\u003cp\u003e(2) Entrepreneurial Activity (\u003cem\u003eStartup\u003c/em\u003e)\u003c/p\u003e\u003cp\u003eUrban public service systems (like makerspaces, Startup cafes, etc.) provide comprehensive social venues and platforms for talent brainstorming and technology exchanges. It not only improves the communication between talents, but also nourishes a vast number of potential entrepreneurial opportunities (Garay et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A favorable entrepreneurial environment can enrich local entrepreneurial abilities (Glaeser et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), strengthen the city's attraction to persons with entrepreneurial aspirations, and thus increase the number of local entrepreneurs and entrepreneurial activities (Jian et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFirstly, \u003cem\u003eTalagg\u003c/em\u003e has the potential to lower the likelihood of adverse selection. It offers entrepreneurial firms with the required skilled workers while reducing recruitment and training costs. And it can also address the technical barriers and budgetary challenges. In other words, it lowers the cost of entrepreneurship on the premise of ensuring the necessary human and material inputs, and lays a good foundation for the growth of entrepreneurial enterprises (Rong et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This will attract more people into entrepreneurial activity. High-quality social groupings among talent provide \u003cem\u003eStartup\u003c/em\u003e with difficult-to-access business information and channels. They can make it easier for entrepreneurs to find partners, investors and customers, as well as reduce the \u003cem\u003eStartup\u003c/em\u003e cycle and risk. Thus, more entrepreneurs are drawn to entrepreneurial activity (Gennaioli et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Secondly, entrepreneurial actions can optimize the allocation of market resources, and guide the development of green industries. \u003cem\u003eTalagg\u003c/em\u003e accelerates economic development while intensifying competition and cooperation among cities (Yang and Pan, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Cooperation across various firms and individuals, while sharing resources and risks, is also continuously increase competitiveness (Combes et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Entrepreneurial enterprises, in particular, use the market competition mechanism to allocate resources toward more promising green and emerging industries. Entrepreneurs take advantage of factor resource utilization to strengthen the green orientation of products, reduce the dependence of the economic on high-pollution and high-energy-consumption models, and then support urban green growth. Therefore, \u003cem\u003eTalagg\u003c/em\u003e can boost the increase of urban entrepreneurial activities and improve the environmental performance of enterprises (Ruthensteiner and Leitner, 2025). In summary, we present the following hypothesis.\u003c/p\u003e\u003cp\u003eH2b: \u003cem\u003eTalagg\u003c/em\u003e can promote \u003cem\u003eUIGG\u003c/em\u003e via \u003cem\u003eStartup\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Moderating Effect\u003c/h2\u003e\u003cp\u003e(1) \u003cem\u003e\"Soft\" Environment\u003c/em\u003e: \u003cem\u003eTalagg\u003c/em\u003e, industry agglomeration (\u003cem\u003eIndagg\u003c/em\u003e) and \u003cem\u003eUIGG\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eTalagg's\u003c/em\u003e function in urban development necessitates the use of specific platforms and carriers, which \u003cem\u003eIndagg\u003c/em\u003e fulfills perfectly. \u003cem\u003eIndagg\u003c/em\u003e is a key feature of modern economic growth and an external representation of productivity agglomeration. The concentration of the same or related industries in a particular region will inevitably lead to the gathering of talents in the region. Similarly, the concentrated flow of talents will also promote enterprise clustering (Jian et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUsing wage signals, \u003cem\u003eIndagg\u003c/em\u003e regulates talent supply and demand of and attracts high-quality and high-skilled workforce. As a result, it encourages more regional R\u0026amp;D and innovation activities, enhances the total factor productivity, and eventually contributes to regional economic growth. First, \u003cem\u003eIndagg\u003c/em\u003e creates numbers job opportunities and attracts professionals from related fields to congregate. At the same time, \u003cem\u003eIndagg\u003c/em\u003e brings more regular formal or informal communication within the same industry. Different ideas of highly qualified labor collide with each other, and the same technical skills of highly skilled personnel are optimized and developed (Squicciarini and Voigtl\u0026auml;nder, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Moretti, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The \"Local Buzzing\" effect facilitates the rapid diffusion and sharing of knowledge and technology (Bathelt et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), stimulates the innovation potential of talent, nurtures urban social networks and innovation ecosystems, and improves the collaboration patterns and innovation efficiency among industries (Huggins and Thompson, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), thereby enhancing urban development potential.\u003c/p\u003e\u003cp\u003eSecondly, \u003cem\u003eIndagg\u003c/em\u003e can broaden \u003cem\u003eTalagg's\u003c/em\u003e impact on green innovation, and support the quick application and popularization of green technologies. \u003cem\u003eTalagg\u003c/em\u003e plays a significant role in enhancing the knowledge-intensive business service (KIBS). The \"Sticky Knowledge\" generated by KIBS \u003cem\u003eIndagg\u003c/em\u003e dynamically matches with the highly skilled labor force, accelerating the transformation of talent resources to talent capital while improving the green technological innovation (Zhang and Guo, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Enterprises that take the lead in adopting new green technologies can gain an early advantage, attract the concentration of industry-related talents, expertise, technology and other resources based on the competitive advantages of the industry. This could increase sector specialization, lower the cost and risk of factor search (Wang M et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and attract related companies to cluster and share the professional service network and ancillary facilities. Finally, it promotes energy conservation and economic efficiency in urban (Giuliano et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurthermore, compared to other locations, the environmental criteria implemented in industrial clusters will be much stricter. According to the Porter Hypothesis, strict environmental regulations can compel talent to research and develop clean technologies (Ouyang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang H et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), strengthen the incentives of green innovation for firms, and pique talent enthusiasm of green innovation (Moretti, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), thereby accelerating inclusive growth. Thus, we put forward the following hypothesis.\u003c/p\u003e\u003cp\u003eH3a: \u003cem\u003eIndagg\u003c/em\u003e has a substantial moderating effect in the process of \u003cem\u003eTalagg\u003c/em\u003e affecting \u003cem\u003eUIGG\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e(2) \u003cem\u003e\"Hard\" Systems\u003c/em\u003e: \u003cem\u003eTalagg\u003c/em\u003e, labor marketization (\u003cem\u003eLabmark\u003c/em\u003e) and \u003cem\u003eUIGG\u003c/em\u003e\u003c/p\u003e\u003cp\u003ePromoting the free movement of labor and the optimal allocation of human resources is a powerful support for the regional economy's sustainable development. As China's most important population mobility management system, the reform of the household registration system can dramatically unleash the vitality of the labor market, which has a significant impact on the mobility of talent and industrial innovation (Cai et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). And it contributes significantly to the high-quality development of the urban economy (Sequeira et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe current labor market segmentation and distortion problems in China are more serious. One of the reasons for this is the household registration constraint (Brandt et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). For foreigners, the various types of social welfare and guarantees (political, employment, education, social security, etc.) attached to the household registration system raise the bar for settling down in cities greatly. Such institutional restraints may make it difficult for talents to move reasonably in accordance with the market economy rules. The factor pricing mechanism cannot be freely determined by market supply and demand, resulting in distortion of talent factor prices, which in turn causes regional talent resource mismatch (Yang and Pan, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Guo et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe reform of the household registration system can effectively tackle the problem of inefficient allocation in the labor market, thereby increasing the degree of \u003cem\u003eLabmark\u003c/em\u003e. The free flow of talents can not only improve the quality of labor market matching, but also give full play to human capital externalities and skill complementarities, enhance urban innovation efficiency (Cai et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zhang and Guo, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and eventually strengthen the momentum of urban economic (Tombe and Zhu, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn addition, well-developed public services are more attractive to competent workers. Equal public basic services, especially for the migrant population, can not only reduces the loss of labor efficiency wages and societal welfare (Diamond, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), but also increase a sense of belonging to the city. That will have a direct impact on urban modernization and harmonious development.\u003c/p\u003e\u003cp\u003eIn summary, we believe that the reform of the household registration system has a favorable impact on the efficiency of human capital allocation, urban economic vitality and the sense of identification of foreign talents. Therefore, we suggests the following hypotheses.\u003c/p\u003e\u003cp\u003eH3b: The reform of the household registration system can play a positive moderating function in the process of \u003cem\u003eTalagg\u003c/em\u003e affecting \u003cem\u003eUIGG\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eBased on the above theoretical analysis, we draw the logic mechanism diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Estimation Strategy and Datas","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Baseline Model\u003c/h2\u003e\u003cp\u003eIn order to study the impact of \u003cem\u003eTalagg\u003c/em\u003e on \u003cem\u003eUIGG\u003c/em\u003e in Chinese, the following baseline regression model is constructed:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{\\text{UIGG}}_{\\text{it}}\\text{=}{\\text{\u0026alpha;}}_{\\text{0}}\\text{+}{\\text{\u0026alpha;}}_{\\text{1}}{\\text{Tal}\\text{agg}}_{\\text{it}}\\text{+}{{\\text{\u0026alpha;}}_{\\text{2}}\\text{Control}}_{\\text{it}}\\text{+CityFE+YearFE+}{\\text{\u0026epsilon;}}_{\\text{it}}\\text{}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eEq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) where \u003cem\u003ei\u003c/em\u003e is the city, \u003cem\u003et\u003c/em\u003e is the year, and \u003cem\u003eα\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e is the constant term. \u003cem\u003eα\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e is the estimated coefficient we interest. \u003cem\u003eUIGG\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e is the level of IGG in city \u003cem\u003ei\u003c/em\u003e in year \u003cem\u003et\u003c/em\u003e. \u003cem\u003eTalagg\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e is the level of \u003cem\u003eTalagg\u003c/em\u003e in city \u003cem\u003ei\u003c/em\u003e in year \u003cem\u003et\u003c/em\u003e. \u003cem\u003eConrtol\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e is a collection of control variables. \u003cem\u003eCityFE\u003c/em\u003e is the city fixed effect, \u003cem\u003eYearFE\u003c/em\u003e is the year fixed effect, and \u003cem\u003eε\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e is the random disturbance term. The coefficient \u003cem\u003eα\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e measures the average difference in the impact of \u003cem\u003eTalagg\u003c/em\u003e on \u003cem\u003eUIGG\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Variables\u003c/h2\u003e\u003cp\u003e(1) Explained variable: \u003cem\u003eUIGG\u003c/em\u003e\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\u003e\u003cem\u003eUIGG\u003c/em\u003e Indicator System\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003cp\u003eIndicators\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003cp\u003eIndicators\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003cp\u003eIndicators\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAttribution\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEconomic Development\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEconomic Output\u003c/p\u003e\u003cp\u003eIncome Level\u003c/p\u003e\u003cp\u003eConsumption Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReal GDP per capita\u003c/p\u003e\u003cp\u003eGDP growth rate %\u003c/p\u003e\u003cp\u003eThe ratio of urban and rural per capita income %\u003c/p\u003e\u003cp\u003eThe ratio of urban and rural per capita consumption %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial\u003c/p\u003e\u003cp\u003eInclusion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEducational Resources\u003c/p\u003e\u003cp\u003eHealthcare Resources\u003c/p\u003e\u003cp\u003eSocial Welfare\u003c/p\u003e\u003cp\u003eEmployment Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTeacher-Student ratio of primary and secondary school %\u003c/p\u003e\u003cp\u003eNumber of physicians per 10,000 people\u003c/p\u003e\u003cp\u003eThe coverage rate of pension insurance %\u003c/p\u003e\u003cp\u003eThe coverage rate of medical insurance %\u003c/p\u003e\u003cp\u003eThe coverage rate of unemployment insurance %\u003c/p\u003e\u003cp\u003eUrban registered unemployment rate %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGreen Livability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLiving Environment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePark green space per capita km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eGreen covered area ha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePollution Control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndustrial wastewater discharge ton\u003c/p\u003e\u003cp\u003eIndustrial SO\u003csub\u003e2\u003c/sub\u003e emissions ton\u003c/p\u003e\u003cp\u003eIndustrial fume (dust) emissions ton\u003c/p\u003e\u003cp\u003eComprehensive utilization rate of industrial solid waste %\u003c/p\u003e\u003cp\u003eHarmless treatment rate of domestic garbage %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: The above data are all from EPS.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eUIGG\u003c/em\u003e is a comprehensive concept. Referring to Sun et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Wang D et\u003c/p\u003e\u003cp\u003eal. (2022), we construct a \u003cem\u003eUIGG\u003c/em\u003e index system based on three dimensions: economic development, social inclusion, and green livability in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and measure it adopting the entropy method.\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\u003eControl Variables\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\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndicator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eStructure\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTertiary GDP/GDP %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEPS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInvest\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe actual use of foreign investment 10 billion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEPS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInternet\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe number of international Internet users/Household population %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEPS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eProperty\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Industrial output value of Hong Kong, Macao and Taiwan Enterprises\u0026thinsp;+\u0026thinsp;Industrial output value of foreign enterprises)/Total industrial output value %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCEIC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePerpost\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePostal and Telecommunication business revenue/Permanent population\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCEIC\u003c/p\u003e\u003cp\u003eCSMAR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePerroad\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReal urban road area /urban population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEPS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eScience\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe science and technology expenditure/The general budget expenditure %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCEIC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePerbook\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal public library book collection/Permanent population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEPS\u003c/p\u003e\u003cp\u003eCEIC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSecurity\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSocial security and employment expenditure/The government budget expenditure %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCEIC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e(2) Explanatory Variable: \u003cem\u003eTalagg\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe stability of the quantity of talents is a strong support for urban development. Compared with academic qualifications, it is more realistic to judge talent by occupation. Given the availability of city-level data,\u003csup\u003e1\u003c/sup\u003e this paper draws on the ideas of Moretti (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Bai et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). \u003cem\u003eTalagg\u003c/em\u003e is represented by the proportion of employees in scientific research, technical services and geological exploration, as well as information transmission, computer services and software industries to the total number of employees. In comparison to other industries, these industries have a comparatively high knowledge reserve and skill level among their workforce. So it is appropriate and practicable to utilize this indication to calculate \u003cem\u003eTalagg\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e(3) Control Variables\u003c/p\u003e\u003cp\u003eWe referred to existing studies and selected the following control variables. The specific definition of variables and data sources are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e(4) Data Description\u003c/p\u003e\u003cp\u003eCombining the comparability and availability of data, and excluding cities with serious missing data, this paper finally compiles and obtains balanced panel data for 270 cities from 2003\u0026ndash;2019. The sample data are mainly from the EPS, CEIC and CSMAR databases, and part of the data are from the \u003cem\u003eChina City Statistical Yearbook\u003c/em\u003e and the \u003cem\u003eChina Urban Construction Statistical Yearbook\u003c/em\u003e. In addition, considering the effect of extreme values and heteroskedasticity, we applied the following to the data treatments: ①All variables involving values are deflated using 2003 as the base year. ②All variables, excluding ratios, are logarithmically treated to reduce the problem of heteroskedasticity. ③baseline variables are supplemented with linear interpolation and moving average methods, and 1% two-way deflator is adopted. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the descriptive statistics of the baseline datas in this paper.\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\u003eDescriptive Statistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMean\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eMax\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eMin\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep50\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep25\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep75\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eUIGG\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1477\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0386\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTalagg\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0316\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eStructure\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.3927\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.7288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.1699\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.3804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.3306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.4469\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInvest\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.2684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.3110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.3412\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInternet\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.1471\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.9794\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.1973\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eProperty\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.1455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.8398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0417\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.2039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eScience\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0157\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSecurity\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.1129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0780\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.1435\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePerpost\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.7839\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0670\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0966\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePerroad\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.1540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.6108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.1007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.1928\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePerbook\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.3485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.4402\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.2822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.1860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.4416\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. Result and Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Baseline Regression\u003c/h2\u003e\u003cp\u003eBefore the baseline regression, we first conducted VIF tests on the baseline variables to avoid multicollinearity issues. The results show that VIF values do not exceed 5 for all baseline variables. Subsequently, adopting high-dimensional fixed-effects model, we proceed stepwise regression to examine the direct effect of \u003cem\u003eTalagg\u003c/em\u003e on \u003cem\u003eUIGG\u003c/em\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the results of the baseline regression.\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\u003eBaseline Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(6)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTalagg\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1250\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1072\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1022\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0988\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0973\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0981\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0136)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0127)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0126)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0120)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0118)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0119)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eStructure\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0093\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0084\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0077\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0078\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0079\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(0.0021)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0020)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInvest\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0050\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0048\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0041\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0041\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0042\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0007)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInternet\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0095\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0079\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0077\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0071\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0013)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0013)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eProperty\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0031\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0019)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0019)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0018)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0018)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eScience\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0735\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0685\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0607\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0129)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0127)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0123)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSecurity\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0126\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0126\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0120\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0049)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0049)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0049)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePerpost\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0051\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0049\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0021)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0020)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePerroad\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0058\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0048\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0019)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePerbook\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0047\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0007)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCity-FE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYear-FE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4590\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9361\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9402\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9409\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eadj. R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9337\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9369\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: Standard errors in parentheses, \u003csup\u003e*\u003c/sup\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.1, \u003csup\u003e**\u003c/sup\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e***\u003c/sup\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs can be seen from columns (1) ~ (6) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the estimated coefficients of \u003cem\u003eTalagg\u003c/em\u003e are all significantly positive at the 1% level, which strongly suggests that an increase in \u003cem\u003eTalagg\u003c/em\u003e can significantly and positively contribute to \u003cem\u003eUIGG\u003c/em\u003e. As analyzed in section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cem\u003eTalagg\u003c/em\u003e can enhance urban development dynamics, guide the green upgrading of industries, promote urban diversification, and enhance urban governance capacity, and thus promoting \u003cem\u003eUIGG\u003c/em\u003e. Our conclusions are similar to Cai et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the control variables, except for \u003cem\u003eProperty\u003c/em\u003e, all the other variables have a significant positive impact on \u003cem\u003eUIGG\u003c/em\u003e. Specifically, the estimated coefficient of \u003cem\u003eStructure\u003c/em\u003e is significantly positive at the 1% statistical level. This indicates that the optimization and upgrading of the industrial structure has a significant improvement to enhancing \u003cem\u003eUIGG\u003c/em\u003e. The rationalization and diversification of industrial structure plays an important role in the high-quality advancement of the urban economy, and has a significant effect in providing jobs, alleviating the pressure of industrial pollution, and boosting the economic growth rate, etc. The coefficient of \u003cem\u003eInvest\u003c/em\u003e is significantly positive at the 1% statistical level. This indicates that foreign capital utilization is beneficial to \u003cem\u003eUIGG\u003c/em\u003e.This is in line with the findings of Ofori et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To some extent, the level of foreign capital utilization also represents the efficiency of local foreign trade and capital utilization. The higher the level of foreign capital utilization, the better the city's trade flow, ability to attract capital and degree of openness and inclusiveness. This has a positive effect on the inclusive development of the city. The estimated coefficient of \u003cem\u003eInternet\u003c/em\u003e is significantly positive at 1% statistical\u003c/p\u003e\u003cp\u003elevel. This indicates that the Internet penetration can significantly boost \u003cem\u003eUIGG\u003c/em\u003e. The Internet, as an important manifestation of the information age, has greatly accelerated the speed of information dissemination among market subjects and increased market activity. The Internet has greatly facilitated the virtual economy such as online transactions and cross-border trade. The popularization of network technology ensures the digital power of urban economic growth. The coefficient of \u003cem\u003eScience\u003c/em\u003e is obviously positive at the 1% statistical level. This indicates that financial science and technology support can significantly improve \u003cem\u003eUIGG\u003c/em\u003e. There is no doubt that science and technology are the primarily driving forces for development. The more the local government's financial expenditure on science and technology investment, the more the city's scientific and technological productivity is guaranteed, and thus the better the city's economic growth performance. Moreover, abundant funds for science and technology can also improve industrial upgrading and green innovation. The coefficient of \u003cem\u003eSecurity\u003c/em\u003e is significantly positive at the 5% statistical level. This indicates that social security is conducive to \u003cem\u003eUIGG\u003c/em\u003e. The aging of China's population is becoming more prominent. Coupled with the continuous decline in the birth rate in recent years, the burden of supporting the labor population has also risen significantly. With the gradual improvement of the social security mechanism, the government's increasing expenditure on social security has greatly reduced the cost of supporting the young and middle-aged labor force, and enhanced the sense of identity of the foreigner to the city. The coefficient of \u003cem\u003ePerpost\u003c/em\u003e is distinctly positive at the 1% statistical level. This manifests that the level of informatization could drive \u003cem\u003eUIGG\u003c/em\u003e. The more developed a city's information industry is, the higher the degree of acceptance of new things, and thus the more vitality economic growth and social inclusion. Moreover, the high frequency of information exchange and dissemination also contributes to the upgrading and updating of the green technology. The estimated coefficient of \u003cem\u003ePerroad\u003c/em\u003e is significantly positive at the statistical level of 5%. This means that infrastructure can significantly facilitate \u003cem\u003eUIGG\u003c/em\u003e. The level of infrastructure plays a fundamental role in the development of a city. It not only reflects the city's productivity, but also promotes social harmony and meets citizens' satisfaction. Complete infrastructure can also effectively support the application and promotion of green technology, thus realizing sustainable urban development. The estimated coefficient of \u003cem\u003ePerbook\u003c/em\u003e is significantly positive at the 1% statistical level. This indicates that cultural capital has a significant positive impact on advancing \u003cem\u003eUIGG\u003c/em\u003e. Cultural capital provides a social foundation for inclusive growth by enhancing residents' sense of cultural identity and belonging, promoting communication and understanding among different groups, and reducing social conflicts. Besides, cultural capital also spurs the blossom of creative industries, creates high value-added employment opportunities, and promotes economic diversification through cultural tourism development.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Endogeneity\u003c/h2\u003e\u003cp\u003eThis paper uses the instrumental variable (IV) way to test the possible endogeneity among variables. Referring to Lin and Tan (2019) and Cai et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), we choose terrain undulation as IV and use the 2SLS method to verify. In order to fulfill the requirement of panel data with variable instrumental variables, the product of terrain undulation (\u003cem\u003eTerrain\u003c/em\u003e) and the number of permanent residents (\u003cem\u003ePopulation\u003c/em\u003e) is selected as the instrumental variable of \u003cem\u003eTalagg\u003c/em\u003e. The IV is somewhat reasonable. On the one hand, for the general labor force, the more complex the topographic relief, the greater the cost of migration. However, the migration costs incurred by the cross-regional flow of talents can almost be negligible. This is because not only do they have a certain economic base, but also the local government will give all kinds of relocation subsidies to the talents. From this, it can be inferred that the IV may have a positive correlation with the explanatory variables, which satisfies the characteristic of correlation with endogenous variables. On the other hand, as an exogenous geographical variable, urban terrain undulation is an objective geographic feature that is almost impossible to change (Chen and Kung, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). It cannot directly affect \u003cem\u003eUIGG\u003c/em\u003e, which satisfies the exogeneity requirement.\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\u003eIV Estimates Results\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\u003e\u003cem\u003eVariables\u003c/em\u003e\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\u003e\u003cem\u003eFirst\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eSecond\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTerrain*Population\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.6450\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(0.0129)\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\u003e\u003cem\u003eTalagg\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1826\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(0.0163)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eControls\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCity-FE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYear-FE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAnderson canon. corr. LM statistic\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1542.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1542.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCragg-Donald Wald F statistic\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2489.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2489.664\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4304\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.514\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNumber of City\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e269\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: Same as Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the results of 2SLS estimation. All estimated coefficients are all significantly positive at the 1% level. From the first stage results, it can be seen that there is a significant positive correlation between IV (\u003cem\u003eTerrain*Population\u003c/em\u003e) and \u003cem\u003eTalagg\u003c/em\u003e, which is in line with the previous analysis and confirms the relevance. And the \u003cem\u003eLM\u003c/em\u003e value of the non-identifiable test and the \u003cem\u003eF\u003c/em\u003e value of the weak instrumental variable test are much larger than the empirical critical value, rejecting the original hypothesis. The second stage results show a substantial and positive correlation between Talent and \u003cem\u003eUIGG\u003c/em\u003e. This is consistent with the baseline results. The above analysis shows that our main conclusions still hold after the introduction of IV to mitigate the impact of potential endogeneity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Robustness\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eHypothesis\u003c/strong\u003e\u003cp\u003e, that \u003cem\u003eTalagg\u003c/em\u003e can significantly boost \u003cem\u003eUIGG\u003c/em\u003e, is proved in the baseline test. In order to strengthen the robustness of this conclusion, we conduct a series of robustness tests as follows, namely, replacing the explanatory variables, adding control variables, changing the clustering robust standard errors, lagging the dependent variable by one period, and eliminating the interference of regional heterogeneity and geographical characteristics. The details are as follows:\u003c/p\u003e\u003c/p\u003e\u003cp\u003e(1) Replace the explanatory variable\u003c/p\u003e\u003cp\u003eIn order to prove the robustness of the baseline results, we first replace the ratio of employees in two industries to the total employees with the ratio of employees in six industries\u003csup\u003e2\u003c/sup\u003e to the total employees, and then recalculating \u003cem\u003eTalent\u003c/em\u003e and running the regression. The results are shown in column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRobustness Tests\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(6)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(7)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTalagg\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0155\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0963\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0981\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0852\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0981\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0981\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0948\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0027)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0114)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0119)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0118)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.0121)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.0119)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(0.0118)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ejd*year\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.1047\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(0.0252)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ewd*year\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(0.0271)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eControls\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCity-FE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYear-FE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCity*Year\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4590\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9383\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.9412\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eadj. R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.9372\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: Same as Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e(2) Add control variables\u003c/p\u003e\u003cp\u003eTo avoid the issue of estimation bias caused by omitted variables, more city-level control variables are added for testing. The additional control variables are as follows: Financial expenditure on education/Local fiscal General Budget Expenditures (\u003cem\u003eEducation\u003c/em\u003e, Source: CEIC); Total bank loans/Total bank deposits (\u003cem\u003eFinance\u003c/em\u003e, Source: CEIC); Fiscal expenditures/GDP (\u003cem\u003eGovern\u003c/em\u003e, Source: CEIC); And per capita mobile phone users (\u003cem\u003eMobile\u003c/em\u003e, Source: EPS). The problem of omitted variables is reduced by controlling for more individual city characteristics. Results are reported in column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e(3) Cluster interaction effects\u003c/p\u003e\u003cp\u003eConsider that within-group correlations (individual and time-varying characteristics) may have an impact on the estimation results. We perform robustness tests by changing the clustered robust standard errors. Specifically, the clustering criteria are refixed at the \u003cem\u003ecity*year\u003c/em\u003e level in regressions to control for time-varying area-level characteristic factors. The results are reported in column (3) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e(4) Explained variable lagged one period\u003c/p\u003e\u003cp\u003eIt takes time for \u003cem\u003eTalagg\u003c/em\u003e to promote urban development. And thus there may be a time lag effect on \u003cem\u003eUIGG\u003c/em\u003e. At the same time, the data may have short-term fluctuations. Using lagged data can help smooth out the impact of short-term fluctuations on the regression results. Therefore, to improve the accuracy of the baseline estimation, the explained variable is used for regression again with a lag of one period. The results are shown in column (4) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e(5) Bootstrap repeated sampling\u003c/p\u003e\u003cp\u003eThe core of Bootstrap is to generate a large number of simulated samples by repeated sampling of the original samples with put-backs, and then estimate the distributional properties of the statistics. Its rationality stems from the direct simulation of sampling variation, which can effectively verify the stability of empirical results. In this paper, the baseline result is re-estimated by repeating the sampling 500 times. The results are shown in column (5) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e(6) Excluding regional variability\u003c/p\u003e\u003cp\u003eDue to special national conditions, the developed degree in eastern China has long been better than that in central and western. In order to exclude regional differences that may affect the results of the baseline estimation, we introduce the interaction term between \u003cem\u003eTalagg\u003c/em\u003e and regional dummy variables (1 for eastern regions and 0 for non-eastern regions) on the basis of the baseline model. The results are presented in column (6) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e(7) Excluding the interference of geographic features\u003c/p\u003e\u003cp\u003eChina is a vast country with complex and variable geographic features. This may have a certain influence on talent mobility. For rule out the possibility that the geographic feature may cause the inaccuracy of the estimated result, we introduce the interaction term between latitude and longitude and year for each city and estimate again. The results are shown in column (7) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eTo sum up, after the above robustness tests, all of \u003cem\u003eTalagg\u003c/em\u003e\u0026rsquo;s estimated coefficients on \u003cem\u003eUIGG\u003c/em\u003e pass the significance test at the 1% statistical level. All results are highly consistent with the baseline estimates, which greatly enhances the credibility of our basic conclusions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Heterogeneity\u003c/h2\u003e\u003cp\u003eConsidering the differences in the individual characteristics of each city, we examined the heterogeneity effect of \u003cem\u003eTalagg\u003c/em\u003e on \u003cem\u003eUIGG\u003c/em\u003e from four aspects, namely, city type, city positioning, geographic location and quantile regression, respectively. The results are shown in Appendix Table\u0026nbsp;4. To visually present the heterogeneous results, we plotted Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e(1) City Location\u003c/p\u003e\u003cp\u003eIn the early stage of reform and opening up, China's eastern coastal provinces leveraged their natural geographical and transportation advantages to become pioneer regions, with economic development levels far surpassing those of central and western provinces. To reduce regional disparities, the Chinese government successively launched the \"Western Development\" and \"Central Revival\" regional development strategies. This paper categorizes cities into east, middle, and west regions based on the standards of the National Bureau of Statistics of China\u003csup\u003e3\u003c/sup\u003e, and conducts empirical tests.\u003c/p\u003e\u003cp\u003eAs shown in Panel A of Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the effect of \u003cem\u003eTalagg\u003c/em\u003e on \u003cem\u003eUIGG\u003c/em\u003e is significantly positive in the east-middle region, but not significant in the west region. This fully demonstrates that the impact of \u003cem\u003eTalagg\u003c/em\u003e on \u003cem\u003eUIGG\u003c/em\u003e exhibits significant geographical location heterogeneity. The influence of \u003cem\u003eTalagg\u003c/em\u003e on east-middle cities is more positive and significant. The underlying reason may align with the polarization theory, where developed region continuously absorbs high-quality factor resources, and the accumulation of factor endowment disparities exacerbates income inequality. Compared to the east-middle region of China, the west region has relatively underdeveloped economic conditions and lagging social security systems, making it less attractive to talent. Most highly skilled talent tends to flow toward economically developed cities with higher welfare levels, resulting in a more obvious effect of \u003cem\u003eTalagg\u003c/em\u003e on \u003cem\u003eUIGG\u003c/em\u003e in east-middle cities.\u003c/p\u003e\u003cp\u003e(2) City Type\u003c/p\u003e\u003cp\u003eThe effect of \u003cem\u003eTalagg\u003c/em\u003e on \u003cem\u003eUIGG\u003c/em\u003e may be affected by city class. Central cities have advantages in terms of market size, resource allocation and policy support, which are conducive to the full utilization of talent resources. Moreover, in reality, compared with ordinary cities, highly skilled talents generally tend to move to the central cities agglomeration. Therefore, according to the city type, this paper sets the municipalities, provincial capitals and sub-provincial cities in the data sample as center cities, and the rest of the cities as ordinary cities.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAs can be seen from Panel A of Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e., the promotion of \u003cem\u003eTalagg\u003c/em\u003e to \u003cem\u003eUIGG\u003c/em\u003e passes the significance test in the center cities, while it did not pass in ordinary cities. This indicates that the effect of \u003cem\u003eTalagg\u003c/em\u003e on \u003cem\u003eUIGG\u003c/em\u003e varies significantly among cities of different administrative levels. By virtue of the huge market advantage, complete public service system, and innate policy resources, central cities provide sufficient space for talents to gather and play. These are not available in ordinary cities.\u003c/p\u003e\u003cp\u003e(3) Quantile Regression\u003c/p\u003e\u003cp\u003eThe results of the baseline regression confirm the uplifting effect of \u003cem\u003eTalagg\u003c/em\u003e on \u003cem\u003eUIGG\u003c/em\u003e. But it is impossible to know whether there is a difference in its impact on cities with different IGG levels. Therefore, this paper employs Quantile Regression to explore the differences in the impact of \u003cem\u003eTalagg\u003c/em\u003e on different \u003cem\u003eUIGG\u003c/em\u003e levels. Panel quantile regression can better handle heterogeneity across individuals, be more robust to outliers and non-normally distributed data, and thus be able to provide differentiated policy recommendations and decision support for different groups (Long et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As an extension of linear regression, quantile regression allows the estimated coefficients to vary with the quantile points of the explanatory variables, thus revealing the heterogeneous effects of the explanatory variables on different levels of the explained variables. Specifically, the 25% and 75% \u003cem\u003eUIGG\u003c/em\u003e quantile points are selected for quantile regression analysis in this paper.\u003c/p\u003e\u003cp\u003eThe test results are shown in Panel D of Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. For the cities with lower \u003cem\u003eUIGG\u003c/em\u003e group (25%), \u003cem\u003eTalent\u003c/em\u003e has a significant positive contribution. For the cities with higher \u003cem\u003eUIGG\u003c/em\u003e group (75%), the impact effect of \u003cem\u003eTalent\u003c/em\u003e is positive but fails the significance test. This suggests that there is a significant difference in the effect of \u003cem\u003eTalagg\u003c/em\u003e in cities with different \u003cem\u003eUIGG\u003c/em\u003e levels. This is also consistent with the marginal utility theory of agglomeration factors. When the city's IGG level is low, \u003cem\u003eTalagg\u003c/em\u003e can provide sufficient intellectual support and guidance suggestions for urban sustainable development, thus significantly enhancing urban inclusiveness and greenness. When the city's IGG level is already at a higher level, the improvement effect of \u003cem\u003eTalagg\u003c/em\u003e may not be so obvious. In other words, as the \u003cem\u003eUIGG\u003c/em\u003e level increases, the effect of \u003cem\u003eTalagg\u003c/em\u003e will gradually weaken.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Mechanisms","content":"\u003cp\u003eThe baseline results show that \u003cem\u003eTalagg\u003c/em\u003e can provide high-quality human capital for urban sustainable development and promote \u003cem\u003eUIGG\u003c/em\u003e. According to the theoretical analysis in section \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e2.2\u003c/span\u003e, we will launch the verification of H2a and H2b in this section.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Mediating Variables\u003c/h2\u003e\u003cp\u003e(1) Green Innovation (\u003cem\u003eGreinn\u003c/em\u003e)\u003c/p\u003e\u003cp\u003eInnovation is the first driving force of development. The improvement of urban innovation capability can inevitably impel the high-quality development of the regional economy. Most scholars use patent applications to measure the level of regional technological innovation, which has a certain degree of rationality. However, in this paper, we believe that compared with the quantity of patent applications, the number of patents obtained can more effectively reflect the regional innovation ability. Therefore, we choose the proportion of green patents obtained to the total number of patents obtained to measure the level of urban \u003cem\u003eGreinn\u003c/em\u003e. The larger its value, the higher the ability of urban green innovation.\u003c/p\u003e\u003cp\u003e(2) Entrepreneurial Activity (\u003cem\u003eStartup\u003c/em\u003e)\u003c/p\u003e\u003cp\u003e\u003cem\u003eStartup\u003c/em\u003e reflects the overall intensity of entrepreneurial activities in a specific region. In addition to start-up capital, entrepreneurship requires excellent professional skills, which is the characteristic of highly-skilled talents. The entrepreneurial atmosphere of a city can directly reflect its economic vitality and social inclusion. Therefore, we refer to the research method of Bai et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). We choose to use the quantity of newly created enterprises per 100 people in a city to measure \u003cem\u003eStartup\u003c/em\u003e. This approach avoids to a certain extent the problem of measurement bias due to the heterogeneity of enterprise sizes in the region, and is able to portray the \u003cem\u003eStartup\u003c/em\u003e of cities more accurately.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Model Setting and Results\u003c/h2\u003e\u003cp\u003eReferring to Guo et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), we construct the following model to test the mechanisms of the role of talent agglomeration on \u003cem\u003eUIGG\u003c/em\u003e.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{\\text{M}}_{\\text{it}}\\text{=}{\\text{\u0026gamma;}}_{\\text{0}}\\text{+}{\\text{\u0026gamma;}}_{\\text{1}}{\\text{Tal}\\text{agg}}_{\\text{it}}\\text{+}{\\text{\u0026sum;}\\text{Control}}_{\\text{it}}\\text{+}{\\text{\u0026mu;}}_{\\text{it}}\\text{}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the above equation, \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e is the mechanism variable, including \u003cem\u003eGreinn\u003c/em\u003e and \u003cem\u003eStartup\u003c/em\u003e. The rest of the variables are defined the same as in Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe estimation results of mechanisms test are shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Column (1) ~(2) are green innovations. Columns (3)~(4) are entrepreneurial activity. The regression results for both are significantly positive at the 1% statistical level. It confirms that \u003cem\u003eTalagg\u003c/em\u003e can indeed promote green innovation technology, increase entrepreneurial activity, and thus enhance \u003cem\u003eUIGG\u003c/em\u003e. H2a and H2b of this paper are confirmed.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMechanisms Results\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\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003eGreinn\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u003cem\u003eStartup\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTalagg\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1921\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1904\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.8111\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.3439\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.0460)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.0488)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.9627)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.8131)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4556\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4556\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4573\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4573\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.4790\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8057\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eadj. \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.4444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4465\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7725\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.7924\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Same as Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"6. Expanded Analysis","content":"\u003cp\u003eIn order to reveal more comprehensively and deeply the influence of \u003cem\u003eTalagg\u003c/em\u003e on \u003cem\u003eUIGG\u003c/em\u003e, referring to the existing studies, we select the two variables of Industrial Agglomeration (\u003cem\u003eIndagg\u003c/em\u003e) and Labor Marketization (\u003cem\u003eLabmark\u003c/em\u003e, i.e., household registration system) as moderating variables. Incorporating them into the baseline model with the interaction term of \u003cem\u003eTalagg\u003c/em\u003e to further conduct the empirical test.\u003c/p\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e6.1 Moderate Variables\u003c/h2\u003e\u003cp\u003e(1) \u003cem\u003eIndagg\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eIndagg\u003c/em\u003e measures the degree of concentration of an industry in a specific geographical area. Referring to the research of Wang Z et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), this paper adopts location entropy (the geographic concentration of industrial) to indicate the degree of \u003cem\u003eIndagg\u003c/em\u003e, which is calculated by the following formula:\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"606\" height=\"103\"\u003e\u003c/p\u003e\u003cp\u003eIn Eq.\u0026nbsp;(3), \u003cem\u003ei\u003c/em\u003e denotes the city, \u003cem\u003et\u003c/em\u003e denotes the year, and n denotes the number of cities. \u003cem\u003eInd\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e denotes the regional Secondary Industry GDP of city \u003cem\u003ei\u003c/em\u003e in year \u003cem\u003et\u003c/em\u003e. \u003cem\u003eArea\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e denotes the administrative land area of city \u003cem\u003ei\u003c/em\u003e in year \u003cem\u003et\u003c/em\u003e. \u003cem\u003eIndagg\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e denotes the level of \u003cem\u003eIndagg\u003c/em\u003e of city \u003cem\u003ei\u003c/em\u003e in year \u003cem\u003et\u003c/em\u003e. The larger the value, the higher \u003cem\u003eIndagg\u003c/em\u003e of the city.\u003c/p\u003e\u003cp\u003e(2) \u003cem\u003eLabmark\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAlthough China has continued to promote labor market reform since the reform and opening up, effectively reducing the barriers to labor mobility. The household registration restriction is still an important obstacle to the free flow of labor in China. Relaxing the household registration system can effectively alleviate the problems of poor labor mobility and inefficient talent allocation, and improve the degree of \u003cem\u003eLabmark\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eIn this paper, we use the household registration policies of each city to test how marketized \u003cem\u003eTalagg\u003c/em\u003e affects \u003cem\u003eUIGG\u003c/em\u003e. We use the settlement index calculated by Zhang et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The household registration data covers 120 major cities in China and is basically representative of the urban household registration characteristics in China. We only use the indices in this dataset that is directly related to talent introduction index. There are three calculation methods for this index: projection method (\u003cem\u003eHousehold_pp\u003c/em\u003e), equal weight method (\u003cem\u003eHousehold_ew\u003c/em\u003e), and entropy value method (\u003cem\u003eHousehold_en\u003c/em\u003e). Since the index contains data from two periods, our paper only uses the index from 2014\u0026ndash;2016 to characterize the urban \u003cem\u003eLabmark\u003c/em\u003e level.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e6.2 Empirical Regression\u003c/h2\u003e\u003cp\u003eReferring to Shehzad et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the interaction terms of \u003cem\u003eTalagg\u003c/em\u003e and \u003cem\u003eIndagg\u003c/em\u003e and \u003cem\u003eTalagg\u003c/em\u003e and \u003cem\u003eLabmark\u003c/em\u003e were respectively included in Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) for regression, and the moderating effects of \u003cem\u003eIndagg\u003c/em\u003e and \u003cem\u003eLabmark\u003c/em\u003e were tested.\u003c/p\u003e\u003cp\u003eThe test results of the moderating effects are reported in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. As shown in column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the coefficients of \u003cem\u003eTalagg*Indagg\u003c/em\u003e are all significantly positive at the 1% statistical level. It fully explains that the conjugated effect of \u003cem\u003eIndagg\u003c/em\u003e and \u003cem\u003eTalagg\u003c/em\u003e can promote \u003cem\u003eUIGG\u003c/em\u003e. In other words, \u003cem\u003eIndagg\u003c/em\u003e can play a positive moderating function in the process of \u003cem\u003eTalagg\u003c/em\u003e on \u003cem\u003eUIGG\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModerate Results\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\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eIndagg\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLabmark\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTalagg*Indagg\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0242***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTalagg*Labmark_pp\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2561\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\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(0.0423)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTalagg*Labmark_ew\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.4250\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.0690)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTalagg*Labmark_en\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4009\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0648)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eControls\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCity-FE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYear-FE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1921\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1921\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1921\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9438\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9484\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9484\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9480\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eadj. R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9444\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Same as Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn columns (2) ~ (4) of Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the coefficients of all \u003cem\u003eTalagg*Labmark\u003c/em\u003e are obviously positive at the 1% statistical level. This fully indicates that the policy synergy effect of household registration system reform and talent introduction can enhance \u003cem\u003eUIGG\u003c/em\u003e. The increase of \u003cem\u003eLabmark\u003c/em\u003e level can play a significant moderating effect in the process of \u003cem\u003eTalagg\u003c/em\u003e on \u003cem\u003eUIGG\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eIn summary, the synergistic effect of \u003cem\u003eIndagg\u003c/em\u003e and \u003cem\u003eTalagg\u003c/em\u003e, as well as the policy cooperation effect of household registration system reform and \u003cem\u003eTalagg\u003c/em\u003e, making the urban economic development more inclusive and greenning. This finding also supports the hypotheses H3a and H3b.\u003c/p\u003e\u003c/div\u003e"},{"header":"7. Conclusions and Implications","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e7.1 Main Conclusions\u003c/h2\u003e\u003cp\u003eTalent, as the first resource for economic and social growth, can provide the necessary intellectual support required for the long-term development of global cities. The agglomeration of highly skilled labor can also fuel urban economic growth, increase social diversity, and encourage the green transformation of industry. This is also the new direction outlined in the UN SDG declaration for future urban development. In this paper, we first construct an \u003cem\u003eUIGG\u003c/em\u003e indicator system containing 17 indicators in three dimensions (economic development, social inclusion and green livability), and then use the entropy method to measure \u003cem\u003eUIGG\u003c/em\u003e. Then we organize and match other explanatory variables, and finally obtain a balanced panel data set for 270 prefecture-level and above cities in China during 2003\u0026ndash;2019.\u003c/p\u003e\u003cp\u003eWe examine the impact of \u003cem\u003eTalagg\u003c/em\u003e on \u003cem\u003eUIGG\u003c/em\u003e in China and its internal mechanisms from both theoretical and empirical perspectives. The findings show that, first, \u003cem\u003eTalagg\u003c/em\u003e can significantly enhance \u003cem\u003eUIGG\u003c/em\u003e. This basic conclusion still holds strongly after a variety of robustness tests. Second, the heterogeneity test finds that the improvement effect of \u003cem\u003eTalagg\u003c/em\u003e on \u003cem\u003eUIGG\u003c/em\u003e is stronger in central cities, east-middle cities, and cities with low IGG levels. Third, the mechanisms test shows that \u003cem\u003eTalagg\u003c/em\u003e promotes the inclusive and greening characteristics of urban economic development by facilitating green technological innovation and increasing entrepreneurial activity. Fourth, the expanded analysis shows that both the horizontal business environment and vertical institutional management have an impact on urban development. Specifically, the conjugate effect of \u003cem\u003eTalagg\u003c/em\u003e and \u003cem\u003eIndagg\u003c/em\u003e, as well as the policy synergy between \u003cem\u003eTalagg\u003c/em\u003e and household registration reform, can better boost \u003cem\u003eUIGG\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e7.2 Policy Implications\u003c/h2\u003e\u003cp\u003eDrawing on the preceding empirical findings and theoretical frameworks, this paper proposes the following key priorities to advance \u003cem\u003eUIGG\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eFirstly, strengthening the talent recruitment policy for \u003cem\u003eUIGG's\u003c/em\u003e intellectual foundation. Aligned with UN SDGs, global cities should deepen consensus on regional inclusive green development and leverage \u003cem\u003eTalagg's\u003c/em\u003e intellectual support. Governments need to build an inclusive talent system\u0026ndash;removing barriers between universities, research institutes, and enterprises\u0026ndash;to align local talent supply with \u003cem\u003eUIGG\u003c/em\u003e needs. Increased investment in education and vocational training will enhance workforce quality, fostering talents for urban diversification and green transition.\u003c/p\u003e\u003cp\u003eSecondly, tailoring policies to regional heterogeneity. Adopt differentiated talent strategies. The core cities should link high-end talents with international innovation. The remote or inland cities may use flexible models (e.g., remote collaboration) to ease geographic constraints on green economic development. The developed or industrialized cities could transfer green technologies and management expertise to underdeveloped cities via industrial gradients. And for low-IGG cities, upgrading public services and infrastructure will boost talent attraction and narrow regional gaps.\u003c/p\u003e\u003cp\u003eThirdly, deepening intermediary mechanisms for \u003cem\u003eTalagg\u003c/em\u003e-\u003cem\u003eGreinn\u003c/em\u003e and \u003cem\u003eTalagg\u003c/em\u003e-\u003cem\u003eStartup\u003c/em\u003e links. Local relevant official departments should encourage green technical innovation by increasing subsidies for talent-led R\u0026amp;D, protecting innovators' incomes, and setting up venture funds to cut innovation costs. At the same time, they could optimize the entrepreneurial ecosystem via tax cuts, simplified approval, and shorter financing chains to enhance talent's entrepreneurial willingness.\u003c/p\u003e\u003cp\u003eFourthly, improving synergies with different policies. On the one hand, urban decision-makers should be committed to use industrial platforms to attract talents, pursue ''dual-wheel drive'' (fostering emerging clusters while upgrading traditional industries), and leverage \u003cem\u003eIndagg\u003c/em\u003e's externality to improve resource allocation. On the other hand, population management authorities should endeavor to relax high-skilled talent settlement rules, optimize cross-regional social security mutual recognition and skill assessment, and base public services on resident population. This promotes service equality, boosts talent belonging, and enhances urban inclusiveness.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eG.C. and F.L. wrote the main manuscript text; G.C. and Y.L. prepared Tables\u0026nbsp;1\u0026ndash;6; G.J. prepared Figs.\u0026nbsp;1 and Figs.\u0026nbsp;2. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Social Science Foundation of China, FJYB036.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research data for this article is sourced from the EPS database (https://www.epsnet.com.cn), CEIC database (https://www.ceicdata.com) and Guotai An database (https://data.csmar.com). All of these are publicly available databases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final version of the manuscript. They consent to its submission for publication and confirm that the work is original, has not been published previously, and is not currently under consideration for publication elsewhere. The authors agree to be accountable for all aspects of the work and ensure that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChenyang Guo\u003csup\u003ea\u003c/sup\u003e , Lan Fang\u003csup\u003ea,b,*\u003c/sup\u003e, Lan Yang\u003csup\u003ec\u003c/sup\u003e, Jiexiao Ge\u003csup\u003ed\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Northwest Institute of Historical Environment and Socio-Economic Development, Shaanxi Normal University, Xi\u0026apos;an, Shaanxi, 710119, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Leibniz Institute of Agricultural Development in Transition Economies (IAMO), Theodor-Lieser-Str. 2, 06120 Halle (Saale), Germany\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003e School of Economics, Zhongnan University of Economics and Law, Wuhan, 430073, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ed\u003c/sup\u003e School of Economics, Beijing Institute of Technology, Beijing, 102481, China\u003c/p\u003e\n\u003cp\u003eFirst author: Chenyang Guo\u003c/p\u003e\n\u003cp\u003eE-mail address: [email protected]\u003c/p\u003e\n\u003cp\u003e* Corresponding author: Lan Fang\u003c/p\u003e\n\u003cp\u003eF-mail address: [email protected] (L. Fang)\u003c/p\u003e\n\u003cp\u003eCo-author: Lan Yang\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eE-mail address: [email protected]\u003c/p\u003e\n\u003cp\u003eCo-author: Jiexiao Ge\u003c/p\u003e\n\u003cp\u003eE-mail address: [email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAtkin, D., Chen, M. K., Popov, A., (2022). The Returns to Face-to-face Interactions: Knowledge Spillovers in Silicon Valley. \u003cem\u003eNational Bureau of Economic Research\u003c/em\u003e. https://doi.org/10.3386/w30147.\u003c/li\u003e\n\u003cli\u003eBai, J., Zhang, Y., Bian, Y., (2022). Does Innovation-driven Policy Increase Entrepreneurial Activity in Cities--Evidence from the National Innovative City Pilot Policy. \u003cem\u003eChina Industrial Economics\u003c/em\u003e, (6), 61-78. (In Chinese). https://doi.org/10.19581/j.cnki.ciejournal.2022.06.016.\u003c/li\u003e\n\u003cli\u003eBathelt, H., Malmberg, A., Maskell, P., (2004). Clusters and knowledge: local buzz, global pipelines and the process of knowledge creation. \u003cem\u003eProgress in Human Geography\u003c/em\u003e, 28(1), 31-56. https://doi.org/10.1191/0309132504ph469oa.\u003c/li\u003e\n\u003cli\u003eBrandt, L., Tombe, T., Zhu, X., (2013). Factor market distortions across time, space and sectors in China. \u003cem\u003eReview of Economic Dynamics\u003c/em\u003e, 16(1), 39-58. https://doi.org/10.1016/j.red.2012.10.002. \u003c/li\u003e\n\u003cli\u003eCai, Q., Liu, H., Pan, Y., Zou, J., (2025). The innovation effects of local talent competition: The case of China\u0026apos;s municipal \u0026quot;Vying for Talent\u0026quot; policies. \u003cem\u003eHabitat International\u003c/em\u003e, 164, 103523. https://doi.org/10.1016/j.habitatint.2025.103523.\u003c/li\u003e\n\u003cli\u003eCai, Q., Wang, S., Liu, H., Shu, S., (2023). Does the Population Agglomeration of Urban Agglomerations Promote Local Firms\u0026apos; Innovation? \u003cem\u003eChina Industrial Economics\u003c/em\u003e, (3), 152-170. (In Chinese). https://doi.org/10.19581/j.cnki.ciejournal.2023.03.006.\u003c/li\u003e\n\u003cli\u003eChemmanur, T. J., Kong, L., Krishnan, K., Yu, Q., (2018). Top Management Human Capital, Inventor Mobility, and Corporate Innovation. \u003cem\u003eJournal of Financial and Quantitative Analysis\u003c/em\u003e, 54(6), 2383-2422. https://doi.org/10.1017/S0022109018001497.\u003c/li\u003e\n\u003cli\u003eChen, T., Kung, J. K. S., (2016). Do land revenue windfalls create a political resource curse? Evidence from China. \u003cem\u003eJournal of Development Economics\u003c/em\u003e, 123, 86-106. https://doi.org/10.1016/j.jdeveco.2016.08.005.\u003c/li\u003e\n\u003cli\u003eCombes, P. P., Duranton, G., Gobillon, L., Puga, D., Roux, S., (2012). The Productivity Advantages of Large Cities: Distinguishing Agglomeration From Firm Selection. \u003cem\u003eEconometrica\u003c/em\u003e, 80(6), 2543-2594. https://doi.org/10.3982/ECTA8442.\u003c/li\u003e\n\u003cli\u003eDavis, M. A., FISHER, J. D. M., WHITED, T. M., (2014). Macroeconomic Implications of Agglomeration. \u003cem\u003eEconometrica\u003c/em\u003e, 82(2), 731-764. https://doi.org/10.3982/ECTA9029.\u003c/li\u003e\n\u003cli\u003eDiamond, R., (2016). The Determinants and Welfare Implications of US Workers\u0026apos; Diverging Location Choices by Skill: 1980\u0026ndash;2000. \u003cem\u003eAmerican Economic Review\u003c/em\u003e, 106(3), 479-524. https://doi.org/10.1257/aer.20131706.\u003c/li\u003e\n\u003cli\u003eFang, Z., Wolski, M., (2021). Human capital, energy and economic growth in China: evidence from multivariate nonlinear Granger causality tests. \u003cem\u003eEmpirical Economics\u003c/em\u003e, 60(2), 607-632. https://doi.org/10.1007/s00181-019-01781-7.\u003c/li\u003e\n\u003cli\u003eFarndale, E., Beamond, M., Corbett-Etchevers, I., Xu, S., (2022). Accessing host country national talent in emerging economies: A resource perspective review and future research agenda. \u003cem\u003eJournal of World Business\u003c/em\u003e, 57(1), 101256. https://doi.org/10.1016/j.jwb.2021.101256.\u003c/li\u003e\n\u003cli\u003eFontagn\u0026eacute;, L., Santoni, G., (2019). Agglomeration economies and firm-level labor misallocation. \u003cem\u003eJournal of Economic Geography\u003c/em\u003e, 19(1), 251-272. https://doi.org/10.1093/jeg/lby007.\u003c/li\u003e\n\u003cli\u003eGaray, L., Font, X., Pereira-Moliner, J., (2017). Understanding sustainability behaviour: The relationship between information acquisition, proactivity and performance. \u003cem\u003eTourism Management\u003c/em\u003e, 60, 418-429. https://doi.org/10.1016/j.tourman.2016.12.017.\u003c/li\u003e\n\u003cli\u003eGennaioli, N., La Porta, R., Lopez-de-Silanes, F., Shleifer, A., (2013). Human Capital and Regional Development. \u003cem\u003eThe Quarterly Journal of Economics\u003c/em\u003e, 128(1), 105-164. https://doi.org/10.1093/qje/qjs050.\u003c/li\u003e\n\u003cli\u003eGharbi, S., Zarrad, O., Kalai, M., \u0026amp; Helali, K., (2025). Examining the symmetric and the asymmetric impacts of green innovation and digitalization on the European Union\u0026rsquo;s green economic growth. \u003cem\u003eDiscover Sustainability\u003c/em\u003e, 6(1), https://doi.org/929.10.1007/s43621-025-01531-z.\u003c/li\u003e\n\u003cli\u003eGiuliano, G., Kang, S., Yuan, Q., (2019). Agglomeration economies and evolving urban form. \u003cem\u003eThe Annals of Regional Science\u003c/em\u003e, 63(3), 377-398. https://doi.org/10.1007/s00168-019-00957-4.\u003c/li\u003e\n\u003cli\u003eGlaeser, E. L., Kerr, S. P., Kerr, W. R., (2015). Entrepreneurship and urban growth: An empirical assessment with historical mines. \u003cem\u003eThe Review of Economics and Statistics\u003c/em\u003e, 97(2), 498-520. https://doi.org/10.1162/REST_a_00456.\u003c/li\u003e\n\u003cli\u003eGu, H., Wang, J., Ling, Y., (2024). Economic geography of talent migration and agglomeration in China: A dual-driver framework. \u003cem\u003eChina Economic Review\u003c/em\u003e, 86, 102180. https://doi.org/10.1016/j.chieco.2024.102180.\u003c/li\u003e\n\u003cli\u003eGu, K., Dong, F., Sun, H., Zhou, Y., (2021). How economic policy uncertainty processes impact on inclusive green growth in emerging industrialized countries: A case study of China. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e, 322, 128963. https://doi.org/10.1016/j.jclepro.2021.128963. \u003c/li\u003e\n\u003cli\u003eGuo, M., Luo, D., Liu, C., (2024). City civilization, employment creation and talent agglomeration: Empirical evidence from the \u0026quot;National Civilized City\u0026quot; policy in China. \u003cem\u003eChina Economic Review\u003c/em\u003e, 87, 102215. https://doi.org/10.1016/j.chieco.2024.102215.\u003c/li\u003e\n\u003cli\u003eHsieh, C. T., Hurst, E., Jones, C. I., Klenow, P. J., (2019). The Allocation of Talent and U.S. Economic Growth. \u003cem\u003eEconometrica\u003c/em\u003e, 87(5), 1439-1474. https://doi.org/10.3982/ECTA11427.\u003c/li\u003e\n\u003cli\u003eHuggins, R., Thompson, P., (2023). Human agency, network dynamics and regional development: the behavioural principles of new path creation. \u003cem\u003eRegional Studies\u003c/em\u003e, 57(8), 1469-1481. https://doi.org//10.1080/00343404.2022.2060958.\u003c/li\u003e\n\u003cli\u003eJian, X., Du, D., Liang, D., (2024). Scale or effectiveness? The nonlinear impact of talent agglomeration on high-quality economic development in China. \u003cem\u003eHeliyon\u003c/em\u003e, 10(9). https://doi.org/10.1016/j.heliyon.2024.e30121.\u003c/li\u003e\n\u003cli\u003eKamguia, B., Tadadjeu, S., Ndoya, H., Djeunankan, R., (2025). Assessing the nexus between industrialization and inclusive green growth in Africa. The critical role of energy efficiency. \u003cem\u003eEcological Economics\u003c/em\u003e, 233,108610. https://doi.org/10.1016/j.ecolecon.2025.108601.\u003c/li\u003e\n\u003cli\u003eKebede, J., Naranpanawa, A., Selvanathan, S., (2023). Financial inclusion and income inequality nexus: A case of Africa. \u003cem\u003eEconomic Analysis and Policy\u003c/em\u003e, 77, 539-557. https://doi.org/10.1016/j.eap.2022.12.006.\u003c/li\u003e\n\u003cli\u003eKerr, S. P., Kerr, W., \u0026Ouml;zden, \u0026Ccedil;., Parsons, C., (2017). High-Skilled Migration and Agglomeration. \u003cem\u003eAnnual Review of Economics\u003c/em\u003e, 9(1), 201-234. https://doi.org/10.1146/annurev-economics-063016-103705.\u003c/li\u003e\n\u003cli\u003eLiu, X., Liu, F., Ren, X., (2023). Firms\u0026apos; digitalization in manufacturing and the structure and direction of green innovation. \u003cem\u003eJournal of Environmental Management\u003c/em\u003e, 335, 117525. https://doi.org/10.1016/j.jenvman.2023.117525.\u003c/li\u003e\n\u003cli\u003eLong, H., Feng, G. F., Gong, Q., Chang, C. P., (2023). ESG performance and green innovation: An investigation based on quantile regression. \u003cem\u003eBusiness Strategy and the Environment\u003c/em\u003e, 32(7), 5102-5118. https://doi.org/10.1002/bse.3410.\u003c/li\u003e\n\u003cli\u003eLong, X., Ji, X., (2019). Economic Growth Quality, Environmental Sustainability, and Social Welfare in China - Provincial Assessment Based on Genuine Progress Indicator (GPI). \u003cem\u003eEcological Economics\u003c/em\u003e, 159, 157-176. https://doi.org/10.1016/j.ecolecon.2019.01.002.\u003c/li\u003e\n\u003cli\u003eLucas, R. E., (2015). Human Capital and Growth. \u003cem\u003eAmerican Economic Review\u003c/em\u003e, 105(5), 85-88.20. https://doi.org//10.1257/aer.p20151065.\u003c/li\u003e\n\u003cli\u003eMcGuirk, H., Lenihan, H., Hart, M., (2015). Measuring the impact of innovative human capital on small firms\u0026apos; propensity to innovate. \u003cem\u003eResearch Policy\u003c/em\u003e, 44(4), 965-976. https://doi.org/10.1016/j.respol.2014.11.008.\u003c/li\u003e\n\u003cli\u003eMoretti, E., (2021). The Effect of High-Tech Clusters on the Productivity of Top Inventors. \u003cem\u003eAmerican Economic Review\u003c/em\u003e, 111(10), 3328-3375. https://doi.org/10.1257/aer.20191277.\u003c/li\u003e\n\u003cli\u003eOfori, I. K., Gbolonyo, E. Y., Ojong, N., (2023). Foreign direct investment and inclusive green growth in Africa: Energy efficiency contingencies and thresholds. \u003cem\u003eEnergy Economics\u003c/em\u003e, 117, 106414. https://doi.org/10.1016/j.eneco.2022.106414.\u003c/li\u003e\n\u003cli\u003eOuyang, X., Li, Q., Du, K., (2020). How does environmental regulation promote technological innovations in the industrial sector? Evidence from Chinese provincial panel data. \u003cem\u003eEnergy Policy\u003c/em\u003e, 139, 111310. https://doi.org/10.1016/j.enpol.2020.111310.\u003c/li\u003e\n\u003cli\u003ePeri, G., Shih, K., Sparber, C., (2015). STEM workers, H-1B visas, and productivity in US cities. \u003cem\u003eJournal of Labor Economics\u003c/em\u003e, 33(S1), S225-S255. https://doi.org/10.1086/679061.\u003c/li\u003e\n\u003cli\u003eRen, M., Zhou, T., Wang, C., (2024). New energy vehicle innovation network, innovation resources agglomeration externalities and energy efficiency: Navigating industry chain innovation. \u003cem\u003eTechnological Forecasting and Social Change\u003c/em\u003e, 200, 123114. https://doi.org/10.1016/j.techfore.2023.123114.\u003c/li\u003e\n\u003cli\u003eRen, Y., He, X., Jiang, Q., Zhang, F., Zhang, B., (2024). Advancing high-quality development in China: Unraveling the dynamics, disparities, and determinants of inclusive green growth at the prefecture level. \u003cem\u003eEcological Indicators\u003c/em\u003e, 169, 112898. https://doi.org/10.1016/j.ecolind.2024.112898.\u003c/li\u003e\n\u003cli\u003eRong, C., Cristia, J. F. E., Marian, M. L., Alzuman, A., Comite, U., (2024). Does green entrepreneurial orientation impact entrepreneurial success through green innovation capability in the manufacturing and services sector of emerging economies? \u003cem\u003eInternational Entrepreneurship and Management Journal\u003c/em\u003e, 21(1), 51. https://doi.org/10.1007/s11365-024-01059-0.\u003c/li\u003e\n\u003cli\u003eSequeira, S., Nunn, N., Qian, N., (2020). Immigrants and the Making of America. \u003cem\u003eThe Review of Economic Studies\u003c/em\u003e, 87(1), 382-419. https://doi.org/10.1093/restud/rdz003.\u003c/li\u003e\n\u003cli\u003eShan, S., Yılmaz, G. S., Waqas, K. H., Gheorghita, D., (2021). Role of green technology innovation and renewable energy in carbon neutrality: A sustainable investigation from Turkey. \u003cem\u003eJournal of Environmental Management\u003c/em\u003e, 294, 113004. https://doi.org/10.1016/j.jenvman.2021.113004.\u003c/li\u003e\n\u003cli\u003eShan, Y., Fang, S., Cai, B., Zhou, Y., Li, D., Feng, K., Hubacek, K., (2021). Chinese cities exhibit varying degrees of decoupling of economic growth and CO2 emissions between 2005 and 2015. \u003cem\u003eOne Earth\u003c/em\u003e, 4(1), 124-134. https://doi.org/10.1016/j.oneear.2020.12.004.\u003c/li\u003e\n\u003cli\u003eShehzad, M. U., Zhang, J., Latif, K. F., Jamil, K., Waseel, A. H., (2023). Do green entrepreneurial orientation and green knowledge management matter in the pursuit of ambidextrous green innovation: A moderated mediation model. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e, 388, 135971. https://doi.org/10.1016/j.jclepro.2023.135971.\u003c/li\u003e\n\u003cli\u003eSong, Y., Huang, H., Li, Y., Xia, J., (2024). Towards inclusive green growth in China: Synergistic roles and mechanisms of new infrastructure construction. \u003cem\u003eJournal of Environmental Management\u003c/em\u003e, 353, 120281. https://doi.org/10.1016/j.jenvman.2024.120281.\u003c/li\u003e\n\u003cli\u003eSquicciarini, M. P., Voigtl\u0026auml;nder, N., (2015). Human Capital and Industrialization: Evidence from the Age of Enlightenment. \u003cem\u003eThe Quarterly Journal of Economics\u003c/em\u003e, 130(4), 1825-1883. https://doi.org/10.1093/qje/qjv025.\u003c/li\u003e\n\u003cli\u003eSun, Y., Ding, W., Yang, Z., Yang, G., Du, J., (2020). Measuring China\u0026apos;s regional inclusive green growth. \u003cem\u003eScience of the Total Environment\u003c/em\u003e, 713, 136367. https://doi.org/10.1016/j.scitotenv.2019.136367.\u003c/li\u003e\n\u003cli\u003eTang, K., Wang, Y. Y., Wang, H. J., (2024). The impact of innovation capability on green development in China\u0026apos;s urban agglomerations. \u003cem\u003eTechnological Forecasting and Social Change\u003c/em\u003e, 200, 123128. https://doi.org/10.1016/j.techfore.2023.123128.\u003c/li\u003e\n\u003cli\u003eTeslenko, V., Melnikov, R., Bazin, D., (2021). Evaluation of the impact of human capital on innovation activity in Russian regions. \u003cem\u003eRegional Studies, Regional Science\u003c/em\u003e, 8(1), 109-126. https://doi.org/10.1080/21681376.2021.1900744.\u003c/li\u003e\n\u003cli\u003eTombe, T., Zhu, X., (2019). Trade, Migration, and Productivity: A Quantitative Analysis of China. \u003cem\u003eAmerican Economic Review\u003c/em\u003e, 109(5), 1843-1872. https://doi.org/10.1257/aer.20150811.\u003c/li\u003e\n\u003cli\u003eWang, D., Hou, Y., Li, X., Xu, Y., (2022). Developing a functional index to dynamically examine the spatio-temporal disparities of China\u0026apos;s inclusive green growth. \u003cem\u003eEcological Indicators\u003c/em\u003e, 139, 108861. https://doi.org/10.1016/j.ecolind.2022.108861.\u003c/li\u003e\n\u003cli\u003eWang, H., Deng, W., Zhang, Z., Li, M., (2024). Does government\u0026apos;s environmental attention improve urban energy efficiency? \u003cem\u003eInternational Review of Financial Analysis\u003c/em\u003e, 91, 103046. https://doi.org/10.1016/j.irfa.2023.103046.\u003c/li\u003e\n\u003cli\u003eWang, M., Wu, Y., Zhang, X., Lei, L., (2024). How does industrial agglomeration affect internal structures of green economy in China? An analysis based on a three-hierarchy meta-frontier DEA and systematic GMM approach. \u003cem\u003eTechnological Forecasting and Social Change\u003c/em\u003e, 206, 123560. https://doi.org/10.1016/j.techfore.2024.123560.\u003c/li\u003e\n\u003cli\u003eWang, Z., Yan, E., Zhang, Z., Li, C., Gong, Q., (2025). The impact and mechanism of government environmental policy innovation on industrial agglomeration: An empirical study based on a Quasi-natural experiment of the river chief system policy in the Yangtze River economic belt. \u003cem\u003eEconomic Analysis and Policy\u003c/em\u003e, 86, 893-911. https://doi.org/10.1016/j.eap.2025.04.014.\u003c/li\u003e\n\u003cli\u003eWorld Bank, (2012). Inclusive Green Growth: The Pathway to Sustainable Development. \u003c/li\u003e\n\u003cli\u003eWu, J., Segerson, K., Langpap, C., (2025). What drives the long-term relationship between economic development and environmental quality? The role of spatial dispersion vs. agglomeration. \u003cem\u003eJournal of Environmental Economics and Management\u003c/em\u003e, 131, 103126. https://doi.org/10.1016/j.jeem.2025.103126.\u003c/li\u003e\n\u003cli\u003eXu, A., Dai, Y., Hu, Z., Qiu, K., (2025). Can green finance policy promote inclusive green growth? Based on the quasi-natural experiment of China\u0026apos;s green finance reform and innovation pilot zone. \u003cem\u003eInternational Review of Economics \u0026amp; Finance\u003c/em\u003e, 100, 104090. https://doi.org/10.1016/j.iref.2025.104090.\u003c/li\u003e\n\u003cli\u003eXu, J. H., Huang, Y. C., (2021). Can the technological talents agglomeration promote regional green development? \u003cem\u003eModern Economic Research\u003c/em\u003e, (12), 116-125. (In Chinese). https://doi.org/10.13891/j.cnki.mer.2021.12.013. \u003c/li\u003e\n\u003cli\u003eYang, J., Wang, Y., Tang, C., Zhang, Z., (2024). Can digitalization reduce industrial pollution? Roles of environmental investment and green innovation. \u003cem\u003eEnvironmental Research\u003c/em\u003e, 240, 117442. https://doi.org/10.1016/j.envres.2023.117442.\u003c/li\u003e\n\u003cli\u003eYang, Z., Pan, Y., (2020). Human capital, housing prices, and regional economic development: Will \u0026quot;vying for talent\u0026quot; through policy succeed? \u003cem\u003eCities\u003c/em\u003e, 98, 102577. https://doi.org/10.1016/j.cities.2019.102577.\u003c/li\u003e\n\u003cli\u003eZhang, H., Guo, R., (2025). How does the spatial agglomeration of human capital affect strategic innovation in China? \u003cem\u003eJournal of Asian Economics\u003c/em\u003e, 96, 101868. https://doi.org/10.1016/j.asieco.2024.101868.\u003c/li\u003e\n\u003cli\u003eZhang, J., Wang, R., Lu, C., (2018). A quantitative analysis of Hukou reform in Chinese cities: 2000\u0026ndash;2016. \u003cem\u003eGrowth and Change\u003c/em\u003e, 50(1), 201-221. https://doi.org/10.1111/grow.12284.\u003c/li\u003e\n\u003cli\u003eZhang, R., Ying, W., Wu, K., Sun, H., (2024). The impact of innovative human capital agglomeration on urban green development efficiency: Based on panel data of 278 Cities in China. \u003cem\u003eSustainable Cities and Society\u003c/em\u003e, 111, 105566. https://doi.org/10.1016/j.scs.2024.105566.\u003c/li\u003e\n\u003cli\u003eZheng, L., Ulrich, K., Sendra-Garc\u0026iacute;a, J., (2021). Qualitative comparative analysis: Configurational paths to innovation performance. \u003cem\u003eJournal of Business Research\u003c/em\u003e, 128, 83-93. https://doi.org/10.1016/j.jbusres.2021.01.044.\u003c/li\u003e\n\u003cli\u003eZhou, Y., Guo, Y., Liu, Y., (2018). High-level talent flow and its influence on regional unbalanced development in China. \u003cem\u003eApplied Geography\u003c/em\u003e, 91, 89-98. https://doi.org/10.1016/j.apgeog.2017.12.023.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Note: Since the statistical number of personnel in these two types of industries in the statistical yearbook is only published until 2019, the data span is set to 2003\u0026ndash;2019 in this paper to ensure the consistency of the panel data.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Information transmission, computer services and software industry; Scientific research, technical services and geological exploration industry; Financial industry; Leasing and business services industry; Education industry; Culture, sports and entertainment industry.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e East: Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan. Middle: Shanxi, Jilin, Heilongjiang, Henan, Hubei, Hunan, Anhui, Jiangxi. West: Inner Mongolia, Chongqing, Sichuan, Guangxi, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang, Tibet.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"Urban Inclusive Green Growth, Talent Agglomeration, High-end Human Capital, Sustainable Development","lastPublishedDoi":"10.21203/rs.3.rs-8023565/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8023565/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAmid slowing global economic growth, the \u0026ldquo;demographic dividend\u0026rdquo; has diminishing returns, making it essential to leverage the \u0026ldquo;talent dividend\u0026rdquo; to achieve urban inclusive green growth (\u003cem\u003eUIGG\u003c/em\u003e). Using panel data from 270 prefecture-level cities in China (2003\u0026ndash;2019), this study examines the impact and mechanisms of talent agglomeration (\u003cem\u003eTalagg\u003c/em\u003e) on \u003cem\u003eUIGG\u003c/em\u003e. The findings show that: (1) \u003cem\u003eTalagg\u003c/em\u003e significantly and directly promotes \u003cem\u003eUIGG\u003c/em\u003e. This finding still holds after a number of robustness tests. (2) Heterogeneity studies show that the effect varies obviously by geographic location, city type, and development level. (3) Mechanism analyses identify two key pathways through which \u003cem\u003eTalagg\u003c/em\u003e improves \u003cem\u003eUIGG\u003c/em\u003e: green technology innovation and entrepreneurial activity. (4) Further analysis highlights synergistic effects between \u003cem\u003eTalagg\u003c/em\u003e and industrial agglomeration, as well as labor marketization, which plays a crucial moderating role in enhancing \u003cem\u003eUIGG\u003c/em\u003e. This study provides a new insight for tackling environmental challenges and social inequality in cities, offering policy implications for China and other emerging economies seeking to transition from traditional development models.\u003c/p\u003e","manuscriptTitle":"Unlocking Pathways for Urban Inclusive Green Growth: Talent Agglomeration —A New Perspective on High-End Human Capital Agglomeration","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-17 12:15:47","doi":"10.21203/rs.3.rs-8023565/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":"6e002dc9-254f-46c4-b288-6a181f009ccc","owner":[],"postedDate":"November 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-03T09:09:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-17 12:15:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8023565","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8023565","identity":"rs-8023565","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}