Engineering Mayors and Urban Carbon Governance: Evidence from China

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This paper investigates whether mayors’ professional backgrounds are associated with better performance in achieving emission reduction outcomes. Using panel data from prefecture-level cities between 2005 and 2016, we find that mayors with engineering backgrounds significantly reduce carbon emission intensity. This effect is pronounced in larger, more industrialized cities. Mechanism analysis reveals two key channels: engineering-trained mayors are more likely to implement high-intensity low-carbon policies, especially with market-based policy instruments, and to promote local green technological innovation. low-carbon carbon emission intensity engineering mayor 1. Introduction The global imperative to address climate change has placed unprecedented demands on national and local governments to reduce greenhouse gas emissions and advance low-carbon economic development. As the world’s largest carbon emitter, China faces mounting pressure to fulfill its dual carbon targets—peaking carbon emissions before 2030 and achieving carbon neutrality by 2060. While these national commitments are ambitious, their realization depends heavily on the governance capacity of subnational governments. In China’s decentralized administrative structure, local officials—particularly mayors—play a pivotal role in formulating and implementing environmental policies. Understanding how the characteristics of these political leaders influence low-carbon outcomes is therefore critical to evaluating the country’s decarbonization trajectory. Existing research has demonstrated that, with the increasing emphasis on carbon emission accountability, local leaders are playing a vital role in addressing climate change and driving innovation in carbon reduction policies (Pan et al.,2022). Lu et al. ( 2020 ) find that mayors who are younger, more highly educated, and environmentally conscious are more effective in improving urban environmental governance. Given that environmental governance is closely tied to energy efficiency improvements and the development and diffusion of clean technologies, the scientific rigor and practical feasibility of related policies are critical determinants of actual emission reduction outcomes. In China’s local bureaucratic system, a considerable proportion of officials possess academic backgrounds in engineering. These individuals typically exhibit stronger technical competence and systems thinking, which may, in theory, enhance their capacity to identify and implement complex decarbonization policies. However, to date, no empirical study has systematically examined the relationship between officials’ professional expertise and carbon reduction performance. This paper seeks to address this gap by focusing on mayors with academic backgrounds in engineering and evaluating their impact on urban carbon emission intensity. By introducing the perspective of bureaucratic professional expertise, this study aims to deepen the understanding of how local governance capacity influences low-carbon transitions and to provide empirical evidence on the determinants of environmental policy effectiveness. This paper employs a two-way fixed effects model and panel data from Chinese prefecture-level cities covering the period 2005–2016 to examine the impact of mayors' technical backgrounds on carbon outcomes. The results indicate that mayors with academic training in engineering significantly reduce urban carbon emission intensity. This effect is robust across alternative outcome measures, including CO₂ emissions per unit of GDP and per capita emissions. The reduction is particularly pronounced in cities with larger populations and in those with a higher proportion of industrial activity in their economic structure. Mechanism analyses further reveal that engineering-trained mayors tend to align local low-carbon policies more closely with national emission reduction goals. In terms of policy instruments, they are more inclined to adopt market-oriented approaches rather than relying solely on command-and-control mechanisms. Additionally, the study finds that mayors with technical expertise place greater emphasis on the development and application of green technologies, suggesting that the promotion of green innovation constitutes an important channel through which carbon emissions are reduced. The remainder of this paper is structured as follows. Section 2 reviews the relevant literature and outlines the theoretical hypotheses. Section 3 describes the data sources, variable definitions, and empirical model. Section 4 presents the main regression results, along with robustness checks and heterogeneity analysis. Section 5 conducts the mechanism analysis. Section 6 concludes and discusses policy implications. 2. Literature Review and Theoretical Hypotheses 2.1 Literature Review A growing body of literature underscores the critical role that the personal characteristics of local government officials play in shaping public policy and influencing economic outcomes. Attributes such as gender (Brollo & Troiano, 2016; Hessami & da Fonseca, 2020 ), hometown affiliation (Do & Lee, 2017 ; Chu et al., 2021 ; Ning & Zhang, 2022 ), educational attainment (Dreher et al., 2009 ; Besley et al., 2011; Sørensen, 2023 ), and career background (Cheng et al., 2024 ) have been shown to significantly affect officials’ policy preferences, governance capacity, and decision-making behavior. For example, Besley et al. (2011) find that better-educated leaders are associated with stronger local economic performance, while Cheng et al. ( 2024 ) show that officials with military backgrounds exhibit stronger risk aversion and are more effective at reducing excessive fiscal expenditure and deficits. In the context of China, local leaders wield substantial authority over economic activities and exercise significant discretion in policymaking. Under the incentive structure of the promotion tournament, empirical studies have demonstrated that local officials play a pivotal role in driving regional economic growth (Li & Zhou, 2005; Jia et al., 2015 ; Yao & Zhang, 2015 ). As environmental governance and pollution control have become increasingly important in official performance evaluations, scholarly attention has begun to shift toward understanding how local officials perform in environmental domains and what factors influence such performance (Wu & Cao, 2021; Yin & Wu, 2022; Jiang & Tang, 2023 ). Responding to national mandates for green transformation, local governments are now tasked with restructuring energy use, addressing climate change, and advancing low-carbon economic development. A number of studies have investigated the role of central government policy initiatives—such as the low-carbon city pilot program—in reducing urban emissions (Zheng et al,2014; Tie et al., 2020; Pan et al., 2022;Yu & Zhang, 2021 ༛Zeng et al.,2023). Pan et al. (2022) find that the behavior and engagement of local leaders significantly affect the success of subnational carbon mitigation policies. Similarly, Lu et al. ( 2020 ) provide evidence that younger, well-educated and environmentally conscious mayors tend to deliver better environmental efficiency outcomes. Given that energy transition, emission control, and the adoption of clean technologies require highly technical policy design and execution, environmental governance places elevated demands on administrative professionalism. However, despite extensive literature on officials' personal traits, little is known about how professional expertise—especially technical training—influences environmental policy performance. This study aims to address this gap by examining whether officials with technical backgrounds are more effective in facilitating carbon emission reductions. Specifically, the empirical analysis focuses on mayors with academic training in engineering, thereby contributing to a deeper understanding of bureaucratic capacity in the context of low-carbon governance. 2.2 Theoretical Hypotheses This paper introduces a human capital-based perspective to examine how technocratic leader affect urban low-carbon governance. We propose that mayors with engineering backgrounds are more likely to reduce local carbon emission intensity due to two key mechanisms: policy capacity, innovation orientation. First, mayors with technical training possess stronger policy capacity in technical domains. Technical education provides analytical tools and problem-solving skills that mayors can apply to formulate more effective low-carbon strategies. They may be better equipped to assess environmental trade-offs, interpret technical reports, and design sector-specific interventions. Second, these mayors may exhibit a stronger innovation orientation, especially in relation to green technology adoption. With a deeper appreciation of technological pathways, they are more likely to support R&D investment, foster partnerships with research institutions, and design incentives for clean innovation. Their leadership may create a more favorable environment for firms to adopt cleaner production techniques. Third, the governance style of technocratic leaders tends to favor structured planning, target-setting, and the use of performance indicators. These traits align closely with China's target-based accountability system for emissions control. Engineering-trained mayors may also prefer market-based policy tools—such as carbon pricing or green finance—that align with long-term sustainability objectives. Based on these theoretical foundations, we propose the following testable hypotheses: H1: Cities governed by mayors with engineering backgrounds exhibit significantly lower carbon emission intensity than those governed by non-technical mayors. H2: The carbon-reducing effect of engineering-trained mayors is mediated through (a) stronger and more detailed low-carbon policy instruments, and (b) greater levels of green technological innovation. This framework links individual-level attributes of political elites to broader developmental outcomes, contributing to both the literature on bureaucratic influence and the emerging field of subnational climate governance. 3. Data and Empirical Strategy 3.1 Data source Local Officials Data . This study uses data on local officials from the CCER Officials Dataset , which includes biographical information on over 4,000 government officials ever serving at the central, provincial, and prefectural levels in China. The dataset provides detailed records such as birthplace, year of birth, educational attainment, career experience, and office held. For data consistency and availability, we focus on the period from 2005 to 2016 and extract individual-level information on mayors of prefecture-level cities. Based on their major (including both undergraduate and part-time education), we define a mayor as having an engineering background if their degree was in engineering. During 2005–2016, a total of 953 individuals held mayoral positions, of whom 200 had engineering backgrounds, representing approximately 21% of the sample. We also collected other relevant mayoral attributes, including whether the mayor held a college degree or above, their year of birth, whether their hometown province is the province of current appointment, and whether they had previous experience in central government agencies. Additional control variables include the number of years the mayor had served in the current post and whether a mayoral turnover occurred during the year. If a turnover occurred, the new mayor's term is counted starting from the following year. Carbon Emissions Data. Carbon emissions data are obtained from the Center for Global Environmental Research (CGER) , which provides annual CO₂ emissions at high spatial resolution based on fossil fuel combustion, cement production, and natural gas usage. These emissions are originally distributed in raster format with a spatial resolution of 1 km × 1 km. We extract and retain grid data covering the territory of China and aggregate them to the prefecture-level. The sample period covers the years 2005 to 2016. The primary dependent variable is carbon intensity, measured as CO₂ emissions (in tons) per billion RMB of GDP. As a robustness check, we also use per capita CO₂ emissions (tons per 1,000 residents) as an alternative dependent variable. To address skewness and improve interpretability, all emissions-related variables are transformed using natural logarithms. Green Innovation Data . To measure the level of low-carbon technological innovation, we use the number of green invention patent applications at the city level as a proxy. Patent data are obtained from the incoPat patent database. Given that invention patents typically involve greater technological complexity and are more indicative of innovative activity, this study focuses specifically on invention patent applications. Following the "IPC Green Inventory" introduced by the World Intellectual Property Organization (WIPO) in 2010, we define green patents as those whose IPC classification falls within the green technology list. The number of green invention patent applications is aggregated annually at the prefecture level. Table 1 Variable Definitions and Descriptive Statistics Variable Definition Obs Mean Std. Dev. Min Max Mayoral characteristic variables Mayor_Engineer Whether the mayor majored in engineering 3417 0.214 0.410 0 1 Mayor_Col Whether the mayor holds a college degree or above 3417 0.669 0.471 0 1 Mayor_Ex Whether the mayor was reassigned to another position during the year 3417 0.262 0.440 0 1 Mayor_Pro Whether the mayor was born in the province of current appointment 3417 0.594 0.491 0 1 Mayor_Dur Number of years in current position 3371 3.440 1.489 1 12 Mayor_Birth Year of birth 3371 1959.681 4.845 1945 1975 Mayor_Exp_uform Whether the mayor has prior work experience in the central government 3347 0.067 0.250 0 1 Carbon intensity variables ln_CO2_GDP Logarithm of CO₂ emissions per unit of GDP 3415 3.974 0.681 1.481 6.002 ln_per_CO2 Logarithm of per capita CO₂ 3416 9.550 0.832 6.443 12.109 City-level control variables ln_GDP Logarithm of GDP (in billions) 3415 16.006 0.985 12.997 19.148 ln_Pop Logarithm of population (in thousands) 3406 8.125 0.675 5.149 9.675 Ind2 Share of secondary industry in GDP 3299 0.364 0.160 0.005 0.984 Ind3 Share of tertiary industry in GDP 3299 0.425 0.158 0.014 0.974 Pilot City Whether the city is a low-carbon pilot city 3417 0.173 0.378 0 1 City-level green technology innovation variables ln_per_gre Logarithm of per capita green patent applications + 1 3406 0.021 0.044 0 0.482 City-level low-carbon policy intensity variables pi_city Policy intensity of low-carbon 2848 2.742 4.221 0 46.750 pi_city_cc Policy instrument intensity of command-and-control 2848 1.278 2.679 0 34.250 pi_city_mb Policy instrument intensity of market-based 2848 1.124 2.323 0 29.500 pi_city_cm Policy instrument intensity of composite tools 2848 0.340 1.168 0 18 Low-Carbon Policy Intensity Data . Data on low-carbon policy intensity are obtained from the China’s Low-carbon Policy Intensity Dataset . This dataset employs machine learning techniques to quantify the strength of manufacturing-related low-carbon policies issued by different levels of government. Given that the dataset begins in 2007, the sample period used in this study spans from 2007 to 2016. Specifically, we use the policy intensity index of low-carbon policies issued by prefecture-level governments, along with three types of policy instruments: command-and-control, market-based, and composite tools. By merging these datasets, we construct a panel of Chinese prefecture-level cities from 2005 to 2016 (excluding centrally-administered municipalities). In addition, we compile a set of economic control variables at the city level, including population size, GDP, the share of the secondary and tertiary sectors in GDP, and whether the city was officially designated as a national low-carbon pilot city. Variable definitions and descriptive statistics are presented in Table 1 . 3.2 Empirical Strategy To examine whether mayors with engineering backgrounds contribute to the development of low-carbon economies at the prefecture level, we employ a two-way fixed effects panel regression model. The baseline model is specified as follows: $$\:{CO2\_Inten}_{it}=\alpha\:+\beta\:{Mayor\_Engineer}_{it}+\gamma\:{X}_{it}+{\mu\:}_{i}+{\lambda\:}_{t}+{\epsilon\:}_{it\:\:}\:\:\:\:\:\:\:\:\:\:\:\:\:$$ 1 \(\:{CO2\_Inten}_{it}\) represents the carbon emission intensity of city \(\:i\) in year \(\:t\) , measured as the natural logarithm of CO₂ emissions per unit of GDP. As a robustness check, we also use the logarithm of per capita CO₂ emissions. \(\:{Mayor\_Engineer}_{it}\) is a dummy indicator equal to 1 if the mayor of city \(\:i\) in year has an engineering educational background, and 0 otherwise. \(\:{X}_{it}\) denotes a vector of control variables that includes both mayoral characteristics and city-level economic indicators. The mayoral characteristics include whether the mayor holds a college or higher degree, year of birth, whether the mayor’s birthplace is within the province of current appointment, prior work experience in central government, the number of years in the current position, and whether the mayor was reassigned to another position during the year. In addition, we control for key city-level economic variables, including the logarithm of the resident population, the logarithm of GDP, and the city’s industrial structure, specifically the share of the secondary and tertiary sectors in GDP. To account for the potential impact of national low-carbon pilot programs, we also control for whether the city (or the province it belongs to) was designated as a low-carbon pilot area in year \(\:t\) (coded as 1 if yes, 0 otherwise). \(\:{\mu\:}_{i}\:\) denote city fixed effects, which control for unobserved, time-invariant characteristics at the regional level. \(\:{\lambda\:}_{t}\) represents year fixed effects, capturing nationwide temporal shocks and trends. \(\:{\epsilon\:}_{it\:\:}\) is the error term, and robust standard errors are used throughout. 4. Empirical Results 4.1 Baseline Regression Table 2 Baseline Regression: Engineering Background of Mayors and Carbon Emission Intensity ln_CO2_GDP (1) (2) (3) Mayor_Engineer -0.014** -0.011*** -0.012*** (0.007) (0.004) (0.004) ln_Pop -0.038 -0.037 (0.029) (0.029) ln_GDP -0.852*** -0.845*** (0.014) (0.014) Ind2 -0.052*** -0.045*** (0.015) (0.015) Ind3 0.007 0.009 (0.015) (0.015) Pilot City -0.007 -0.008 (0.006) (0.006) Mayor_Col 0.007* (0.004) Mayor_Ex -0.000 (0.003) Mayor_Pro -0.001 (0.004) Mayor_Dur -0.000 (0.001) Mayor_Birth -0.001*** (0.000) Mayor_Exp_uform -0.020*** (0.006) Cons -0.626*** 13.360*** 15.561*** (0.002) (0.304) (0.921) City FE YES YES YES Year FE YES YES YES N 3415 3298 3229 Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. Table 2 presents the regression estimates examining the relationship between mayors’ engineering backgrounds and urban carbon emission intensity, where the dependent variable is the logarithm of CO₂ emissions per unit of GDP. In Column (1), without any control variables, the estimated coefficient on the engineering background indicator is − 0.014 and statistically significant at the 5% level. This suggests that cities governed by engineering-background mayors tend to have significantly lower carbon intensity. In Column (2), after adding city-level control variables, the coefficient slightly decreases in magnitude to − 0.011 and becomes significant at the 1% level, indicating a more robust relationship. Notably, among the controls, we find that a higher share of secondary industry is significantly associated with a greater reduction in carbon intensity, which may reflect improved energy efficiency or structural upgrading within industrial cities. Column (3) introduces additional controls for mayor-level characteristics. The coefficient on engineering background remains stable at − 0.012 and significant at the 1% level. This specification serves as our preferred baseline model. The result implies that, on average, cities led by mayors with engineering backgrounds experience a 1.2% reduction in carbon intensity relative to those without such backgrounds. In addition, we find that mayoral age and central government experience are significantly associated with environmental performance. Specifically, younger mayors are associated with greater reductions in carbon intensity, and those with prior experience in central government posts appear to perform better in advancing carbon mitigation at the local level. 4.2 Robustness Checks To ensure the robustness of the baseline results, we conduct a series of tests by redefining both the dependent and explanatory variables, using lagged carbon outcomes, and excluding specific subsamples such as provincial capital cities. First, we replace the dependent variable with an alternative measure of carbon emissions—logarithm of per capita CO₂ emissions. Column (1) of Table 3 reports the results, with the estimated coefficient being − 0.012 and statistically significant at the 1% level. This finding once again confirms that mayors with engineering backgrounds significantly lower the urban carbon intensity, thereby supporting the view that such backgrounds facilitate the development of low-carbon economies. Second, to address potential concerns about reverse causality—namely, that cities with stronger low-carbon performance may be more likely to appoint technically qualified mayors—we re-estimate the model using next year’s carbon emission intensity as the dependent variable. The results, reported in Column (2), remain significantly negative, further suggesting that the observed effect is not driven by endogenous mayoral selection. Moreover, the time lag between policy formulation and observable outcomes is also reasonably accounted for. Table 3 Robustness Check (1) (2) (3) (4) ln_per_CO2 F. ln_CO2_Inten Restricted to full-time education Excluding provincial capital cities Mayor_Engineer -0.012*** -0.017*** -0.008** -0.013** (0.004) (0.006) (0.004) (0.005) Controls YES YES YES YES City FE YES YES YES YES Year FE YES YES YES YES N 3229 3229 3229 2941 Control variables are consistent with the baseline regression. Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. Considering that there may be differences in the level of specialization between an official’s initial (full-time) degree and subsequent on-job education, we conduct a robustness check by redefining the engineering background variable. Specifically, we classify mayors as having a technical background only if their first degree was in engineering and their education level was college or above. Columns (3) of Table 3 report the results based on this stricter definition. The estimated coefficients remain negative and statistically significant at the 5% level, confirming the robustness of our main findings. Finally, to mitigate the influence of large and administratively privileged cities, we exclude all provincial capital cities and re-run the regressions. The results, presented in Column (4), remain consistently negative and significant at the 5% level, demonstrating that the main findings are not driven by large-city outliers. 4.3 Heterogeneity Analysis We further investigate whether the effect of mayors’ technical backgrounds on carbon emission performance varies across different types of cities. Specifically, we examine heterogeneity by city size and industrial structure. First, we assess whether the effect differs between larger and smaller cities. Based on population size in the baseline year (2005), we classify cities with population above the median as large cities and those below the median as small cities. Columns (1) and (2) of Table 4 report the subsample regression results. While the estimated coefficients are negative in both subsamples, the effect is statistically significant at the 1% level only for large cities. This suggests that the policy influence of engineering-background mayors in reducing carbon intensity is more pronounced in larger urban areas. Table 4 Heterogeneity Analysis (1) (2) (3) (4) Large cities Small cities Industrial cities Non-industrial cities Mayor_Engineer -0.017*** -0.004 -0.019*** -0.002 (0.006) (0.007) (0.005) (0.007) Controls YES YES YES YES City FE YES YES YES YES Year FE YES YES YES YES N 1783 1446 1688 1541 Control variables are consistent with the baseline regression. Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. Next, we explore heterogeneity based on industrial composition. Using the 2005 share of secondary industry in local GDP as a proxy, we define cities above the median as industrial-oriented cities and those below the median as non-industrial-oriented. Columns (3) and (4) present the corresponding regression results. Again, both coefficients are negative, but the effect is statistically significant only for industrial-oriented cities at the 1% level. This implies that mayors with engineering expertise are particularly effective in curbing carbon emissions in cities with stronger industrial dependence. Taken together, these results suggest that the baseline findings are largely driven by large cities and cities with a stronger industrial base. In such contexts, engineering-background mayors are better positioned to accelerate structural transformation and reduce carbon emission, thereby promoting greener and more sustainable urban development. 5. Mechanism Analysis How do mayors with engineering backgrounds contribute to reductions in urban carbon emission intensity? One plausible mechanism is through their influence on local policy formulation. Since local governments in China play a central role in both economic development and environmental governance, a mayor’s policy orientation is a critical channel through which their background may shape environmental outcomes. Mayors with technical expertise may place greater emphasis on low-carbon development. Due to their stronger professional knowledge base, it could lead them not only to introduce a greater number of relevant policies but also to formulate more detailed, actionable, and implementable measures. To test this hypothesis, we examine whether engineering-background mayors are more likely to issue stronger low-carbon policies. We use the city-level low-carbon policy intensity index from the China’s Low-carbon Policy Intensity Dataset as the dependent variable. Column (1) of Table 5 presents the regression result, showing a positive and statistically significant coefficient at the 10% level. This suggests that mayors with engineering backgrounds tend to issue more comprehensive and technically detailed low-carbon policies compared to their peers from non-technical backgrounds. Table 5 Mechanism Analysis — Mayor’s Engineering Background and Low-Carbon Policy Intensity (1) (2) (3) (4) Pi_city PI_city_cc PI_city_mb PI_city_cm Mayor_Engineer 0.413* 0.093 0.258** 0.051 (0.240) (0.162) (0.131) (0.072) Controls YES YES YES YES City FE YES YES YES YES Year FE YES YES YES YES N 2682 2682 2682 2682 The sample covers the years from 2007 to 2016. Control variables include those from the baseline regression, with the addition of provincial-level low-carbon policy intensity. Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. To explore further, we examine whether such mayors differ in the types of policy tools they prefer. Different types of policy instruments—such as command-and-control regulations, market-based mechanisms, and hybrid approaches—have varying implications for enterprise behavior and environmental outcomes. The dataset also provides disaggregated indices for these three types of instruments. We run separate regressions using the sub-indices for command-and-control (PI_city_cc), market-based (PI_city_mb), and hybrid tools (PI_city_cm) as dependent variables. The results, shown in Columns (2)–(4), indicate positive coefficients across all policy types, with the largest and most statistically significant effect observed for market-based instruments. The coefficient for the market-based policy intensity index is 0.258 and significant at the 5% level, suggesting that mayors with technical backgrounds are more inclined to adopt market-oriented policy tools—such as fiscal subsidies, tradable certificates, and investment incentives. These tools are flexible and incentive-compatible, allowing local governments to guide firms toward emission reductions more effectively. Moreover, such instruments tend to be better suited to local economic conditions and are more sustainable in the long run. Moreover, mayors with technical expertise may place greater emphasis on reducing carbon emissions through technological means—for example, by encouraging firms to adopt cleaner production processes? To test this hypothesis, we further investigate whether engineering-background mayors are more likely to promote green technological innovation at the city level. We use the number of green invention patent applications per 1,000 residents (log-transformed after adding one) as the dependent variable. To mitigate the confounding effects of overall innovation activity across cities, we control for the total number of invention patent applications per 1,000 residents (also log-transformed after adding one). Table 6 Mechanism Analysis — Mayor’s Engineering Background and Green Technological Innovation (1) (2) ln_per_greenpat F.ln_per_greenpat Mayor_Engineer 0.001* 0.003** (0.001) (0.001) Controls YES YES City FE YES YES Year FE YES YES N 3229 3221 Control variables include those from the baseline regression, with the additional control for the logarithm of city-level per capita invention patent applications. Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. Column (1) of Table 6 reports the results. The estimated coefficient on the engineering background variable is 0.10 and statistically significant at the 10% level, suggesting that mayors with engineering training are associated with a higher number of green patent applications—an indication of enhanced development and diffusion of clean technologies. Given the inherent time lag between R&D efforts and patent filings, we further use the number of green patent applications in the following year as the dependent variable. The results, shown in Column (3), reveal an even larger and more significant coefficient. We place greater confidence in this result, as it captures the delayed effect of policy-driven innovation. Overall, the findings suggest that engineering-background mayors, drawing on their technical expertise, are more likely to reduce carbon intensity through fostering green technological innovation. 6. Conclusion and Policy Implications This paper investigates the role of mayoral professional expertise—specifically, engineering educational backgrounds—in shaping urban carbon reduction outcomes in China. Drawing on a panel dataset of prefecture-level cities from 2005 to 2016, we provide robust empirical evidence that mayors with engineering backgrounds significantly reduce carbon emission intensity. This effect holds across different measures of emissions (e.g., per unit of GDP and per capita), persists after a battery of robustness checks, and is particularly pronounced in cities with larger populations and stronger industrial bases. Our mechanism analysis reveals two important channels through which technical expertise contributes to emission reductions. First, engineering-trained mayors tend to issue high intensity low-carbon policies, particularly favoring market-based instruments. Second, such mayors are more likely to promote green innovation at the local level, as indicated by higher levels of green invention patent applications. These findings contribute to the broader literature on bureaucratic competence and climate governance, offering new evidence that the professional composition of political elites matters for environmental outcomes. They suggest that beyond institutional design and performance evaluation systems, the technical qualifications of local leaders can meaningfully shape the effectiveness of climate-related policies. As China advances toward its dual carbon goals—peaking carbon emissions and achieving carbon neutrality—this study offers the following policy recommendations from the perspective of local official selection and appointment: First, technical competence should be systematically incorporated into the criteria for evaluating and appointing local officials. In regions with more urgent decarbonization demands, appointing leaders with relevant technical backgrounds may yield stronger analytical and policy design capacity, thereby enhancing the effectiveness of local climate governance. Second, for officials without formal engineering or scientific backgrounds, structured training programs on clean technology deployment and carbon markets may help bridge the knowledge gap and improve implementation capacity. Declarations Ethics and Consent to Participate declarations Not applicable. Consent to Publish declaration : All authors have read and agreed to the content of the manuscript and are accountable for all aspects of its accuracy and integrity in accordance with ICMJE criteria. The article is original, has not been published previously, and is not under consideration elsewhere. All authors agree to the terms of the BioMed Central Copyright and License Agreement. Competing interests: The authors declare that they have no competing interests. Funding: Lyubing Feng would like to acknowledge the support from the Key Program of the National Natural Science Foundation of China [No. 72034006]; the ESG and Sustainable Development Reaserch Center, HZCU. 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Cite Share Download PDF Status: Published Journal Publication published 24 Oct, 2025 Read the published version in Carbon Balance and Management → Version 1 posted Editorial decision: Revision requested 25 Jul, 2025 Reviews received at journal 03 Jul, 2025 Reviews received at journal 30 Jun, 2025 Reviewers agreed at journal 23 Jun, 2025 Reviewers agreed at journal 23 Jun, 2025 Reviewers invited by journal 23 Jun, 2025 Editor assigned by journal 07 Jun, 2025 Submission checks completed at journal 07 Jun, 2025 First submitted to journal 04 Jun, 2025 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6820961","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475269639,"identity":"a0064f6c-3f3f-45b0-ad32-a136f185d93c","order_by":0,"name":"Lyubing Feng","email":"","orcid":"","institution":"Hangzhou City University","correspondingAuthor":false,"prefix":"","firstName":"Lyubing","middleName":"","lastName":"Feng","suffix":""},{"id":475269640,"identity":"53314abb-053f-4c99-b772-b57b127e64e7","order_by":1,"name":"Shirong Zeng","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Shirong","middleName":"","lastName":"Zeng","suffix":""},{"id":475269641,"identity":"106b996a-b516-4c28-98a0-1a75cdf16312","order_by":2,"name":"Sai Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYLCCBBsGOQaGA0AWG9Fa0gyMSdTCkGaQ2ABmEKPF4AD7ww8PEv6kz288Y8DwoewwA//sBkJaeIwlEhIMchsbzhgwzjh3mEHizgH8WswO8DBIJP4wyG1mOGPAzNt2mMFAIoGQFvbHP4C2pLOBtPwlTguDGchhCTwgLYzEaLE/wGNmkZBgbDiD4VjBwZ5z6TwSNwhokWxgf3zzR4KcvPyMwxsf/CizluOfQUALg/wDKEPiADgyeQioRwb8DSQoHgWjYBSMghEFAKQGQc52QBuMAAAAAElFTkSuQmCC","orcid":"","institution":"Hangzhou City University","correspondingAuthor":true,"prefix":"","firstName":"Sai","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-06-04 13:23:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6820961/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6820961/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13021-025-00327-y","type":"published","date":"2025-10-24T16:16:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":94490262,"identity":"ef8cf83d-a1e2-4219-bb8d-b0258db1d2f6","added_by":"auto","created_at":"2025-10-27 17:08:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":920883,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6820961/v1/73e7072e-9964-4fec-b8b8-7952d936b870.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Engineering Mayors and Urban Carbon Governance: Evidence from China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe global imperative to address climate change has placed unprecedented demands on national and local governments to reduce greenhouse gas emissions and advance low-carbon economic development. As the world\u0026rsquo;s largest carbon emitter, China faces mounting pressure to fulfill its dual carbon targets\u0026mdash;peaking carbon emissions before 2030 and achieving carbon neutrality by 2060. While these national commitments are ambitious, their realization depends heavily on the governance capacity of subnational governments. In China\u0026rsquo;s decentralized administrative structure, local officials\u0026mdash;particularly mayors\u0026mdash;play a pivotal role in formulating and implementing environmental policies. Understanding how the characteristics of these political leaders influence low-carbon outcomes is therefore critical to evaluating the country\u0026rsquo;s decarbonization trajectory.\u003c/p\u003e \u003cp\u003eExisting research has demonstrated that, with the increasing emphasis on carbon emission accountability, local leaders are playing a vital role in addressing climate change and driving innovation in carbon reduction policies (Pan et al.,2022). Lu et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) find that mayors who are younger, more highly educated, and environmentally conscious are more effective in improving urban environmental governance. Given that environmental governance is closely tied to energy efficiency improvements and the development and diffusion of clean technologies, the scientific rigor and practical feasibility of related policies are critical determinants of actual emission reduction outcomes.\u003c/p\u003e \u003cp\u003eIn China\u0026rsquo;s local bureaucratic system, a considerable proportion of officials possess academic backgrounds in engineering. These individuals typically exhibit stronger technical competence and systems thinking, which may, in theory, enhance their capacity to identify and implement complex decarbonization policies. However, to date, no empirical study has systematically examined the relationship between officials\u0026rsquo; professional expertise and carbon reduction performance. This paper seeks to address this gap by focusing on mayors with academic backgrounds in engineering and evaluating their impact on urban carbon emission intensity. By introducing the perspective of bureaucratic professional expertise, this study aims to deepen the understanding of how local governance capacity influences low-carbon transitions and to provide empirical evidence on the determinants of environmental policy effectiveness.\u003c/p\u003e \u003cp\u003eThis paper employs a two-way fixed effects model and panel data from Chinese prefecture-level cities covering the period 2005\u0026ndash;2016 to examine the impact of mayors' technical backgrounds on carbon outcomes. The results indicate that mayors with academic training in engineering significantly reduce urban carbon emission intensity. This effect is robust across alternative outcome measures, including CO₂ emissions per unit of GDP and per capita emissions. The reduction is particularly pronounced in cities with larger populations and in those with a higher proportion of industrial activity in their economic structure. Mechanism analyses further reveal that engineering-trained mayors tend to align local low-carbon policies more closely with national emission reduction goals. In terms of policy instruments, they are more inclined to adopt market-oriented approaches rather than relying solely on command-and-control mechanisms. Additionally, the study finds that mayors with technical expertise place greater emphasis on the development and application of green technologies, suggesting that the promotion of green innovation constitutes an important channel through which carbon emissions are reduced.\u003c/p\u003e \u003cp\u003eThe remainder of this paper is structured as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reviews the relevant literature and outlines the theoretical hypotheses. Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e3\u003c/span\u003e describes the data sources, variable definitions, and empirical model. Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the main regression results, along with robustness checks and heterogeneity analysis. Section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e5\u003c/span\u003e conducts the mechanism analysis. Section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e6\u003c/span\u003e concludes and discusses policy implications.\u003c/p\u003e"},{"header":"2. Literature Review and Theoretical Hypotheses","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Literature Review\u003c/h2\u003e \u003cp\u003eA growing body of literature underscores the critical role that the personal characteristics of local government officials play in shaping public policy and influencing economic outcomes. Attributes such as gender (Brollo \u0026amp; Troiano, 2016; Hessami \u0026amp; da Fonseca, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), hometown affiliation (Do \u0026amp; Lee, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Chu et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ning \u0026amp; Zhang, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), educational attainment (Dreher et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Besley et al., 2011; S\u0026oslash;rensen, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and career background (Cheng et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) have been shown to significantly affect officials\u0026rsquo; policy preferences, governance capacity, and decision-making behavior. For example, Besley et al. (2011) find that better-educated leaders are associated with stronger local economic performance, while Cheng et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) show that officials with military backgrounds exhibit stronger risk aversion and are more effective at reducing excessive fiscal expenditure and deficits.\u003c/p\u003e \u003cp\u003eIn the context of China, local leaders wield substantial authority over economic activities and exercise significant discretion in policymaking. Under the incentive structure of the promotion tournament, empirical studies have demonstrated that local officials play a pivotal role in driving regional economic growth (Li \u0026amp; Zhou, 2005; Jia et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Yao \u0026amp; Zhang, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). As environmental governance and pollution control have become increasingly important in official performance evaluations, scholarly attention has begun to shift toward understanding how local officials perform in environmental domains and what factors influence such performance (Wu \u0026amp; Cao, 2021; Yin \u0026amp; Wu, 2022; Jiang \u0026amp; Tang, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResponding to national mandates for green transformation, local governments are now tasked with restructuring energy use, addressing climate change, and advancing low-carbon economic development. A number of studies have investigated the role of central government policy initiatives\u0026mdash;such as the low-carbon city pilot program\u0026mdash;in reducing urban emissions (Zheng et al,2014; Tie et al., 2020; Pan et al., 2022;Yu \u0026amp; Zhang, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e༛Zeng et al.,2023). Pan et al. (2022) find that the behavior and engagement of local leaders significantly affect the success of subnational carbon mitigation policies. Similarly, Lu et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) provide evidence that younger, well-educated and environmentally conscious mayors tend to deliver better environmental efficiency outcomes.\u003c/p\u003e \u003cp\u003eGiven that energy transition, emission control, and the adoption of clean technologies require highly technical policy design and execution, environmental governance places elevated demands on administrative professionalism. However, despite extensive literature on officials' personal traits, little is known about how professional expertise\u0026mdash;especially technical training\u0026mdash;influences environmental policy performance. This study aims to address this gap by examining whether officials with technical backgrounds are more effective in facilitating carbon emission reductions. Specifically, the empirical analysis focuses on mayors with academic training in engineering, thereby contributing to a deeper understanding of bureaucratic capacity in the context of low-carbon governance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Theoretical Hypotheses\u003c/h2\u003e \u003cp\u003eThis paper introduces a human capital-based perspective to examine how technocratic leader affect urban low-carbon governance. We propose that mayors with engineering backgrounds are more likely to reduce local carbon emission intensity due to two key mechanisms: policy capacity, innovation orientation.\u003c/p\u003e \u003cp\u003eFirst, mayors with technical training possess stronger policy capacity in technical domains. Technical education provides analytical tools and problem-solving skills that mayors can apply to formulate more effective low-carbon strategies. They may be better equipped to assess environmental trade-offs, interpret technical reports, and design sector-specific interventions.\u003c/p\u003e \u003cp\u003eSecond, these mayors may exhibit a stronger innovation orientation, especially in relation to green technology adoption. With a deeper appreciation of technological pathways, they are more likely to support R\u0026amp;D investment, foster partnerships with research institutions, and design incentives for clean innovation. Their leadership may create a more favorable environment for firms to adopt cleaner production techniques.\u003c/p\u003e \u003cp\u003eThird, the governance style of technocratic leaders tends to favor structured planning, target-setting, and the use of performance indicators. These traits align closely with China's target-based accountability system for emissions control. Engineering-trained mayors may also prefer market-based policy tools\u0026mdash;such as carbon pricing or green finance\u0026mdash;that align with long-term sustainability objectives.\u003c/p\u003e \u003cp\u003eBased on these theoretical foundations, we propose the following testable hypotheses:\u003c/p\u003e \u003cp\u003e \u003cem\u003eH1: Cities governed by mayors with engineering backgrounds exhibit significantly lower carbon emission intensity than those governed by non-technical mayors.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eH2: The carbon-reducing effect of engineering-trained mayors is mediated through (a) stronger and more detailed low-carbon policy instruments, and (b) greater levels of green technological innovation.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThis framework links individual-level attributes of political elites to broader developmental outcomes, contributing to both the literature on bureaucratic influence and the emerging field of subnational climate governance.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Data and Empirical Strategy","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data source\u003c/h2\u003e \u003cp\u003e \u003cb\u003eLocal Officials Data\u003c/b\u003e. This study uses data on local officials from the \u003cem\u003eCCER Officials Dataset\u003c/em\u003e, which includes biographical information on over 4,000 government officials ever serving at the central, provincial, and prefectural levels in China. The dataset provides detailed records such as birthplace, year of birth, educational attainment, career experience, and office held. For data consistency and availability, we focus on the period from 2005 to 2016 and extract individual-level information on mayors of prefecture-level cities. Based on their major (including both undergraduate and part-time education), we define a mayor as having an engineering background if their degree was in engineering. During 2005\u0026ndash;2016, a total of 953 individuals held mayoral positions, of whom 200 had engineering backgrounds, representing approximately 21% of the sample. We also collected other relevant mayoral attributes, including whether the mayor held a college degree or above, their year of birth, whether their hometown province is the province of current appointment, and whether they had previous experience in central government agencies. Additional control variables include the number of years the mayor had served in the current post and whether a mayoral turnover occurred during the year. If a turnover occurred, the new mayor's term is counted starting from the following year.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCarbon Emissions Data.\u003c/b\u003e Carbon emissions data are obtained from the \u003cem\u003eCenter for Global Environmental Research (CGER)\u003c/em\u003e, which provides annual CO₂ emissions at high spatial resolution based on fossil fuel combustion, cement production, and natural gas usage. These emissions are originally distributed in raster format with a spatial resolution of 1 km \u0026times; 1 km. We extract and retain grid data covering the territory of China and aggregate them to the prefecture-level. The sample period covers the years 2005 to 2016. The primary dependent variable is carbon intensity, measured as CO₂ emissions (in tons) per billion RMB of GDP. As a robustness check, we also use per capita CO₂ emissions (tons per 1,000 residents) as an alternative dependent variable. To address skewness and improve interpretability, all emissions-related variables are transformed using natural logarithms.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGreen Innovation Data\u003c/b\u003e. To measure the level of low-carbon technological innovation, we use the number of green invention patent applications at the city level as a proxy. Patent data are obtained from the incoPat patent database. Given that invention patents typically involve greater technological complexity and are more indicative of innovative activity, this study focuses specifically on invention patent applications. Following the \"IPC Green Inventory\" introduced by the World Intellectual Property Organization (WIPO) in 2010, we define green patents as those whose IPC classification falls within the green technology list. The number of green invention patent applications is aggregated annually at the prefecture level.\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\u003eVariable Definitions and Descriptive 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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMayoral characteristic variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMayor_Engineer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhether the mayor majored in engineering\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMayor_Col\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhether the mayor holds a college degree or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMayor_Ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhether the mayor was reassigned to another position during the year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMayor_Pro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhether the mayor was born in the province of current appointment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMayor_Dur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of years in current position\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMayor_Birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear of birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1959.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1975\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMayor_Exp_uform\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhether the mayor has prior work experience in the central government\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCarbon intensity variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eln_CO2_GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLogarithm of CO₂ emissions per unit of GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eln_per_CO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLogarithm of per capita CO₂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCity-level control variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eln_GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLogarithm of GDP (in billions)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eln_Pop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLogarithm of population (in thousands)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.675\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInd2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShare of secondary industry in GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInd3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShare of tertiary industry in GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePilot City\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhether the city is a low-carbon pilot city\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCity-level green technology innovation variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eln_per_gre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLogarithm of per capita green patent applications\u0026thinsp;+\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCity-level low-carbon policy intensity variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epi_city\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolicy intensity of low-carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46.750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epi_city_cc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolicy instrument intensity of command-and-control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34.250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epi_city_mb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolicy instrument intensity of market-based\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29.500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epi_city_cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolicy instrument intensity of composite tools\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eLow-Carbon Policy Intensity Data\u003c/b\u003e. Data on low-carbon policy intensity are obtained from the \u003cem\u003eChina\u0026rsquo;s Low-carbon Policy Intensity Dataset\u003c/em\u003e. This dataset employs machine learning techniques to quantify the strength of manufacturing-related low-carbon policies issued by different levels of government. Given that the dataset begins in 2007, the sample period used in this study spans from 2007 to 2016. Specifically, we use the policy intensity index of low-carbon policies issued by prefecture-level governments, along with three types of policy instruments: command-and-control, market-based, and composite tools.\u003c/p\u003e \u003cp\u003eBy merging these datasets, we construct a panel of Chinese prefecture-level cities from 2005 to 2016 (excluding centrally-administered municipalities). In addition, we compile a set of economic control variables at the city level, including population size, GDP, the share of the secondary and tertiary sectors in GDP, and whether the city was officially designated as a national low-carbon pilot city. Variable definitions and descriptive statistics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Empirical Strategy\u003c/h2\u003e \u003cp\u003eTo examine whether mayors with engineering backgrounds contribute to the development of low-carbon economies at the prefecture level, we employ a two-way fixed effects panel regression model. The baseline model is specified as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{CO2\\_Inten}_{it}=\\alpha\\:+\\beta\\:{Mayor\\_Engineer}_{it}+\\gamma\\:{X}_{it}+{\\mu\\:}_{i}+{\\lambda\\:}_{t}+{\\epsilon\\:}_{it\\:\\:}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{CO2\\_Inten}_{it}\\)\u003c/span\u003e \u003c/span\u003e represents the carbon emission intensity of city \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e in year \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e, measured as the natural logarithm of CO₂ emissions per unit of GDP. As a robustness check, we also use the logarithm of per capita CO₂ emissions. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Mayor\\_Engineer}_{it}\\)\u003c/span\u003e\u003c/span\u003e is a dummy indicator equal to 1 if the mayor of city \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e in year has an engineering educational background, and 0 otherwise.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{it}\\)\u003c/span\u003e \u003c/span\u003e denotes a vector of control variables that includes both mayoral characteristics and city-level economic indicators. The mayoral characteristics include whether the mayor holds a college or higher degree, year of birth, whether the mayor\u0026rsquo;s birthplace is within the province of current appointment, prior work experience in central government, the number of years in the current position, and whether the mayor was reassigned to another position during the year. In addition, we control for key city-level economic variables, including the logarithm of the resident population, the logarithm of GDP, and the city\u0026rsquo;s industrial structure, specifically the share of the secondary and tertiary sectors in GDP. To account for the potential impact of national low-carbon pilot programs, we also control for whether the city (or the province it belongs to) was designated as a low-carbon pilot area in year \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e (coded as 1 if yes, 0 otherwise). \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{i}\\:\\)\u003c/span\u003e\u003c/span\u003edenote city fixed effects, which control for unobserved, time-invariant characteristics at the regional level. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\lambda\\:}_{t}\\)\u003c/span\u003e\u003c/span\u003e represents year fixed effects, capturing nationwide temporal shocks and trends. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{it\\:\\:}\\)\u003c/span\u003e\u003c/span\u003eis the error term, and robust standard errors are used throughout.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Empirical Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Baseline Regression\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Regression: Engineering Background of Mayors and Carbon Emission Intensity\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eln_CO2_GDP\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMayor_Engineer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.014**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.011***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.012***\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(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.004)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eln_Pop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.037\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(0.029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.029)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eln_GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.852***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.845***\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(0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInd2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.052***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.045***\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(0.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.015)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInd3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\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(0.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.015)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePilot City\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.008\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(0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.006)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMayor_Col\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\u003e0.007*\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(0.004)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMayor_Ex\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\u003e-0.000\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(0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMayor_Pro\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\u003e-0.001\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(0.004)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMayor_Dur\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\u003e-0.000\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(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMayor_Birth\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\u003e-0.001***\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(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMayor_Exp_uform\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\u003e-0.020***\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(0.006)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.626***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.360***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.561***\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(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.304)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.921)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eRobust standard errors in parentheses. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the regression estimates examining the relationship between mayors\u0026rsquo; engineering backgrounds and urban carbon emission intensity, where the dependent variable is the logarithm of CO₂ emissions per unit of GDP. In Column (1), without any control variables, the estimated coefficient on the engineering background indicator is \u0026minus;\u0026thinsp;0.014 and statistically significant at the 5% level. This suggests that cities governed by engineering-background mayors tend to have significantly lower carbon intensity. In Column (2), after adding city-level control variables, the coefficient slightly decreases in magnitude to \u0026minus;\u0026thinsp;0.011 and becomes significant at the 1% level, indicating a more robust relationship. Notably, among the controls, we find that a higher share of secondary industry is significantly associated with a greater reduction in carbon intensity, which may reflect improved energy efficiency or structural upgrading within industrial cities.\u003c/p\u003e \u003cp\u003eColumn (3) introduces additional controls for mayor-level characteristics. The coefficient on engineering background remains stable at \u0026minus;\u0026thinsp;0.012 and significant at the 1% level. This specification serves as our preferred baseline model. The result implies that, on average, cities led by mayors with engineering backgrounds experience a 1.2% reduction in carbon intensity relative to those without such backgrounds. In addition, we find that mayoral age and central government experience are significantly associated with environmental performance. Specifically, younger mayors are associated with greater reductions in carbon intensity, and those with prior experience in central government posts appear to perform better in advancing carbon mitigation at the local level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Robustness Checks\u003c/h2\u003e \u003cp\u003eTo ensure the robustness of the baseline results, we conduct a series of tests by redefining both the dependent and explanatory variables, using lagged carbon outcomes, and excluding specific subsamples such as provincial capital cities.\u003c/p\u003e \u003cp\u003eFirst, we replace the dependent variable with an alternative measure of carbon emissions\u0026mdash;logarithm of per capita CO₂ emissions. Column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reports the results, with the estimated coefficient being \u0026minus;\u0026thinsp;0.012 and statistically significant at the 1% level. This finding once again confirms that mayors with engineering backgrounds significantly lower the urban carbon intensity, thereby supporting the view that such backgrounds facilitate the development of low-carbon economies.\u003c/p\u003e \u003cp\u003eSecond, to address potential concerns about reverse causality\u0026mdash;namely, that cities with stronger low-carbon performance may be more likely to appoint technically qualified mayors\u0026mdash;we re-estimate the model using next year\u0026rsquo;s carbon emission intensity as the dependent variable. The results, reported in Column (2), remain significantly negative, further suggesting that the observed effect is not driven by endogenous mayoral selection. Moreover, the time lag between policy formulation and observable outcomes is also reasonably accounted for.\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\u003eRobustness Check\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(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eln_per_CO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF. ln_CO2_Inten\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRestricted to full-time education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExcluding provincial capital cities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMayor_Engineer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.012***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.017***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.008**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.013**\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(0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eControl variables are consistent with the baseline regression. Robust standard errors in parentheses. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\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\u003eConsidering that there may be differences in the level of specialization between an official\u0026rsquo;s initial (full-time) degree and subsequent on-job education, we conduct a robustness check by redefining the engineering background variable. Specifically, we classify mayors as having a technical background only if their first degree was in engineering and their education level was college or above. Columns (3) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e report the results based on this stricter definition. The estimated coefficients remain negative and statistically significant at the 5% level, confirming the robustness of our main findings.\u003c/p\u003e \u003cp\u003eFinally, to mitigate the influence of large and administratively privileged cities, we exclude all provincial capital cities and re-run the regressions. The results, presented in Column (4), remain consistently negative and significant at the 5% level, demonstrating that the main findings are not driven by large-city outliers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Heterogeneity Analysis\u003c/h2\u003e \u003cp\u003eWe further investigate whether the effect of mayors\u0026rsquo; technical backgrounds on carbon emission performance varies across different types of cities. Specifically, we examine heterogeneity by city size and industrial structure.\u003c/p\u003e \u003cp\u003eFirst, we assess whether the effect differs between larger and smaller cities. Based on population size in the baseline year (2005), we classify cities with population above the median as large cities and those below the median as small cities. Columns (1) and (2) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e report the subsample regression results. While the estimated coefficients are negative in both subsamples, the effect is statistically significant at the 1% level only for large cities. This suggests that the policy influence of engineering-background mayors in reducing carbon intensity is more pronounced in larger urban areas.\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\u003eHeterogeneity Analysis\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(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLarge cities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSmall cities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIndustrial cities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-industrial cities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMayor_Engineer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.017***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.019***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.002\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(0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1541\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eControl variables are consistent with the baseline regression. Robust standard errors in parentheses. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\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\u003eNext, we explore heterogeneity based on industrial composition. Using the 2005 share of secondary industry in local GDP as a proxy, we define cities above the median as industrial-oriented cities and those below the median as non-industrial-oriented. Columns (3) and (4) present the corresponding regression results. Again, both coefficients are negative, but the effect is statistically significant only for industrial-oriented cities at the 1% level. This implies that mayors with engineering expertise are particularly effective in curbing carbon emissions in cities with stronger industrial dependence.\u003c/p\u003e \u003cp\u003eTaken together, these results suggest that the baseline findings are largely driven by large cities and cities with a stronger industrial base. In such contexts, engineering-background mayors are better positioned to accelerate structural transformation and reduce carbon emission, thereby promoting greener and more sustainable urban development.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Mechanism Analysis","content":"\u003cp\u003eHow do mayors with engineering backgrounds contribute to reductions in urban carbon emission intensity? One plausible mechanism is through their influence on local policy formulation. Since local governments in China play a central role in both economic development and environmental governance, a mayor\u0026rsquo;s policy orientation is a critical channel through which their background may shape environmental outcomes.\u003c/p\u003e \u003cp\u003eMayors with technical expertise may place greater emphasis on low-carbon development. Due to their stronger professional knowledge base, it could lead them not only to introduce a greater number of relevant policies but also to formulate more detailed, actionable, and implementable measures. To test this hypothesis, we examine whether engineering-background mayors are more likely to issue stronger low-carbon policies.\u003c/p\u003e \u003cp\u003eWe use the city-level low-carbon policy intensity index from the China\u0026rsquo;s Low-carbon Policy Intensity Dataset as the dependent variable. Column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the regression result, showing a positive and statistically significant coefficient at the 10% level. This suggests that mayors with engineering backgrounds tend to issue more comprehensive and technically detailed low-carbon policies compared to their peers from non-technical backgrounds.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMechanism Analysis \u0026mdash; Mayor\u0026rsquo;s Engineering Background and Low-Carbon Policy Intensity\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(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePi_city\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePI_city_cc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePI_city_mb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePI_city_cm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMayor_Engineer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.413*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.258**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.051\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(0.240)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.162)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.131)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.072)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eThe sample covers the years from 2007 to 2016. Control variables include those from the baseline regression, with the addition of provincial-level low-carbon policy intensity. Robust standard errors in parentheses. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\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\u003eTo explore further, we examine whether such mayors differ in the types of policy tools they prefer. Different types of policy instruments\u0026mdash;such as command-and-control regulations, market-based mechanisms, and hybrid approaches\u0026mdash;have varying implications for enterprise behavior and environmental outcomes. The dataset also provides disaggregated indices for these three types of instruments.\u003c/p\u003e \u003cp\u003eWe run separate regressions using the sub-indices for command-and-control (PI_city_cc), market-based (PI_city_mb), and hybrid tools (PI_city_cm) as dependent variables. The results, shown in Columns (2)\u0026ndash;(4), indicate positive coefficients across all policy types, with the largest and most statistically significant effect observed for market-based instruments. The coefficient for the market-based policy intensity index is 0.258 and significant at the 5% level, suggesting that mayors with technical backgrounds are more inclined to adopt market-oriented policy tools\u0026mdash;such as fiscal subsidies, tradable certificates, and investment incentives. These tools are flexible and incentive-compatible, allowing local governments to guide firms toward emission reductions more effectively. Moreover, such instruments tend to be better suited to local economic conditions and are more sustainable in the long run.\u003c/p\u003e \u003cp\u003eMoreover, mayors with technical expertise may place greater emphasis on reducing carbon emissions through technological means\u0026mdash;for example, by encouraging firms to adopt cleaner production processes? To test this hypothesis, we further investigate whether engineering-background mayors are more likely to promote green technological innovation at the city level. We use the number of green invention patent applications per 1,000 residents (log-transformed after adding one) as the dependent variable. To mitigate the confounding effects of overall innovation activity across cities, we control for the total number of invention patent applications per 1,000 residents (also log-transformed after adding one).\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\u003eMechanism Analysis \u0026mdash; Mayor\u0026rsquo;s Engineering Background and Green Technological Innovation\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\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 \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\u003eln_per_greenpat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF.ln_per_greenpat\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMayor_Engineer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003**\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(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eControl variables include those from the baseline regression, with the additional control for the logarithm of city-level per capita invention patent applications. Robust standard errors in parentheses. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\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\u003eColumn (1) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e reports the results. The estimated coefficient on the engineering background variable is 0.10 and statistically significant at the 10% level, suggesting that mayors with engineering training are associated with a higher number of green patent applications\u0026mdash;an indication of enhanced development and diffusion of clean technologies. Given the inherent time lag between R\u0026amp;D efforts and patent filings, we further use the number of green patent applications in the following year as the dependent variable. The results, shown in Column (3), reveal an even larger and more significant coefficient. We place greater confidence in this result, as it captures the delayed effect of policy-driven innovation. Overall, the findings suggest that engineering-background mayors, drawing on their technical expertise, are more likely to reduce carbon intensity through fostering green technological innovation.\u003c/p\u003e"},{"header":"6. Conclusion and Policy Implications","content":"\u003cp\u003eThis paper investigates the role of mayoral professional expertise\u0026mdash;specifically, engineering educational backgrounds\u0026mdash;in shaping urban carbon reduction outcomes in China. Drawing on a panel dataset of prefecture-level cities from 2005 to 2016, we provide robust empirical evidence that mayors with engineering backgrounds significantly reduce carbon emission intensity. This effect holds across different measures of emissions (e.g., per unit of GDP and per capita), persists after a battery of robustness checks, and is particularly pronounced in cities with larger populations and stronger industrial bases. Our mechanism analysis reveals two important channels through which technical expertise contributes to emission reductions. First, engineering-trained mayors tend to issue high intensity low-carbon policies, particularly favoring market-based instruments. Second, such mayors are more likely to promote green innovation at the local level, as indicated by higher levels of green invention patent applications.\u003c/p\u003e \u003cp\u003eThese findings contribute to the broader literature on bureaucratic competence and climate governance, offering new evidence that the professional composition of political elites matters for environmental outcomes. They suggest that beyond institutional design and performance evaluation systems, the technical qualifications of local leaders can meaningfully shape the effectiveness of climate-related policies. As China advances toward its dual carbon goals\u0026mdash;peaking carbon emissions and achieving carbon neutrality\u0026mdash;this study offers the following policy recommendations from the perspective of local official selection and appointment: First, technical competence should be systematically incorporated into the criteria for evaluating and appointing local officials. In regions with more urgent decarbonization demands, appointing leaders with relevant technical backgrounds may yield stronger analytical and policy design capacity, thereby enhancing the effectiveness of local climate governance. Second, for officials without formal engineering or scientific backgrounds, structured training programs on clean technology deployment and carbon markets may help bridge the knowledge gap and improve implementation capacity.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics and Consent to Participate declarations\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eConsent to Publish declaration\u003c/b\u003e: \u003cp\u003eAll authors have read and agreed to the content of the manuscript and are accountable for all aspects of its accuracy and integrity in accordance with ICMJE criteria. The article is original, has not been published previously, and is not under consideration elsewhere. All authors agree to the terms of the BioMed Central Copyright and License Agreement.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eLyubing Feng would like to acknowledge the support from the Key Program of the National Natural Science Foundation of China [No. 72034006]; the ESG and Sustainable Development Reaserch Center, HZCU.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLyubing Feng proposed research idea and provided essential funding support for this project. Sai Wang was responsible for data cleaning and conducting the empirical analysis.Shirong Zeng wrote the main manuscript text.All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCheng C, Huang B, Wang Y, Hu L. 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Do green technology innovations contribute to carbon dioxide emission reduction? Empirical evidence from patent data. Technol Forecast Soc Chang. 2019;146:297\u0026ndash;303.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHessami Z, da Fonseca ML. Female political representation and substantive effects on policies: A literature review. Eur J Polit Econ. 2020;63:101896.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia R, Kudamatsu M, Seim D. Political selection in China: The complementary roles of connections and performance. J Eur Econ Assoc. 2015;13(4):631\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang Q, Tang P. All roads lead to Rome? Carbon emissions, pollutant emissions and local officials\u0026rsquo; political promotion in China. Energy Policy. 2023;181:113700.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu J, Li B, Li H, Zhang X. Characteristics, exchange experience, and environmental efficiency of mayors: Evidence from 273 prefecture-level cities in China. J Environ Manage. 2020;255:109916.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNing S, Zhang Q. Hometown favoritism: Effect of environmental governance of politicians returning home. Econ Model. 2022;117:106070.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026oslash;rensen RJ. Educated politicians and government efficiency: Evidence from Norwegian local government. J Econ Behav Organ. 2023;210:163\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYao Y, Zhang M. Subnational leaders and economic growth: Evidence from Chinese cities. J Econ Growth. 2015;20:405\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu Y, Zhang N. Low-carbon city pilot and carbon emission efficiency: Quasi-experimental evidence from China. Energy Econ. 2021;96:105125.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng S, Jin G, Tan K, Liu X. Can low-carbon city construction reduce carbon intensity? Empirical evidence from low-carbon city pilot policy in China. J Environ Manage. 2023;332:117363.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng S, Kahn ME, Sun W, Luo D. Incentives for China's urban mayors to mitigate pollution externalities: The role of the central government and public environmentalism. Reg Sci Urban Econ. 2014;47:61\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"carbon-balance-and-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cbam","sideBox":"Learn more about [Carbon Balance and Management](https://cbmjournal.biomedcentral.com/)","snPcode":"13021","submissionUrl":"https://submission.nature.com/new-submission/13021/3","title":"Carbon Balance and Management","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"low-carbon, carbon emission intensity, engineering mayor","lastPublishedDoi":"10.21203/rs.3.rs-6820961/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6820961/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnder China\u0026rsquo;s carbon peaking targets, local leaders are responsible for implementing national low-carbon policies. This paper investigates whether mayors\u0026rsquo; professional backgrounds are associated with better performance in achieving emission reduction outcomes. Using panel data from prefecture-level cities between 2005 and 2016, we find that mayors with engineering backgrounds significantly reduce carbon emission intensity. This effect is pronounced in larger, more industrialized cities. Mechanism analysis reveals two key channels: engineering-trained mayors are more likely to implement high-intensity low-carbon policies, especially with market-based policy instruments, and to promote local green technological innovation.\u003c/p\u003e","manuscriptTitle":"Engineering Mayors and Urban Carbon Governance: Evidence from China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-26 12:40:32","doi":"10.21203/rs.3.rs-6820961/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-26T02:17:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-03T15:56:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-01T02:52:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311919550965235644996932405364187575481","date":"2025-06-23T15:02:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"74963987194416998749575871875820135022","date":"2025-06-23T09:07:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-23T08:36:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-07T06:35:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-07T05:54:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Carbon Balance and Management","date":"2025-06-04T13:19:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"carbon-balance-and-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cbam","sideBox":"Learn more about [Carbon Balance and Management](https://cbmjournal.biomedcentral.com/)","snPcode":"13021","submissionUrl":"https://submission.nature.com/new-submission/13021/3","title":"Carbon Balance and Management","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"03545a78-7f68-4288-9339-875dcf173658","owner":[],"postedDate":"June 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T16:25:15+00:00","versionOfRecord":{"articleIdentity":"rs-6820961","link":"https://doi.org/10.1186/s13021-025-00327-y","journal":{"identity":"carbon-balance-and-management","isVorOnly":false,"title":"Carbon Balance and Management"},"publishedOn":"2025-10-24 16:16:25","publishedOnDateReadable":"October 24th, 2025"},"versionCreatedAt":"2025-06-26 12:40:32","video":"","vorDoi":"10.1186/s13021-025-00327-y","vorDoiUrl":"https://doi.org/10.1186/s13021-025-00327-y","workflowStages":[]},"version":"v1","identity":"rs-6820961","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6820961","identity":"rs-6820961","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}