How does public policy drive urban energy transition? Evidence from China

Li, Jun; Li, Shuqi; Qiu, Yifeng Article How does public policy drive urban energy transition? Evidence from China Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Li, Jun; Li, Shuqi; Qiu, Yifeng (2025) : How does public policy drive urban energy transition? Evidence from China, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 13, Iss. 7, pp. 1-16, https://doi.org/10.3390/economies13070195 This Version is available at: https://hdl.handle.net/10419/329475 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Received: 13 May 2025 Revised: 19 June 2025 Accepted: 24 June 2025 Published: 8 July 2025 Citation: Li, J., Li, S., & Qiu, Y. (2025). How Does Public Policy Drive Urban Energy Transition? Evidence from China. Economies,13(7), 195. https:// doi.org/10.3390/economies13070195 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Article How Does Public Policy Drive Urban Energy Transition? Evidence from China Jun Li 1, Shuqi Li 2and Yifeng Qiu 3,* 1School of Public Management, South China Agricultural University, Guangzhou 510642, China; [email protected] 2College of Business and Logistics, Luohe Vocational Technology College, Luohe 462002, China; [email protected] 3Center for China Special Economic Zone Studies, Shenzhen University, Shenzhen 518060, China *Correspondence: [email protected] Abstract Promoting urban energy transition is essential for achieving environmental sustainability, yet how to effectively guide this process through public policy remains a key research question. This study aims to evaluate the effectiveness of government policy in facilitating urban energy transition, with a specific focus on China’s National New Energy Demonstration City Construction (NEDC) Policy. Using a difference-in-differences model with panel data from 274 Chinese cities, the empirical results indicate that the NEDC policy significantly advances urban energy transition, resulting in a notable increase of 0.571 units in the Urban Energy Transition Index and an improvement of 0.0321 units in the Urban Energy Transition Efficiency Index. Mechanism analysis further reveals that the NEDC policy promotes urban energy transition primarily by advancing financial development, strengthening environmental regulations, and encouraging capital-biased technological progress. Heterogeneity analysis indicates that the NEDC policy significantly boosts urban energy transition in resource-based cities, whereas it exerts a suppressive effect on urban energy transition in non-resource-based cities. This study offers valuable policy implications for developing countries seeking sustainable urban transformation. Keywords: urban energy transition; capital-biased technological progress; energy structure; energy consumption efficiency 1. Introduction Understanding how public policy can effectively promote urban energy transition is a critical research aim in the context of achieving global environmental sustainability. The urgency of environmental sustainability has prompted nations worldwide to explore new urban development models that balance economic growth and ecological preservation. China’s systematic practices in eco-city transformation have emerged as a representative case study in the exploration of new urban paradigms (Islam,2024;Y. Wu & Zhang,2024; Dai,2025). To address environmental pressures from rapid urbanization and industrial expansion, the Chinese government implemented the New Energy Demonstration Cities Construction (NEDC) policy in 2014, aiming to increase the proportion of renewable energy consumption in designated pilot cities (Jiao et al.,2024;J. Zhang et al.,2020). However, there is still a lack of empirical research examining the impact of similar policies on urban energy transition. Therefore, this study seeks to address this gap by systematically evaluating the effectiveness and underlying mechanisms of the NEDC policy in promoting urban energy Economies 2025,13, 195 https://doi.org/10.3390/economies13070195 Economies 2025,13, 195 2 of 16 transition, thereby providing valuable policy insights for developing countries pursuing sustainable urban transformation. As the largest developing country, China’s experience offers a distinctive case, and although the empirical analysis is grounded in the Chinese context, the core mechanisms and policy rationale—such as differentiated governance strategies and institutional innovation—may serve as useful insight for other developing countries confronting similar challenges in their urban energy transitions. Some studies indicate that Chinese urban areas accounted for approximately 85% of national carbon emissions (Z. Liu et al.,2022), making urban transformation outcomes critical determinants for achieving China’s national dual carbon goals. Existing studies indicate that the influential factors of urban energy utilization mainly include financial development levels (Irfan et al.,2023), energy prices (Geng et al.,2021), technological innovation (Marcantonini & Ellerman,2015), government incentives (Shahbaz et al.,2022), and industrial structures (Zhao et al.,2023). The urban energy transition policy incentive essentially represents a paradigmatic innovation that integrates ecological values into economic growth systems (Dai et al.,2025;Xu et al.,2024). Empirical findings also show that the NEDC program has significantly improved environmental performance (Shao et al.,2024) and accelerated ecological transitions (J. Wu et al.,2018;Q. Zhang et al.,2022) in pilot cities through policy-guided technological innovation and industrial upgrading. Additionally, prior research tends to focus on the theoretical and domestic dimensions of urban energy policy impacts, with limited attention given to potential cross-border effects, such as technology spillovers and international cooperation. Therefore, this study seeks to address these contradictions by conducting a comprehensive and systematic analysis of the NEDC policy’s impact on urban energy transitions in China. In particular, it aims to clarify the mechanisms driving energy transition, thereby contributing to a more nuanced understanding of urban transformation under dual carbon constraints. Using a quasi-natural experiment design based on China’s 2014 NEDC policy implementation, this study employs panel data from 274 Chinese prefecture-level cities (2011–2018) to examine policy impacts and mechanisms via a difference-in-differences (DID) model. Findings reveal that the NEDC policy increased energy structure transformation by 0.57 units and energy efficiency transformation by 0.03 units in pilot cities compared to non-pilot counterparts. Results remain robust after adjusting cluster-robust standard errors, adding time/city fixed effects, and some further robustness checks using staggered policy timing, alternative treatment variables, and placebo analysis confirm estimation reliability. Mechanism analysis identifies three impact pathways: promoting financial development, strengthening environmental regulations, and stimulating capital-biased technological progress. This study enhances the understanding of urban energy transition policies in emerging economies. This study makes three key contributions to the existing literature. First, it addresses an important research gap by empirically examining the effectiveness of China’s public policy (New Energy Demonstration City Policy) in driving urban energy transition—a policy area that has received limited attention in prior studies. While much of the existing literature has focused on the national level, the specific impacts of city-level demonstration policies on energy transition remain underexplored. By focusing on the NEDC policy initiative, this study enriches the field of energy economics with fresh empirical evidence on how place-based policies can reshape urban energy systems. Second, this study advances the understanding of the mechanisms underlying policy-driven urban energy transitions. Specifically, it identifies three critical channels—financial development, environmental regulation, and capital-biased technological progress—through which the NEDC policy facilitates urban energy transition. This mechanism analysis provides valuable insights for designing differentiated and targeted policy interventions in future sustainable urban de- Economies 2025,13, 195 3 of 16 velopment strategies. Third, this research employs a multi-period difference-in-differences (DID) methodology to rigorously identify the causal effect of the NEDC policy on urban energy transition outcomes, effectively addressing potential endogeneity and selection bias issues that may have undermined the validity of earlier studies. The adoption of this robust empirical strategy enhances the credibility of the findings and supports the formulation of evidence-based policy recommendations for developing countries aiming to implement similar eco-city initiatives. 2. Theoretical Analysis and Research Hypotheses As a core policy within the institutional innovation framework of the new energy governance paradigm, China’s National New Energy Demonstration City Construction (NEDC) policy distinguishes itself through its vertical integration mechanism of “goal anchoring-technological adaptation-scenario development”. Unlike traditional one-way energy planning models, the NEDC policy employs an urban-subjective development logic, linking innovative energy demand-side management tools (e.g., green electricity trading mechanisms) with supply-side structural reforms to form multi-scale nested sustainable energy ecosystems. Building on this foundation, the NEDC policy functions as a systematic strategy to accelerate the transition from fossil fuel-based to sustainable energy systems. Prior studies show that the policy reduces enterprise energy intensity via technological innovation and tax incentives (X. Liu et al.,2023). For instance, Chen et al. (2023) finds that NEDC directly drives energy structural transformation by boosting renewable energy use, while efficiency gains are indirect results of technological progress. Q. Zhang et al. (2022) confirms this dual transition enhances urban energy carbon performance (ECP). J. Yang et al. (2024) demonstrate that government guidance combined with green technological innovation under NEDC yields systematic improvements in both energy structure and efficiency. Collectively, these findings suggest that NEDC’s composite approach leverages both direct promotion of new energy development and indirect facilitation of technological progress. Based on the theoretical analysis above, we propose the following: Hypothesis 1: The NEDC policy exerts positive effects on urban energy transition. Financial development plays a pivotal role in this process by enabling efficient capital allocation to green industries, reducing financing costs and risks associated with renewable projects. At the firm level, improved financial accessibility helps overcome funding constraints, accelerating green investments (He et al.,2021). The growth of green financial instruments, such as sustainability-linked bonds, further supports capital-intensive clean energy projects. Macroeconomically, financial development strengthens fiscal and regulatory frameworks, enabling targeted subsidies, tax incentives, and public investment in green infrastructure. Financial liberalization and emerging green finance markets, including carbon trading and ESG-based strategies, create an enabling environment aligning financial decisions with sustainability goals (C. Li et al.,2022). These mechanisms integrate closely with the NEDC policy’s financial incentives to drive urban energy transition. Based on the theoretical analysis above, we propose the following: Hypothesis 2: NEDC policy promotes urban energy transition by promoting financial development. Beyond financial development, fiscal resources and environmental regulations are also vital facilitators of energy transition. A stable fiscal revenue base provides necessary funding for renewable energy innovation and enables governments to strengthen environmental oversight (Hou et al.,2024). The NEDC policy leverages fiscal subsidies and tax Economies 2025,13, 195 4 of 16 incentives to build a regulatory framework that encourages clean energy adoption. Unlike financial development, which mainly influences energy transition via capital allocation and investment efficiency, environmental regulations operate through enforcement and institutional constraints that shape corporate and consumer behavior toward sustainability. These regulations promote energy transition through multiple pathways: imposing compliance costs on polluting industries to drive greener production; incentivizing R&D in renewable technologies to accelerate innovation; and employing market-based instruments like carbon pricing and emission trading to shift energy consumption patterns. The effectiveness of these regulations is enhanced by financial development, which supplies the capital firms need to comply and invest in low-carbon technologies. This interplay aligns with previous findings emphasizing the role of innovative financial instruments in renewable energy growth (Song et al.,2023), the positive effects of financial openness on structural energy transformation (Jia et al.,2024), and the influence of green finance on consumption and pollution reduction (C. Li et al.,2022). Based on the theoretical analysis above, we propose the following: Hypothesis 3: NEDC policy promotes urban energy transition by strengthening environmental regulations. Technological progress, especially capital-biased technological progress (IBTE), is a core driver of energy structural transformation (Hassan et al.,2022;Weber & Domazlicky,1999). Under the NEDC policy, binding renewable energy and carbon intensity targets phase out energy-intensive firms, redirecting capital to green innovation sectors. The policy’s combination of R&D subsidies and carbon market incentives creates a reinforcing innovation loop, enhancing capital-biased technological advances (Wang & Yi, 2021). City-type heterogeneity moderates this effect: resource-based cities, with established green regulatory frameworks and heavy capital stocks, experience stronger capital-biased progress due to supportive institutional and financial conditions (X. Zhang et al.,2022). Based on the theoretical analysis above, we propose the following: Hypothesis 4: NEDC policy promotes urban energy transition by stimulating capital-biased technological progress. 3. Data and Method 3.1. Sample Selection and Data Sources 3.1.1. Dependent Variable Urban energy transition, the dependent variable of this study, encompasses two dimensions: energy supply-side structural optimization and demand-side efficiency improvement (Fan & Yi,2021). This study focuses on the dimension of energy structure transition (UET), while simultaneously examining the impact of policies on energy efficiency transition (CTFP). For measuring UET, referring to the research framework of Shen et al. (2023), a two-dimensional assessment system is developed, incorporating energy system transition performance and energy transition readiness. The former measures the current state of the energy system, covering energy structure and environmental sustainability, while the latter reflects transition-driving factors, including economic foundation, capital investment, technological application, and talent reserves. In addition, to verify the internal consistency and reliability of the variable scale, this study calculates the Cronbach’s Alpha coefficient. This coefficient is used to assess the internal consistency among multiple indicators within a measurement tool, with values ranging from 0 to 1. Generally, a value of α > 0.7 is Economies 2025,13, 195 5 of 16 considered to indicate high reliability (Nunnally,1978). In this study, the Cronbach’s Alpha coefficient for the UET variable exceeds 0.75 (Shen et al.,2023), demonstrating that the constructed measurement system has good consistency and stability in reflecting the core concept. For energy efficiency evaluation, we innovatively introduce the carbon total factor productivity (CTFP) indicator. Rooted in the environmental production technology theory proposed by Färe et al. (1989,1993), CTFP incorporates carbon emissions as a non-desirable output into the TFP calculation framework using the Shepherd distance function. Unlike traditional green total factor productivity (GTFP) that only considers pollutants, CTFP further distinguishes the heterogeneous impacts of energy input and carbon emissions through the following formula: CTFP =Y LαKβEγCOδ 2 (1) where Y represents regional GDP, L, K, and E denote labor, capital, and energy inputs, respectively, CO 2 is the non-desirable output, and α , β , γ , δ are the output elasticity coefficients of corresponding factors. A higher CTFP value indicates higher comprehensive efficiency in achieving economic growth and carbon emission control under the same factor inputs. 3.1.2. Independent Variable The core independent variable is China’s National New Energy Demonstration City (NEDC) policy, represented by the interaction term DID it between the policy dummy variable and time dummy variable. This policy adopts a purposeful sampling strategy based on the 2014 NEDC list issued by the National Energy Administration, selecting 59 pilot cities as the treatment group (excluding remote areas like Tibet and Xinjiang). When establishing the NEDC, China set specific entry criteria for selected cities, requiring them to meet standards in economic, social, energy, and environmental development capacity. Pilot cities were also required to complete national and provincial targets for total pollutant emission reduction within the designated timeframe (X. Yang et al.,2021; X. Liu et al.,2023). The scientific rationale of this sampling design lies in the fact that the pilot cities were uniformly designated by the central government, which helps eliminate selection bias stemming from cities’ own energy characteristics (Jacobson et al.,1993). The control group, consisting of 215 non-pilot cities, was determined based on a dual matching principle: geographic proximity, to control for differences in climate conditions and energy infrastructure; and industrial structure similarity, to ensure the comparability of energy consumption characteristics. This sampling design aligns with the requirements of the DID model’s counterfactual framework and effectively isolates the net effect of policy intervention. Ultimately, this approach forms a balanced panel dataset comprising 274 cities over the period 2011–2018. The policy implementation year is set as 2014, with the DID it variable assigned a value of 1 for treatment group cities in years ≥2014, and 0 otherwise. 3.1.3. Mechanism Variables The mechanism variables include financial development, environmental regulations, and capital-biased technological progress. Financial development (FDI) is measured using the inclusive finance index developed by G. Li et al. (2023), which comprehensively reflects financial accessibility and service efficiency through 12 indicators such as bank outlet density and mobile payment coverage. Environmental regulations (IPCI) follow the methodology provide by He et al. (2021) and Ye et al. (2021), calculated as the ratio of Economies 2025,13, 195 6 of 16 industrial pollution control investment to environmental protection investment. A higher IPCI value indicates greater fiscal investment intensity in pollution governance. The measurement of capital-biased technological progress (IBTE) follows the methodology of Weber and Domazlicky (1999) and Färe et al. (1997). Based on the Malmquist input productivity index, total factor productivity (TFP) change is decomposed into overall technical efficiency change (OTEC) and technological progress (TECH). Technological progress is further decomposed into the product of output-biased technological progress (OBTE), input-biased technological progress (IBTE), and neutral technological progress (MATE). The IBTE coefficient is used to quantify the technology bias characteristics of capital factors. The calculation data is sourced from C. Li et al. (2022). The specific calculation equations are as follows: MALM =OTEC ∗TECH (2) TECH =OBTE ∗IBTE ∗MATE (3) 3.1.4. Control Variables In the selection and measurement of control variables, this study systematically incorporates some key variables that may influence urban energy transition (UET), with indicator construction strictly adhering to theoretical logic and measurement standards established in prior research. Industrial structure level (IND) is measured using the natural logarithm of secondary industry added value, effectively capturing the structural impact of energy-intensive sectors such as manufacturing and heavy industries on urban energy consumption. Economic development stage (XGDP) is characterized by the natural logarithm of per capita GDP, controlling for differential effects of urban economic scale and living standards on energy demand elasticity. Green technological innovation capacity (GTFP) is calculated as the natural logarithm of annual green patent counts (including utility and invention patents) plus one, a treatment that avoids zero-value bias while quantifying the marginal contribution of environmentally friendly technologies to energy system optimization. Technological R&D intensity (RD) is measured by the ratio of R&D expenditure to GDP. The selection of these control variables aligns with broad consensus in energy economics literature and corresponds to the theoretical framework positing three-dimensional impacts of industrial structure, economic foundation, and technological innovation on energy transition. Table 1presents the definitions of the relevant variables. Table 1. Definition of Variables. Variable Name Definition UET Urban Energy Transition Measures structural change in urban energy systems, based on transition performance and readiness. CTFP Carbon Total Factor Productivity Evaluates energy efficiency, incorporating carbon emissions as undesirable outputs within a TFP framework. DID Policy Treatment Indicator Interaction term for NEDC pilot cities and post-policy period (post-2014), core explanatory variable. FDI Financial Development Index Measures financial accessibility and service efficiency using inclusive finance indicators. IPCI Environmental Regulation Intensity Ratio of industrial pollution control investment to total environmental protection investment. IBTE Capital-Biased Technological Progress Input-biased technological progress calculated via Malmquist index decomposition. Economies 2025,13, 195 7 of 16 Table 1. Cont. Variable Name Definition IND Industrial Structure Logarithm of the added value of the secondary industry, representing energy-intensive economic activity. XGDP Economic Development Level Logarithm of per capita GDP, controlling for differences in city development stages. GTFP Green Innovation Capacity Logarithm of the number of green patents (utility and invention) plus one. RD R&D Intensity Ratio of R&D expenditure to GDP, capturing innovation investment strength. CCT Carbon Trading Policy Dummy Indicates whether a city is covered by the national carbon emission trading scheme, used in robustness tests. 3.2. Model Regarding empirical model construction, this study treats the 2014 New Energy Demonstration Cities (NEDC) policy implementation as a quasi-natural experiment. A difference-in-differences (DID) model is employed to estimate the net effects of policy intervention on urban energy structure transformation (UET) and energy efficiency enhancement (CTFP). Leveraging the list of policy pilots, cities are categorized into treatment groups (policy pilot cities) and control groups (non-pilot cities). The DID design offers significant methodological advantages: First, the exogenous nature of policy shocks substantially mitigates endogeneity caused by reverse causality, as local government selection of pilot cities typically follows central policy directives rather than inherent urban energy endowments. Second, the inclusion of two-way fixed effects can reduce estimation bias from omitted variables. The model specification, as shown in Equations (4) and (5), directly reflects the magnitude of energy transition improvement in treatment groups relative to control groups post-policy implementation through its interaction term coefficient. CETit =β0+β1DIDit +β2controlit +λt+γi+εit (4) CTFPit =β0+β1DIDi,t+β2controlit +λt+γi+εit (5) CETit and CTFPi,t represent the dependent variables, which mean the urban energy transition and urban energy efficiency transition of city iin year t, respectively. DIDi,t is a strategic dummy variable, reflecting the implementation of NEDC policy in year tby city i, which is counted as 1 for implementation and 0 for non-implementation. For the control variables controli,t , including industrial level (IND), economic development level (XGDP), green innovation level (GTFP) and technology research and development level (RD); γi represents the fixed effect of cities, which is used to control the inherent characteristics of different cities; λt is a fixed effect of the years to control the influence of the macro environment over time; εit is an error term that covers random interference factors that are not considered by the model. The data used in this study are sourced from the Urban Statistical Yearbooks of various Chinese cities and from relevant data manually collected by the authors from different cities. Table 2presents the descriptive statistics of the variables. Table 3presents the descriptive statistics for the treatment and control groups, before and after the implementation of the policy. Economies 2025,13, 195 8 of 16 Table 2. Descriptive statistics. Variable N Mean Std Min Max UET 2192 46.284 12.536 17.598 82.724 CTFP 2129 −0.029 0.212 −4.073 0.762 DID 2192 0.135 0.341 0 1 FDI 2192 4.942 0.507 2.972 5.714 IPCI 2188 11.445 7.261 0.221 42.622 IBTE 2192 0.999 0.008 0.967 1.214 IND 2192 15.808 0.948 13.046 18.416 XGDP 2192 10.676 0.565 8.772 13.055 GTFP 2192 4.772 1.67 0 10.122 RD 2188 0.296 0.311 0.125 5.033 CCT 2192 0.1 0.301 0 1 Table 3. Descriptive Statistics: Treatment and Control Groups Before and After Policy Implementation. Pre-Policy Post-Policy Control Group Treatment Group Difference Control Group Treatment Group Difference Difference in Difference UET 43.814 43.931 0.117 48.552 49.178 0.626 0.509 Energy Consumption (Unit: 100 million tons of standard coal) 1330 1336 6 1272 1212 −60 −66 Energy Intensity (Unit: 100 million tons of standard coal per 10,000 USD) 4.023 4.035 0.012 3.211 3.031 −0.18 −0.192 FDI 4.416 4.418 0.002 5.245 5.392 0.147 0.145 IPCI 9.689 9.652 −0.037 10.266 10.457 0.191 0.228 IBTE 1.0004 1.0006 0.0002 0.9986 0.9997 0.0011 0.0009 Number of city 215 59 215 59 4. Empirical Results and Analysis 4.1. Baseline Regression Results In Table 4, the baseline regression results based on the difference-in-differences (DID) model reveal that China’s New Energy Demonstration City (NEDC) policy significantly promotes urban energy transition. Specifically, columns (1) and (2), where the Urban Energy Transition Index (UET) serves as the dependent variable, indicate that the implementation of the NEDC policy has led to an increase of 0.571 units in UET in the pilot cities, after controlling for other variables. The coefficient is statistically significant at the 5% level. Columns (3) and (4), which use the City-level Energy Transition Efficiency Index (CTFP) as the dependent variable, indicate that the NEDC policy led to a 0.0321-unit increase in CTFP, with the coefficient being statistically significant at the 5% level. All models control for both city-specific and year-specific fixed effects. These findings support Research Hypothesis 1, suggesting that the NEDC policy can promote urban energy transition. Economies 2025,13, 195 15 of 16 S.L.; visualization, J.L.; supervision, J.L.; project administration, J.L.; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript. Funding: National Natural Science Foundation of China Project (Grant No. 72364014). Jiangxi Provincial Social Science Foundation Project (Grant No. 25JL06). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request. Conflicts of Interest: The authors declare no conflict of interest. References Chen, B., Jin, F., Li, G., & Zhao, Y. (2023). Can the new energy demonstration city policy promote green and low-carbon development? Evidence from China. Sustainability,15(11), 8727. [CrossRef] Dai, J. (2025). Is policy pilot a viable path to sustainable development? Attention allocation perspective. International Review of Financial Analysis,98, 103923. [CrossRef] Dai, J., Mehmood, U., & Nassani, A. A. (2025). 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