
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39223238
71728
10.1038/s41598-024-71728-1
Article
The carbon emission reduction effect of green fiscal policy: a quasi-natural experiment
Wang Shuguang 1
Zhang Zequn 1
Zhou Zhicheng 2
Zhong Shen 102714@hrbcu.edu.cn

2
1 https://ror.org/03zsxkw25 grid.411992.6 0000 0000 9124 0480 School of Public Finance and Administration, Harbin University of Commerce, Harbin, 150028 China
2 https://ror.org/03zsxkw25 grid.411992.6 0000 0000 9124 0480 School of Finance, Harbin University of Commerce, Harbin, 150028 China
2 9 2024
2 9 2024
2024
14 203177 3 2024
30 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Carbon emission reduction is crucial for mitigating global climate change, and green fiscal policies, through providing economic incentives and reallocating resources, are key means to achieve carbon reduction targets. This paper uses data covering 248 cities from 2003 to 2019 and applies a multi-period difference-in-differences model (DID) to thoroughly assess the impact of energy conservation and emission reduction (ECER) fiscal policies on enhancing carbon emission (CE1) reduction and carbon efficiency (CE2). It further analyzes the mediating role of Green Innovation (GI), exploring how it strengthens the impact of ECER policies. We find that: (1) ECER policies significantly promote the improvement of carbon reduction and CE2, a conclusion that remains robust after excluding the impacts of concurrent policy influences, sample selection biases, outliers, and other random factors. (2) ECER policies enhance CE1 reduction and CE2 in pilot cities by promoting green innovation, and this conclusion is confirmed by Sobel Z tests. (3) The effects of ECER policies on CE1 reduction and the improvement of CE2 are more pronounced in higher-level cities, the eastern regions and non-resource cities. This research provides policy makers with suggestions, highlighting that incentivizing green innovation through green fiscal policies is an effective path to achieving carbon reduction goals.

Keywords

Carbon emission reduction
Fiscal policies for energy conservation and emission reduction
Multi-period difference-in-differences method
Quasi-natural experiment
Subject terms

Environmental impact
Climate-change impacts
Climate-change mitigation
issue-copyright-statement© Springer Nature Limited 2024
==== Body
pmcIntroduction

Efforts to mitigate global climate change through the reduction of CE1 have emerged as a shared objective among nations globally1. From the initiation of the United Nations Framework Convention on Climate Change to the enactment of the Kyoto Protocol and the adoption of the Paris Agreement, these pacts reflect the unified resolve of nations to tackle global climate change2,3. With the acceleration of global industrialization and the continuous increase in energy demand, there has been a significant rise in the emissions of greenhouse gases, especially carbon dioxide, posing an unprecedented challenge to the Earth’s climate system4. These issues encompass the escalation of average global temperatures, a surge in severe weather occurrences, accelerated glacier melt, and a persistent increase in sea levels5–7, which threaten the balance of natural ecosystems and have profound impacts on the economic development and well-being of human societies. Therefore, adopting effective carbon reduction strategies to slow these climate change trends has become an urgent task faced globally.

In the current field of CE1 reduction research, the focus is mainly on implementing policies such as carbon emission trading8, smart city pilot policies9, and low-carbon city pilot policies10. Among these policies, green fiscal policy, as a core strategy to mitigate the impact of climate change, is increasingly recognized by the academic community and policymakers for its importance in promoting CE1 reduction11,12. This policy directly impacts CE1 in economic activities through adjustments in the tax system, provision of fiscal subsidies, and increased investments in renewable energy and low-carbon technologies13. Green fiscal policies differ from traditional environmental protection measures by employing a mechanism that combines incentives and constraints, aiming to encourage enterprises to adopt emission reduction measures. In the implementation process of green fiscal policies, governments encourage enterprises to reduce CE1 by adjusting tax policies14. Specifically, the ECER policy impacts the carbon emissions of demonstration cities through a combination of financial incentives and target constraints. The demonstration period lasts for three years, during which the central government provides reward funds for demonstration projects. The amount of these rewards is determined by the category of the city: 600 million RMB annually for municipalities and city clusters, 500 million RMB annually for sub-provincial cities and provincial capitals, and 400 million RMB annually for other cities. Local governments have the discretion to decide how to utilize these funds, while the central government is responsible solely for project record management. Additionally, the central government conducts annual and overall target assessments of the demonstration cities. The results of the annual assessment influence the reward funds for the following year: cities that perform excellently will receive an additional 20% of reward funds, while those that fail to meet the standards will have 20% of their funds withdrawn. The overall assessment results are linked to the demonstration qualification and reward funds; cities that fail to meet the overall targets or have serious issues will lose their demonstration status and have all reward funds withdrawn. This financial incentive mechanism ensures that local governments have sufficient financial support when implementing green technologies and projects, promoting increased energy efficiency and the widespread adoption of clean energy. Simultaneously, through the target constraint mechanism, the central government strictly supervises and incentivizes local governments’ efforts to reduce emissions, ensuring effective policy implementation. Under the dual pressure of financial incentives and performance assessments, local governments actively adopt various measures to promote energy conservation and emission reduction, including investing in green infrastructure, promoting energy-saving technologies, and optimizing energy structures, thereby achieving significant reductions in carbon emissions.

Furthermore, innovation and technological breakthroughs significantly enhance the effectiveness of green fiscal policies in reducing carbon emissions. Specifically, technological advancements improve energy efficiency, reducing the energy consumption per unit of output; they lower the production costs of clean energy, promoting its widespread adoption; and they advance carbon capture and storage technologies, directly reducing industrial carbon dioxide emissions. These technological improvements bolster the impact of green fiscal policies, making them more effective in achieving carbon reduction targets. However, the implementation of green fiscal policies also faces some challenges. Firstly, balancing the relationship between economic development and environmental protection to avoid potential negative impacts such as job losses and industrial relocation during policy execution is an issue that policymakers need to consider. Secondly, the effective implementation of green fiscal policies requires strong policy support and regulatory mechanisms to ensure that policy measures are effectively executed and can adapt to constantly changing economic and environmental conditions. Therefore, evaluating the carbon reduction effect of such policies is of significant importance for achieving long-term environmental sustainability and promoting the green economic transformation.

This paper analyzes the impact of green fiscal policies on carbon emissions and carbon efficiency. Relevant research mainly focuses on the following two areas: studies on the factors influencing carbon emissions, and research related to environmental regulations and energy conservation and emission reduction fiscal policies.

Firstly, a substantial body of literature focuses on the factors influencing carbon emissions, with some studies specifically examining the impact of government intervention and environmental regulation on CO2 emissions. These studies are closely related to the theme of this paper. From an economic perspective, numerous studies have demonstrated that economic growth significantly impacts carbon emissions15–17. Generally, increased economic activity is associated with higher energy consumption, leading to higher carbon emissions. However, as economies reach a certain level of development, the Environmental Kuznets Curve (EKC) phenomenon may occur, where carbon emissions begin to decrease after reaching a certain economic threshold18,19. Research has also confirmed that economic growth increases the ecological footprint, leading to environmental degradation20. For example, economic growth, income inequality, and energy poverty have increased environmental pressure in BRICS countries21. In Pakistan, institutional quality has led to higher CO2 emissions, but economic development can help reduce these emissions22. From a social perspective, the acceleration of urbanization is typically accompanied by increased energy consumption, thereby raising carbon emissions. There is a long-term and short-term U-shaped relationship between urbanization and the environment23. Upgrading existing infrastructure can enable various sectors to produce minimal waste that impacts emissions24. Changes in consumption levels and population structure also significantly affect carbon emissions25. From a policy perspective, government-enacted environmental regulations and policies, such as carbon taxes, carbon trading markets, emission standards, and renewable energy subsidies, play a crucial role in reducing carbon emissions. Innovations and environmental policies contribute to emission reductions both in the long and short term. Additionally, carbon pricing can reduce emissions in specific regions, although its impact is often more targeted at specific countries26. Carbon taxes and mitigation technologies are helping to achieve sustainable development goals for carbon mitigation27. Green energy investments are significantly associated with greenhouse gas emissions and support environmental quality28. However, these studies often overlook the impact of energy conservation and emission reduction fiscal policies on carbon emissions.

Secondly, there is a body of literature focusing on environmental regulation, which can be divided into two main areas: the impact of environmental regulation on the environment and its impact on the economy. On the one hand, extensive research has explored the environmental impact of regulation. Studies generally agree that stringent environmental regulations help reduce pollutant emissions and improve environmental quality. Environmental regulations significantly enhance the synergy between carbon reduction and air pollution control29. Target-based pollutant reduction policies effectively constrain the sulfur dioxide emissions of regulated enterprises, lowering their sulfur dioxide emission intensity, thereby demonstrating that stringent environmental regulations facilitate green transitions for businesses30. However, in some developing countries or regions with weak enforcement, the effectiveness of environmental regulations may be compromised. Despite strict regulatory policies being in place, inadequate enforcement or a lack of regulatory capacity may result in actual pollutant reduction falling short of expectations. On the other hand, part of the literature examines the economic impact of environmental regulation. Some studies suggest that environmental regulation can drive technological innovation and industrial upgrading, thereby promoting economic growth31. Strict environmental standards force companies to improve production processes and develop new environmental technologies, which can create new economic opportunities and growth points32. Environmental regulations significantly enhance green technological innovation33, and they have notably promoted green innovation across European countries34. Conversely, environmental regulations may increase operational costs for businesses, particularly in the short term due to compliance costs, which could inhibit economic growth. This is especially true for regions or countries that rely heavily on high-pollution, high-energy-consumption industries, where environmental regulation might lead to a slowdown in economic growth. Given that energy conservation and emission reduction fiscal policies are a form of environmental regulation, it is necessary to evaluate their effectiveness.

Thirdly, some literature evaluates the governance effectiveness of energy conservation and emission reduction fiscal policies. From an environmental perspective, these policies can reduce pollutants and enhance efficiency. On average, such policies have reduced industrial SO2 (sulfur dioxide) emissions by 23.8% and industrial wastewater discharge by 17.5%35. Additionally, energy conservation and emission reduction fiscal policies can effectively improve green total factor carbon efficiency36. From an economic perspective, these policies can promote investment and economic growth37. They have significantly improved green credit for enterprises and can facilitate sustainable urban development38.

In summary, there are two significant gaps in the existing literature. Firstly, although numerous studies have extensively explored the factors influencing carbon emissions from economic, social, and policy perspectives, relatively few have examined the relationship between ECER policies and carbon emissions. Specifically, most of the existing literature focuses on the impact of macroeconomic policies, industrial structure adjustments, and technological innovation on carbon emissions. However, there is a lack of systematic empirical analysis on how specific fiscal incentives directly affect carbon emissions, limiting our comprehensive understanding of the actual effects of fiscal policies on emission reduction. Secondly, most of the existing studies investigate carbon dioxide emissions from a single perspective, such as focusing on total carbon emissions, carbon intensity, or carbon efficiency. These studies lack a multi-faceted exploration of the relationship between a single policy and carbon emissions. Typically, research adopts a specific metric to measure policy effects, but this approach overlooks how different metrics might reveal various aspects of policy impact. Consequently, these studies fail to capture the multi-dimensional effects of policies on reducing carbon emissions comprehensively. This single-perspective research methodology cannot adequately reflect the multiple impacts of policies on carbon emissions across different scenarios and time periods. This paper aims to evaluate the impact of the ECER policy, jointly introduced by the Ministry of Finance and the National Development and Reform Commission in 2011, on CE1 and CE2. Given that the ECER policy was implemented in three batches of pilot cities, this study employs a multi-period Difference-in-Differences (DID) model for analysis. The advantage of this model lies in its ability to compare the effects of the policy before and after its implementation across multiple time points, thereby capturing the dynamic impacts of the policy. Furthermore, this article explores the mediating role of green innovation in the impact process of the ECER policy, revealing the policy’s varying effects on CE1 and CE2 across different regions through heterogeneity analysis.The marginal contributions of this article: Firstly, this paper evaluates the relationship between ECER policies and carbon emissions, addressing a significant gap in the existing research. Although numerous studies have explored various factors influencing carbon emissions from different perspectives, there is a lack of systematic research on the actual effects of specific fiscal policies on energy conservation and emission reduction, particularly their direct impact on carbon emissions. Through empirical analysis and data validation, this study thoroughly investigates the specific mechanisms and effects of ECER policies on carbon emissions in practice, thus filling this research gap. Secondly, this paper systematically assesses the relationship between ECER policies and carbon emissions from two key perspectives: total carbon emissions and carbon efficiency. By considering these two important indicators, this study not only examines the impact of ECER fiscal policies on overall carbon emissions but also analyzes their role in improving carbon efficiency. Through an in-depth analysis of these two metrics, this paper provides a more comprehensive and multi-dimensional view, systematically evaluating the effectiveness and mechanisms of ECER policies.

The remainder of the article is organized as follows: the second part discusses the policy background and theoretical analysis; the third part details the model settings and variable explanations; the fourth part presents the empirical analysis; the fifth part analyzes regional heterogeneity; and the last part concludes with conclusions and policy recommendations.

Policy background and theoretical analysis

Policy background

In 2011, the Ministry of Finance and the National Development and Reform Commission issued the “Notice on Conducting Comprehensive Demonstration Work of Fiscal Policies for Energy Conservation and Emission Reduction,” deciding to carry out comprehensive demonstrations of fiscal policies for ECER in some cities during the “Twelfth Five-Year” period. Beijing, Shenzhen, Chongqing, Hangzhou, Changsha, Guiyang, Jilin, and Xinyu were selected as the first batch of demonstration cities. In the subsequent years of 2013 and 2014, 10 and 12 cities were respectively chosen as pilot cities for the fiscal policies on ECER. Specifically, this policy uses cities as platforms and integrates fiscal policies as a means to comprehensively carry out urban ECER demonstrations in various aspects, including industrial decarbonization, transportation clean-up, building greening, service intensification, major pollutant reduction, and large-scale utilization of renewable energy. Its main goal in terms of CE1 reduction is to establish a concept of green, circular, and low-carbon development in the demonstration cities, achieve widespread promotion of low-carbon technologies in industries, construction, transportation, and other fields, lead the pilot cities in ECER efforts across society, and significantly enhance their capacity for sustainable development. Figure 1 presents the spatial distribution of ECER policy pilot cities in the years 2011, 2013, and 2014 (This figure was created using ArcMap software).Fig. 1 Distribution of ECER Policy Pilot Areas (Plan Approval Number GS(2019)1822).

Theoretical analysis

Carbon emission reduction effect of green fiscal policy

Green fiscal policy, as a significant environmental governance tool, promotes the transformation of the economic and social system towards low-carbon, sustainable development through fiscal measures39. Its CE1 reduction effects can be described from the following aspects. Firstly, green fiscal policy encourages the research and application of green technologies through economic incentives (such as tax reductions and fiscal subsidies)40. These technologies include energy efficiency improvement technologies, clean energy technologies, and carbon capture and storage technologies, which directly reduce energy consumption and CE1 in economic activities. Secondly, green fiscal policy influences the behavior of consumers and producers by affecting the price mechanism. The imposition of a carbon tax raises the cost of CE1, reflecting the external cost of CE1 on the environment, encouraging enterprises to take emission reduction measures, and prompting consumers to prefer low-carbon products and services41. The change in price signals promotes the transformation of the entire society’s energy consumption structure towards more efficient and low-carbon directions. Furthermore, green fiscal policy can support CE1 reduction-related infrastructure construction and public service improvements through the guidance and redistribution of funds. This includes the construction and optimization of public transportation systems, urban greening, and forest conservation projects, which not only directly or indirectly reduce CE1 but also enhance the carbon absorption capacity of cities and regions. Lastly, green fiscal policies, by raising public environmental awareness and participation, create a conducive atmosphere for all sectors of society to join in carbon reduction efforts42. Governments can increase public awareness of climate change and inspire a low-carbon lifestyle through the promotion and education of fiscal policies, providing broader social support for carbon reduction43.

Green fiscal policies not only drive a reduction in CE1 but also stimulate sustainable economic growth. By taxing high-carbon activities, offering financial subsidies and incentives for green projects, these policies channel capital towards low-carbon and green industries. This not only mitigates negative environmental impacts but also fosters the development of emerging green technologies and sectors. As the green industry expands and low-carbon technologies become more widespread, economic growth increasingly relies on clean and efficient energy use44, thereby enhancing the CE2. Thus, the implementation of green fiscal policies demonstrates a commitment to transitioning towards a low-carbon economy, playing a crucial role in the global response to climate change, achieving a win–win for environmental protection and economic growth.

Based on this, the article proposes hypothesis 1: Green fiscal policies can promote CE1 reduction effects and enhance CE2.

Mechanism analysis

Green innovation is a key factor in driving sustainable development, particularly playing a significant role in CE1 reduction and efficiency enhancement. By introducing and adopting new environmentally friendly technologies and processes, green innovation not only significantly reduces greenhouse gas emissions but also enhances the efficiency of energy use and resource management, thus promoting a harmonious coexistence between economic activity and environmental protection. Green innovation, through the development and adoption of renewable energy technologies such as solar, wind, and biomass energy, directly reduces reliance on fossil fuels and the corresponding CE1. The application of these technologies not only reduces the carbon footprint but also promotes the diversification of energy supply and enhances energy security45. Green innovation also plays an essential role in improving energy efficiency. By adopting more efficient production processes and energy-using equipment, businesses and households can accomplish the same tasks or meet the same living needs with lower energy consumption, thus reducing CE146. Additionally, green innovation encompasses the concepts and practices of the circular economy, which encourages the reuse, recycling, and recovery of materials, reducing the extraction and processing of new materials and further lowering CE1s in the production process47. Green innovation includes the development of Carbon Capture, Utilization, and Storage (CCUS) technologies, which can directly capture carbon dioxide from industrial emissions and either convert it into useful products or safely store it, thereby reducing the carbon content in the atmosphere48. On the policy and management level, green innovation also involves establishing and refining mechanisms such as carbon pricing, green taxes, and carbon trading, which promote the adoption of low-carbon and environmentally friendly technologies and behaviors among businesses and individuals through economic incentives49. Based on this, the article proposes hypothesis H2: Green fiscal policies can promote CE1 reduction effects and CE2 by fostering green innovation.

In conclusion, the theoretical framework, as shown in Fig. 2.Fig. 2 Theoretical framework.

Model setting and variable description

Model

To address the limitations faced by traditional regression models in evaluating policy implementation effects, this study utilizes DID model for analysis. Given the variation in the policy implementation years in this paper, the traditional DID model cannot be used50. Accordingly, this paper draws on the approach of Beck et al.51, employing a DID with multiple time periods to assess the policy effects, with the model set up as follows:1 Yit=β0+β1Treatedi×Postit+λ∑Controlsit+νi+τt+εit

Y in the model is the explained variable, indicating CE1 and CE2 of the city i in the annual t. Treatedi is the group variable, where it takes the value 1 if city i belongs to the treatment group, and 0 if it belongs to the control group; Postit is the post-treatment period dummy variable, where it takes the value 1 for city i in year t if ECER policy has been officially implemented, and 0 if it has not been officially implemented. This study investigates the impact of energy conservation and emission reduction fiscal policies on urban CE1 and CE2 by examining the effect of the interaction term Treated × Postit on the dependent variable. The coefficient β1 measures the impact of the policy on the dependent variable. Controls in this study represent control variables, specifically urbanization rate (lnur), foreign direct investment level (lnfdi), industrial structure (lnis), level of scientific and technological expenditure (lnsst), and fiscal revenue and expenditure level (lnfre), among others. ν, τ and ε represent city fixed effects, time fixed effects, and random error terms, respectively.

Considering the three-year implementation period of green fiscal policies, it is necessary to establish an exit mechanism for the treatment group. Drawing on existing literature12, this paper constructs the following treatment groups: the first batch of pilot cities from 2011 to 2014 is set to 1; the second batch of pilot cities from 2013 to 2016 is set to 1; the third batch of pilot cities from 2014 to 2017 is set to 1, with other years set to 0. The pilot cities are shown in Fig. 3.Fig. 3 ECER policy implementation period.

Variables and data sources

Explained variables

Carbon Emissions: Drawing from existing literature, this article utilizes current CE1 data to calculate CE152,53. It follows the guidelines on greenhouse gas emission allocations by the IPCC, taking into account the emissions of carbon dioxide within the administrative boundaries of each city. Territorial emissions refer to emissions occurring within the managed territory and maritime areas under the jurisdiction of a region54, including emissions from socio-economic sectors and direct residential activities within regional boundaries55.

Carbon Efficiency: Following existing literature, this paper measures CE2 using the ratio of CE1 to GDP56.

In examining the correlation between CE1 and economic efficiency, Fig. 4a provides an overview of the evolution of CE1 from 2003 to 2019, while Fig. 4b offers a detailed portrayal of the progress in CE2 over the same period. Figure 4a reveals a steady increase in total CE1 beginning in 2002, with a notable acceleration post-2009, peaking in 2017. Despite some fluctuations and a slight dip in 2018, the figures for 2019 remained just below the peak, overall indicating an upward trajectory. In contrast, Fig. 4b demonstrates a year-on-year improvement in CE2, measured in tens of thousands of yuan output per ton of carbon emitted, starting in 2003. The pace of growth accelerated significantly after 2011, reaching its zenith in 2019. This signifies a substantial rise in the economic output efficiency per unit of carbon emitted, revealing a reduction in carbon dependency within economic activities. The combined analysis of both figures indicates that, alongside economic growth, there has been a notable advancement in optimizing CE2.Fig. 4 Trends in CE1 (a) and CE2 (b) (2003–2019).

Control variables

To eliminate the interference of omitted variables on the research results, this article selects the following control variables57,58: Urbanization rate (lnur), which refers to the ratio of urban population to total population; Level of foreign direct investment (lnfdi), the ratio of actual foreign investment to the GDP; Industrial structure (lnis), the proportion of the secondary industry in GDP; Level of science and technology expenditure (lnsst), the ratio of science and technology expenditure in ten thousand to GDP in hundred billion; Fiscal revenue and expenditure level (lnfre), the sum of local fiscal budget revenue and expenditure to GDP. To reduce heteroscedasticity in the data, this article takes the logarithm of all control variables. Table 1 reports the definitions of the main variables in this paper.Table 1 Variable definition.

	Variable	Abbreviation	Definition	
Explained Variable	Carbon emission	CE1		
Carbon efficiency	CE2	Ratio of GDP to CE1	
Explanatory Variable	Fiscal policies for energy conservation and emission reduction	ECER		
Mediator Variable	Green innovation	GI	Number of green invention patent grants	
Total number of green patents per 10,000 people	
Control Variables	Urbanization	lnur	Ratio of urban population to total population	
Foreign direct investment	lnfdi	Ratio of actual foreign investment in ten thousand US dollars to the GDP in hundred billion	
Industrial structure	lnis	Secondary industry in GDP	
Science and technology expenditure	lnsst	Ratio of science and technology expenditure in ten thousand to GDP in hundred billion	
Fiscal revenue and expenditure	lnfre	Sum of local fiscal budget revenue and expenditure to GDP in hundred billion	

Sample selection and data source

We selects cities at the prefecture level in China from 2003 to 2019 as the research sample. Considering that missing data can affect the results, this paper excludes samples with missing data, ultimately obtaining 3134 samples. The CE1 data in this paper comes from the China Emissions Accounts and Datasets (CEADs), which provides CE1 data from 1997 to 2019, so the sample period for this paper ends in 2019. The control variable data are all sourced from the China City Statistical Yearbook covering the years 2004 to 2020. Table 2 provides descriptive statistics for the main variables in this paper.Table 2 Descriptive statistics.

Variable	N	Mean	SD	Min.	Max.	
CE1	3134	3.299	0.866	0.775	6.129	
CE2	3134	3.947	0.777	1.522	6.426	
ECER	3134	0.031	0.172	0	1	
lnur	3134	0.409	0.109	0.106	0.693	
lnfdi	3134	0.032	0.031	0	0.429	
lnis	3134	0.394	0.069	0.139	0.620	
lnsst	3134	0.181	0.156	0	1.638	
lnfre	3134	7.631	0.355	6.005	8.948	

Eliminating interference

In a quasi-natural experiment, various factors may influence the relationship between the implementation of green fiscal policies and the reduction of carbon emissions. To address this, we employed multiple methods to control for these potential confounding variables. Firstly, we introduced control variables to eliminate or reduce the interference of external factors on the main research relationship, ensuring the accurate estimation of the effects of green fiscal policies. Secondly, we adopted a two-way fixed effects model to control for time-invariant city characteristics and potential common time trends. Thirdly, we conducted parallel trend tests to verify whether the trends of the treatment and control groups were consistent before the policy implementation, ensuring the validity of the Difference-in-Differences (DID) estimates. Additionally, we performed multiple robustness checks, including propensity score matching and excluding the effects of other concurrent policies, to test the robustness of the results. Finally, we confirmed the reliability of the results through placebo tests. These methods collectively help to effectively reduce the interference of external variables, ensuring the accuracy and reliability of the research findings.

Empirical results

Benchmark regression analysis

We employs a two-way fixed effects model for the empirical analysis of the CE1 reduction effects of ECER policies, with the estimation results presented in Table 3. Columns (1) to (3) of Table 3 report the estimation results of green fiscal policies on CE1. The results show that, when the model does not include control variables, the implementation of green fiscal policies has an estimated coefficient of − 0.070 for CE1, significant at the 1% level, indicating that the CE1 of pilot cities are 7.0% lower than those of non-pilot cities. After adding control variables, the results do not change significantly. Columns (4) to (6) report the estimation results of green fiscal policies on CE2. The results indicate that, when the model does not include control variables, the implementation of green fiscal policies has an estimated coefficient of 0.099 for CE2, significant at the 1% level, suggesting that the CE2 of pilot cities is 9.9% higher than that of non-pilot cities. After including control variables, the results remain largely unchanged. This provides evidence for Hypothesis 1: ECER policies have a significant CE1 reduction effect and also significantly promote CE2.Table 3 Results of the benchmark regression analysis.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	
CE1	CE2	
ECER	− 0.070***	− 0.063***	− 0.063***	0.099***	0.103***	0.094***	
(− 3.115)	(− 2.756)	(− 2.774)	(4.145)	(4.355)	(3.949)	
lnur		0.090	0.080		0.154	0.208	
	(0.707)	(0.623)		(1.142)	(1.578)	
lnfdi		− 0.726***	− 0.731***		0.564**	0.984***	
	(− 2.999)	(− 2.990)		(2.042)	(3.495)	
lnis		1.217***	1.233***		0.560**	0.464**	
	(6.996)	(7.019)		(2.348)	(2.180)	
lnsst			0.118*			0.103	
		(1.922)			(1.640)	
lnfre			− 0.031			− 0.367***	
		(− 0.938)			(− 7.853)	
_cons	3.302***	2.808***	3.024***	3.944***	3.643***	6.430***	
(822.125)	(37.375)	(11.594)	(902.383)	(38.426)	(17.740)	
City/Year	YES	YES	YES	YES	YES	YES	
N	3134	3134	3134	3134	3134	3134	
R2	0.939	0.941	0.941	0.912	0.912	0.916	
z statistics in parentheses *p < 0.1, **p < 0.05, ***p < 0.01.

To further illustrate the step-by-step changes in the coefficients, this paper presents Fig. 5. The horizontal axis of Fig. 5 represents the number of control variables, while the vertical axis indicates the coefficients, with the grey area denoting the error bars. As evident from Fig. 5, the coefficients and error bars exhibit minimal variation with the increase in control variables, indicating a negligible impact of the number of control variables on the coefficients and highlighting their stability. This finding suggests that the primary regression coefficients remain consistent even when more control variables are included in the analysis, underscoring the model’s robustness.Fig. 5 Plot of coefficient variation based on the step by step method.

Parallel trend test

The prerequisite for using DID model to evaluate policies is the parallel trends assumption. This implies that, before the policy intervention, the treatment group and the control group should exhibit similar trends without systematic differences. After the policy intervention, the trends between these two groups should diverge significantly. Following existing literature50,59,60, this paper employs an event study approach to analyze the effects before and after the policy implementation.2 Yit=∑k=-8,k≠-18βkTreated(k)++λ∑Controlsit+νi+τt+εit

In Eq. (2), the variable Treated still represents cities that have been approved to establish pilot ECER policies. To avoid perfect multicollinearity, this paper uses the year before policy implementation as the baseline group, meaning that k = − 1 is not included in the regression equation, and the other parts of the model are consistent with the baseline model. If the coefficient is not significant when k < 0, it indicates that the estimated results satisfy the parallel trends assumption. Figure 6 shows that, before the implementation of the policy, all coefficients are not significant, and in the fifth year after policy implementation, the coefficients start to become significant. This indicates that the implementation of ECER policies has a significant promotional effect on CE1 reduction and CE2 in the pilot areas, but this effect has some lag.Fig. 6 Parallel trend test of CE1 (a) and CE2 (b).

Robustness test

Exclusion of contemporaneous policies

The smart city construction policy began with the “Notice on Carrying out the National Smart City Pilot Work” issued by the Ministry of Housing and Urban–Rural Development in 2012, with smart city pilots being established in 2012, 2013, and 201461. This paper excludes all smart pilot cities and re-runs the regression, with results shown in columns (1) and (2) of Table 4. The results indicate that contemporaneous policies during the sample period caused some interference with the estimated coefficients, but the extent is very limited. The implementation of ECER policies still has statistically and economically significant effects on promoting CE1 reduction and CE2 in pilot cities.Table 4 Results of nearest neighbor matching within the calipers.

Variable	Unmatched	Mean	%reduct	t-test	V(T)/V(C)	
Matched	Treated	Control	%bias	|bias|	t	p >|t|	
lnur	U	0.4941	0.4061	86.0		7.90	0.000	0.80	
M	0.4941	0.4907	3.3	96.2	0.24	0.813	0.92	
lnfdi	U	0.0334	0.0317	5.7		0.51	0.611	0.69	
M	0.0334	0.0329	1.6	71.8	0.12	0.903	0.97	
lnis	U	0.3805	0.3943	− 19.4		− 1.93	0.053	1.15	
M	0.3805	0.3792	1.8	91.0	0.11	0.910	0.88	
lnsst	U	0.2761	0.1781	58.8		6.09	0.000	1.32	
M	0.2761	0.2847	− 5.2	91.2	− 0.31	0.760	0.71	
lnfre	U	7.7759	7.6267	46.0		4.07	0.000	0.66*	
M	7.7759	7.7927	− 5.2	88.7	− 0.38	0.702	0.84	

PSM-DID

We employs the Propensity Score Matching (PSM) method to process the data, aiming to reduce data bias and the impact of confounding factors62,63. Through PSM-DID analysis, the results show that after matching, the absolute bias (|bias|) of all variables decreases by more than 70%, and the p-values are not statistically significant. This comparative analysis reveals the effectiveness of PSM in reducing the initial bias between the treatment and control groups. Therefore, the matching process successfully achieves balance in characteristics between the two groups across key indicators, making the assessment of the treatment effect more accurate and reliable.

Table 4 reports the results of the PSM. The propensity score matching results show a substantial decrease in |bias| for variables, highlighting an enhanced balance between treated and control groups post-matching. For instance, the absolute bias for “lnur” dropped from 86.0% to just 3.3%, showcasing a 96.2% reduction in bias, which underscores the effectiveness of the matching process. Similarly, other variables like “lnfdi”, “lnis”, and “lnsst” experienced significant reductions in bias. The p >|t| values, mostly above 0.05 post-matching, indicate that the differences between groups are not statistically significant, affirming the success of the matching in minimizing discrepancies and improving comparability.

Figure 7 displays the matching results of PSM. The results indicate that after the matching process, the percentage bias (%bias) for the control variables all remain below 10%. This finding fully confirms the effectiveness of the PSM method in balancing key characteristics between the experimental and control groups, thereby ensuring the accuracy and reliability of subsequent analyses.Fig. 7 Balance test.

This paper conducts an empirical analysis using matched data, with the results shown in columns (3) and (4) of Table 5. The results indicate that ECER policy still has a significant CE1 reduction effect and also significantly promotes CE2. This suggests that there is no significant impact of self-selection bias on the regression results in this study.Table 5 Robustness test.

Variables	Exclusion of Contemporaneous Policies	PSM-DID	Winsorize	Replacement Sample Time	
(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	
CE1	CE2	CE1	CE2	CE1	CE2	CE1	CE2	
ECER	− 0.100***	0.126***	− 0.065***	0.104***	− 0.062***	0.097***	− 0.067***	0.098***	
(− 3.241)	(3.991)	(− 2.785)	(4.235)	(− 2.767)	(4.167)	(− 2.913)	(4.087)	
lnur	− 0.350**	0.550***	0.145	0.141	0.051	0.252*	0.065	0.204	
(− 2.157)	(3.097)	(0.810)	(0.717)	(0.403)	(1.953)	(0.496)	(1.541)	
lnfdi	− 0.972**	1.104***	− 0.588**	0.918***	− 1.022***	1.273***	− 0.784***	0.949***	
(− 2.571)	(2.595)	(− 2.490)	(3.446)	(− 4.140)	(4.134)	(− 2.780)	(2.965)	
lnis	1.129***	0.324	1.307***	0.570*	1.292***	0.431**	1.149***	0.505**	
(5.126)	(1.177)	(5.633)	(1.871)	(7.578)	(2.056)	(6.269)	(2.229)	
lnsst	0.229*	− 0.112	0.056	0.167***	0.164***	0.101	0.129**	0.080	
(1.955)	(− 0.964)	(0.913)	(2.919)	(2.729)	(1.298)	(1.970)	(1.188)	
lnfre	− 0.037	− 0.362***	0.039	− 0.335***	− 0.051	− 0.358***	− 0.014	− 0.351***	
(− 0.881)	(− 5.855)	(0.897)	(− 5.513)	(− 1.390)	(− 7.530)	(− 0.407)	(− 7.099)	
_cons	3.105***	6.297***	2.524***	6.279***	3.164***	6.345***	2.917***	6.267***	
(9.414)	(12.748)	(6.700)	(11.969)	(11.116)	(17.319)	(10.923)	(16.543)	
City/Year	YES	YES	YES	YES	YES	YES	YES	YES	
N	1857	1857	2298	2298	3134	3134	3009	3009	
R2	0.932	0.899	0.956	0.939	0.941	0.915	0.942	0.915	

Winsorize

To reduce the impact of outliers on regression analysis, this paper adopts a winsorization process39,64, which involves replacing observations below a certain threshold with the 1st percentile and those above the threshold with the 99th percentile before conducting the regression. Columns (5) and (6) of Table 5 display the analysis results after this treatment, showing that the impact of outliers on the regression results is not significant.

Replacement sample time

Considering the potential unique impact of the COVID-19 pandemic on CE1 and CE2 in 2019, this paper decided to exclude data from 2019 to ensure the robustness of the research results, thus avoiding the interference of pandemic-related outliers in the analysis. Subsequently, the paper conducted an empirical analysis based on the updated dataset, with the analysis results presented in columns (7) and (8) of Table 5. The analysis results indicate that after excluding the special impact of the COVID-19 pandemic, the CE1 reduction effect of the green fiscal policy remains significant, and there is still a significant promotional effect on CE2.

Placebo test

The DID model is based on the common trends assumption, which posits that, in the absence of an intervention, the trends of the treatment and control groups would have been similar65. By conducting a placebo test on data from before the intervention, this assumption can be tested for validity. If significant ‘intervention effects’ are also found during the placebo test conducted before the intervention or at irrelevant time points, this indicates that the effects estimated by DID are actually caused by other unobserved factors, rather than the intervention itself66. Referencing the placebo practices in existing literature59, this paper tests for the impact of unobservable factors on the estimation results. The study randomizes the impact of ECER policies across cities, selecting treatment groups randomly from 248 cities, with the remaining cities serving as control groups. This randomization process is repeated 500 times to generate a distribution graph of the regression coefficients, where the dashed line in the graph represents the actual regression coefficient, as specifically shown in Fig. 8. Figure 8a represents the placebo test for CE1, and Fig. 8b for CE2. From Fig. 8, it is evident that after randomizing the core explanatory variables, the mean of the coefficients is close to 0, and the mean of the coefficients after randomization significantly deviates from their true values. This indicates that, excluding the interference of other random factors on the empirical results, the green fiscal policy has a significant effect on CE1 reduction and significantly promotes CE2.Fig. 8 Placebo test of CE1 (a) and CE2 (b).

Mechanism test

The analysis results presented earlier indicate that the ECER policy has significantly promoted CE1 reduction and the improvement of CE2 in pilot cities. Accordingly, this study will further explore the mechanism of action of ECER policy and has constructed the following model:3 GIit=β0+β1Treatedi×Postit+λ∑Controlsit+νi+τt+εit

GI refers to green innovation. Following existing literature, this study uses the number of green invention patent grants (lngi_invention) and the total number of green patents per 10,000 people (lnpgi_total) as proxy variables for green innovation67,68. Due to the evident causal inference flaws in the three-stage mediation mechanism test69, this study refers to the mediation effect test model by Niu et al.70 and employs the Sobel test to further evaluate the regression results, thereby enhancing the completeness and credibility of the mechanism test71. The regression results are shown in Table 6. Columns (1) and (4) report the impact of the ECER policy on green innovation, with significant results. This confirms hypothesis H2: green fiscal policies can promote CE1 reduction effects and CE2 by fostering green innovation. Moreover, the Sobel Z coefficients are greater than 2.58, indicating that the mediating variable has a sufficiently strong explanatory power for the total effect.Table 6 Mechanism test: GI.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	
lngi_invention	CE1	CE2	lnpgi_total	CE1	CE2	
ECER	0.129**	− 0.062***	0.091***	0.035*	− 0.061***	0.089***	
(2.361)	(− 2.716)	(3.821)	(1.693)	(− 2.686)	(3.822)	
lngi_invention		− 0.010*	0.023**				
	(− 1.146)	(2.505)				
lnpgi_total					− 0.073**	0.133***	
				(− 2.314)	(4.021)	
lnur	1.227***	0.092	0.180	− 0.301***	0.058	0.248*	
(4.417)	(0.714)	(1.369)	(− 4.039)	(0.448)	(1.876)	
lnfdi	− 2.102***	− 0.752***	1.032***	− 0.915***	− 0.798***	1.106***	
(− 4.113)	(− 3.038)	(3.601)	(− 2.922)	(− 3.126)	(3.745)	
lnis	− 2.007***	1.213***	0.510**	− 1.530***	1.121***	0.668***	
(− 5.077)	(6.885)	(2.375)	(− 11.594)	(6.225)	(2.959)	
lnsst	1.797***	0.135**	0.062	0.890***	0.183**	− 0.016	
(10.257)	(2.085)	(0.968)	(9.230)	(2.425)	(− 0.219)	
lnfre	− 0.469***	− 0.036	− 0.357***	− 0.342***	− 0.056	− 0.322***	
(− 5.232)	(− 1.067)	(− 7.564)	(− 9.174)	(− 1.595)	(− 6.654)	
_cons	5.652***	3.078***	6.303***	3.443***	3.274***	5.972***	
(8.008)	(11.632)	(17.053)	(11.778)	(11.536)	(15.372)	
City/Year	YES	YES	YES	YES	YES	YES	
Sobel Z		5.748	5.426		4.349	4.254	
N	3134	3134	3134	3134	3134	3134	
R2	0.918	0.941	0.916	0.863	0.941	0.916	

Heterogeneity analysis

By city grade

In the process of urbanization and industrialization, a city’s level often reflects its level of economic development, capacity for technological innovation, infrastructure completeness, and the comprehensiveness of its public services. This paper categorizes the sample cities based on their tier into higher-level cities (provincial capitals, sub-provincial cities, and municipalities directly under the Central Government) and general cities, and conducts regression analysis. The regression results shown in Table 7, specifically in columns (1), (2), (6), and (7), indicate that in higher-tier cities, the coefficients of the ECER policy on CE1 and CE2 for pilot cities are -0.098 and 0.118, respectively, significant at the 1% level. However, in general cities, the absolute values of the coefficients are smaller and not significant. From this, we can conclude that the ECER policy’s effect on CE1 reduction and the enhancement of CE2 is more significant in higher-tier cities compared to general cities. Higher-level cities, with their advanced economic structures, abundant fiscal resources, high levels of technological innovation, and strong policy enforcement capabilities, make the green fiscal policy more effective in these areas in terms of CE1 reduction and the promotion of CE2. Firstly, economically developed higher-tier cities have more sufficient fiscal funds and investment capacity, which can support large-scale green infrastructure construction and green technology R&D, thereby directly reducing urban CE1 and improving energy use efficiency. Secondly, technological innovation is a key factor in improving CE2. As centers of technological innovation and information exchange, higher-level cities are more likely to attract and gather high-tech companies and research institutions, promoting the development and application of green technologies, and effectively reducing CE1. Additionally, higher-tier cities usually have more comprehensive laws, regulations, and policy enforcement mechanisms, ensuring the effective implementation and regulation of green fiscal policies. Also, residents in these cities often have higher environmental awareness and a preference for green consumption, which helps to create a favorable social atmosphere for the implementation of green fiscal policies. Finally, due to their strong regional influence and exemplary role, higher-tier cities can promote green transformation and low-carbon development in surrounding areas and even the entire country through policy guidance and market incentives, further amplifying the CE1 reduction effect and enhancing the impact on CE2 of green fiscal policies.Table 7 Results of urban grade and geographic location heterogeneity analysis.

Variables	High	Low	East	Centre	West	High	Low	East	Centre	West	
(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	
CE1	CE2	
ECER	− 0.098***	− 0.044	− 0.091***	− 0.076*	− 0.042	0.118***	0.066*	0.119***	0.091*	0.051	
	(− 3.053)	(− 1.316)	(− 2.760)	(− 1.651)	(− 0.862)	(4.081)	(1.932)	(3.417)	(1.859)	(1.042)	
lnur	0.464**	− 0.051	0.018	− 0.030	0.148	0.101	0.307**	0.151	0.333*	0.041	
	(2.064)	(− 0.337)	(0.102)	(− 0.174)	(0.341)	(0.391)	(2.039)	(0.787)	(1.823)	(0.111)	
lnfdi	− 0.112	− 0.996***	− 0.345	− 0.487	− 1.706	1.058**	1.029***	0.758**	− 1.444**	1.992*	
	(− 0.303)	(− 3.120)	(− 1.274)	(− 0.958)	(− 1.455)	(2.255)	(3.051)	(2.381)	(− 2.150)	(1.757)	
lnis	2.577***	1.043***	1.535***	1.761***	− 0.041	− 0.282	0.742***	− 0.114	0.195	0.947*	
	(4.708)	(5.579)	(5.118)	(6.746)	(− 0.075)	(− 0.526)	(3.132)	(− 0.330)	(0.604)	(1.854)	
lnsst	− 0.014	0.177**	− 0.043	0.387***	− 0.787***	0.258**	− 0.014	0.322***	− 0.136	0.983***	
	(− 0.134)	(2.249)	(− 0.485)	(4.652)	(− 2.934)	(2.390)	(− 0.179)	(4.021)	(− 1.418)	(3.595)	
lnfre	− 0.207*	− 0.035	0.004	− 0.040	− 0.088	− 0.099	− 0.338***	− 0.386***	− 0.453***	− 0.141	
	(− 1.879)	(− 0.963)	(0.062)	(− 0.805)	(− 1.018)	(− 0.883)	(− 6.645)	(− 5.601)	(− 6.416)	(− 1.625)	
_cons	4.365***	3.041***	2.886***	2.674***	4.003***	5.031***	6.003***	6.902***	7.201***	4.264***	
	(4.708)	(10.833)	(5.945)	(6.848)	(5.099)	(5.615)	(14.964)	(13.110)	(13.237)	(5.731)	
City/Year	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	
N	522	2612	1373	1179	582	522	2612	1373	1179	582	
R2	0.950	0.932	0.938	0.948	0.934	0.946	0.906	0.897	0.935	0.921	

By geographic location

Given the significant differences in economic development levels, resource endowments, and institutional environments across regions in China, the implementation effects of the ECER policy may exhibit heterogeneity. Therefore, this paper divides the sample into eastern, central, and western regions for analysis and conducts regressions separately. The regression results are presented in Table 7. Columns (3) to (5) and (8) to (9) of Table 7 show the regression results for CE1s and CE2, respectively, with columns (3) and (8) representing the results for the eastern region. The analysis indicates that, in the eastern region, the ECER policy significantly promotes carbon reduction and CE2. Although the policy’s effects in the central region are less than those in the eastern region, they still exhibit a positive impact. In contrast, in the western region, the ECER policy’s promotional effects on carbon reduction and CE2 are not significant.

This analysis reveals that, within the regional development pattern of China, the eastern regions exhibit more significant outcomes in terms of the CE1 reduction effect and the enhancement of CE2 under green fiscal policies compared to the central and western regions. Firstly, as the most economically developed area in China, the eastern region, with its leading total economic output, industrialization, and urbanization levels, provides a solid fiscal support and technological foundation for the implementation of green fiscal policies. This economic advantage enables the eastern region to allocate more resources to the research, development, and application of green technologies, as well as related infrastructure construction, thereby effectively promoting CE1 reduction and energy efficiency improvement. Secondly, environmental policies and regulations in the eastern region are generally stricter and more advanced. Coupled with a higher public awareness of environmental protection, this creates a favorable social environment and policy atmosphere for the implementation of green fiscal policies and carbon reduction. Additionally, the industrial structure in the eastern region is more optimized and high-end compared to the central and western regions, with a larger proportion of the service industry and high-tech industries, which typically have lower energy consumption intensity and CE1, facilitating the improvement of overall CE2. Furthermore, as an important gateway for international trade and investment, the eastern region is more open to adopting and introducing advanced green technologies and management practices from abroad, accelerating the pace of green transformation. Lastly, the dense urban network and well-developed transportation and logistics systems in the eastern region provide convenient conditions for the effective implementation of green fiscal policies. Therefore, due to comprehensive advantages in economic development level, industrial structure, policy environment, technological innovation capability, and infrastructure, the eastern region demonstrates more significant performance in the CE1 reduction effect and the promotion of CE2 under green fiscal policies.

Figure 9 reports the main regression coefficients and error bars from the heterogeneity analysis, clearly illustrating the distribution of coefficients.Fig. 9 Results of heterogeneity analysis.

Classification by resource-based city

Resource-based cities center on industries involved in the extraction and processing of local natural resources, including minerals and forests72–74. Due to their unique urban characteristics, these cities may have a specific impact on the efficacy of ECEP policy. Consequently, this paper follows the guidelines set forth by the State Council in the “National Plan for Sustainable Development of Resource-based Cities (2013–2020),” dividing the sample into resource-based and non-resource-based cities for separate regression analyses, the results of which are presented in Table 8. Columns (1) and (2) detail the regression outcomes for CE1, while columns (3) and (4) address CE2. The findings reveal that, compared to resource-based cities, the effect of ECEP policies on carbon reduction is more pronounced in non-resource-based cities, with a similarly more substantial impact on the promotion of CE2.Table 8 Heterogeneity analysis.

Variables	Resource	Non-resource	Resource	Non-resource	
(1)	(2)	(3)	(4)	
CE1	CE2	
ECER	0.011	− 0.096***	0.043	0.111***	
(0.203)	(− 4.106)	(0.857)	(4.371)	
lnur	0.560**	− 0.142	− 0.469**	0.556***	
(2.188)	(− 0.999)	(− 2.109)	(3.427)	
lnfdi	− 1.274**	− 0.579**	1.079*	1.096***	
(− 2.185)	(− 2.207)	(1.770)	(3.305)	
lnis	1.578***	1.151***	0.733**	0.146	
(4.775)	(5.474)	(1.987)	(0.566)	
lnsst	− 0.149	0.190***	0.305***	− 0.016	
(− 1.479)	(2.936)	(2.712)	(− 0.197)	
lnfre	0.058	− 0.080*	− 0.383***	− 0.358***	
(1.202)	(− 1.690)	(− 5.813)	(− 5.182)	
_cons	2.146***	3.455***	6.241***	6.580***	
(5.567)	(9.414)	(12.184)	(12.345)	
City/Year	YES	YES	YES	YES	
N	1067	2067	1067	2067	
R2	0.933	0.945	0.905	0.897	

Upon conducting a thorough analysis of the disparities in how non-resource-based cities and resource-based cities respond to ECER policies, a significant finding emerges: non-resource-based cities, due to their diversified industrial structures and lower reliance on highly polluting and energy-intensive heavy industries and mineral resource extraction, demonstrate a stronger capacity to adopt and promote new energy, clean energy, and energy-efficient technologies. This characteristic of their industrial structure not only facilitates effective carbon reduction efforts but also propels a shift in economic growth models towards services, high-tech industries, and innovation-driven sectors, which are associated with lower energy consumption and carbon intensities. Therefore, the potential for ECER policies to enhance CE2 and reduce CE1 is greater in these cities. In contrast, resource-based cities, due to their long-standing dependence on resource extraction, exhibit significant inertia in their economic structure, technological levels, and employment opportunities. This inertia not only complicates their transition and industrial restructuring but also increases the associated costs. Against this backdrop, non-resource-based cities are more likely to achieve notable successes in implementing ECER policies compared to their resource-based counterparts.

Conclusions and policy recommendations

Conclusions

Based on the city-level dataset from 2003 to 2019, this paper employs a multi-time point difference-in-differences model to thoroughly explore the impact of the ECER policy on CE1 reduction and CE2, reaching the following conclusions:

The ECER policy is confirmed to play a significant role in promoting the reduction of CE1 and enhancing CE2. This conclusion remains robust even after controlling for factors that might affect the accuracy of the assessment, such as contemporaneous policy interferences, sample selection biases, extreme value treatments, and other random factors. This indicates that the ECER policy has important practical implications in mitigating climate change impacts, and its effects are not significantly influenced by the aforementioned potential interferences. The ECER policy effectively promotes CE1 reduction and CE2 improvements by incentivizing the research and application of green technologies. This finding underscores the mediating role of green innovation in environmental policies, highlighting that fiscal incentives such as tax breaks and subsidies are crucial for promoting technological innovation and application, and further achieving environmental benefits. The CE1 reduction effect and CE2 enhancement of the ECER policy are more pronounced in economically developed, higher-tier cities and in the eastern regions. This may be due to these areas having better infrastructure, higher technological innovation capabilities, more abundant fiscal resources, and stronger public environmental awareness, which all provide strong support for the effective implementation of the ECER policy. Moreover, this variation also suggests that policymakers need to consider regional characteristics when implementing relevant policies to maximize policy effectiveness.

Discussion

Existing literature has explored the role of energy conservation and emission reduction fiscal policies in environmental protection, such as green credit37, ESG performance75, green total factor carbon efficiency36, and sustainable urban development38. These studies report the positive impact of such policies on the environment. However, they do not directly examine the impact of these policies on pollutants. Our study extends the existing literature by investigating the relationship between these policies and carbon emissions. Green fiscal policies significantly promote the reduction of carbon emissions (CE1) and the improvement of carbon efficiency (CE2) through economic incentives, price mechanisms, infrastructure support, and increasing public environmental awareness. Specifically, these policies encourage the research and application of green technologies, change consumer and producer behavior, optimize energy consumption structures, support related infrastructure construction, and increase public participation in low-carbon living. Additionally, green fiscal policies promote sustainable economic growth by directing funds towards low-carbon and green industries, fostering the development of green technologies and industries. Overall, green fiscal policies have not only achieved significant environmental protection results but also played a crucial role in realizing the dual goals of economic growth and environmental protection.

Despite the significant findings, our study has some limitations. Firstly, the data is limited to 248 cities from 2003 to 2019, which may not fully capture the long-term impact of ECER policies. Secondly, reliance on existing data may introduce biases, as not all relevant factors could be considered. Future research could address these limitations by expanding the dataset, including more diverse regions, and employing alternative methods to validate these findings.

Policy recommendations

Based on the above analysis, the policy recommendations of this paper are as follows:Continue to increase fiscal support. The government should continue to enhance fiscal support for the ECER policy, including expanding the scope of tax reductions and increasing the level of fiscal subsidies, especially for those projects and technologies that can significantly improve energy efficiency and reduce CE1. This will further stimulate the innovation motivation of enterprises and research institutions, accelerating the research and development (R&D) and application of low-carbon technologies.

Optimize policy design and implementation mechanisms. Considering the robustness of the ECER policy effects, the government should further refine the policy design to ensure that measures precisely target sectors and aspects with high CE1. Concurrently, it is crucial to establish and enhance the supervision mechanism for policy execution, ensuring effective implementation of policy measures. This approach also necessitates timely adjustments and optimizations of the policy to tackle new challenges effectively.

Establish a dedicated Green Technology Innovation Fund. This fund aims to provide financial support specifically for R&D and promotion of green technologies with high CE2. By offering startup capital, R&D subsidies, and rewards for the successful commercialization of green technologies, the fund can not only stimulate the innovation drive of enterprises and research institutions but also accelerate the transformation of green technologies from theory to practice. Consequently, this will promote CE1 reduction and CE2 enhancement on a broader scale. This initiative directly responds to the importance of fiscal incentive measures for promoting technological innovation and application emphasized in the research, ensuring the ECER policy maximizes its benefits in promoting green development.

Differentiated policy design. Given the variations in the effects of the ECER policy across different regions, policymakers should design and implement differentiated energy-saving and emission reduction policies based on regional factors such as economic development level, industrial structure, and resource endowment. For economically more developed areas with a stronger technological foundation, CE1 reduction can be promoted by introducing higher standards for environmental protection and mechanisms for rewarding technological innovation. For regions that are relatively less economically developed, the focus should be on providing technical support and financial assistance to enhance their capacity for CE1 reduction.

Green fiscal policies play a crucial role in reducing carbon emissions and promoting sustainable economic growth, but their impact on social and income inequality needs careful consideration. Firstly, while policies like carbon taxes are effective in reducing emissions, they may place a significant burden on low-income households, as a larger proportion of their income goes towards energy and basic necessities. To mitigate this inequality, governments can implement redistributive measures, such as using carbon tax revenues for direct subsidies or tax reductions for low-income families, ensuring social equity while achieving emission reductions. Secondly, green fiscal policies encourage investment in green technologies and the implementation of green projects. However, these incentives often favor businesses and wealthy families capable of making such investments, potentially widening income disparities. Therefore, policy design should consider inclusive growth by providing green job training and encouraging small and medium-sized enterprises to participate in green projects, ensuring that various social strata benefit from the green economy. Furthermore, in terms of public investment, governments should prioritize low-income and marginalized communities, ensuring they also benefit from the construction of green infrastructure. This includes prioritizing the development of public transportation and renewable energy projects in these areas, thereby reducing living costs and improving the quality of life for these communities. By adopting these redistributive measures and inclusive policy designs, green fiscal policies can achieve the goals of environmental protection and economic growth while effectively mitigating their negative impacts on social and income inequality, promoting sustainable and inclusive development.

When evaluating various policy tools for achieving carbon reduction goals, it is evident that carbon taxes, renewable energy subsidies, ECER policies, emissions trading systems, and energy efficiency standards each have their unique advantages (see Table 9). Carbon taxes leverage price mechanisms to encourage emissions reduction and provide redistribution opportunities, while renewable energy subsidies promote technological advancement and market development. ECER policies offer direct incentives and support for infrastructure, resulting in long-term environmental benefits. Emissions trading systems combine cap-and-trade controls with market flexibility, and energy efficiency standards provide direct pathways to emissions reduction. In practical applications, the integrated use of multiple policy tools, fully utilizing their respective advantages, can more effectively achieve carbon reduction goals and drive the transition to a low-carbon economy. Policymakers must consider equity, economic impact, and public acceptance when designing these policies to balance environmental protection with economic growth. Through careful integration and balanced implementation, green fiscal policies can significantly reduce carbon emissions while promoting sustainable and inclusive economic development.

Table 9 Advantages and limitations of policy tools related to carbon emission reduction.

Policy tools	Advantages	Limitations	
Carbon tax	• Price Signal Carbon taxes send a strong price signal by raising the cost of carbon emissions, encouraging businesses and consumers to reduce their carbon footprint

• Flexibility Businesses and consumers can choose the most cost-effective ways to reduce emissions, providing flexibility in achieving reductions

• Revenue redistribution Governments can redistribute carbon tax revenues to mitigate the impact on low-income households and invest in green projects

	• Public Acceptance Carbon taxes may face public and political resistance, especially in countries with high tax burdens

• Economic impact If not designed properly, carbon taxes could impose significant economic burdens on certain industries or regions, affecting employment and economic growth

	
Renewable energy subsidies	• Technological advancement Subsidies can lower the costs of renewable energy technologies, accelerating their development and market penetration

• Market development Fiscal support can help establish and expand emerging renewable energy sectors, driving the transition to cleaner energy sources

• Job creation Renewable energy projects often generate significant employment opportunities, boosting local economies

	• Fiscal burden Long-term, substantial subsidies can strain government finances, requiring careful balancing

• Market distortion Over-reliance on subsidies may distort markets and hinder the effective operation of market mechanisms

	
Emissions trading system (ETS)	• Cap-and-trade By setting a cap on emissions, ETS ensures that total emissions remain within a controlled limit

• Economic efficiency Trading emission allowances in the market allows for cost-effective reduction of emissions

• Innovation incentive Companies are incentivized to innovate to reduce emissions and lower the costs of buying allowances

	• Complexity Designing and managing an ETS is complex, requiring robust monitoring and regulatory frameworks

• Price volatility The price of emission allowances can be volatile, increasing market uncertainty

	
ECER policy	• Direct incentives ECER policies provide direct financial incentives, such as subsidies and grants, for implementing energy-saving and emission reduction projects

• Behavioral change By setting clear targets and performance evaluations, ECER policies encourage businesses and local governments to adopt greener practices

• Infrastructure support These policies can fund the development of energy-efficient infrastructure and technologies, leading to long-term environmental benefits

	• Administrative complexity Managing and evaluating the effectiveness of ECER policies can be administratively complex and resource-intensive

• Potential for misallocation Without proper oversight, funds allocated for ECER projects might be misused or fail to target the most effective initiatives

	
Energy efficiency standards and regulations	• Direct impact Efficiency standards and regulations can directly mandate emission reductions, ensuring clear progress toward targets

• Clarity Businesses and consumers have clear guidelines on compliance, facilitating planning and implementation of reduction measures

	• Lack of flexibility Fixed standards and regulations may lack flexibility, failing to account for the specific circumstances of different businesses and sectors

• Compliance costs Strict standards can increase compliance costs for businesses, affecting their competitiveness

	

Author contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by ZC. Z, S. Z. The first draft of the manuscript was written by ZQ. Z and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding

This study is supported by the National Social Science Fund Major Project: “Research on the Policy System and Implementation Path to Accelerate the Formation of New Productive Forces,” Project Number: 23&ZD069.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References

1. Martin G Saikawa E Effectiveness of state climate and energy policies in reducing power-sector CO2 emissions Nat. Clim. Change 2017 7 912 919 10.1038/s41558-017-0001-0
Martin, G. & Saikawa, E. Effectiveness of state climate and energy policies in reducing power-sector CO2 emissions. Nat. Clim. Change 7, 912–919 (2017).10.1038/s41558-017-0001-0
2. Soergel B A sustainable development pathway for climate action within the UN 2030 Agenda Nat. Clim. Change 2021 11 656 664 10.1038/s41558-021-01098-3
Soergel, B. et al. A sustainable development pathway for climate action within the UN 2030 Agenda. Nat. Clim. Change 11, 656–664 (2021).10.1038/s41558-021-01098-3
3. Terhaar J Frölicher TL Aschwanden MT Friedlingstein P Joos F Adaptive emission reduction approach to reach any global warming target Nat. Clim. Change 2022 12 1136 1142 10.1038/s41558-022-01537-9
Terhaar, J., Frölicher, T. L., Aschwanden, M. T., Friedlingstein, P. & Joos, F. Adaptive emission reduction approach to reach any global warming target. Nat. Clim. Change 12, 1136–1142 (2022).10.1038/s41558-022-01537-9
4. Gidden MJ Aligning climate scenarios to emissions inventories shifts global benchmarks Nature 2023 624 102 108 10.1038/s41586-023-06724-y 37993713
Gidden, M. J. et al. Aligning climate scenarios to emissions inventories shifts global benchmarks. Nature 624, 102–108 (2023).37993713 10.1038/s41586-023-06724-y
5. Brown PT Climate warming increases extreme daily wildfire growth risk in California Nature 2023 621 760 766 10.1038/s41586-023-06444-3 37648863
Brown, P. T. et al. Climate warming increases extreme daily wildfire growth risk in California. Nature 621, 760–766 (2023).37648863 10.1038/s41586-023-06444-3
6. Liu Y Cai W Lin X Li Z Increased extreme swings of Atlantic intertropical convergence zone in a warming climate Nat. Clim. Change 2022 12 828 833 10.1038/s41558-022-01445-y
Liu, Y., Cai, W., Lin, X. & Li, Z. Increased extreme swings of Atlantic intertropical convergence zone in a warming climate. Nat. Clim. Change 12, 828–833 (2022).10.1038/s41558-022-01445-y
7. Tebaldi C Extreme sea levels at different global warming levels Nat. Clim. Change 2021 11 746 751 10.1038/s41558-021-01127-1
Tebaldi, C. et al. Extreme sea levels at different global warming levels. Nat. Clim. Change 11, 746–751 (2021).10.1038/s41558-021-01127-1
8. Chaobo Z Qi S Can carbon emission trading policy break China’s urban carbon lock-in? J. Environ. Manag. 2024 353 120129 10.1016/j.jenvman.2024.120129
Chaobo, Z. & Qi, S. Can carbon emission trading policy break China’s urban carbon lock-in?. J. Environ. Manag. 353, 120129 (2024).10.1016/j.jenvman.2024.120129
9. Wu S Smart cities and urban household carbon emissions: A perspective on smart city development policy in China J. Clean Prod. 2022 373 133877 10.1016/j.jclepro.2022.133877
Wu, S. Smart cities and urban household carbon emissions: A perspective on smart city development policy in China. J. Clean Prod. 373, 133877 (2022).10.1016/j.jclepro.2022.133877
10. Zhang H Feng C Zhou X Going carbon-neutral in China: Does the low-carbon city pilot policy improve carbon emission efficiency Sustain. Prod. Consump. 2022 33 312 329 10.1016/j.spc.2022.07.002
Zhang, H., Feng, C. & Zhou, X. Going carbon-neutral in China: Does the low-carbon city pilot policy improve carbon emission efficiency. Sustain. Prod. Consump. 33, 312–329 (2022).10.1016/j.spc.2022.07.002
11. Shan Y Impacts of COVID-19 and fiscal stimuli on global emissions and the Paris Agreement Nat. Clim. Chang. 2021 11 200 206 10.1038/s41558-020-00977-5
Shan, Y. et al. Impacts of COVID-19 and fiscal stimuli on global emissions and the Paris Agreement. Nat. Clim. Chang. 11, 200–206 (2021).10.1038/s41558-020-00977-5
12. Sun L Feng N Research on fiscal policies supporting green and low-carbon transition to promote energy conservation and emission reduction in cities: Empirical evidence from China J. Clean. Prod. 2023 430 139688 10.1016/j.jclepro.2023.139688
Sun, L. & Feng, N. Research on fiscal policies supporting green and low-carbon transition to promote energy conservation and emission reduction in cities: Empirical evidence from China. J. Clean. Prod. 430, 139688 (2023).10.1016/j.jclepro.2023.139688
13. Zhu X Lu Y Open economy, fiscal expenditure of environmental protection and pollution governance: Evidences from China’s provincial and industrial panel data China Popul. Resour. Environ. 2017 27 10 18
Zhu, X. & Lu, Y. Open economy, fiscal expenditure of environmental protection and pollution governance: Evidences from China’s provincial and industrial panel data. China Popul. Resour. Environ. 27, 10–18 (2017).
14. Fan H Liang C The pollutant and carbon emissions reduction synergistic effect of green fiscal policy: Evidence from China Financ. Res. Lett. 2023 58 104446 10.1016/j.frl.2023.104446
Fan, H. & Liang, C. The pollutant and carbon emissions reduction synergistic effect of green fiscal policy: Evidence from China. Financ. Res. Lett. 58, 104446 (2023).10.1016/j.frl.2023.104446
15. Waheed R Sarwar S Wei C The survey of economic growth, energy consumption and carbon emission Energy Rep. 2019 5 1103 1115 10.1016/j.egyr.2019.07.006
Waheed, R., Sarwar, S. & Wei, C. The survey of economic growth, energy consumption and carbon emission. Energy Rep. 5, 1103–1115 (2019).10.1016/j.egyr.2019.07.006
16. Esso LJ Keho Y Energy consumption, economic growth and carbon emissions: Cointegration and causality evidence from selected African countries Energy 2016 114 492 497 10.1016/j.energy.2016.08.010
Esso, L. J. & Keho, Y. Energy consumption, economic growth and carbon emissions: Cointegration and causality evidence from selected African countries. Energy 114, 492–497 (2016).10.1016/j.energy.2016.08.010
17. Li J Li S Energy investment, economic growth and carbon emissions in China-Empirical analysis based on spatial Durbin model Energy Policy 2020 140 111425 10.1016/j.enpol.2020.111425
Li, J. & Li, S. Energy investment, economic growth and carbon emissions in China-Empirical analysis based on spatial Durbin model. Energy Policy 140, 111425 (2020).10.1016/j.enpol.2020.111425
18. Yin J Zheng M Chen J The effects of environmental regulation and technical progress on CO2 Kuznets curve: An evidence from China Energy Policy 2015 77 97 108 10.1016/j.enpol.2014.11.008
Yin, J., Zheng, M. & Chen, J. The effects of environmental regulation and technical progress on CO2 Kuznets curve: An evidence from China. Energy Policy 77, 97–108 (2015).10.1016/j.enpol.2014.11.008
19. Al-Mulali U Saboori B Ozturk I Investigating the environmental Kuznets curve hypothesis in Vietnam Energy Policy 2015 76 123 131 10.1016/j.enpol.2014.11.019
Al-Mulali, U., Saboori, B. & Ozturk, I. Investigating the environmental Kuznets curve hypothesis in Vietnam. Energy Policy 76, 123–131 (2015).10.1016/j.enpol.2014.11.019
20. Danish Hassan ST Baloch MA Mahmood N Zhang J Linking economic growth and ecological footprint through human capital and biocapacity Sustain. Cities Soc. 2019 47 101516 10.1016/j.scs.2019.101516
Danish, Hassan, S. T., Baloch, M. A., Mahmood, N. & Zhang, J. Linking economic growth and ecological footprint through human capital and biocapacity. Sustain. Cities Soc. 47, 101516 (2019).10.1016/j.scs.2019.101516
21. Hassan ST Batool B Zhu B Khan I Environmental complexity of globalization, education, and income inequalities: New insights of energy poverty J. Clean. Prod. 2022 340 130735 10.1016/j.jclepro.2022.130735
Hassan, S. T., Batool, B., Zhu, B. & Khan, I. Environmental complexity of globalization, education, and income inequalities: New insights of energy poverty. J. Clean. Prod. 340, 130735 (2022).10.1016/j.jclepro.2022.130735
22. Hassan ST Danish Khan SU-D Xia E Fatima H Role of institutions in correcting environmental pollution: An empirical investigation Sustain. Cities Soc. 2020 53 101901 10.1016/j.scs.2019.101901
Hassan, S. T., Danish, Khan, S.U.-D., Xia, E. & Fatima, H. Role of institutions in correcting environmental pollution: An empirical investigation. Sustain. Cities Soc. 53, 101901 (2020).10.1016/j.scs.2019.101901
23. Awan A Sadiq M Hassan ST Khan I Khan NH Combined nonlinear effects of urbanization and economic growth on CO2 emissions in Malaysia. An application of QARDL and KRLS Urban Clim. 2022 46 101342 10.1016/j.uclim.2022.101342
Awan, A., Sadiq, M., Hassan, S. T., Khan, I. & Khan, N. H. Combined nonlinear effects of urbanization and economic growth on CO2 emissions in Malaysia. An application of QARDL and KRLS. Urban Clim. 46, 101342 (2022).10.1016/j.uclim.2022.101342
24. Khan K Khurshid A Are technology innovation and circular economy remedy for emissions? Evidence from the Netherlands Environ. Dev. Sustain. 2024 26 1435 1449 10.1007/s10668-022-02766-w
Khan, K. & Khurshid, A. Are technology innovation and circular economy remedy for emissions? Evidence from the Netherlands. Environ. Dev. Sustain. 26, 1435–1449 (2024).10.1007/s10668-022-02766-w
25. Zhu Q Peng X The impacts of population change on carbon emissions in China during 1978–2008 Environ. Impact Assess. Rev. 2012 36 1 8 10.1016/j.eiar.2012.03.003
Zhu, Q. & Peng, X. The impacts of population change on carbon emissions in China during 1978–2008. Environ. Impact Assess. Rev. 36, 1–8 (2012).10.1016/j.eiar.2012.03.003
26. Khurshid A Rauf A Qayyum S Calin AC Duan W Green innovation and carbon emissions: the role of carbon pricing and environmental policies in attaining sustainable development targets of carbon mitigation—Evidence from Central-Eastern Europe Environ. Dev. Sustain. 2023 25 8777 8798 10.1007/s10668-022-02422-3
Khurshid, A., Rauf, A., Qayyum, S., Calin, A. C. & Duan, W. Green innovation and carbon emissions: the role of carbon pricing and environmental policies in attaining sustainable development targets of carbon mitigation—Evidence from Central-Eastern Europe. Environ. Dev. Sustain. 25, 8777–8798 (2023).10.1007/s10668-022-02422-3
27. Li Z Climate change and the UN-2030 agenda: Do mitigation technologies represent a driving factor? New evidence from OECD economies Clean Technol. Environ. Policy 2023 25 195 209 10.1007/s10098-022-02396-w
Li, Z. et al. Climate change and the UN-2030 agenda: Do mitigation technologies represent a driving factor? New evidence from OECD economies. Clean Technol. Environ. Policy 25, 195–209 (2023).10.1007/s10098-022-02396-w
28. Hassan ST Batool B Sadiq M Zhu B How do green energy investment, economic policy uncertainty, and natural resources affect greenhouse gas emissions? A Markov-switching equilibrium approach Environ. Impact Assess. Rev. 2022 97 106887 10.1016/j.eiar.2022.106887
Hassan, S. T., Batool, B., Sadiq, M. & Zhu, B. How do green energy investment, economic policy uncertainty, and natural resources affect greenhouse gas emissions? A Markov-switching equilibrium approach. Environ. Impact Assess. Rev. 97, 106887 (2022).10.1016/j.eiar.2022.106887
29. Liao N Luo X He Y Could environmental regulation effectively boost the synergy level of carbon emission reduction and air pollutants control? Evidence from industrial sector in China Atmos. Pollut. Res. 2024 15 102173 10.1016/j.apr.2024.102173
Liao, N., Luo, X. & He, Y. Could environmental regulation effectively boost the synergy level of carbon emission reduction and air pollutants control? Evidence from industrial sector in China. Atmos. Pollut. Res. 15, 102173 (2024).10.1016/j.apr.2024.102173
30. Meng X Zhang M Zhao Y Environmental regulation and green transition: Quasi-natural experiment from China’s efforts in sulfur dioxide emissions control J. Clean Prod. 2024 434 139741 10.1016/j.jclepro.2023.139741
Meng, X., Zhang, M. & Zhao, Y. Environmental regulation and green transition: Quasi-natural experiment from China’s efforts in sulfur dioxide emissions control. J. Clean Prod. 434, 139741 (2024).10.1016/j.jclepro.2023.139741
31. Chen J Hu L Does environmental regulation drive economic growth through technological innovation: Application of nonlinear and spatial spillover effect Sustainability 2022 14 16455 10.3390/su142416455
Chen, J. & Hu, L. Does environmental regulation drive economic growth through technological innovation: Application of nonlinear and spatial spillover effect. Sustainability 14, 16455 (2022).10.3390/su142416455
32. Zhang Y Li X Environmental regulation and high-quality economic growth: Quasi-natural experimental evidence from China Environ. Sci. Pollut. Res. 2022 29 85389 85401 10.1007/s11356-022-21832-4
Zhang, Y. & Li, X. Environmental regulation and high-quality economic growth: Quasi-natural experimental evidence from China. Environ. Sci. Pollut. Res. 29, 85389–85401 (2022).10.1007/s11356-022-21832-4
33. Wang X Chai Y Wu W Khurshid A The empirical analysis of environmental regulation’s spatial spillover effects on green technology innovation in China Int. J. Environ. Res. Public Health 2023 20 1069 10.3390/ijerph20021069 36673826
Wang, X., Chai, Y., Wu, W. & Khurshid, A. The empirical analysis of environmental regulation’s spatial spillover effects on green technology innovation in China. Int. J. Environ. Res. Public Health 20, 1069 (2023).36673826 10.3390/ijerph20021069
34. Khurshid A Huang Y Cifuentes-Faura J Khan K Beyond borders: Assessing the transboundary effects of environmental regulation on technological development in Europe Technol. Forecast. Soc. Change 2024 200 123212 10.1016/j.techfore.2024.123212
Khurshid, A., Huang, Y., Cifuentes-Faura, J. & Khan, K. Beyond borders: Assessing the transboundary effects of environmental regulation on technological development in Europe. Technol. Forecast. Soc. Change 200, 123212 (2024).10.1016/j.techfore.2024.123212
35. Zhu Y Han S Zhang Y Huang Q Evaluating the effect of government emission reduction policy: Evidence from demonstration cities in China Int. J. Environ. Res. Public Health 2021 18 4649 10.3390/ijerph18094649 33925637
Zhu, Y., Han, S., Zhang, Y. & Huang, Q. Evaluating the effect of government emission reduction policy: Evidence from demonstration cities in China. Int. J. Environ. Res. Public Health 18, 4649 (2021).33925637 10.3390/ijerph18094649
36. Li G Wang X Can green fiscal policy improve green total factor carbon efficiency? Evidence from China J. Environ. Plan. Manag. 2024 10.1080/09640568.2024.2352554
Li, G. & Wang, X. Can green fiscal policy improve green total factor carbon efficiency? Evidence from China. J. Environ. Plan. Manag.10.1080/09640568.2024.2352554 (2024).10.1080/09640568.2024.2352554
37. Cheng Y Xu Z Fiscal policy promotes corporate green credit: Experience from the construction of energy conservation and emission reduction demonstration cities in China Green Financ. 2024 6 1 23 10.3934/GF.2024001
Cheng, Y. & Xu, Z. Fiscal policy promotes corporate green credit: Experience from the construction of energy conservation and emission reduction demonstration cities in China. Green Financ. 6, 1–23 (2024).10.3934/GF.2024001
38. Lin B Zhu J Impact of energy saving and emission reduction policy on urban sustainable development: Empirical evidence from China Appl. Energy 2019 239 12 22 10.1016/j.apenergy.2019.01.166
Lin, B. & Zhu, J. Impact of energy saving and emission reduction policy on urban sustainable development: Empirical evidence from China. Appl. Energy 239, 12–22 (2019).10.1016/j.apenergy.2019.01.166
39. Liu S Xu P Chen X Green fiscal policy and enterprise green innovation: evidence from quasi-natural experiment of China Environ. Sci. Pollut. Res. 2023 30 94576 94593 10.1007/s11356-023-28847-5
Liu, S., Xu, P. & Chen, X. Green fiscal policy and enterprise green innovation: evidence from quasi-natural experiment of China. Environ. Sci. Pollut. Res. 30, 94576–94593 (2023).10.1007/s11356-023-28847-5
40. Nordhaus W The Climate Casino: Risk, Uncertainty, and Economics for a Warming World 2013 Yale University Press
Nordhaus, W. The Climate Casino: Risk, Uncertainty, and Economics for a Warming World (Yale University Press, 2013). 10.2307/j.ctt5vkrpp.
41. Green JF Does carbon pricing reduce emissions? A review of ex-post analyses Environ. Res. Lett. 2021 16 043004 10.1088/1748-9326/abdae9
Green, J. F. Does carbon pricing reduce emissions? A review of ex-post analyses. Environ. Res. Lett. 16, 043004 (2021).10.1088/1748-9326/abdae9
42. Ma B Sharif A Bashir M Bashir MF The dynamic influence of energy consumption, fiscal policy and green innovation on environmental degradation in BRICST economies Energy Policy 2023 183 113823 10.1016/j.enpol.2023.113823
Ma, B., Sharif, A., Bashir, M. & Bashir, M. F. The dynamic influence of energy consumption, fiscal policy and green innovation on environmental degradation in BRICST economies. Energy Policy 183, 113823 (2023).10.1016/j.enpol.2023.113823
43. Hu Y Ding Y Liu J Zhang Q Pan Z Does carbon mitigation depend on green fiscal policy or green investment? Environ. Res. Lett. 2023 18 045005 10.1088/1748-9326/acc4df
Hu, Y., Ding, Y., Liu, J., Zhang, Q. & Pan, Z. Does carbon mitigation depend on green fiscal policy or green investment?. Environ. Res. Lett. 18, 045005 (2023).10.1088/1748-9326/acc4df
44. Nie C Li R Feng Y Chen Z The impact of China’s energy saving and emission reduction demonstration city policy on urban green technology innovation Sci. Rep. 2023 13 15168 10.1038/s41598-023-42520-4 37704747
Nie, C., Li, R., Feng, Y. & Chen, Z. The impact of China’s energy saving and emission reduction demonstration city policy on urban green technology innovation. Sci. Rep. 13, 15168 (2023).37704747 10.1038/s41598-023-42520-4
45. Fischedick, M. et al. Industry. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Technical Report. (2014).
46. Sorrell S Reducing energy demand: A review of issues, challenges and approaches Renew. Sustain. Energy Rev. 2015 47 74 82 10.1016/j.rser.2015.03.002
Sorrell, S. Reducing energy demand: A review of issues, challenges and approaches. Renew. Sustain. Energy Rev. 47, 74–82 (2015).10.1016/j.rser.2015.03.002
47. Ghisellini P Cialani C Ulgiati S A review on circular economy: The expected transition to a balanced interplay of environmental and economic systems J. Clean. Prod. 2016 114 11 32 10.1016/j.jclepro.2015.09.007
Ghisellini, P., Cialani, C. & Ulgiati, S. A review on circular economy: The expected transition to a balanced interplay of environmental and economic systems. J. Clean. Prod. 114, 11–32 (2016).10.1016/j.jclepro.2015.09.007
48. Leung DYC Caramanna G Maroto-Valer MM An overview of current status of carbon dioxide capture and storage technologies Renew. Sustain. Energy Rev. 2014 39 426 443 10.1016/j.rser.2014.07.093
Leung, D. Y. C., Caramanna, G. & Maroto-Valer, M. M. An overview of current status of carbon dioxide capture and storage technologies. Renew. Sustain. Energy Rev. 39, 426–443 (2014).10.1016/j.rser.2014.07.093
49. Stiglitz, J. E. et al. Report of the High-Level Commission on Carbon Prices. 1–61 (2017) 10.7916/d8-w2nc-4103.
50. Callaway B Sant’Anna PHC Difference-in-differences with multiple time periods J. Econom. 2021 225 200 230 10.1016/j.jeconom.2020.12.001
Callaway, B. & Sant’Anna, P. H. C. Difference-in-differences with multiple time periods. J. Econom. 225, 200–230 (2021).10.1016/j.jeconom.2020.12.001
51. Beck T Levine R Levkov A Big bad banks? The winners and losers from bank deregulation in the United States J. Financ. 2010 65 1637 1667 10.1111/j.1540-6261.2010.01589.x
Beck, T., Levine, R. & Levkov, A. Big bad banks? The winners and losers from bank deregulation in the United States. J. Financ. 65, 1637–1667 (2010).10.1111/j.1540-6261.2010.01589.x
52. Shan Y City-level emission peak and drivers in China Sci. Bull. 2022 67 1910 1920 10.1016/j.scib.2022.08.024
Shan, Y. et al. City-level emission peak and drivers in China. Sci. Bull. 67, 1910–1920 (2022).10.1016/j.scib.2022.08.024
53. Shan Y City-level climate change mitigation in China Sci. Adv. 2018 4 eaaq0390 10.1126/sciadv.aaq0390 29963621
Shan, Y. et al. City-level climate change mitigation in China. Sci. Adv. 4, eaaq0390 (2018).29963621 10.1126/sciadv.aaq0390
54. Eggleston, H. S., Buendia, L., Miwa, K., Ngara, T. & Tanabe, K. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. (2006).
55. Shan Y Methodology and applications of city level CO2 emission accounts in China J. Clean. Prod. 2017 161 1215 1225 10.1016/j.jclepro.2017.06.075
Shan, Y. et al. Methodology and applications of city level CO2 emission accounts in China. J. Clean. Prod. 161, 1215–1225 (2017).10.1016/j.jclepro.2017.06.075
56. Xu Q Zhong M Cao M Does digital investment affect carbon efficiency? Spatial effect and mechanism discussion Sci. Total Environ. 2022 827 154321 10.1016/j.scitotenv.2022.154321 35259384
Xu, Q., Zhong, M. & Cao, M. Does digital investment affect carbon efficiency? Spatial effect and mechanism discussion. Sci. Total Environ. 827, 154321 (2022).35259384 10.1016/j.scitotenv.2022.154321
57. Sun W Huang C How does urbanization affect carbon emission efficiency? Evidence from China J. Clean. Prod. 2020 272 122828 10.1016/j.jclepro.2020.122828
Sun, W. & Huang, C. How does urbanization affect carbon emission efficiency? Evidence from China. J. Clean. Prod. 272, 122828 (2020).10.1016/j.jclepro.2020.122828
58. Opoku EEO Boachie MK The environmental impact of industrialization and foreign direct investment Energy Policy 2020 137 111178 10.1016/j.enpol.2019.111178
Opoku, E. E. O. & Boachie, M. K. The environmental impact of industrialization and foreign direct investment. Energy Policy 137, 111178 (2020).10.1016/j.enpol.2019.111178
59. Li P Lu Y Wang J Does flattening government improve economic performance? Evidence from China J. Dev. Econ. 2016 123 18 37 10.1016/j.jdeveco.2016.07.002
Li, P., Lu, Y. & Wang, J. Does flattening government improve economic performance? Evidence from China. J. Dev. Econ. 123, 18–37 (2016).10.1016/j.jdeveco.2016.07.002
60. Guo Q Zhong J The effect of urban innovation performance of smart city construction policies: Evaluate by using a multiple period difference-in-differences model Technol. Forecast. Soc. Chang. 2022 184 122003 10.1016/j.techfore.2022.122003
Guo, Q. & Zhong, J. The effect of urban innovation performance of smart city construction policies: Evaluate by using a multiple period difference-in-differences model. Technol. Forecast. Soc. Chang. 184, 122003 (2022).10.1016/j.techfore.2022.122003
61. Guo Q Wang Y Dong X Effects of smart city construction on energy saving and CO2 emission reduction: Evidence from China Appl. Energy 2022 313 118879 10.1016/j.apenergy.2022.118879
Guo, Q., Wang, Y. & Dong, X. Effects of smart city construction on energy saving and CO2 emission reduction: Evidence from China. Appl. Energy 313, 118879 (2022).10.1016/j.apenergy.2022.118879
62. Abadie A Imbens GW Matching on the estimated propensity score Econometrica 2016 84 781 807 10.3982/ECTA11293
Abadie, A. & Imbens, G. W. Matching on the estimated propensity score. Econometrica 84, 781–807 (2016).10.3982/ECTA11293
63. Lyu C Xie Z Li Z Market supervision, innovation offsets and energy efficiency: Evidence from environmental pollution liability insurance in China Energy Policy 2022 171 113267 10.1016/j.enpol.2022.113267
Lyu, C., Xie, Z. & Li, Z. Market supervision, innovation offsets and energy efficiency: Evidence from environmental pollution liability insurance in China. Energy Policy 171, 113267 (2022).10.1016/j.enpol.2022.113267
64. Park BU Simar L Zelenyuk V Local likelihood estimation of truncated regression and its partial derivatives: Theory and application J. Econ. 2008 146 185 198 10.1016/j.jeconom.2008.08.007
Park, B. U., Simar, L. & Zelenyuk, V. Local likelihood estimation of truncated regression and its partial derivatives: Theory and application. J. Econ. 146, 185–198 (2008).10.1016/j.jeconom.2008.08.007
65. Baker AC Larcker DF Wang CCY How much should we trust staggered difference-in-differences estimates? J. Financ. Econ. 2022 144 370 395 10.1016/j.jfineco.2022.01.004
Baker, A. C., Larcker, D. F. & Wang, C. C. Y. How much should we trust staggered difference-in-differences estimates?. J. Financ. Econ. 144, 370–395 (2022).10.1016/j.jfineco.2022.01.004
66. Arkhangelsky D Athey S Hirshberg DA Imbens GW Wager S Synthetic difference-in-differences Am. Econ. Rev. 2021 111 4088 4118 10.1257/aer.20190159
Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W. & Wager, S. Synthetic difference-in-differences. Am. Econ. Rev. 111, 4088–4118 (2021).10.1257/aer.20190159
67. Cho JH Sohn SY A novel decomposition analysis of green patent applications for the evaluation of R&D efforts to reduce CO2 emissions from fossil fuel energy consumption J. Clean. Prod. 2018 193 290 299 10.1016/j.jclepro.2018.05.060
Cho, J. H. & Sohn, S. Y. A novel decomposition analysis of green patent applications for the evaluation of R&D efforts to reduce CO2 emissions from fossil fuel energy consumption. J. Clean. Prod. 193, 290–299 (2018).10.1016/j.jclepro.2018.05.060
68. Zhao P Lu Z Kou J Du J Regional differences and convergence of green innovation efficiency in China J. Environ. Manag. 2023 325 116618 10.1016/j.jenvman.2022.116618
Zhao, P., Lu, Z., Kou, J. & Du, J. Regional differences and convergence of green innovation efficiency in China. J. Environ. Manag. 325, 116618 (2023).10.1016/j.jenvman.2022.116618
69. Jiang T Mediating and moderating effects in empirical studies of causal inference China Ind. Econ. 2022 10.19581/j.cnki.ciejournal.2022.05.005
Jiang, T. Mediating and moderating effects in empirical studies of causal inference. China Ind. Econ.10.19581/j.cnki.ciejournal.2022.05.005 (2022).10.19581/j.cnki.ciejournal.2022.05.005
70. Niu Z Xu C Wu Y Business environment optimization, human capital effect and firm labor productivity J. Manag. World 2023 39 83 100
Niu, Z., Xu, C. & Wu, Y. Business environment optimization, human capital effect and firm labor productivity. J. Manag. World 39, 83–100 (2023).
71. Aguinis H Edwards JR Bradley KJ Improving our understanding of moderation and mediation in strategic management research Org. Res. Methods 2017 20 665 685 10.1177/1094428115627498
Aguinis, H., Edwards, J. R. & Bradley, K. J. Improving our understanding of moderation and mediation in strategic management research. Org. Res. Methods 20, 665–685 (2017).10.1177/1094428115627498
72. Chen W Exploring the industrial land use efficiency of China’s resource-based cities Cities 2019 93 215 223 10.1016/j.cities.2019.05.009
Chen, W. et al. Exploring the industrial land use efficiency of China’s resource-based cities. Cities 93, 215–223 (2019).10.1016/j.cities.2019.05.009
73. Li B Han Y Wang C Sun W Did civilized city policy improve energy efficiency of resource-based cities? Prefecture-level evidence from China Energy Policy 2022 167 113081 10.1016/j.enpol.2022.113081
Li, B., Han, Y., Wang, C. & Sun, W. Did civilized city policy improve energy efficiency of resource-based cities? Prefecture-level evidence from China. Energy Policy 167, 113081 (2022).10.1016/j.enpol.2022.113081
74. Wang Y Has the sustainable development planning policy promoted the green transformation in China’s resource-based cities Resour. Conserv. Recycl. 2022 180 106181 10.1016/j.resconrec.2022.106181
Wang, Y. et al. Has the sustainable development planning policy promoted the green transformation in China’s resource-based cities. Resour. Conserv. Recycl. 180, 106181 (2022).10.1016/j.resconrec.2022.106181
75. Miao S Tuo Y Zhang X Hou X Green fiscal policy and ESG performance: Evidence from the energy-saving and emission-reduction policy in China Energies 2023 16 3667 10.3390/en16093667
Miao, S., Tuo, Y., Zhang, X. & Hou, X. Green fiscal policy and ESG performance: Evidence from the energy-saving and emission-reduction policy in China. Energies 16, 3667 (2023).10.3390/en16093667
