Does Low-Carbon City Construction Promote Integrated Economic, Energy, and Environmental Development? An Empirical Study Based on the Low-Carbon City Pilot Policy in China

Abstract

Low-carbon urban transformation is considered the path to green economic growth in dual-carbon contexts. The low-carbon city pilot policy (LCCP) in China has vast potential to enhance the integrated development of the economy, energy, and environment (3Es). Taking 240 cities in China from 2005 to 2019 as research samples, this paper investigated the impact of LCCP on the integrated development level of the 3Es using the progressive difference-in-differences model and analyzed the mechanisms of influence. In addition, the implementation effects on different cities were analyzed using the triple difference model. The findings show that implementing the LCCP policy significantly improves the 3Es integrated development level in the sample cities, and a variety of robustness tests were used to validate this conclusion. The influence mechanism analysis shows that the primary avenues for pilot programs to contribute and successfully advance the enhancement of the low-carbon city 3Es integrated development level are technological innovation and financial development. Moreover, the heterogeneity analysis of resource endowment and geographical location shows that LCCP is beneficial to the 3Es integrated development in non-resource-based cities and eastern cities. Consequently, policy recommendations include the continuation of low-carbon pilot city expansion, joint promotion of regional 3Es integration, improving the level of technological innovation and financial development, and tailoring of policy to local circumstances. This study provides theoretical support for the evaluation of China’s LCCP and, to a certain extent, proves that the measures taken by China in the process of exploring green economic growth and realizing the dual-carbon goal are correct.

1. Introduction

In terms of carbon emissions, China presently leads the globe [1], already reaching 12.039 billion tons of total carbon emissions by 2021. Figure 1 depicts China’s carbon emissions between 2001 and 2021. In the Second Tenth Report, proposals were drawn to vigorously advance the energy revolution, hasten the shift to a more environmentally friendly development model, and promote the peaceful coexistence of people and nature. In consequence, China, the greatest consumer of primary energy in the world, is putting more of an emphasis on how energy efficiency, economic growth, and emission reduction are all evolving together. However, one of the primary causes of environmental pollution, resource crises, and climate warming is the elevated energy consumption of cities, which in turn highly impacts urban growth and human activities [2,3]. In this regard, it is essential to maintain the notion of sustainable development and insist on the implementation of a green economic development model. Such efforts prevent the aggravation of the adverse effects of global warming and can thereby ensure national energy security, supply new opportunities for economic growth, and boost new levels of national competitiveness.

Promoting low-carbon, green, and sustainable cities depends on the integrated and coordinated growth of the 3Es. Energy serves as the structural underpinning for social progress and is a major driver of societal economic advancement. The environment serves as both a conduit for energy and a habitat for diverse species populations. As a result, energy demand is determined by economic growth, which is either aided or constrained by the environment. The economy, energy, and environment complement each other and cooperate with each other. Only through the mutual support of these systems can the system as a whole be in a positive cycle that fosters thorough and well-coordinated 3Es development. However, the excessive use of energy and high levels of pollutant emissions that have led to the conflict between the 3Es are more clear [4]. This increase is the result of an unwavering focus on economic expansion [5]. Therefore, for the coordinated growth of cities and the successful implementation of green policies, a detailed analysis of the overall development level of the 3Es offers crucial conditions and essential guarantees. Additionally, the unremitting promotion and pursuit of comprehensive development of 3Es is a common responsibility of all nations worldwide. In addition, what measures should China take to promote 3Es integrated development?

China has established a dual-carbon goal to confront the substantial problems posed by climate change [6]. Cities must alter their growth patterns since they are vital to promoting carbon reduction and reducing the heat island effect [7]. In many nations and regions around the world, the low-carbon city pilot policy (LCCP) has become one of the most significant development initiatives. In China, a common approach to exploring suitable policies for national conditions is to gradually expand and experiment with the establishment of pilot projects [8,9]. China published the Notice on the Piloting of Low-Carbon Provinces, Regions, and Cities on 19 July 2010, and announced the second and third batches of pilot regions in 2012 and 2017. Can LCCP promote 3Es integrated development? What are the impact mechanisms, and is there resource endowment as well as geographic heterogeneity in the effectiveness of LCCP implementation? This essay provides theoretical, analysis-based solutions to these queries.

Under the dual-carbon target and the new economic situation, realizing energy conservation and emission reduction while fostering economic growth has become a long-term objective pursued by the Chinese government [10]. The LCCP strategy intends to reduce greenhouse gas emissions, improve energy security, and promote green economic growth through various methods, including optimizing energy structures, fostering technological innovation, and constructing low-carbon industrial systems. Therefore, it is essential to investigate the impact of LCCP from the standpoint of integrated 3Es development. However, this performance assessment has primarily focused on the completion of emission reductions and other social effects in a single dimension, with few studies examining the policy’s impact on the comprehensive and coordinated development of cities from a multi-dimensional perspective; there is no literature on how this policy has affected the comprehensive development of 3E; and even less research has explored specific paths to achieve integrated 3Es development in Chinese cities under the LCCP policy. This paper innovatively explores LCCP effects from the perspective of integrated urban 3Es development by adopting progressive difference-in-differences (DID) while analyzing the impact mechanisms and heterogeneity characteristics, using 240 Chinese cities as a research sample from 2005 to 2019. This study has important reference value for properly handling the interaction between economic development and environmental protection in the process of urban construction, realizing green economic growth, and building low-carbon cities.

The following are the primary contributions of this study: (1) Most evaluations of the impact of LCCP in earlier research came from a single dimension, such as energy conservation or carbon reduction, but this study switched to evaluating the efficacy of the policy in the overall development effects on energy, economics, and the environment. (2) Considering the current situation of LCCP being implemented in batches in China, the general development of the 3Es was experimentally tested by innovatively using the progressive DID in this paper. Moreover, robustness tests were performed to ensure the conclusion’s reliability. (3) Meanwhile, this study analyzed the routes between LCCP and 3Es integrated development and validated the impact mechanisms using a three-stage mediated effects model to fill the research gap between the two. (4) To further investigate the contribution of resource endowment and geographical location heterogeneity to the pilot policy, the triple difference method was innovatively used; relevant suggestions were made for various types of cities and regional cities based on the findings. In conclusion, this study contributes to the early achievement of the dual-carbon target and offers a theoretical foundation for China to realize 3Es integrated development and green economic growth.

The structure of the present study is as follows: Section 2 presents a comprehensive literature review and theorized mechanisms. The model design and data sources are discussed in Section 3. Section 4 reports the analysis of empirical data and tests for robustness. Section 5 analyzes the influence mechanism of the LCCP policy. Section 6 presents the influence of urban resource endowments and geographic variability on the level of integrated urban 3Es development. The key conclusions and related recommendations are outlined in Section 7.

2. Literature Review and Theorized Mechanisms

2.1. Literature Review

Currently, academic research on 3Es focuses primarily on the interaction mechanism between the three and the evaluation of index measurement. Some scholars, both in China and worldwide, have studied the relationship between the three interactions using the EKC (environmental Kuznets curve) theory. Shahbaz et al. [11] argued that the EKC theory can be useful in determining the relationship between the 3Es. Joshi and Beck [12] claimed the economy does not cause environmental degradation or provide feedback from energy consumption. Several researchers assessed the coordination of various systems using the coupled coordinated development theory. Cui D. et al., for example, presented an integrated approach comprising system dynamics and coupled coordination degree models to evaluate socio-economic development patterns and the effectiveness of water conservation measures in improving CCD [13]. Moreover, based on a mechanism analysis of the economic-energy-environment system, Li et al. [14] used exploratory spatial data analysis tools and PLS pathway models to investigate the integrated development of 3Es complex systems in Chinese provinces.

Since the LCCP policy’s adoption, most researchers have concentrated on the theoretical concepts [15], implementation strategies, and impact assessments [16]. The most pertinent research for this study is how to measure the results of LCCP. Regarding the evaluation of LCCP, researchers have found that this policy can significantly lower CO₂ emissions, increase carbon efficiency [17], enhance air quality [18], and advance the creation of green cities [19]. By optimizing energy structure, lowering fossil energy consumption, and lowering energy intensity, the LCCP policy has aided the energy transition [20]. These findings are evidence that the LCCP strategy is superior as a means of achieving green and sustainable development [21,22]. This policy also creates favorable conditions for the early attainment of the dual carbon objective by upgrading industrial structures [16]. Some scholars have proposed low-carbon road maps for various types of regions in China from the viewpoint of urban heterogeneity, variations in development stages, obstacles, and opportunities [23]. The Heckman selection model has also been used to conduct an empirical investigation, which demonstrated that local leaders’ actions positively promoted policy innovation during the construction of LCCP [8].

Despite the fact that much research concentrates on the LCCP policy, none of them explores the benefits of this policy from the 3Es comprehensive development perspective. On the one hand, the information entropy approach was used in this study to assign weights to individual indicators during the calculation of a comprehensive index; the examination of the correlation degree and information between these indicators allowed the quantification of the weights of the indicators while avoiding the deficiency of subjective judgment [24]. On the other hand, the DID approach, which has a good model-fitting effect, is currently the most popular method for evaluating such policies. It is also a crucial model for the assessment of China’s progressive policy reforms. First, using regression on city panel data, the DID approach establishes the statistical significance of the impact of the policy more easily than static comparison methods. Second, the interaction effect between the explanatory variables and the explained variables is effectively controlled by investigating the policy shock of LCCP as an independent variable and modeling it as a dummy variable, which prevents the endogeneity issue. Furthermore, the DID method efficiently negates the effect on the explanatory variables due to unobservable individual variability, allowing the net effect of the pilot program to be obtained. Consequently, combining the aforementioned benefits of DID and accounting for LCCP being launched and executed in stages, this study uses the progressive DID model.

2.2. Theoretical Analysis of Influence Mechanisms

The LCCP policy is a comprehensive environmental strategy to address climate change and energy issues while maintaining stable economic growth, prompting a gradual shift in the urban development model to quality-centered green growth [25], which will undoubtedly contribute to the 3Es integrated development. LCCP can stimulate technological innovation through regulation and incentives to achieve green urban economic growth [26]. First, because of the restrictions on pollution emissions, enterprises understand that they must implement green technological innovation to transform and advance industrial development and produce low-carbon and environmentally friendly industrial output [27]; otherwise, they will incur significant costs for environmental protection. Second, the public’s consumption demand for green products has grown significantly as a result of the benefits of technological innovation permeating and enhancing many aspects of urban residents’ lives. This unquestionably creates positive green demand feedback on the production side, further encouraging enterprises to actively pursue technological innovation [28]. Also, technological innovation can not only optimize the urban industrial structure and improve energy efficiency but also reduce pollution emissions, all of which would help to advance 3Es integrated development.

Realizing green and sustainable growth is inextricably linked to the finance sector’s steadfast support. First, as the pilot policy is implemented, companies face technological innovation. The financial market can offer them financial security, diversify the risks associated with technological research, and address the issue of market failure brought on by information asymmetry to promote the advancement of green technology and the green economy. Second, with better environmental regulations and stricter enforcement, outdated production capacity and industries require large amounts of capital for environmental investment and pollution control, while financial development can ease financing constraints and reduce the cost of environmental investment and financing, thus effectively promoting the implementation of policies and the green transformation of cities. A series of green financial policies, including government subsidies, tax exemptions, and preferential lending rates, have been implemented throughout LCCP implementation, which can enable the effective allocation of funds, promote green technology development, the use of clean energy, and the realization of industrial green and low-carbon development, which will, in turn, promote the 3Es integrated development.

Based on the above discussion, Figure 2 illustrates how LCCP influences 3E.

3. Method Selection and Variable Description

3.1. Model Construction

To overcome selective bias due to the non-random nature of policy interventions, quasi-experimental techniques, such as the matching method, DID method, and breakpoint regression, are frequently used in the study of policy implementation impacts [29]. The matching method is a non-experimental method that must meet strong assumptions and have a considerable amount of data [30]. The breakpoint regression approach states that when the value of an individual’s main variable exceeds the critical value, the individual accepts policy intervention. The DID technique is by far the most used methodology for assessing policies, and it is frequently applied to LCCP evaluation [28]. DID’s good fit brings the policy assessment more in line with economic reality. Simultaneously, policy evaluation requirements are lowered by allowing for the presence of unobservables. Furthermore, the methodology avoids the endogeneity problem and the impact on the dependent variables caused by unobservable individual variability, allowing the net effect of the pilot program to be calculated. Therefore, after conducting a comparative analysis and considering that LCCP was implemented in batches, this paper adopts the progressive DID to determine whether the LCCP policy has an impact on the 3Es integrated development. As shown in Equation (1), α₁ is the net effect of the policy, where α₁ > 0 indicates that LCCP improves the 3Es integrated development level of a pilot city compared with a non-pilot city. The model is expressed as follows:

$$Y_{i,t}={\alpha }_0+{\alpha }_{1}du\times dt+\phi X_{i,t}+{\mu }_i+{\lambda }_t+{\epsilon }_{i,t}$$

(1)

where Y_i,t represents the city’s 3Es integrated development level; the i-th city and the t-th year, respectively, are denoted by the subscripts i and t; du represents the individual dummy variable for the study city (1 for the experimental group and 0 for the control group); dt denotes the temporal dummy variable (1 for the experimental group and 0 for the control group); and the multiplier term du × dt is the core explanatory variable, indicating whether city i implemented the LCCP strategy in year t. The core estimator is represented by α₁. A series of control variables is represented by X_i,t. City-fixed effects are denoted by μ_i, time-fixed effects are denoted by λ_t and ε_i,t is a random error term.

3.2. Sample Selection

Currently, China implemented the first batch of LCCP projects in 2010 and continued to expand pilot cities in 2012 and 2017. The first batch of pilots comprised five provinces, including Guangdong, and 8 cities, including Tianjin; the second batch comprised 1 province, Hainan, and 28 cities, including Beijing; and the third batch comprised 41 cities, as well as 4 districts and counties, including Wuhai City.

Given the considerable disparities in resource endowments and development levels of those cities at different levels, as well as the lack of statistical data for some pilot cities, this study did not consider provincial-level, district-level, and county-level pilot cities, as well as pilot cities with missing data. Therefore, 65 pilot cities were identified in three batches as the experimental group, and 175 non-pilot cities were selected as the control group [31]. Furthermore, since the second batch of LCCP was announced on 26 November 2012, close to the end of the year, considering the initiation time of the policy and the lag in policy implementation, this study defined the implementation time of the three batches of the LCCP policy as 2010, 2013, and 2017.

3.3. Variables

3.3.1. Dependent Variables

Scholars frequently construct index systems to study the relationship between 3E. Based on relevant studies [32], this study correspondingly improved the operability and rationality of the study’s results and established an urban 3Es comprehensive development evaluation index system in

Table 1. Evaluation indicators for integrated development of urban economic-energy-environmental systems.

Target Layer	System Layer	Indicator Layer	Indicators and Nature
Integrated development of Urban economic-energy-environmental systems	Economy system	Gross regional product	Positive index
		Average of urban workers	Positive index
	Energy system	Total social electricity consumption	Negative index
	Ecology system	Rate of industrial SO2 emissions	Negative index
		Rate of domestic waste disposal	Positive index
		CO2 emissions	Negative index

.

The economic system is expressed by the gross regional product and the average of urban workers on the job; the former refers to the results of all resident units’ production activities throughout a certain time period, whereas the latter refers to all individuals working in the unit and paid by the unit, both of which show the region’s economic strength and market scale. The energy system is expressed by total social electricity consumption, considering all areas of electricity consumption, such as primary, secondary, and tertiary industries, and can effectively reflect regional energy consumption. The environmental system is expressed by the rate of industrial SO₂ emissions, CO₂ emissions, and the rate of domestic waste disposal, all three of which can effectively reflect urban environmental pollution and its treatment.

To minimize the involvement of human variables, the weights were determined using the entropy value approach through the information entropy principle and objectively and accurately evaluated for the research object. According to the theoretical underpinnings of information theory, information is a measure of system orderliness, whereas entropy is a measure of system disorderliness. Additionally, based on the entropy weight theory, more information present in the 3Es integrated development evaluation index system results in smaller uncertainty and higher weightage [33]. The following is a specific evaluation model:

• Standardization of indicators: with n years, s prefecture-level cities, m indicators, and x′_ij being the standardized value of indicator j in the year i, x′_ij can be expressed as:

  $${{\displaystyle x^{\prime }}}_{ij}=\frac{{{\displaystyle x}}_{it}-\min \left({{\displaystyle x}}_{ij}\right)}{\max \left({{\displaystyle x}}_{ij}\right)-\min \left({{\displaystyle x}}_{ij}\right)} (\mathrm{Positive} \mathrm{index})$$

  (2)

  $${{\displaystyle x^{\prime }}}_{ij}=\frac{\max \left({{\displaystyle x}}_{ij}\right)-{{\displaystyle x}}_{ij}}{\max \left({{\displaystyle x}}_{ij}\right)-\min \left({{\displaystyle x}}_{ij}\right)} (\mathrm{Negative} \mathrm{index})$$

  (3)
• Determination of indicator weights:

  $${{\displaystyle p}}_{ij}=\raisebox{1ex}{${{\displaystyle x^{\prime }}}_{ij}$}\!\left/ \!\raisebox{-1ex}{${\displaystyle {\sum }_{i=1}^n{{\displaystyle x^{\prime }}}_{ij}}$}\right.$$

  (4)
• Information entropy of the j-th indicator:

  $${{\displaystyle \mathrm{e}}}_j=-ln{(k)}^{-1}{\displaystyle \sum _{i=1}^m{p}_{ij}}\cdot ln{p}_{ij}, \mathrm{among} \mathrm{them}, k>0, k=ln(ns)$$

  (5)
• j-th indicator information utility value:

  $$d_j=1-E_j$$

  (6)
• Determination of the weights of each indicator:

  $$w_i=\frac{d_j}{{\displaystyle {\sum }_{j=1}^m{d}_j}}$$

  (7)
• City 3Es integrated development evaluation index value:

  $$Y_{i,t}={\displaystyle {\sum }_{j=i}^m\left({{\displaystyle w_j{x}^{\prime }}}_{it}\right)}$$

  (8)

3.3.2. Independent Variables

This study used dummy variables (dudt) to evaluate the LCCP policy. The dummy variables dt are set to 1 for all years starting in the year of implementation and thereafter, with a value of 0 assigned before the year of implementation; du = 1 for cities implementing the LCCP policy; and du = 0 for the rest of the cities.

3.3.3. Mechanism Variables

The mechanism variables include technological innovation and financial development. The former uses the ratio of year-end research, technical service, and geological exploration employees to year-end employees to assess the city’s technological innovation capacity [34]. The latter is calculated by dividing financial institutions’ total year-end loans by their GDP.

3.3.4. Control Variables

Some representative control variables that may influence the comprehensive development level of urban 3Es were selected to solve the problem of estimation bias caused by omitted variables, including: the level of urbanization (urb) as measured by the proportion of urban inhabitants in cities at the end of the year, which reflects whether the concept of green development is implemented in the process of urbanization and coordinates the relationship between urbanization level and green development level; the regional industrial structure (ind) as determined by the proportion of tertiary industry’s added value in the regional GDP, which reflects whether to promote green economic growth through industrial structure upgrading; carbon sink resources (crs) measured by the urban green cover, which reflects the impact of economic development on the environment; and the level of government involvement (gov) as indicated by the proportion of government expenditure to regional GDP, which indicates the level of attention given to sustainable development in government policy initiatives.

3.4. Data Collection

Based on the consistency of statistical quality and data availability, 240 prefecture-level cities’ panel data from 2005 to 2019 were selected for this study. The data for the independent variable LCCP policy was generated considering relevant papers issued by the state. The data used in the construction of the city 3Es comprehensive development index system and the control variables were selected from the China City Yearbook from 2006 to 2020, the CEADs database, as well as from statistical bulletins and statistical yearbooks of each city. The missing values of a few variables in individual years were uniformly filled by interpolation. With 2005 as the base period, all nominal variables of the aforementioned data were modified to eliminate the effect of inflation.

Table 2. Descriptive statistics of all variables.

Variable	Observations	Mean	Std. Dev.	Min	Max
Yi,t	3600	0.0719	0.0779	0.0185	0.890
urb	3600	52.98	15.64	11.83	100
ind	3600	0.437	0.253	0.111	5.435
crs	3600	38.68	7.680	0.6931	95.25
gov	3600	0.186	0.191	0.0427	6.041
tech	3600	0.0177	0.0139	0	0.150
loan	3600	0.991	0.824	0.113	16.88

presents descriptive statistics for all variables.

4. Empirical Results

4.1. Regression Results and Analysis

To accurately analyze the influence of the pilot policy and consider the small values of some indicators, all variables in this study were logged to keep the data stable and eliminate heteroskedasticity to avoid pseudo-regressions.

Table 3. Basic regression results.

Variables	lnY	lnY	lnY	lnY	lnY	lnY
	(1)	(2)	(3)	(4)	(5)	(6)
dudt	0.0400 ***	0.0394 ***	0.0231 ***	0.0325 ***	0.0322 ***	0.0212 ***
	(0.0063)	(0.0062)	(0.0059)	(0.0068)	(0.0067)	(0.0054)
lnurb				0.0416 ***	0.0395 ***	−0.0287 **
				(0.0042)	(0.0047)	(0.0106)
lnind				0.0305 ***	0.0266 ***	0.0309 ***
				(0.0055)	(0.0052)	(0.0068)
lncrs				0.0016	0.0016	−0.0028 *
				(0.0016)	(0.0016)	(0.0014)
lngov				−0.0329 ***	−0.0264 ***	−0.0563 ***
				(0.0069)	(0.0064)	(0.0090)
year	NO	NO	YES	NO	NO	YES
city	NO	YES	YES	NO	YES	YES
cons	0.0631 ***	0.0632 ***	0.0486 ***	−0.1117 ***	−0.1028 ***	0.1619 ***
	(0.0031)	(0.0006)	(0.0014)	(0.0161)	(0.0172)	(0.0389)
N	3600	3600	3600	3600	3600	3600
R2	0.220	0.220	0.437	0.307	0.307	0.473

Note: Significance levels of 10%, 5%, and 1% are indicated by the symbols *, **, and ***, respectively. Standard errors are shown by the values in brackets.

displays the regression findings. The results of models (1) to (6), as follows, represent the random effects model with and without control variables, the city fixed effects model, and the city and time dual fixed effects model, respectively.

(1) The effect of LCCP on the 3Es integrated development level. Regardless of whether control variables were included or excluded or whether time or city fixed effects were included, the coefficients of all regressions were significantly positive at the 1% level. This suggests that the policy significantly raised the 3Es comprehensive development level in the pilot cities. Given that model (6) is the regression result after considering all other components, it indicated LCCP can greatly increase 3Es comprehensive development compared with that in non-pilot cities with an impact coefficient of 0.0212, while all other variables remain constant. The following factors are among the reasons behind this discovery: First, both central and local governments support the LCCP program, which can effectively play the role of policy guidance, incentive impact, and demonstration and promote high participation of enterprises and the public in the building of LCCP; second, traditional industries have been encouraged to adopt green technologies, realize low-carbon upgrades, and reduce the emission of polluting gases as a result of the city’s environmental regulations being gradually strengthened and financial constraints being gradually eased. The green technology progress associated with the policy penetrated and benefited urban residents’ lives at several levels with the increased supply of green products. In addition, the policy raised urban residents’ understanding of environmentally friendly consumption, which significantly decreased the amount of carbon emissions generated by domestic electricity, transportation, and heating [35].

(2) The estimated coefficient of urbanization level (urb) was considerably negative at the 5% level, showing that the increase in urbanization level impedes the developmental level for urban 3Es integrated development. This may result from the crescent need for infrastructure construction as urbanization progresses, as well as from the large demand for housing among urban residents, which raises the energy resource requirement and, in turn, increases the pollution caused by energy use. In addition, the presence of numerous sloppy development and weight-light phenomena in the fast urbanization process is impeding the urban 3Es integrated development level.

(3) The estimated coefficient of regional industrial structure (ind) was significantly positive at the 1% level, showing that the upgrading of regional industrial structure promotes urban 3Es integrated development level growth. This increase could be attributed to the shift of urban functions from industrialized to service trade functions, which promotes the expansion of low-carbon industries. Meanwhile, with steadily tightening environmental rules, regional industries have enhanced their awareness of green development principles, optimized industrial access principles, adjusted energy structure, reduced consumption of coal, implemented clean production, and improved energy efficiency. Additionally, it actively fosters low-carbon energy and quickens the growth of the new energy sector, thereby reducing fossil fuel use, lowering carbon dioxide and other greenhouse gas emissions, and supporting the urban 3Es integrated development level. Such results are consistent with those of Liu et al. [28], who concluded that industrial structure has significant impacts on LCCP construction.

(4) The estimated coefficient of carbon sink resources (crs) was significantly negative at the 10% level. Accelerated industrialization and population growth, excessive exploitation and consumption of energy resources, unsustainable land use, and significant deforestation have all possibly contributed to a continuing decline in forest carbon sink supplies. In turn, this contribution decreases their capacity to absorb carbon dioxide from the atmosphere, leading to an increase in the amount of greenhouse gases that enter the atmosphere. Additionally, due to soil degradation brought on by human activities, plants are unable to grow and reproduce normally, which has a negative effect on the intricate ecological systems in forests and causes previously fixed carbon to be released into the atmosphere. It can be shown that the benefits of carbon sink resources have not yet been realized, therefore preventing urban 3Es integrated development.

(5) The estimated coefficient of the degree of government intervention (gov) was significantly negative at the 1% level, indicating that the increase in the degree of government intervention suppresses that of the 3Es integrated development. On the one hand, under the GDP-only theoretical orientation, local governments tend to prioritize attaining economic data unilaterally, causing local governments to focus on investments and light services, resulting in inefficient fiscal and financial resources. On the other hand, under the promotion tournament mechanism, the decisions of local governments, out of the need for performance assessment, have led to the short-sightedness of economic goals, resulting in light public services as well as human capital and construction duplication. Consequently, cities have achieved rapid economic growth while overlooking an overall balanced socio-economic development, resulting in inefficient financial investment, massive energy consumption, and increased environmental pollution.

4.2. Robustness Tests

(1)

Parallel trend test

The parallel trend assumption should be satisfied before measuring policy effectiveness using the Progressive DID model. The following model is set up:

$$\begin{array}{l}ln{Y}_{i,t}={\alpha }_0+{\displaystyle \sum _{t=-4}^6{\delta }_t}{D}_{i,t}+{\beta }_1\cdot lnur{b}_{i,t}+{\beta }_2\cdot lnin{d}_{i,t}\\ +{\beta }_3\cdot lncs{r}_{i,t}+{\beta }_4\cdot lngo{v}_{i,t}+{\mu }_i+{\lambda }_t+{\epsilon }_{i,t}\end{array}$$

(9)

D_i,t is a series of dummy variables representing policy variables generated in each relative year, with the pilot year as a reference. Their assignment rules are as follows: D_i,t is equal to 0 for pilot cities prior to policy implementation, 1 for all years beginning and ending after policy implementation, and 0 for non-pilot cities. In this study, the data t is set to −5 for all years before the policy implementation for 5 years and above, and t is set to 6 for all years after the policy implementation for 6 years and above. Figure 3 shows the test findings of the parallel trend test.

It can be concluded that prior to the implementation of the LCCP, the estimated coefficient fluctuated around 0 and had no statistical significance in almost all periods, implying that there is no significant difference between the 3Es integrated development level of pilot cities and non-pilot cities. The coefficient of each period following the adoption of the policy is noticeably favorable, showing that the LCCP has greatly raised urban 3Es integrated development. According to the aforementioned variations, the research sample of this study passed the parallel trend test.

(2)

Placebo test

Drawing on Li et al. [36], 65 cities among all sample cities were randomly selected as a “pseudo-experimental group”, to exclude the influence of unobserved city sample characteristics on the regression results and to avoid the impacts of random elements and missing variables. Their product and the time dummy variables were chosen as the core explanatory variables to re-run the regression; this random sampling process was repeated 500 times to obtain the estimated coefficients of the regression of the LCCP policy on 3Es integrated development with their corresponding p-values for this test. As shown in Figure 4, the estimated coefficients under the random treatment are distributed around 0 and follow a normal distribution. This distribution might be because the regression results of the policy on low-carbon cities were not significant for the randomly sampled sample. Also, the vertical dashed line shows that the estimated coefficients under the random treatment exclude the estimated coefficients of the true regressions, indicating that it is impossible for the random sample to have coefficients equal to the true coefficients, implying that the policy is effective in the selected experimental groups. Accordingly, the effect of unobservable factors can be excluded; this observation demonstrates that the primary conclusions remain reliable.

(3)

Winsorized method test

The maximum and minimum values of several variables were significantly different, and extreme values were present. To avoid their influence on the accuracy of the baseline regression results, the Winsorized method was used. The study sample was truncated by 1%, and we re-ran the regression; the estimation results are shown in

Table 4. Winsorized method results.

Variables	lnY	lnY	lnY	lnY	lnY	lnY
	(1)	(2)	(3)	(4)	(5)	(6)
dudt	0.0312 ***	0.0309 ***	0.0152 ***	0.0216 ***	0.0215 ***	0.0138 ***
	(0.0040)	(0.0039)	(0.0038)	(0.0043)	(0.0042)	(0.0036)
lnurb				0.0513 ***	0.0497 ***	−0.0141 **
				(0.0038)	(0.0038)	(0.0068)
lnind				0.0334 ***	0.0294 ***	0.0404 ***
				(0.0065)	(0.0063)	(0.0089)
lncrs				0.0044 *	0.0042 *	−0.0062 **
				(0.0025)	(0.0025)	(0.0025)
lngov				−0.0345 ***	−0.0264 **	−0.0909 ***
				(0.0092)	(0.0088)	(0.0141)
year	NO	NO	YES	NO	NO	YES
city	NO	YES	YES	NO	YES	YES
cons	0.0624 ***	0.0605 ***	0.0460 ***	−0.1619 ***	−0.1562 ***	0.1172 ***
	(0.0029)	(0.0004)	(0.0011)	(0.0154)	(0.0166)	(0.0277)
N	3528	3528	3528	3302	3302	3302
R2	0.197	0.197	0.511	0.384	0.384	0.560

Note: Significance levels of 10%, 5%, and 1% are indicated by the symbols *, **, and ***, respectively. Standard errors are shown by the values in brackets.

. After truncating the extreme values, the estimated coefficients are consistent with the basic regression results.

(4)

Alternative bandwidths

The effects of the policy after implementation are the focus of current research. Considering that the sample policy will not be implemented immediately, the research method of Zhang et al. [37] was employed to change the bandwidth treatment. This change was achieved by shortening the study period and establishing the sample study time from 2008 to 2018 to evaluate the differing periods of the policy’s impact on 3Es integrated development; the results are reported in

Table 5. Replacement bandwidth results.

Variables	lnY	lnY	lnY
	(1)	(2)	(3)
dudt	0.0298 ***	0.0291 ***	0.0159 ***
	(0.0050)	(0.0048)	(0.0046)
year	NO	NO	YES
city	NO	YES	YES
cons	0.0668 ***	0.0668 ***	0.0553 ***
	(0.0035)	(0.0006)	(0.0011)
N	2640	2640	2640
R2	0.174	0.174	0.415

Note: Significance levels of 1% are indicated by the symbols ***, respectively. Standard errors are shown by the values in brackets.

. The results were in close proximity to those of the basic regression. Therefore, the previous conclusions are robust.

(5)

Excluding interference of concurrent policies

Concurrent policies may affect the urban 3Es integrated development level in the sample period and thus influence baseline regression results. Two interfering policies were identified, namely the new energy demonstration city policy and the pilot program for supporting innovative cities. Therefore, these pilot policies were added to the baseline regression model, and the control variables were set as the cross product of whether they are new energy demonstration cities and the implementation time (New energy), and whether they are innovative city pilots and the implementation time (Innovation), respectively. The regression results are shown in

Table 6. Results excluding other policy disturbances.

Variables	lnY	lnY	lnY	lnY
	(1)	(2)	(3)	(4)
dudt	0.0231 ***	0.0200 ***	0.0212 ***	0.0188 ***
	(0.0059)	(0.0059)	(0.0054)	(0.0055)
New energy	0.0021		0.0024
	(0.0040)		(0.0039)
Innovation		0.0202 ***		0.0177 ***
		(0.0035)		(0.0035)
lnurb			−0.0290 **	−0.0250 **
			(0.0107)	(0.0106)
lnind			0.0308 ***	0.0294 ***
			(0.0069)	(0.0062)
lncrs			−0.0028 *	−0.0020
			(0.0015)	(0.0013)
lngov			−0.0561 ***	−0.0469 ***
			(0.0091)	(0.0083)
year	YES	YES	YES	YES
city	YES	YES	YES	YES
cons	0.0486 ***	0.0486 ***	0.1630 ***	0.1451 ***
	(0.0014)	(0.0013)	(0.0391)	(0.0388)
N	3600	3600	3600	3600
R2	0.437	0.479	0.473	0.504

Note: Significance levels of 10%, 5%, and 1% are indicated by the symbols *, **, and ***, respectively. Standard errors are shown by the values in brackets.

. Among them, model (1) and model (2) are the results that include only policy variables, whereas model (3) and model (4) are regression results that further introduce control variables. After eliminating concurrent policy disturbances, the sign and significance of the dudt coefficients are identical to those in the basic regression; this finding demonstrates that these policies did not bias the basic regression results.

5. Mechanism Analysis

According to the theoretical mechanism, LCCP may have an impact on the 3Es integrated development via technological innovation and financial development. Therefore, we adopt the three-stage intermediary effect model to empirically investigate the impact mechanism of LCCP. The specific model is as follows:

$$ln{Y}_{i,t}={\alpha }_0+{\alpha }_{1}du\times dt+\phi X_{i,t}+{\mu }_i+{\lambda }_t+{\epsilon }_{i,t}$$

(10)

$$ln{w}_{i,t}={\delta }_0+{\delta }_{1}du\times dt+\phi X_{i,t}+{\mu }_i+{\lambda }_t+{\epsilon }_{i,t}$$

(11)

$$ln{Y}_{i,t}={\gamma }_0+{\gamma }_{1}du\times dt+{\gamma }_2{w}_{i,t}+\phi X_{i,t}+{\mu }_i+{\lambda }_t+{\epsilon }_{i,t}$$

(12)

where w_i,t denotes the intermediary variables and ${\delta }_1$ is the influence of LCCP on the intermediary variable. The product of ${\delta }_1$ and ${\gamma }_2$ represents the intermediary impact mechanism, which depicts the indirect impact of the policy on the dependent variable. ${\gamma }_1$ is the direct influence after removing the mediating effect. If the coefficients ${\alpha }_1$, ${\delta }_1$, and ${\gamma }_2$ are significant, the intermediation effect exists.

Table 7. Test results of the influence mechanism.

Variables	lnY	lntech	lnY	lnloan	lnY
	(1)	(2)	(3)	(4)	(5)
dudt	0.0138 ***	0.0023 **	0.0136 ***	0.0683 *	0.0136 ***
	(0.0036)	(0.0010)	(0.0036)	(0.0392)	(0.0036)
lnurb	−0.0141 **	−0.0020	−0.0140 **	−0.2922 **	−0.0133 *
	(0.0068)	(0.0021)	(0.0068)	(0.0971)	(0.0068)
lnind	0.0404 ***	0.0050 *	0.0398 ***	1.2022 ***	0.0371 ***
	(0.0089)	(0.0030)	(0.0089)	(0.2459)	(0.0088)
lncrs	−0.0062 **	−0.0010	−0.0061 **	0.0014	−0.0062 **
	(0.0025)	(0.0008)	(0.0025)	(0.0357)	(0.0025)
lngov	−0.0909 ***	−0.0075	−0.0902 ***	2.8219 ***	−0.0989 ***
	(0.0141)	(0.0050)	(0.0141)	(0.4397)	(0.0151)
lntech			0.1524 *
			(0.0897)
lnloan					0.0028 **
					(0.0014)
year	YES	YES	YES	YES	YES
city	YES	YES	YES	YES	YES
cons	0.1172 ***	0.0258 **	0.1148 ***	1.1705 **	0.1138 ***
	(0.0277)	(0.0084)	(0.0274)	(0.3853)	(0.0276)
N	3302	3354	3302	3354	3302
R2	0.560	0.065	0.561	0.672	0.562

Note: Significance levels of 10%, 5%, and 1% are indicated by the symbols *, **, and ***, respectively. Standard errors are shown by the values in brackets.

reports the effect mechanism of LCCP on 3E. Column (1) shows the estimated results of Equation (10), and columns (2) and (4) show the estimated results of Equation (11); the mechanism variables are technological innovation and financial development, respectively. Columns (3) and (5) show the estimated results of Equation (12). The findings show that when the mechanism variables are included, column (1) agrees with the baseline regression results. Technology innovation is a mechanism variable with a significant positive estimated coefficient in column 2, demonstrating that LCCP can successfully encourage urban technology innovation. The coefficient value in column (3) is smaller and significantly positive, demonstrating the establishment of the intermediary mechanism of technological innovation. It indicates that building the LCCP enhances the construction of a low-carbon talent team, improves the speed of low-carbon technology research, accelerates the decarbonization of industrial structures, improves energy efficiency, and facilitates the greening of residents’ lives, thus effectively improving the 3Es integrated development.

The estimated coefficient in column (4) is notably positive when financial development is the mechanism variable, demonstrating that building low-carbon cities is helpful in raising the level of financial development. The coefficient value in column (5) is smaller and significantly positive, indicating that the intermediary mechanism of financial development is established. It demonstrates that during the implementation of LCCP, a series of green financial policies introduced have provided financial protection for green technology research, alleviated financing constraints for industrial upgrading, and reduced the cost of environmental investment and financing, resulting in the effective allocation of funds and thus promoting the 3Es integrated development.

6. Further Analysis

Resource endowment and geographical location are essential variables contributing to regional development differences in China. They may have distinct influences on the construction of low-carbon cities. A triple difference approach was applied to conduct heterogeneity tests [38], described in further detail below. The types of cities are detailed in Appendix A.

6.1. Heterogeneity Analysis Based on Resource Endowment

The research sample was divided into resource-based cities and non-resource-based cities for further comparative analysis. The models can be expressed as:

$$\begin{array}{l}ln{Y}_{i,t}={\alpha }_0+{\alpha }_{1}du\times dt\times dr+{\beta }_1\cdot lnur{b}_{i,t}+{\beta }_2\cdot lnin{d}_{i,t}\\ +{\beta }_3\cdot lncs{r}_{i,t}+{\beta }_4\cdot lngo{v}_{i,t}+{\mu }_i+{\lambda }_t+{\epsilon }_{i,t}\end{array}$$

(13)

where dr denotes the city’s resource endowment. Its value was assigned to 1 for resource-based cities and 0 for non-resource-based cities. The estimated coefficient β₁ of du × dt × dr represents the heterogeneity of resource endowment of LCCP on the urban 3Es integrated development level, which is critical for the present study.

As shown in

Table 8. Results of the test for resource endowment heterogeneity.

	Resource-Based Cities		Non-Resource-Based Cities
Variables	lnY	lnY	lnY	lnY
	(1)	(2)	(3)	(4)
dudtdr	−0.0131 ***	−0.0118 ***	0.0336 ***	0.0311 ***
	(0.0022)	(0.0023)	(0.0072)	(0.0066)
lnurb		−0.0332 **		−0.0256 **
		(0.0121)		(0.0098)
lnind		0.0294 ***		0.0291 ***
		(0.0074)		(0.0065)
lncrs		−0.0030 **		−0.0026 *
		(0.0015)		(0.0013)
lngov		−0.0604 ***		−0.0517 ***
		(0.0104)		(0.0084)
year	YES	YES	YES	YES
city	YES	YES	YES	YES
cons	0.0486 ***	0.1804 ***	0.0486 ***	0.1498 ***
	(0.0015)	(0.0443)	(0.0013)	(0.0357)
N	3600	3600	3600	3600
R2	0.381	0.427	0.479	0.509

Note: Significance levels of 10%, 5%, and 1% are indicated by the symbols *, **, and ***, respectively. Standard errors are shown by the values in brackets.

, the regression results of models (1) and (2) illustrate that, irrespective of control variables, the LCCP policy had a significant negative impact on the level of 3Es integrated development in resource-based cities, whereas it had a significant driving effect in non-resource-based cities. Resource-based cities are industrial cities whose primary industries are the mining and processing of local natural resources, such as minerals and forests; this exploitation provokes issues such as resource depletion and ecological damage. Restricted by weak technology, functional locking, and insufficient endogenous motivation for transformation and development, resource-based cities adopting the LCCP policy face strong emission reduction tasks due to their large carbon emission base, emerging technology, and unreasonable industrial layout during the early phases of construction, which in turn results in major pressure and challenges for low-carbon construction and poor implementation of the policy [39]. A study suggested that improving governance is likely to effectively avoid a “resource curse” [40].

6.2. Heterogeneity Analysis Based on Geographical Location

To further measure the effects generated by geographical locations, the sample cities were divided into three, namely, eastern, central, and western cities. The model can be expressed as follows:

$$\begin{array}{l}ln{Y}_{i,t}={\alpha }_0+{\alpha }_{1}du\times dt\times dl+{\beta }_1\cdot lnur{b}_{i,t}+{\beta }_2\cdot lnin{d}_{i,t}\\ +{\beta }_3\cdot lncs{r}_{i,t}+{\beta }_4\cdot lngo{v}_{i,t}+{\mu }_i+{\lambda }_t+{\epsilon }_{i,t}\end{array}$$

(14)

where dl denotes the geographical location of the city and was set to 1 for cities in the east and 0 for cities in the central and western regions, dl was assigned in the same manner. The estimated coefficient β₁ of du × dt × dl represents the geographical heterogeneity of the policy on 3Es integrated development.

The regression results with and without the control variables are represented by models (1) and (2) in

Table 9. Results of the geographic location heterogeneity test.

	Eastern Cities		Central Cities		Western Cities
Variables	lnY	lnY	lnY	lnY	lnY	lnY
	(1)	(2)	(3)	(4)	(5)	(6)
dudtdl	0.0455 ***	0.0422 ***	−0.0032	−0.0024	0.0018	0.0015
	(0.0108)	(0.0102)	(0.0040)	(0.0037)	(0.0067)	(0.0065)
lnurb		−0.0232 **		−0.0335 **		−0.0335 **
		(0.0087)		(0.0121)		(0.0122)
lnind		0.0225 ***		0.0299 ***		0.0303 ***
		(0.0063)		(0.0073)		(0.0073)
lncrs		−0.0031 **		−0.0030 **		−0.0030 **
		(0.0013)		(0.0015)		(0.0015)
lngov		−0.0482 ***		−0.0610 ***		−0.0612 ***
		(0.0082)		(0.0104)		(0.0105)
year	YES	YES	YES	YES	YES	YES
city	YES	YES	YES	YES	YES	YES
cons	0.0486 ***	0.1440 ***	0.0486 ***	0.1815 ***	0.0486 ***	0.1815 ***
	(0.0013)	(0.0320)	(0.0015)	(0.0445)	(0.0015)	(0.0446)
N	3600	3600	3600	3600	3600	3600
R2	0.499	0.525	0.376	0.422	0.376	0.422

Note: Significance levels of 5%, and 1% are indicated by the symbols **, and ***, respectively. Standard errors are shown by the values in brackets.

. The coefficient of the regression was significantly positive at the 1% level in eastern cities; however, it did not significantly contribute to central and western cities. Eastern cities have a high starting point for urbanization, quick development, and diversification because their geographical locations attract numerous talented individuals and gather rich human capital, which in turn provides persistent internal energy to propel LCCP construction. Moreover, eastern cities offer advantages in terms of industrial structure, infrastructure, transportation conditions, and technological innovation [41]. They diligently promote the adoption of LCCP programs, reaping the rewards and effectively driving the urban 3Es integrated development. Conversely, due to the relative lack of inventive talents and a homogenous industrial structure, the economic growth of central and western cities mostly depends on labor-intensive and resource-based industries. Furthermore, central and western cities exhibit low enforcement of environmental regulations while being under pressure to cut emissions, which prevents the efficient implementation of LCCP initiatives, which in turn affects the ecological environment.

7. Discussion

This research uses the progressive DID to thoroughly investigate the impact of LCCP on the complete development level of 3Es and its mechanisms in Chinese cities. In contrast to other research, this paper takes a novel approach to analyzing how LCCP policies affect the expansion of the green economy by looking at it through the lens of 3Es integrated development. The findings demonstrate that low-carbon pilot cities have greatly improved the integrated 3Es development when compared to non-pilot cities, and the conclusion remains reliable even after a number of robustness tests. This is sufficient evidence of the necessity and correctness of China’s environmental regulations to support green economic growth and meet the dual-carbon goal.

The influence mechanism confirms that the primary avenues for LCCP to contribute are financial development and technological innovation. These avenues may effectively foster the enhancement of low-carbon city 3Es integrated development level. It demonstrates how the adoption of low-carbon policies has led to the introduction of a number of green financial policies that guarantee funding for the study and advancement of green technologies, foster technological innovation, and subsequently make it easier to upgrade the industrial structure and optimize the energy structure, all of which have a positive impact on the urban 3E’s integrated development level.

The heterogeneity analysis of the research samples was further conducted based on the geographic location and resource endowment variations of Chinese cities. LCCP plays a more significant role in promoting the 3Es integrated development of non-resource-based cities and eastern cities. The technological level is reasonably developed, industrial transformation is simpler, resource utilization efficiency is higher, and the implementation of LCCP rules is convenient because of location and resource advantages. In the future, this will also provide theoretical support for China to adopt policies according to local conditions.

8. Conclusions and Policy Implications

8.1. Conclusions

China has implemented three batches of cities under the low-carbon city pilot strategy since 2010 to establish a win-win path for the economy and the environment, as well as to promote the low-carbon and green transformation of cities. Under the influence of carbon peaking and carbon neutral targets, assessing policy effectiveness has become essential by examining if they have promoted integrated economic, energy, and environmental development in cities. Accordingly, this paper applied a comprehensive evaluation method to measure the city’s 3Es integrated development index. This paper uses 240 Chinese cities from 2005 to 2019 as research samples, and it creatively examines the effects of LCCP and analyzes the influence mechanism from the perspective of 3Es integrated development by using the progressive DID. It also introduces the heterogeneity characteristics by building a triple-difference model. The main conclusions drawn are described below.

First, the promotion effect on 3Es integrated development was much greater in pilot cities than in non-pilot cities by enacting the LCCP policy. This result remained true after several robustness tests, which objectively prove the necessity and correctness of the low-carbon city policy adopted by China to promote green economic development; furthermore, the parallel trend test indicates that the coordinated promotion effect gradually strengthens with the prolonged duration of the policy implementation. This finding is primarily attributable to the increased significance of environmental regulation with strong government support, which promotes the growth of green technologies and raises public participation, which in turn efficiently stimulates the promotion effect of the policy on the urban 3Es integrated development level.

Second, the regional industrial structure had a positive impact on the urban 3Es integrated development level, which majorly responds to the ongoing transformation of an urban industry to a service-oriented model and to the gradual adoption of a clean energy structure. Meanwhile, the level of urbanization, carbon sink resources, and the degree of government intervention had negative effects, mainly due to accelerated urbanization, overly one-sided pursuit of economic development, and excessive use of energy resources.

Third, the analysis of the impact mechanism found that LCCP improves the level of urban 3Es integrated development through technological innovation and financial development. Through the progressive advancement of technological innovation, the policy has sped the upgrading of industrial decarbonization, improved energy efficiency, and encouraged the greening of residents’ lifestyles. Meanwhile, by improving the level of financial development, the policy alleviates financial limitations and offers financial security for the city’s low-carbon transition, promoting 3Es integrated growth. The findings of the impact mechanism research provide an essential direction for future low-carbon construction and the development of green economies in cities.

Finally, resource endowment and regional heterogeneity both had an impact on how the LCCP strategy was implemented. Non-resource-based cities and eastern cities were more effective at implementing LCCP than resource-based cities and central and western cities. Such resource and location advantages had a great influence over policy implementation under the dual-carbon objective; this is mainly because most of the eastern cities are non-resource-based, implying that they do not generally exhibit resource “path dependency” and may easily accomplish industrial transformation. In addition, their resource utilization efficiency is enhanced by their innate advantages and fast technological development, effectively promoting the construction of low-carbon cities.

8.2. Policy Implications

The following policy implications are presented based on the previous conclusions:

First, the active promotion of the LCCP policy is recommended, especially under the pressure of the double-carbon goal. This study demonstrates that putting into practice the LCCP program can greatly increase 3Es integrated development, which accumulates experience that contributes to the achievement of low-carbon city transformations and exploring new economic growth points. Based on the scientific evaluation of the policy implementation effect, the Chinese government is encouraged to gradually broaden the scope of low-carbon cities by following the model of “first pilot and then extension” and leveraging the policy to its full potential for achieving the double-carbon goal.

Second, actively coordinating the integrated regional 3Es development is advisable. For instance, discarding the “GDP-only theory” in city construction, adopting reasonable development planning, encouraging the shift to a green model of economic development, and maintaining stable economic growth within the bounds of available energy and environmental resources are endeavors that should be pursued. Other strategies are adjusting the energy structure, focusing on resource rationalization, promoting clean energy development, and enhancing the ecological carrying capacity of resources and the environment. In addition, traditional industries should implement the new development concept, eliminate backward production capacity, optimize industrial structure constantly, achieve coordinated development of industries, and rationalize and advance in meeting the growing needs of society.

Third, to boost technological innovation and financial development. We will increase investment in technology, create a favorable environment for innovation, expand the ranks of low-carbon technical personnel, and increase the conversion rate of scientific research results to promote technological innovation and achieve high-quality economic development in China. Optimize and improve urban green credit and financial policies, innovate investment and financing models, extend financing channels for enterprises, raise fiscal subsidies and incentives, and ensure the development of green technology to encourage the growth of the urban green economy.

Ultimately, it is recommended that local conditions be considered while implementing the LCCP policy. Chinese cities present significant geographical and development differences; therefore, implementation strategies should be continuously adjusted and improved. Non-resource-based cities and eastern cities should be favored in the first phase to leverage their strengths, which could aid in the vigorous development of green low-carbon industries, overcome the unstructured development mode, and thoroughly realize green economic growth. In turn, the spillover effect of the policy might drive resource-based and central and western cities to effectively implement the LCCP policy.

This study empirically analyzed the implementation effect of LCCP from the perspective of 3Es integrated development. It offers a fresh viewpoint for the investigation of urban green transformation, and the analysis of influence mechanisms and heterogeneity has important guiding significance for low-carbon construction and the growth of the green economy. However, this study may also have the following limitations: This article solely focuses on the influence of LCCP on 3Es integrated development at the macro level of prefecture-level cities, with no empirical data at the micro level of enterprises. Future scholars can directly explore the LCCP policy on 3Es integrated development from the micro level, which has more scientific and practical value. Moreover, most environmental policies will have spatial spillover effects. In the future, we can combine spatial measurement methods with DID models to investigate how LCCP policies affect both the cities themselves and their surrounding cities.