Implementation Effect, Long-Term Mechanisms, and Industrial Upgrading of the Low-Carbon City Pilot Policy: An Empirical Study Based on City-Level Panel Data from China

Abstract

The green, low-carbon transition is a broad and profound change. The low-carbon city pilot policy (LCCP) is one of the most important strategies in China, aimed at dealing with climate change and realizing the green, low-carbon transition. Therefore, a quantitative evaluation of the implementation effect of the pilot policy is conducive to better promoting low-carbon work in the pilot areas. Based on 283 cities in China from 2005 to 2021, this paper constructs a double-difference model to empirically test the implementation effect, long-term mechanisms, and industrial upgrading of the pilot policy. The results show that the LCCP significantly promotes high-quality economic development and employment stability. After a series of robustness and endogenic tests, the conclusions in this study are still valid. Further analysis of the findings in this paper shows that the pilot programs promote the optimization and upgrading of industrial structures. The mediating effect shows that the LCCP has established three long-term mechanisms: developing alternative industries, expanding the level of openness, and promoting innovation. The heterogeneity analysis shows that the pilot policy’s implementation effect is more significant in cities located in central and western regions, as well as in non-resource-based cities. This study proposes the continuation of the promotion and implementation of the pilot policy, an increase in financial funds and policy support, the need to strengthen the labor market’s adaptability and protection mechanisms, the adaptation to local conditions to promote policy implementation, and the need to step up efforts to publicize pilot policies.

1. Introduction

Climate change is one of the biggest challenges that humans face in the 21st century [1]. Increased carbon emissions are a key factor in global warming, worsening the greenhouse effect and worsening extreme weather conditions, according to research. According to a new study by a team of scientists who were working on a global carbon budget in 2023, global carbon dioxide (CO₂) emissions from fossil fuels are rising again to record levels. They are expected to reach 36.8 BN tons, which is up 1.1% from 2022 [2]. UN Secretary-General Guterres, in a video message in 2024 at the 6th UN Environment Assembly in June, called on the leaders attending the conference to push for a solution to combat climate change, biodiversity loss, and pollution. Countries should take action to reduce carbon emissions and protect the Earth’s environment. Concerns about climate change can be traced back to the 1972 Global Conference on Environment and Development, which introduced the idea of “One Earth” to kick-start the focus on climate change’s impact on the planet. In December 1997, representatives from 149 countries and territories adopted the Kyoto Protocol, the first legally binding cap on greenhouse gas emissions. As an important player in the field of climate change, the EU has been committed to promoting global carbon reduction and sustainable development. On 28 November 2018, the EU adopted the EU strategic long-term vision for 2050, which sets the target of achieving carbon neutrality by 2050. In April 2021, the European Council and the European Parliament agreed on the European Climate Act, further setting a 55% emission reduction target for member states by 2030 (compared with 1990). In November 2014, the US released the US–China Joint Statement on climate change, which set a 26–28% reduction target for greenhouse gas emissions, from 2005 levels, by 2050. On 28 May 2024, the US government issued a set of rules to regulate carbon credits from voluntary carbon markets (VCMs), setting out strict criteria for the development of carbon credit projects to ensure that emission reductions are tangible and measurable. Cutting carbon emissions, reducing pollution, expanding green development, and pursuing economic growth are the focus of global attention [3].

The National Congress of the Communist Party of China (NCCPC), held in October 2022, reaffirmed that high-quality development is the primary task of building a modern socialist country in an all-encompassing way [4] and that promoting green and low-carbon development is the engine of high-quality development. As a major responsible country, China has actively assumed the responsibility of reducing emissions and has made significant strategic decisions on carbon peaking and carbon neutrality. In order to achieve positive results and implement sustainable development approaches, China has formulated a number of policies in the energy sector to promote the reduction of carbon emissions. These include suggestions on improving the institutional mechanisms and policy measures for the green and low-carbon energy transition [5], the 14th five-year plan for a modern energy system [6], and the medium and long-term plan for the development of the hydrogen energy industry (2021–2035) [7]. In order to actively respond to the call for action to reduce carbon emissions, local governments have formulated a total of 156 local policies and action plans.

Since 2010, China has carried out 81 national LCCP projects in three batches in order to achieve sustainable and innovative urban development and encourage institutional innovation. Such projects cover cities with different levels of development, resource allocation, and infrastructure. This series of pilot projects has not only explored and achieved positive progress in key areas, such as the transition to a low-carbon economy, the establishment of relevant policy mechanisms, the application of innovative technologies, the effective implementation of related projects, and cross-sectoral cooperation, but it has also accumulated useful experience in promoting low-carbon development models.

As an environmental regulatory policy, the LCCP is a vital tool to help realize the goal of reducing carbon emissions, and the related research focuses on assessing the environmental, economic, and social effects of the policy. When assessing the environmental effects of the policy, the main focus is on the reduction of carbon emissions and pollution control. Existing studies have described in detail the carbon emission reduction effect of the LCCP. At the macro level, the pilot policy has been effective in reducing urban carbon emissions, improving energy efficiency, and improving the quality of energy [8], energy efficiency [9], and inclusive low-carbon growth [10]. At the micro level, the pilot policy has been found to increase the market value of listed companies in pilot locations. At the same time, it can also regulate the cost of energy savings and emission reduction [11], promote green enterprise technology innovation [12], and improve corporate performance regarding environmental, social, and governance [13]. In the area of pollution control, some scholars believe that the LCCP can improve air quality [14], play an important role in reducing air pollution levels [15] and improving green total factor productivity [16] in cities, and can contribute to sustainable urban development [17]. Pilot policies can also improve the supply of urban green products, increase residents’ awareness of green consumption, encourage the formation of green lifestyles, and solve environmental pollution and ecological damage problems at the source [18]. In terms of the economic effects, scholars have found that the LCCP can significantly increase urban wealth and promote urban green economic efficiency [19], thus realizing high-quality economic development. In addition, the pilot policy encourages the adoption of efficient energy and clean technology, which further encourages enterprises to optimize their production processes, improve the quality of their export products, and promote their high-quality development [20]. In terms of social effects, existing studies have found that LCCP can promote residents’ consumption upgrading, raise employment levels, promote labor force development [21], and enhance the level of employment, which promotes the flow of labor to the tertiary industry [22].

Based on the above literature, it can be seen that most studies focus on a single perspective, with relatively little research on the implementation effects of the pilot policy. Moreover, economic growth and employment are rarely analyzed in the same framework, with less attention to the establishment of long-term policy mechanisms. However, as pilot policies continue to be implemented and deepened, a question remains: has the establishment of LCCP achieved the expected results? Can it promote high-quality economic development and employment stability? Are its economic, environmental, and social effects only short-term, or has it formed a long-term sustainable development mechanism? On this basis, this paper analyzes the impact and regional heterogeneity of LCCP on high-quality economic development, employment stability, and industrial upgrading while also testing the long-term mechanism of the policy.

This paper regards the LCCP as a quasi-natural experiment. Based on panel data from 283 Chinese cities from 2005 to 2021, the study quantitatively evaluates the implementation effect, long-term mechanism, and impact on industrial upgrading of LCCP. The main contributions are as follows: First, the effectiveness of LCCP on the economic and employment levels is explored by measuring high-quality economic development using the entropy method and the employment level based on the ILO standard definition of unemployment. This helps assess the general implementation effects. In addition, the study measures and estimates the impact of mechanisms such as alternative industry development, the level of foreign investment, and technological innovation in resource-exhausted cities. Therefore, it is verified whether LCCP has promoted long-term mechanisms. Second, this paper constructs a difference-in-differences model to analyze the impact of pilot policies on low-carbon cities. It uses a series of robust methods to verify the long-term mechanism of pilot policies. A quasi-natural experiment also effectively alleviates endogeneity issues and provides a more reliable policy basis for urban low-carbon development. Third, based on differences in urban location and resource endowment, the paper analyzes the effect of LCCP and provides insights and management strategies for the promotion of ecological civilization and green and low-carbon development.

This study is structured as follows: Section 2 presents a comprehensive literature review and theoretical mechanisms. Section 3 discusses the model setup, selected variables, and data sources. Section 4 conducts an empirical analysis, including benchmark regression and robustness tests. Section 5 further analyzes the long-term mechanism of policy and introduces the impact of geographic differences and resource endowments on policy effectiveness. Finally, Section 6 presents the main conclusions and related recommendations.

2. Theoretical Analysis and Research Hypotheses

LCCP is an environmental regulation with weak incentives and constraints. Its aim is to cut carbon emissions, improve the environment, and achieve sustainable development, thus promoting high-quality urban economic development [23]. Unlike previous environmental regulations, the national government doesn’t have specific objectives for LCCP, such as timelines for peak carbon emissions or emission criteria for certain sectors. Conversely, it delegated authority to the pilot governments, allowing each city to implement low-carbon initiatives based on its own circumstances. This approach was more experimental in comparison to previous environmental regulation measures with well-defined objectives. The effectiveness of this weak-binding environmental policy has yet to be definitively determined. Several researchers found that LCCP can raise awareness of low-carbon practices in cities, cut carbon emissions, optimize urban resource uses, and improve the well-being of urban residents. Not only does it contribute to carbon reduction, but it also facilitates the relocation of polluting industries. This policy also optimizes and upgrades the industrial structure, contributes to reasonable resource allocation, establishes a sustainable green industry system, and ultimately improves economic performance and drives regional economic development [24]. Further studies show that LCCP incentivizes enterprises to promote innovation, offset pollution control costs, reduce energy consumption, and cut emissions, thus fulfilling a win-win situation for both economic development and environmental protection [25]. LCCP imposes higher demands on enterprise production and management, promoting enterprises’ innovation to stay competitive [26]. In addition, LCCP creates new jobs and promotes a virtuous cycle of employment creation. The continued implementation of LCCP can generate more green jobs, improve the overall environmental standards of enterprises, and enhance the quality of their human resources. As a result, companies are more likely to adopt an expansion strategy that will eventually translate to increased hiring. At the same time, investments in green technologies will boost labor demand.

However, according to the “follow-cost” theory in neoclassical economics, environmental regulations can give rise to higher environmental management costs for enterprises [27]. These costs may limit their R&D, production expansion, and enterprise capital expenditure management, as well as reduce corporate profits, therefore affecting their ability to reinvest in growth. At the same time, the introduction of pollution-intensive enterprises in some regions in pursuit of rapid economic development has created a “pollution paradise” [28], which is detrimental to overall environmental quality. The potential negative impacts can be illustrated through three aspects: First, industrial upgrading may be inhibited. Strict environmental regulation can increase costs for enterprises as they are required to make extra investments in pollution prevention and control [29]. These increased costs may reduce profit margins and market competitiveness, therefore inhibiting industrial upgrading. Additionally, due to the proximity transfer effect of pollution, pollution-intensive enterprises may relocate to regions with relaxed environmental regulations to offset the profit losses from high environmental costs. This relocation, in turn, influences the industrial transformation of the receiving area. Second, stricter environmental regulations may hinder a country’s openness. Studies have shown that countries with looser environmental regulations tend to enjoy a cost advantage when they attract FDI compared to their counterpart with stricter policies [30]. As a result, higher environmental regulations may inhibit FDI, thus reducing openness. Third, technological progress may be hampered. Studies indicate that environmental regulations may lead to an “innovation bubble” [31], where the quantity of innovations increases but the quality declines. On the one hand, enterprises may submit lower-cost, low-quality patent applications to take advantage of preferential policies and subsidies, therefore inhibiting real innovation. On the other hand, when companies access low-cost financing, they do not allocate it toward true innovation. This phenomenon may lead to “greenwashing” [32], in which companies ostensibly pursue green innovation but fail to advance environmental technologies substantially. At the same time, the absence of green innovation, in conjunction with competition among local governments to draw in investment, has emerged as a crucial barrier to the quality of green innovation.

Therefore, this paper proposes the following hypotheses:

Hypothesis 1. 

LCCP can promote high-quality economic development, stabilize employment, and promote industrial transformation and upgrading.

Hypothesis 2. 

LCCP can improve high-quality economic development and employment through the establishment of three long-term mechanisms: developing alternative industries, increasing openness, and upgrading technological progress.

3. Method Selection and Variable Description

3.1. Model Construction

To explore the impact of LCCP on high-quality economic development and employment stability, this paper utilizes data from three batches of low-carbon city pilots published in 2010, 2012, and 2017. A difference-in-differences model is constructed to evaluate the impact of pilot policies and establish causal relationships.

$${\mathit{Quality}}_{\mathit{it}}={\alpha }_1+{\beta }_1{\mathit{Treat}}_{\mathrm{i}}\times {\mathit{Post}}_{\mathit{it}}+{\mathrm{X}}_{\mathit{it}}{\gamma }_1+{\lambda }_{\mathit{it}}+{\lambda }_{\mathit{i}}+{\lambda }_{\mathit{t}}+{\epsilon }_{\mathit{it}}$$

(1)

$${\mathit{Employ}}_{\mathit{it}}={\alpha }_2+{\beta }_2{\mathit{Treat}}_{\mathit{i}}\times {\mathit{Post}}_{\mathit{it}}+{\mathit{X}}_{\mathit{it}}{\gamma }_2+{\lambda }_{\mathit{it}}+{\lambda }_{\mathit{i}}+{\lambda }_{\mathit{t}}+{\xi }_{\mathit{it}}$$

(2)

In the model, Quality_it and Employ_it are the dependent variables, which indicate the level of high-quality economic development and employment in prefecture-level cities, respectively. The interaction term Treat_i × Post_it is the core independent variable. The control variables, X_it include urbanization level, financial investment, human capital level, environmental regulation, population density, and government intervention. Additionally, λ_i is city-specific fixed effects, λ_t is time-specific fixed effects, ε_it and ξ_it are random error terms.

3.2. Variables

3.2.1. Dependent Variables

High-quality economic development is sustainable development that takes into account the comprehensive balance of economic, social, and environmental factors. This variable is the first dependent variable [33]. Therefore, based on Jia L. [34], the indicator system is built around five dimensions: innovation, coordination, greenness, openness, and sharing in

Table 1. Indicator system for high-quality economic development.

Disaggregated Indicators	Sub-Indicators	Specific Indicators	Unit of Measure	Indicator Direction
				Forward	Suitably	Negative
Innovative development	investment in science and education	Science and technology inputs/financial expenditures	%		√
		Investment in education/financial expenditure	%		√
	Patent level	Patent acquisition	Pieces	√
Coordinated development	Financial development	Balance of financial deposits/balance of financial loans	%		√
	people’s livelihood	Per capita income per unit	Yuan	√
		Non-real estate investment/investment in fixed assets	%		√
	industrial structure	Share of tertiary sector	%	√
open development	Overview of foreign investment	Foreign capital utilization	Hundred Million USD	√
	Overview of Foreign Enterprises	Gross output value of foreign-owned enterprises	Billion	√
		Number of foreign-owned enterprises	Pieces	√
Green development	three wastes emission	Industrial wastewater discharge/industrial output	Tons/10,000 yuan			√
		Industrial sulfur dioxide emissions/industrial output value	Tons/10,000 yuan			√
		Industrial fume (dust) emissions/industrial output value	Tons/10,000 yuan			√
	sewage treatment	Comprehensive utilization rate of general industrial solid waste	%	√
		Centralized treatment rate of sewage treatment plants	%	√
		Non-hazardous treatment rate of domestic waste	%	√
Shared development	social welfare	Number of physicians/populations	Pieces/10,000 people	√
		Wages of employed workers	Yuan	√
		Urban greening rate	%	√
	consumption level	Consumption of social retail goods/GDP	%	√
	Government burden	Fiscal expenditure/revenue	%		√

Note: √ indicates the choice of Indicator Direction.

.

The second dependent variable is the employment level. According to the International Labor Organization’s (ILO) criteria for unemployment [35], unemployment is measured by four conditions: being of working age, not being employed at the time of the survey, being part of the labor force, and being willing to work. In this paper, we measure employment using the ratio of the unemployed population to the total population in a prefecture-level city.

3.2.2. Independent Variables

According to the basic principle of difference-in-differences modeling, the interaction term of the time and policy dummy variables for LCCP is used as a proxy variable. Treat_i × Post_it is defined as follows: if the city is a low-carbon city, it is coded as 1; if not, it is coded as 0. If the city is in the pilot period, it is coded as 1; otherwise, it is coded as 0.

3.2.3. Control Variables

This paper, drawing from relevant literature, selects six control variables to better identify the impact effects of LCCP: (1) urbanization level, measured by the ratio of the urban resident population to the total resident population; (2) fiscal investment strength, measured by the ratio of the fixed asset investment to the general expenditure of the government treasury; (3) human capital level: measured by students enrolled in the general undergraduate and tertiary institutions to the total population at the end of the year; (4) environmental regulation, measured by the ratio of the comprehensive utilization rate of general solid waste; (5) population density, measured by the ratio of regional resident population to an urban area; (6) government intervention level, measured by the ratio of general government fiscal expenditure to regional GDP.

Table 2. Variable definitions and descriptions.

Variable Type	Variable Symbol	Variable Name	Description of Variables
explanatory variable	Qualityit	High-quality economic development	Entropy method to measure the index of high-quality economic development
	Employit	unemployment rate	Unemployed population/total population in prefecture-level cities
control variable	Urb	urbanization level (of a city or town)	Urban resident population/total resident population
	Fis	Financial investment efforts	Investment in fixed assets/general government expenditure
	Hum	Level of human capital	Number of students enrolled in general undergraduate programs/total population at the end of the year
	Er	environmental regulation	Comprehensive utilization rate of general industrial solid waste
	Pop	population density	Regional resident population/urban area
	Grov	Level of government intervention	General government expenditure/gross regional product

lists basic definitions and descriptions of these variables.

3.3. Sample Selection

This paper selects a research sample of 283 cities in China from 2005 to 2021.

Table 3. Descriptive Statistics.

Variables	Obs	Mean	SD	Min	Max
Qualityit	4673	0.0025	0.0047	0.0000	0.0595
Employit	4673	3.2358	1.2015	−5.0000	34.8667
Treatit × Postit	4673	0.2628	0.4401	0.0000	1.0000
Urb	4673	0.5186	0.4402	0.1141	1.0000
Fis	4673	4.7790	2.0825	0.0124	17.1682
Hum	4673	0.0200	0.0264	0.0005	0.1928
Er	4673	79.4644	23.5162	0.2400	223.8400
Pop	4673	5.7342	0.9212	0.6831	7.8866
Grov	4673	0.1780	0.1016	0.0426	1.9363

presents descriptive statistics for all variables. The raw data were obtained from the China Urban Statistical Yearbook and the statistical annual reports of several prefecture-level cities [36]. To ensure data rigor, we excluded samples with missing data for major variables and used linear interpolation to supplement the missing values and replace the outliers. Ultimately, we obtained 4673 observation samples from 283 cities.

4. Empirical Results

4.1. Regression Results and Analysis

Table 4. Base regression results.

	(1)	(2)	(3)	(4)
	Qualityit	Qualityit	Employit	Employit
Treatit × Postit	0.0004 ***	11.3696 ***	−0.0187 ***	−0.0194 ***
	(0.0001)	(2.7592)	(0.0044)	(0.0044)
time effect	Yes	Yes	Yes	Yes
individual effect	Yes	Yes	Yes	Yes
control variable	No	Yes	No	Yes
constant	0.0016 ***	0.5527 ***	0.0704 ***	0.7062 ***
	(0.0001)	(0.1656)	(0.0228)	(0.1669)
N	4673	4673	4673	4673

Note: Standard errors are reported in parentheses; *, **, and *** are significant at the 10%, 5%, and 1% levels, respectively. Same for the following tables.

demonstrates the results of Equations (1) and (2), which analyze the impact of LCCP on the high-quality economic development and employment level. In particular, columns (2) and (4) incorporate the impact of the control variable X. The results show that the estimated coefficients of the core explanatory variables are significant. Specifically, from the estimated coefficients in Column (2) and Column (4), it can be seen that LCCP has an effect of about 0.0004 on the high-quality economic development of the city, while the unemployment rate for urban and rural workers decreases by 0.0187 percentage points. This shows that LCCP significantly contributes to local economic development and employment stability, showing both statistical and economic significance, thus verifying Hypothesis 1.

4.2. Robustness Tests

4.2.1. Parallel Trend Test

Satisfying the parallel trend assumption is a prerequisite for the use of the difference-in-differences method. As a result, this study tests the parallel trend hypothesis by drawing on existing research in Figure 1 [37,38]. Before the implementation of LCCP, the results were not significant. Following the implementation, the regression coefficient for high-quality economic development gradually increased, while the coefficient for the unemployment rate gradually decreased. Both variables passed the significance test, therefore satisfying the assumption of parallel trends. Additionally, in the fifth year after the implementation of LCCP, the study found that the impact of LCCP on employment stabilization exhibits a lag effect. In addition, the coefficient for high-quality economic development decreased, and the coefficient for the unemployment rate increased. This could be attributed to the fact that following the success of the early pilot cities, the subsequently approved pilot cities may emulate their low-carbon measures, therefore gradually diminishing the overall impact of LCCP.

4.2.2. Placebo Test

Figure 2 displays the distribution of coefficients from the placebo test, a commonly used method of robustness testing. This test involves the random generation of a treatment group and the selection of three different year combinations, as well as a thousand simulation tests to enhance the reliability of the analysis. The estimated coefficients generated through random sampling are concentrated around 0 and essentially follow a normal distribution. In contrast, the actual estimated coefficients, represented by the dashed line, are apparent outliers in this distribution, in line with the expectations of the placebo test. This indicates that the main estimation results in this paper are unlikely to be the result of chance, suggesting that unobservable stochasticity factors do not significantly influence them and ruling out the influence of other unobservable factors on the empirical results.

4.2.3. Policy Lag Effects

Considering that the implementation of green and low-carbon development is a gradual process [39], there may be a lagged effect on the high-quality development of the urban economy and employment stability. Therefore, this paper regresses LCCP with a one-period lag, and the analytical results of the lag effect are demonstrated in columns (1) and (2) of

Table 5. Robustness test results.

Variables and Statistical Parameters	(1)	(2)	(3)	(4)	(5)	(6)
	F1.Qualityit	F1.Employit	Qualityit	Employit	Qualityit	Employit
Treatit × Postit	0.0002 **	−0.2022 ***	0.0007 ***	−0.2341 ***	0.0003 ***	−0.2369 ***
	(0.0001)	(0.0471)	(0.0001)	(0.0407)	(0.0001)	(0.0531)
time effect	Yes	Yes	Yes	Yes	Yes	Yes
individual effect	Yes	Yes	Yes	Yes	Yes	Yes
control variable	Yes	Yes	Yes	Yes	Yes	Yes
constant	0.0037 ***	4.4455 ***	−0.0058 ***	5.8009 ***	0.0037 ***	4.5718 ***
	(0.0009)	(0.5098)	(0.0004)	(0.1514)	(0.0009)	(0.5795)
N	4334	4272	4612	4545	4673	4673

Note: Standard errors are reported in parentheses; *, **, and *** are significant at the 10%, 5%, and 1% levels, respectively. Same for the following tables.

. The estimated coefficients of Treat_i × Post_it are significant, indicating a positive effect on high-quality economic development and a negative effect on the unemployment rate. After considering the potential lagged effect of LCCP, it still significantly promotes high-quality economic development, reduces unemployment rates, and enhances employment stability. Thus, the core conclusions of this paper remain robust.

First, we excluded samples from municipalities directly under the central government. Since Beijing, Tianjin, Shanghai, and Chongqing may exhibit special characteristics in terms of policy implementation and differ from other cities in terms of their level of economic development and urban-rural administrative systems, their inclusion could lead to significant differences between selected pilot cities and non-pilot cities, which in turn affects the accuracy of the estimation. This paper excludes the four municipalities, reruns the regression, and confirms the robustness of the results, as shown in columns (3) and (4) of Table 5.

Second, the potential impact of extreme values in the control variables is addressed using a 1% shrinkage treatment. The estimation results are shown in columns (5) and (6) of Table 5. The estimated coefficients of Treat_i × Post_it remain significant at the 1% level after eliminating extreme values, confirming the robustness of the findings.

Third, the interference of other policies is excluded. Given the longtime frame of the study, LCCP may be influenced by similar policies, including “the PM2.5 key monitoring policy” introduced in 2012, “Interim Measures of the Ministry of Environmental Protection” by the Ministry of Environmental Protection that began in 2014, “the Environmental Protection Tax Law (Draft for Public Comments)” introduced in 2015, and the Innovative Cities Pilot Policy. To test whether the impact effect is influenced by other national policies, this paper re-estimates the dummy variables for these four policy types by adding them to Equation (1). The estimation results are shown in columns (7) and (8) of

Table 6. Consider the robustness test results of other environmental policies.

Variables and Statistical Parameters	(7)	(8)
	Qualityit	Employit
Treatit × Postit	0.0002 ***	−0.2585 ***
	(0.0001)	(0.0535)
PM2.5 focused on monitoring policy	0.0004	−0.4525 **
	(0.0004)	(0.2265)
Policy on Special Emission Limits for Air Pollutants	−0.0004	−0.1109
	(0.0003)	(0.01987)
Interim Measures for Interviews by the Ministry of Environmental Protection	0.0000	0.0178
	(0.0001)	(0.0630)
Pilot policy for innovative cities	0.0011 ***	0.1941 ***
	(0.0001)	(0.0642)
time effect	Yes	Yes
individual effect	Yes	Yes
control variable	Yes	Yes
constant	0.0040 ***	4.6267 ***
	(0.0009)	(0.5797)
N	4673	4673

Note: Standard errors are reported in parentheses; *, **, and *** are significant at the 10%, 5%, and 1% levels, respectively. Same for the following tables.

, where the estimated coefficients of Treat_i × Post_it remain significant.

In summary, the core findings of this paper remain robust.

4.3. Endogenous Test

To mitigate the endogenous bias caused by omitted variables and bidirectional causality, a two-stage least squares of instrumental variables is used. We set the undulation of the terrain as the instrumental variable (IV) since it is a natural geographical factor that affects the flow and exchange of regional resources. For the weak instrumental test, the Wald F statistic for Kleibergen–Paap R K was 24.368, greater than the 10% threshold of 16.38 established by the Stock-Yogo weak instrument test, indicating that the instrument meets the correlation characteristics. Additionally, the Wald F statistic for Kleibergen–Paap R K was 21.542, and the instrument meets the correlation characteristics. The test results presented in

Table 7. Two-stage least squares results.

Variables	The First Stage	The Second Stage	The First Stage	The Second Stage
iv	0.053 ***		0.050 ***
	(4.9364)		(4.6413)
Treatit × Postit		0.014 ***		−0.825 *
		(3.4651)		(−1.9531)
time effect	Yes	Yes	Yes	Yes
individual effect	Yes	Yes	Yes	Yes
control variable	Yes	Yes	Yes	Yes
constant	−0.526 ***	−0.004 **	−0.565 ***	5.562 ***
	(−8.6030)	(−2.3852)	(−8.0919)	(24.9804)

Note: Standard errors are reported in parentheses; *, **, and *** are significant at the 10%, 5%, and 1% levels, respectively. Same for the following tables.

provide evidence for the validity of the instrumental variables used in this paper.

5. Further Analysis

5.1. Impact on Industrial Upgrading

To further test Hypothesis 1, this paper examines industrial upgrading, with a specific focus on the heightening and rationalization of industrial structures, to determine whether LCCP encourages such upgrading.

5.1.1. Heightened Industrial Structure

The heightening of industrial structure means changing the structure of an industry to make it more advanced, efficient, and high-value [40,41]. This usually involves a gradual transition from low-end industries, which are labor- and resource-intensive, to high-end industries that rely on technology and knowledge. Generally, industrial heightening is measured by assessing the growth of the tertiary sector. However, these indicators are mainly analyzed from a quantitative dimension—the proportion of different industries—without full consideration of the core quality of industrial structure evolution. This can lead to what is known as “superficial intensification”. In fact, industrial structure enhancement involves both quantitative growth and qualitative improvement. Such qualitative improvement is reflected in changes in industry proportions and improvements in labor productivity. A region can truly reflect a high level of industrial sophistication when high-productivity industries constitute a larger share of its economy. This paper defines heightened industrial structure as the weighted product of the change in the proportion of industries and their labor productivity, calculated by the following formula:

$${\mathit{Ais}}_{\mathit{i},\mathit{t}}={\displaystyle {\sum }_{m=1}^3{y}_{i,m,t}}\times l{p}_{i,m,t},m=1,2,3\dots \dots$$

(3)

In Equation (3), y_{i, m, t} denotes the proportion of the m industry in i area to the GDP in period t while lp_{i, m, t} denotes the labor productivity of industry m in area i during period t.

5.1.2. Rationalization of Industrial Structure

Rationalization of industrial structure refers to the optimization and adjustment of the industrial structure to ensure that each industry effectively meets the needs of social and economic development and achieves efficient allocation and utilization of resources [42]. This process covers various aspects, primarily the adjustment of industrial proportion, enhancement of the industrial chain, coordination of the regional economy, and efficient resource utilization. It is of great significance in enhancing the international competitiveness of a country or region, realizing economic transformation and upgrading, promoting social employment, and improving living standards [43,44]. At present, limited quantitative research has been conducted on the rationalization of industrial structure, with inconsistencies in the selection of indicators. In this paper, the Tel index is used as a tool to measure the degree of rationalization in the industrial structures of prefecture-level cities. The specific calculation formula is as follows:

$${\mathit{Theil}}_{\mathit{i},\mathit{t}}={\displaystyle {\sum }_{m=1}^3(y_{i,m,t}}/l_{i,m,t}),m=1,2,3\dots \dots$$

(4)

where y_{i, m, t} corresponds to the values defined in Equation (3) and l_i,m,t denotes the proportion of employees in industry m in area i during period t.

As shown in columns (1) to (4) of

Table 8. Impact on industrial upgrading.

	(1)	(2)	(3)	(4)
	${\mathit{A}\mathit{i}\mathit{s}}_{\mathit{i},\mathit{t}}$		${\mathit{T}\mathit{h}\mathit{e}\mathit{i}\mathit{l}}_{\mathit{i},\mathit{t}}$
Treatit × Postit	0.0145 **	0.0148 **	0.0012	0.0250 *
	(0.0070)	(0.0070)	(0.0060)	(0.0060)
time effect	Yes	Yes	Yes	Yes
individual effect	Yes	Yes	Yes	Yes
control variable	No	Yes	No	Yes
constant	0.7141 ***	0.7755 ***	0.2795 ***	0.2679 ***
	(0.0072)	(0.0769)	(0.0061)	(0.0653)
N	4673	4673	4673	4673

Note: Standard errors are reported in parentheses; *, **, and *** are significant at the 10%, 5%, and 1% levels, respectively. Same for the following tables.

, the benchmark regressions, which utilize clustered robust standard errors and control for city and year-fixed effects, show that the introduction of LCCP has promoted industrial upgrading. Specifically, the regression coefficients of the core explanatory variables related to industrial structure heightening are all positively significant at the 5% level, indicating that LCCP promotes the heightening of industrial structure. However, the promotion effect of LCCP on the rationalization of industrial structure is not significant enough, indicating that LCCP may lack effective measures for resource integration and optimization. To sum up, LCCP can promote industrial upgrading, but its promoting effect on the rationalization of industrial structure requires improvement.

5.2. Long-Term Mechanism Test

This section tests the long-term mechanism of LCCP as proposed in the theoretical analysis. This paper selects three mediating variables for this mechanism test: value added of the tertiary industry (reflecting the development of alternative industries), the level of FDI (reflecting the level of openness to the outside world), and technological innovation (reflecting the level of technological development). According to Equations (5)–(7), the mediating effect analysis method is used to conduct the intrinsic mechanism test. The mediating effect M covers the value added by the tertiary industry, FDI level, and technological innovation.

$${\mathit{M}}_{\mathit{it}}={\alpha }_1+{\beta }_1{\mathit{Treat}}_{\mathit{i}}\times {\mathit{Post}}_{\mathit{it}}+{\mathit{X}}_{\mathit{it}}{\gamma }_1+{\lambda }_{\mathit{it}}+{\lambda }_{\mathit{i}}+{\lambda }_{\mathit{t}}+{\epsilon }_{\mathit{it}}$$

(5)

$${\mathit{Quality}}_{\mathit{it}}={\alpha }_2+{\beta }_2{\mathit{Treat}}_{\mathit{i}}\times {\mathit{Post}}_{\mathit{it}}+{\theta }_2{\mathit{M}}_{\mathit{it}}{+\mathit{X}}_{\mathit{it}}{\gamma }_2+{\lambda }_{\mathit{it}}+{\lambda }_{\mathit{i}}+{\lambda }_{\mathit{t}}+{\delta }_{\mathit{it}}$$

(6)

$${\mathit{Employ}}_{\mathit{it}}={\alpha }_3+{\beta }_3{\mathit{Treat}}_{\mathit{i}}\times {\mathit{Post}}_{\mathit{it}}+{\theta }_3{\mathit{M}}_{\mathit{it}}{+\mathit{X}}_{\mathrm{it}}{\gamma }_3+{\lambda }_{\mathit{it}}+{\lambda }_{\mathit{i}}+{\lambda }_{\mathit{t}}+{\zeta }_{\mathit{it}}$$

(7)

5.2.1. Development of Alternative Industries

LCCP, as a formal environmental regulation, has increased the entry and production costs for pollution-intensive industries. As a result, some industrial enterprises, unable to break away from high-carbon production, are gradually withdrawing from the market [45]. Simultaneously, tertiary industries, such as the service sector, gain an advantage in development, therefore promoting industrial restructuring towards higher-end levels. This fosters new industries and generates new momentum for green economic growth. The estimation results in Column (1) of

Table 9. Impact mechanism test.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
	Value Added of Tertiary Industry	Level of Foreign Investment	Technological Innovation	Qualityit	Qualityit	Qualityit	Employit	Employit	Employit
Treati × Postit	0.0121 ***	0.0024 *	0.0029 ***	0.0003 ***	0.0003 ***	0.0003 ***	−0.2337 ***	−0.2336 ***	−0.2280 ***
	(0.0041)	(0.0014)	(0.0005)	(0.0001)	(0.0001)	(0.0001)	(0.0531)	(0.0537)	(0.0533)
Value added of tertiary industry				0.0014 ***			−0.3832 *
				(0.0004)			(0.2068)
Level of foreign investment					0.0014 ***			−1.6107 ***
					(0.0004)			(0.5838)
technological innovation						0.0261 ***			−2.9955 ***
						(0.0023)			(1.5013)
control variable	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
individual effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
time effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
constant	0.2884 ***	−0.0004	0.0122 **	0.0041 ***	0.0048 ***	0.0042 ***	4.3192 ***	4.2129 ***	4.2476 ***
	(0.0453)	(0.0168)	(0.0059)	(0.0009)	(0.0010)	(0.0009)	(0.5906)	(0.6448)	(0.5879)
N	4673	4673	4673	4673	4673	4673	4673	4673	4673

Note: Standard errors are reported in parentheses; *, **, and *** are significant at the 10%, 5%, and 1% levels, respectively. Same for the following tables.

show that the LCCP significantly improves the proportion of tertiary industries. In addition, the results in columns (4) and (7) show that LCCP promotes high-quality economic development and employment levels through the development of the tertiary industry, with the mediating effect being significant. This indicates that LCCP can promote high-quality economic development and employment stability using the development of the tertiary industry as a long-term mechanism.

5.2.2. Level of Foreign Investment

Since China’s reform and opening up, FDI has steadily increased and has become an important pillar of Chinese economic growth. However, the introduction of FDI has also presented China with numerous challenges. Environmental regulation can have a significant impact on the costs and profits of foreign enterprises, thus affecting their decisions to enter or exit the market [46]. Exploration of the impact of LCCP on FDI can help determine whether FDI in China remains in the “pollution shelter” or has reached the “pollution halo” stage. The estimation results in Column (2) of Table 9 show that LCCP significantly reduces the level of FDI. In addition, the results in columns (5) and (8) of Table 9 indicate that LCCP promotes high-quality economic development and employment stability by increasing the level of FDI, with significant mediating effects. This indicates that LCCP can promote high-quality economic development and employment stability through the long-term mechanism of enhancing the level of FDI.

5.2.3. Technological Innovation

In the pursuit of environmental development, local governments have actively taken measures to incentivize enterprises to innovate in low-carbon technologies. This includes engaging enterprises in the research, development, and application of green technologies, as well as promoting the practice of cleaner production to achieve ecological and environmental objectives [47]. Consequently, LCCP promotes not only environmentally sustainable development but also provides foreign-invested enterprises with a new avenue for consideration: investment in low-carbon technologies to reduce environmental costs and partially offset rising labor costs. The estimation results in Column (3) of Table 9 show that the LCCP significantly increases the level of technological innovation. Furthermore, the results in columns (6) and (9) of Table 9 indicate that LCCP promotes high-quality economic development and employment levels through increased technological innovation, with a significant mediating effect. This indicates that LCCP can promote high-quality economic development and employment stability through the long-term mechanism of enhancing technological innovation.

Overall, LCCP promotes high-quality economic development and employment stability through the long-term mechanism of developing alternative industries, attracting FDI, and upgrading technological innovation, thus verifying Hypothesis 2.

5.3. Heterogeneity Analysis

5.3.1. Geographical Differences

Geographical differences influence the economic development, industrial distribution, openness, and environmental conditions of China’s regions. These factors are likely to influence the effects of LCCP. To investigate the regional differences in LCCP, this paper divides the sample into three groups: the Eastern Region, the Central Region, and the Western Region. The results in

Table 10. Heterogeneity test results based on urban location.

	(1)	(2)	(3)	(4)	(5)	(6)
	Qualityit			Employit
	the east	central section	western part	the east	central section	western part
Treati × Postit	0.0001	0.0006 ***	0.0005 ***	−0.0911 *	−0.1691	−0.1276 ***
time effect	(0.0004)	(0.0001)	(0.0001)	(0.0494)	(0.1089)	(0.0439)
individual effect	Yes	Yes	Yes	Yes	Yes	Yes
control variable	Yes	Yes	Yes	Yes	Yes	Yes
constant	−0.0191 ***	−0.0013 ***	−0.0013 ***	6.2147 ***	6.3146 **	3.9921 ***
	(0.0017)	(0.0002)	(0.0003)	(0.2462)	(0.4097)	(0.1378)
N	1662	1679	1332	1662	1679	1332

Note: Standard errors are reported in parentheses; *, **, and *** are significant at the 10%, 5%, and 1% levels, respectively. Same for the following tables.

show that LCCP significantly promotes high-quality economic development and employment in the central and western regions. However, its promotion effect is weaker in the eastern region. This difference may be due to the eastern region’s advanced economic and technological status, which already offers a foundation for low-carbon development. Therefore, the additional contribution of LCCP in these areas is not statistically significant. Furthermore, factors such as policy implementation, resource allocation, and conformity with the local economic structure may also influence its effectiveness, leading to a less favorable implementation effect in the eastern region than initially anticipated.

5.3.2. Heterogeneity of Resource Endowments

The city’s resource endowment also affects the effectiveness of the implementation of LCCP. Compared with non-resource cities, resource cities usually have a more homogeneous industrial structure and a relatively weak foundation for green and low-carbon development. Based on the classification in “The National Sustainable Development Plan for Resource-Based Cities (2013–2020)”, this study classifies cities into two categories, resource-based and non-resource-based, and carries out a differentiation analysis, the results of which are presented in columns (1) to (4) of

Table 11. Results of heterogeneity test based on resource endowment.

	(1)	(2)	(3)	(4)
	Qualityit		Employit
	resource-based city	Non-resource-based cities	resource-based city	Non-resource-based cities
Treati × Postit	−0.0001 ***	0.0006 **	−0.1599 ***	−0.1154 **
	(0.0000)	(0.0002)	(0.0509)	(0.0582)
time effect	Yes	Yes	Yes	Yes
individual effect	Yes	Yes	Yes	Yes
control variable	Yes	Yes	Yes	Yes
constant	−0.0004 ***	−0.0120 ***	4.5439 ***	6.3611 ***
	(0.0001)	(0.0009)	(0.1719)	(0.2332)
N	1850	2823	1850	2823

Note: Standard errors are reported in parentheses; *, **, and *** are significant at the 10%, 5%, and 1% levels, respectively. Same for the following tables.

. The findings indicate that LCCP has a significant impact on high-quality economic development and employment stability in non-resource-dependent cities. However, in resource-dependent cities, the policy shows a negative effect. This may be because resource-dependent cities, with their long-term economic growth model reliant on resource extraction and processing, have developed a more rigid development path. The industrial structure of these cities is characterized by high energy consumption and pollution, which leads to a “low-end lock-in” of economic growth and makes it challenging to transform into a more efficient and environmentally friendly development model. Therefore, in promoting economic structural transformation and the transformation of old and new energies, the impact of low-carbon strategies is limited. In the short term, it is difficult to significantly improve the harmonization of economic development and the ecological environment, and it may even be a constraint on their development.

6. Conclusions and Recommendations

6.1. Conclusions

Low-carbon development is essential for China’s adaptation to climate change and the transformation of its economic growth model. LCCP plays a crucial role in the achievement of China’s greenhouse gas emission reduction targets. Since its first low-carbon pilot projects in 2010, China has enacted rules and regulations, embedded low-carbon technologies, and charted a mutually beneficial trajectory for both the economy and the environment. Therefore, it is important to evaluate the efficacy of these policies. Based on data from 283 cities in China from 2005 to 2021, this paper constructs a difference-to-difference model to measure the implementation effects, long-term mechanisms, and industrial upgrading related to LCCP, which enriches the existing literature. The main conclusions drawn are as follows:

First, benchmark regression results show that LCCP promotes high-quality regional economic development and employment stability. The policy’s impact on high-quality urban economic development is about 0.0003, and it has reduced the unemployment rate among urban and rural workers by 20.67 percentage points. Various robustness tests have objectively confirmed the necessity and effectiveness of LCCP in the promotion of the green economy in China. The parallel trend test results show that LCCP exhibits a lagged effect on employment stability. Furthermore, five years after the implementation of LCCP, there has been a decrease in the coefficient of high-quality economic development, an increase in the unemployment rate, and a gradual decrease in the overall effectiveness of the LCCP.

Second, LCCP promotes the industrial structure; however, its impact on the rationalization of this structure is not significant, as there are no effective measures to integrate and optimize resource allocation.

Third, LCCP advances high-quality local economic development and stable employment through a long-term mechanism of developing alternative industries, attracting FDI, and enhancing technological innovation.

Finally, LCCP promotes high-quality economic development and employment stability in the central and western regions, but the effect in the eastern regions is less significant. This diminished effect could be attributed to the eastern region’s existing economic and technical advancements, which provide a solid foundation for low-carbon development. Therefore, the additional contribution of low-carbon pilot policies is not statistically significant. As for resource endowment, LCCP plays a more significant role in the high-quality economic development and employment stability of non-resource-based cities. This may be because resource-dependent cities, with their long-term economic growth model reliant on resource extraction and processing, have developed a more rigid development model. These cities often lag in the development and innovation of low-carbon technologies, making it difficult to reduce carbon emissions and improve energy efficiency.

6.2. Policy Implications

The findings of this paper have the following policy implications:

First, LCCP should continue to be promoted. Currently, it is important to broaden LCCP, investigate low-carbon development models in the context of low-carbon city construction, gather experience in low-carbon governance, and offer references for the comprehensive implementation of a low-carbon strategy. At the same time, the fulfillment of low-carbon goals is a complex and long-term task that requires unwavering efforts to promote a low-carbon strategy and maintain steady progress to avoid short-term campaign-style carbon reduction.

Second, local governments should increase the financial resources and policy support for technology research and development, improve workforce skills and knowledge, and create an innovation-friendly environment. This will facilitate the full utilization of the compensating effects of innovation and mitigate any decline in total factor productivity caused by the LCCP. Simultaneously, it is essential to encourage all regions to expedite the rational allocation of industrial structures and foster a green lifestyle through a well-designed mix of policies.

Third, it is crucial to improve the adaptability of the labor market and protective mechanisms, taking into account the varying costs for workers resulting from increased carbon emissions and employment elasticity. This approach should include appropriate training, re-employment programs, and social security for workers affected by low-carbon policies. The effective implementation of these policies is essential for labor market stability and the protection of the rights and interests of workers. By incorporating the talent factor input, local governments can enhance the efficiency of the talent service system, establish comprehensive talent service platforms, and give full play to the benefits of skilled labor. The objective is to motivate firms to invest in vocational education and training for their employees, thus enhancing their adaptability to high-skilled positions. This will amplify the positive impact of LCCP on employment and overall job quality.

Fifth, we should continue to raise awareness of LCCP, guide residents to understand and support the implementation of these policies, stimulate innovators’ initiative, and make the concept of low-carbon life deeply rooted in people’s minds, ultimately achieving high-quality economic development.

However, this study is limited by its exclusive focus on the city level, disregarding the influence of the pilot strategy on the micro level of enterprises by considering only one viewpoint. In the future, we can continue to explore the effectiveness of pilot policy, combining the enterprise perspective and spatial econometric models.