Impact of China’s Low-Carbon City Pilot Policies on Enterprise Energy Efficiency

Abstract

An increase in energy efficiency is an essential element and a powerful driving force for the in-depth implementation of the sustainable development strategies necessary in accelerating the promotion of green, circular, and low-carbon development, as well as to promote the comprehensive green transformation of economic and social development. An important question with regard to this paper is thus: can the low-carbon city pilot policy promote energy efficiency improvement, and if so, through what mechanisms? This paper uses the SBM–Malmquist–Luenberger index method to measure the green total factor energy efficiency and examines the impact and pathways of the pilot policy on the energy efficiency of enterprises, using a sample of listed manufacturing enterprises in 230 prefecture-level cities in China from 2007 to 2020. Additionally, the time-varying difference-in-differences (DID) method is approached in this paper. After replacing energy efficiency with slack-based measure directional distance function model (SBM-DDF) and conducting a series of robustness tests, this study found that the pilot policy can significantly improve the energy efficiency of manufacturing enterprises. A mechanism test shows that this policy can promote green innovation effect and agglomeration effect to improve enterprises’ energy efficiency. The low-carbon city pilot policy has contributed the most to energy efficiency through enterprise investment in green innovation and manufacturing agglomeration. Heterogeneity analysis found that policy effect differs among firms in terms of different sizes and properties, and the pilot policy plays different roles among different regions. This paper provides firm-level theoretical support and empirical evidence for evaluating low-carbon city pilot policy and offers policy recommendations.

1. Introduction

China’s rapid urbanization and industrialization have led to enormous energy consumption and CO₂ emissions, and it has become a national strategy to build a clean and efficient energy system and a high-quality development model characterized by green and low-carbon. Recently, China has accelerated the construction of an energy-saving and low-carbon energy system, with the proportion of coal consumption continuing to decline and that of clean energy consumption steadily increasing, which have led to a remarkable structural energy reform. However, as China’s energy resources are dominated by coal, China’s total coal consumption is high and has remained in a growth phase; furthermore, the energy structure for power generation in China has been dominated by coal for a long period of time. The improvement of energy efficiency is faced with more significant structural constraints on energy supply, thus resulting in China’s low energy efficiency, and in the context of an increased pressure on climate governance and the ecological environment, improving energy efficiency is an inevitable choice [1]. Energy is a hot topic in the economic field and is an essential carrier for environmental governance, which is constrained or stimulated by environmental regulations. Regarding the impact of environmental regulations on energy efficiency, the academic community still needs to form a unified view. As a comprehensive policy aimed at promoting economic and social low-carbon development, the low-carbon city pilot policy has no impact on whether energy efficiency can be improved and if green low-carbon cities can be realized. The construction of the development model therefore shas essential research value.

Cities are major spatial clusters of industry, population, and socio-economic activities and are becoming increasingly vital implementation agents in low-carbon development and climate governance, making the development of low-carbon cities a fundamental unit of action for achieving China’s target of greenhouse gas (GHG) emission control [2]. Currently, China’s central policies to control GHG emissions include the low-carbon city pilot policy, a carbon sequestration plan, carbon emission trading pilot policy, and the action for achieving carbon peak and carbon neutrality. Among these, the low-carbon city pilot policy was implemented the earliest and covers the largest area. China’s National Development and Reform Commission launched the pilot low-carbon provinces and cities in 2010. It announced the second and third batch of pilot cities in 2012 and 2017, which now include six provinces and 81 cities. Pilot cities under this policy are required to implement long-term transformation and development plans for a low-carbon economy based on low-carbon production and consumption, with specific requirements for energy consumption control and carbon peak target, making energy efficiency improvements a pivotal area to be addressed in order to reduce energy consumption and achieve green development in China.

Considering that the low-carbon city pilot policy combines market-based and command-based environmental regulation [3,4], command-based environmental regulation is vital to promote carbon emission reduction and green technology innovation in enterprises. Wang et al. (2019) illustrate that command-based environmental regulation is effective in inducing patents for inventions with a higher innovation content [5], and some scholars find that command-based environmental regulation is conducive to improving the overall environmental effectiveness in China [6]. Furthermore, research demonstrates the role of market-based environmental regulation in promoting innovation among Chinese firms [7]. Xie et al. (2017) used a slacks-based measure and Luenberger productivity index to measure and find that market-based environmental regulation has increased green productivity in most Chinese provinces [8], which is beneficial for improving energy efficiency, owing to the fact that technological advancement is a major factor in energy efficiency improvements [9]. Therefore, considering the direct and indirect effects of environmental regulations on energy efficiency, it is helpful to review the existing research on the impact of environmental regulation on energy efficiency to explore the pilot policy’s implementation effects. The existing literature on the impact of environmental regulation on energy efficiency is controversial in terms of the relationship between the two. The first category is the cost crowding-out effect, which argues that environmental regulations constrain energy efficiency [10,11]; the second category is the innovation compensation effect, which argues that environmental regulations effectively promote energy efficiency [12,13]; and the third category is the non-linear effect, which argues that environmental regulations have a variable impact on energy efficiency [14]. In particular, Pan et al. (2019) argue for an inverted U-shaped relationship between facilitation and inhibition [15]; Wu et al. (2020) argue that this non-linear relationship is a U-shaped relationship that inhibits and then promotes [16]; and Wang et al. (2017) support the non-linear theory of inverted U-shaped relationships and find a single threshold effect [17]. On the one hand, research on the low-carbon city pilot policy has focused on the impact of the policy on urban carbon and pollution emissions [18,19,20,21]; on the other hand, the difference-in-differences model is the primary method used by scholars to test the economic effects of low-carbon city pilot policies in terms of foreign direct investment [22], strategic emerging industries [23], technological innovation [24], industrial structure optimization [25], and high-quality development [26].

The above studies have analyzed and discussed energy efficiency and low-carbon city pilot policies from different perspectives. However, there are limitations in the following aspects: ① The existing literature has conducted rich tests on the Porter hypothesis, examining the impact of environmental policies on energy efficiency from the perspective of environmental regulation, with most studies mainly examining a single environmental policy, while some scholars have divided environmental policies into different types to examine their impact on energy efficiency; however, the tests on comprehensive environmental policies are relatively limited [27]. ② The literature has extensively used DID to evaluate the policy effects of the low-carbon city pilot policy. However, the evaluation perspective is focused chiefly on carbon emissions and economic development and fails to explore the promotion effect of the low-carbon city pilot policy on energy efficiency from a deeper perspective. There is a paucity of the literature examining the impact of the low-carbon city pilot policy on energy efficiency in terms of innovative mechanisms and agglomeration effects [28]. ③ Most of the current studies have conducted policy evaluation at the city level. They cover different dimensions and levels of urban development and can assess the policy effects in a relatively comprehensive manner. However, the selection and weighting of indicators are subjective, which, to a certain extent, affect the assessment of the net effect of the policy and makes it difficult to measure the effectiveness of the implementation of the pilot policy from a unified perspective.

To address the limitations of existing studies, the main marginal contributions of this paper include: ① This is the first evaluation of the impact of low-carbon city pilot policies on green total factor energy efficiency via a quasi-natural experiment of China’s low-carbon city pilot policies, which not only expands to a certain extent the research content on the analysis of the low-carbon city pilot policy effects, but also further enriches the research system on low-carbon in the Chinese context at the policy level. ② The SBM–Malmquist–Luenberger index method was used to measure sample enterprises’ green total energy efficiency and construct a database of green total factor energy efficiency at an enterprise level, reflecting China’s low-carbon green transformation and energy efficiency progress from a microscopic perspective. ③ Based on the comprehensive application of innovation theory and industry cluster theory, the intrinsic mechanism of low-carbon city pilot policies affecting energy efficiency is revealed in depth in terms of enterprise green technology innovation effect and industrial agglomeration effect, which enrich the research literature on the impact of comprehensive government policies on enterprise energy efficiency. It also meticulously analyzes the heterogeneous effects of enterprise size, enterprise property rights, and urban location based on enterprise and regional differences, thus providing more in-depth theoretical support for implementing urban transformation with low carbon and insights from relevant government departments to formulate low-carbon development policies in a targeted manner.

The remainder of the paper is organized as follows. Section 2 presents the policy background and theoretical analysis and proposes the research hypothesis. Section 3 introduces the research design of the paper. Section 4 analyzes the empirical results and conducts robustness tests on them. Section 5 examines the impact mechanism of the green innovation and agglomeration effect induced by the pilot policy on enterprises. Section 6 conducts a heterogeneity analysis in three dimensions: enterprise ownership attributes, enterprise size, and spatial location. The final part contains conclusions and policy implications.

2. Policy Background and Theoretical Analysis

2.1. Policy Background

The low-carbon city pilot policy is a meaningful initiative for China to actively explore the green development of industrialized urbanization and build an economic model and industrial system characterized by low-carbon emissions. Compared to the low-carbon policies of other significant countries, China’s low-carbon city pilot policy is characterized by a more robust combination of sector specificity and policies. Germany introduced the 2050 climate action plan in 2016, proposing to control carbon emissions and enhance the use of energy resources, mainly utilizing carbon taxes and tradable emission rights. In 2019, the UK Committee on climate change released a report proposing that the UK will offset carbon emissions through technical means such as afforestation, carbon capture, and carbon sequestration. The European Union has promulgated the European Green Deal, the Sustainable Investment Plan, and the European Climate Law, mainly through green finance to build a low-carbon society. As can be seen, China’s pilot policy on low-carbon cities uses prefecture-level cities as a policy vehicle. It is a systematic solution to achieve a nationwide transition to low-carbon economic production and social life. To meet the challenges of climate change caused by GHG emissions, the notice on piloting low-carbon provinces, regions, and cities issued by the National Development and Reform Commission in 2010 pointed out that actively addressing climate change is a major strategy for China’s economic and social development, and a major opportunity to accelerate the transformation of the economic development mode and economic restructuring, and to promote several cities to take the lead in entering the ranks of low-carbon cities to provide a model for other cities. It is also a major opportunity to accelerate the transformation of the economic development mode and economic restructuring. As shown in Figure 1, 2017 saw the largest number of cities approved as low-carbon pilot cities in the entire progressive reform process. In particular, the National Development and Reform Commission approved eight prefecture-level cities—Tianjin, Chongqing, Shenzhen, Xiamen, Hangzhou, Nanchang, Guiyang, and Baoding—and five provinces—Guangdong, Liaoning, Hubei, Shaanxi, and Yunnan—as the first batch of pilot low-carbon regions. Hainan Province and 28 cities, including Beijing, Shanghai, Shijiazhuang, Jilin, and Suzhou, were established as the second batch of low-carbon pilot provinces and cities. In 2017, 45 low-carbon pilot cities passed the field acceptance of the National Development and Reform Commission and became the third batch of pilot cities to carry out low-carbon construction. At present, the National Development and Reform Commission has approved the construction of low-carbon pilot cities in six provinces and eighty-one cities, including seventy-seven prefecture-level cities and four county-level cities, ultimately achieving at least one low-carbon pilot city in each province and spreading the exploration and construction of low-carbon cities across the country.

As mentioned above, the National Development and Reform Commission developed three batches of low-carbon city pilot lists in 2010, 2012, and 2017, respectively. The first batch of pilot cities were mainly first-tier cities with a stronger economic strength and higher levels of development. In order to explore the realization paths for different types of regions to shift to low-carbon development, the second and third batches of cities descended from first-tier cities to second, third, and fourth-tier cities (see Figure 2) to drive and promote national green and low-carbon development as a whole. Overall, the selection of low-carbon pilot cities mainly takes into account factors such as the foundation of the pilot cities’ preliminary work, resource endowment, regional characteristics and representation, which illustrates that the low-carbon city pilot policy is a valuable exploration by the central government to encourage local governments to carry out pilot work in light of the actual situation. Since the implementation of the low-carbon city pilot works, pilot cities in each province and city have prepared detailed plans and critical tasks, following the overall objectives of the national low-carbon city pilot policy, mainly focusing on the establishment of low-carbon science and technology innovation mechanisms, low-carbon enterprise demonstration systems, carbon data management, low-carbon products and technology promotion and other aspects of detailed implementation of low-carbon city construction tasks. Regarding policy implementation effects, the study found that the low-carbon city pilot policy significantly improved total factor energy efficiency [3]. However, further detailed empirical testing of the effect mechanisms at play is needed.

2.2. Theoretical Analysis

2.2.1. Low-Carbon City Pilot Policies and Energy Efficiency

Similar to the design idea of an emissions trading system [29], the core idea of low-carbon city pilot policies is to set a theoretical peak carbon emission target and total carbon emissions for a region, establish a sound low-carbon development system, optimize the energy structure, and reduce the proportion of high-carbon industries to achieve green development in fields of high pollution, high energy consumption, and high emissions, focusing on promoting the optimal use of energy, building a low-carbon industrial system and a resource-saving and environmentally friendly energy eco-system. Energy efficiency directly reduce carbon dioxide emissions and builds a low-carbon economy and energy ecosystem. Energy efficiency is affected by many factors, including industrial structure [30], foreign direct investment [31], energy consumption structure [32], technology level [33], and market level [34], and so on. Therefore, energy efficiency improvement requires comprehensive government policies that can provide systemic solutions in strategic planning. According to Shi et al. (2013), relying on moderate government intervention is a necessary path to improve energy efficiency [35], and removing distortions in factor markets with reasonable government interventions such as taxes and subsidies can significantly improve energy efficiency [36]. In theoretical analysis, the municipal government of pilot cities can accelerate the construction of new electricity systems, promote the establishment of clean electricity-based energy systems, close and rectify high-carbon industries, promote industrial restructuring, increase the proportion of low-carbon industries, promote circular economy in economic production and social life, and improve the energy price formation mechanism. The series of measures as mentioned above can break down the operational barriers and processes between administrative departments and form a virtuous circle to optimize energy efficiency. They also avoid, to a certain extent, the division of interests between different economic agents caused by a single policy and prevent conflicts between policy agents from weakening the effect of the policy.

Since its implementation, low-carbon city pilot policies have received widespread attention from the central government and the public. National ministries, commissions, and local superintendents have attached great importance to the implementation of pilot projects in the pilot provinces and cities and have stepped up public awareness of the pilot work. At the same time, the National Development and Reform Commission establishes a direct contact mechanism with the development and reform departments of pilot areas to evaluate the progress of the pilot regularly [3]. On the social level, pilot areas’ governments regularly announce the construction of environmental management and low-carbon projects to the social mass, and social supervision and public complaints exert public pressure on local governments to improve the quality of environmental management. In summary, this paper puts forward the following hypotheses:

H1: 

Low-carbon city pilot policy can significantly improve the energy efficiency of enterprises.

2.2.2. Low-Carbon City Pilot Policy, Enterprise Green Innovation, and Energy Efficiency

Low-carbon city pilot policy is a city-level comprehensive policy to achieve China’s climate governance action goals. The implementation of the policy will lead to the de-carbonization of economic production and the overall development of the industrial system toward low carbon, which requires the improvement of energy efficiency, the reduction in total energy consumption and energy intensity, and thus the reduction i total carbon emissions and carbon intensity at the city level. Therefore, the policy is characterized by industry-specific and policy-oriented features [37]. The industry-specific approach is reflected in the low-carbon city pilot policy’s focus on green development with low carbon in key fields of high energy-consuming and high-emission urban areas, such as industry, construction, transport, energy supply, and waste management, through targeted pollution reduction and carbon governance in high energy-consuming and high-emission industries and urban areas. Additionally, the establishment of a sound low-carbon governance system with key industries and areas as a grip, to achieve the goal of controlling greenhouse gas emissions at the city level, induces green innovation among enterprises in the process. The low-carbon city pilot policy exhibits a stronger focus on corporate green innovation than other city-level pilot policies. Innovation-driven policy focuses on increasing investment in innovation, stimulating enterprise innovation, promoting transformation of innovation results, and optimizing innovation and entrepreneurship environment to enhance the intensity and effectiveness of innovation in all aspects of economic, scientific, technological, educational, and social development in a city [38]. A smart city pilot policy focuses on modernizing urban governance systems and capabilities through digital foundations and information technology and by using modern information technology to generate technological, configurational, and structural effects to drive innovation in urban development paths [39]. Unlike the abovementioned pilot policies, which target specific areas, the low-carbon city pilot policy is based on a city’s economic and social situation, industrial characteristics, and factor endowments to formulate corresponding low-carbon development plans and comprehensively build a city development model, featuring low-carbon emissions, which includes the different types of policy tools, such as command-and-control, market-based, voluntary policies, and green financial policies, etc. The combination of various policy tools promotes the decoupling of urban development from carbon emissions and improves the performance of enterprises in green innovation. Command-and-control policies directly promote green innovation and the transformation of enterprises into green development, a process that is inevitably accompanied by an increased investment in green innovation and the development of green innovation outputs that meet the needs of low-carbon development. At the same time, under the market incentive policy, enterprises’ positive innovation behavior can improve production efficiency, reduce their innovation and production costs, and improve product differentiation, enabling them to gain competitive advantages in the market, effectively enhancing their green innovation investment and efficiency. On the other hand, the low-carbon city pilot policy releases a strong signal of government support for enterprises to achieve green energy-saving and high-quality development, which can stimulate and mobilize more social capital to invest in green industries and the green upgrading of enterprises, thereby effectively discouraging polluting investments, promote technological progress in environmental protection and energy-saving fields, create a better institutional environment for enterprise development, optimize resource allocation, property rights protection, environmental information disclosure and supervision systems, and improve the efficiency of green innovation.

Green innovation is a complex process of converting innovation inputs into innovation outputs. Innovation inputs are designed to address issues such as innovation incentives as well as the willingness and ability to innovate. In contrast, innovation outputs are closely related to the efficiency of converting innovation inputs into innovation outputs. The innovation theory of neoclassical economics argues that innovation has externalities and that benefits, which firms obtain from innovation, are often lower than their inputs, leading to a lack of incentive for firms to invest in innovation [40]. In this context, government intervention in innovation activities can be an important complementary mechanism to address market failures and the lack of innovation by enterprises. Typical government support, such as industrial policies, tax incentives, financial subsidies, and extraordinary financial support, can effectively reduce innovation costs and increase enterprises’ input and output [41]. More importantly, government intervention sends a signal that the government wants enterprises to achieve green, low-carbon, and high-quality development, which will attract more human, material, and financial resources to invest in enterprise innovation and research and development, and to a certain extent alleviate financial and technical pressure brought by technological innovation, effectively increasing the innovation output of enterprises [42]. In addition, government intervention can create a relatively fair and favorable institutional environment, expand the competitive advantage of industries, compensate for market failures, and protect property rights, and stimulate the effects of green innovation. At the same time, for contract-intensive industries such as high-tech manufacturing and productive services, a favorable institutional environment can facilitate the effective implementation of contracts, reduce compliance frictions and unpredictable risks [43], and improve the efficiency of the allocation of innovation resources, thus promoting the innovation efficiency of enterprises in the region. In summary, this paper proposes the following hypothesis:

H2: 

Low-carbon city pilot policy can positively influence energy efficiency by promoting enterprises’ green innovation.

2.2.3. Low-Carbon City Pilot Policy, Agglomeration Effect and Energy Efficiency

The low-carbon city pilot policy is a comprehensive policy aimed at promoting the transformation of urban development models, focusing on building a low-carbon innovation system, promoting low-carbon technologies and products, and creating an industrial system with low-carbon characteristics. The policy provides innovative mechanisms and institutional elements for the productive services sector and a broad market for demand and clear policy guidance and technological innovation dynamics for the manufacturing sector. The implementation of the low-carbon city pilot policy can give rise to the synergy effect of various specialized production factors so that production factors matching low-carbon industries can be effectively brought into play, and low-carbon industries, low-carbon-related industries, and low-carbon-supporting industries with intra-industry and extra-industry cooperation in a specific region can achieve geographical agglomeration, forming low-carbon industrial clusters and ultimately improving energy efficiency. Therefore, the low-carbon city pilot policy can effectively accelerate the process of industrial agglomeration. Industrial agglomeration is a kind of industrial development law that occurs in a specific spatial scope. With the leap of an economic development stage, an integrated economic development mode of multi-industry integration becomes mainstream. The response to industrial agglomeration is beyond the traditional superposition of single industries in a specific space. The synergistic agglomeration is carried out according to the internal linkage of industrial chain and the relevance of external industrial chain. A representative example is that regions with a high concentration of productive services tend to have a relatively developed manufacturing sector [44]. Marshall district theory analyzes the positive effects of industrial agglomeration from the perspective of externalities, i.e., industrial agglomeration reduces matching costs between enterprises and workers by gathering a large number of skilled labor and technical experts; it also improves the production efficiency of enterprises so that they have more resource to invest in technological research and development, which can optimize energy use processes and increase energy efficiency. This is because the concentration of factors in a given space facilitates the saving of various costs and increases the efficiency of their use. The agglomeration process facilitates the efficiency of energy use and environmental factors when energy and the environment are considered production input factors [45].

According to Hoover’s classification of specialized agglomeration and diversified agglomeration [46], the mechanism of agglomeration’s effect on energy efficiency is divided into manufacturing agglomeration, productive service agglomeration, and synergistic agglomeration for analysis in this paper. From the perspective of industrial agglomeration, productive services need to transform potential service demand into actual industrial demand, while manufacturing agglomeration and industrial synergy agglomeration have strong policy orientation and require technological innovation to achieve regional industrial spatial pattern optimization [47]. Thus, the low-carbon city pilot policy can effectively target and promote the phenomenon of industrial agglomeration under its strong policy orientation and its promotion effect on enterprise innovation. Specifically, in terms of the three types of agglomeration effects: firstly, the agglomeration of manufacturing industries provides opportunities for collaboration between upstream and downstream enterprises, geographical proximity reduces transport costs and energy consumption, and chain economy formed by the proximity to industries leads to energy efficiency improvements throughout the chain. The spatial agglomeration of economic activities allows producers in the same market or industry zone to flexibly employ labor according to market or production needs, while keeping total energy consumption constant and increasing productivity per unit of energy. The clustering of firms can induce the proximity of R&D investment and technological innovation between firms, and the concentration of R&D departments in spatial locations can also enhance formal and informal contacts and exchanges between R&D personnel in different institutions, further strengthening knowledge and spillover effects [48]. Knowledge spillovers contribute to the development of cluster innovation networks and the growth of cluster economies. They are a source of an increased innovation output and productivity in clusters, which will contribute to improving the energy efficiency of firms [49]. Secondly, with the optimization and upgrading of economic development and industrial structure, some knowledge-intensive enterprises are spun off from within the manufacturing industry. In contrast, a large number of technology-intensive enterprises are attracted to each other in the same space, giving rise to the agglomeration of productive service industries. On the one hand, the production service industry has characteristics of high added value, high technological content, and low energy consumption and pollution, which can lead to the use of more clean outsourcing services in the economic production of the manufacturing industry, which can significantly reduce energy consumption and emissions of the manufacturing industry, but also enable it to focus on the research and development of core technologies, which is conducive to the improvement of energy efficiency. Finally, as a particular form of agglomeration economy, i.e., diversified industrial agglomeration, it mainly refers to co-agglomeration of manufacturing and production service industries, which also has positive externalities of industrial agglomeration and can effectively improve economic efficiency, energy conservation, and emission reduction, and promote energy efficiency. Unlike specialized agglomeration, co-agglomeration can effectively expand market demand on basis of industrial integration, deepen professional division of labor between manufacturing and productive services, effectively reduce production costs, transaction costs, information costs and professional intermediate input costs of enterprises, and to a certain extent, save the use of energy factors. Co-agglomeration is conducive to the sharing and centralized use of energy infrastructure and improves energy. In addition, synergistic agglomeration will accelerate the flow of factors, thus generating a “structural dividend”, i.e., energy factors will be transferred from inefficient sectors to efficient sectors, thus improving overall energy efficiency. In summary, this paper proposes the following hypothesis:

H3: 

Low-carbon city pilot policy can positively impact energy efficiency through agglomeration effects.

3. Research Design

3.1. Construction of Benchmark Regression Model

In recent years, scholars have extensively employed the difference-in-differences (DID) model to evaluate the effects of pilot policies. The wide-ranging application of this method stems from its capacity to treat the implementation of new policies or institutional changes as exogenous natural experiments. By employing the DID model, researchers can accurately assess the impact of pilot policies on energy efficiency and provide robust evaluation evidence for energy efficiency investments. For instance, Greenstone et al. (2019) employed the DID method to examine the effects of weatherization assistance policies on energy consumption, thereby evaluating the effectiveness of energy efficiency investments [50]. Similarly, Haimeng Liu et al. (2022) conducted empirical analysis using the DID model to investigate the implementation effects of air pollution prevention and control policies during autumn and winter in the Beijing–Tianjin–Hebei region and its surrounding areas [51]. They accurately assessed the net impact of the policy on energy efficiency by comparing pre-implementation differences in energy efficiency between the pilot and non-pilot areas. In this paper, the pilot policy of low-carbon cities is considered a quasi-natural experiment, and the DID model is employed to investigate its impact on energy efficiency in the manufacturing sector. Specifically, considering the policy’s implementation in three phases, a time-varying DID approach is utilized for estimation. This method enables a more precise capture of the dynamic effects during the implementation of the pilot policy, thus providing a comprehensive assessment of its influence on energy efficiency. The specific model is presented as follows:

(1)${Gml}_{it}={\beta }_0+{\beta }_1({Treat}_{ir}\times {Time}_{rt})+{CV}_{it}+{\gamma }_t+{\alpha }_i+{\delta }_i+{\epsilon }_{it}$

where ${Gml}_{it}$ denotes energy efficiency of firm i in year t, β₀ is a constant term. Treat_ir is a dummy variable for policy cities. Treat_ir takes the value of 1 if the city r—in which firm i is located—belongs to the pilot region, and 0 otherwise. Time_rt is a dummy variable for the time of policy implementation. Time_rt takes the value of 1 if the city r in that year is greater than or equal to the year of policy implementation, and 0 otherwise. CV_it is a matrix of control variables. γ_t controls for firm-fixed effects over time, α_i controls individual firm effects, and ε_it is a random disturbance term. The model focuses on the coefficient of Treat_ir × Time_rt, i.e., β₁, and this interaction term is denoted by DID in the later section. β₁ captures the net effect of the low-carbon city pilot policy on energy efficiency. If β₁ is significantly positive, it indicates that the low-carbon policy can facilitate energy efficiency for firms in low-carbon pilot areas. Conversely, it indicates that policy inhibits energy efficiency for firms in pilot areas.

3.2. Mechanism Analysis

The previous DID model was designed to explore whether the low-carbon city pilot policy can promote energy efficiency; therefore, how does this enhancement effect materialize? This requires an in-depth exploration of the intrinsic impact mechanisms. The theoretical analysis and research hypothesis in Section 2 have led to the theoretical hypothesis that the low-carbon city pilot policy can enhance the energy efficiency of enterprises through the green innovation effect and industrial agglomeration effect.

In terms of model construction, this paper embedded green innovation and industrial agglomeration variables affecting energy efficiency into the benchmark model of Equation (1) to examine the significance of the impact mechanism, and the model is presented as follows:

(2)$\begin{array}{l}{Gml}_{it}={\beta }_0+{\beta }_1({Treat}_{ir}\times {Time}_{rt}\times {Tec}_{it})+{\beta }_2({Treat}_{ir}\times {Tec}_{it})\\ +{\beta }_3({Treat}_{ir}\times {Tec}_{it}){\beta }_4({Time}_{rt}\times {Tec}_{it})+{CV}_{it}+{\gamma }_t+{\delta }_i+{\alpha }_i+{\epsilon }_{it}\end{array}$

(3)$\begin{array}{l}{Gml}_{it}={\beta }_0+{\beta }_1({Treat}_{ir}\times {Time}_{rt}\times {Agg}_{it})+{\beta }_2({Treat}_{ir}\times {Time}_{rt})\\ +{\beta }_3({Treat}_{ir}\times {Agg}_{it})+{\beta }_4({Time}_{rt}\times {Agg}_{it})+{CV}_{it}+{\gamma }_t+{\delta }_i+{\alpha }_i+{\epsilon }_{it}\end{array}$

where ${Gml}_{it}$ represents energy efficiency of firm i in year t; Tec_it represents green innovation effect; and Agg_it represents industrial agglomeration variable. Equations (2) and (3) are mainly concerned with the significance of the coefficients ${\beta }_1$ of the interaction terms ${Treat}_{ir}\times {Time}_{rt}\times M_{it}$, and the other variables are defined in the same way as in Equation (1).

3.3. Data Sources

This study constructs panel data with a sample of Chinese-listed manufacturing enterprises from 2007 to 2020. In the sample selection process, this paper performs the following steps on the data: (1) to avoid the influence of the company’s operating status, we exclude sample enterprises that are marked as ST, *ST, and those which are about to be delisted; (2) to prevent the influence of outliers of the variables, a 1% tail reduction (Winsorize) is applied to the left and right ends of the continuous variables; (3) sample enterprises with missing core variables are excluded; and (4) the sample with serious missing indicators are exclude after matching enterprise data with city and province data. The final unbalanced panel data for 14 years were obtained for 2528 listed production enterprises, including 1872 enterprises in the experimental group located in pilot regions and 672 enterprises in the control group located in non-pilot regions, which were spread across 230 cities in China. The firm data are sourced from China Stock Market and Accounting Research Database (CSMAR) and the city data from the China City Statistical Yearbook.

3.4. Variable Definition and Measurement

3.4.1. Explained Variable: Energy Efficiency

This paper examines the effectiveness of the low-carbon city pilot policy through the lens of corporate energy efficiency research. Labor (Labor), capital (capital), and energy (energy) are selected as input variables; gross regional product (gdp) as desired output; and industrial sulfur dioxide (SO₂), industrial smoke (smoke), and industrial wastewater (effluents) emissions as non-desired outputs. The SBM–Malmquist–Luenberger method measures each a prefecture-level city’s green total factor energy efficiency (Gml).

Figure 3 shows the spatial and temporal quantile of green total factor energy efficiency for 284 prefecture-level cities, with 2007 on the left and 2020 on the right. Longitudinally, energy efficiency has increased over time in all regions of the country, partly because the low-carbon city pilot policy combines two types of environmental regulations, market-based and command-based, to improve energy efficiency through the innovative compensatory effect of green technology innovation. On the other hand, by promoting the evolution of regional industries, low-carbon reform can promote regional industrial structure upgrade much more than that which inhibits the rationalization of regional industrial structure and can effectively enhance the optimization and upgrading of regional industrial structures. The low-carbon city pilot policy requires pilot provinces and cities to accelerate the construction of an industrial system, featuring low-carbon emissions in conjunction with local characteristic industries and development strategies, which enhances the rationalization and advancement of urban industrial structures, thereby improving energy efficiency. Furthermore, Figure 3 illustrates to some extent that most prefecture-level cities in China showed varying degrees of improvement in energy efficiency between 2007 and 2020, and whether there is a tangible causal effect between this improvement and the pilot low-carbon city policy is yet to be empirically tested in further detail.

3.4.2. Policy Variable

This study of low-carbon city pilot policy includes three batches of provinces and cities. The three batches of low-carbon pilots’ region were promulgated in 2010, 2012, and 2017, respectively, by Song et al. (2019) [19], considering all prefecture-level cities in pilot provinces as low-carbon pilot cities, and a region belonging to multiple batches of the pilot city list is defined by the earliest time.

3.4.3. Control Variable

The issue of other variables that potentially contribute to the omission of low-carbon city pilot policy on the energy efficiency of firms is considered. Based on existing studies, this paper selects firm size (Size), asset–liability rate (Lev), profitability (ROA), total asset turnover (ATO), cash flow (Cashflow), fixed asset ratio (FIXED), growth rate of business revenue (Growth), loss (Loss), board size (Board), whether chairman and general manager are the same person (Dual), shareholding ratio of the largest shareholder (Top1), the nature of the firm (SOE), and the number of years the firm has been listed (ListAge) are used as control variables.

Table 1 shows the descriptive statistics of variables. During the sample period, the mean value of energy efficiency for Chinese listed production enterprises was 1.026, with a standard deviation of 1.015, close to the mean, indicating that the data were close to a normal distribution; the maximum and minimum values varied widely. Overall, there is some variation in the energy efficiency of different enterprises, i.e., the energy efficiency level varies from enterprise to enterprise.

4. Result of Empirical Analysis

4.1. Baseline Regression Result

Based on the above DID model, the impact of the low-carbon city pilot policy on the energy efficiency of manufacturing enterprises was tested. According to the results in Table 2, the coefficient of the interaction term DID in column (1) is positive and significant at the 1% level (β1 = 0.036, p < 0.01) before the inclusion of the control variables. After adding the control variables, the coefficient of the interaction term DID in column (2) is also positive and significant at the 1% level (β1 = 0.008, p < 0.01). In column (3), after adding the control variables with individual and year fixed effects, the coefficient on the interaction term DID is positive and also significant at the 1% level (β1 = 0.017, p < 0.01). This suggests that the control variables affect energy efficiency and that controlling for them benefits the reasonability of the policy impact results. As seen from Table 2, the low-carbon city pilot policy does enhance the energy efficiency of manufacturing enterprises in the pilot region to a certain extent, and Hypothesis H1 was tested.

4.2. Parallel Trend Test Result

The basic premise of the difference-in-differences test is the parallel trend hypothesis, i.e., it is the experimental and control groups that have the same trend of change prior to the low-carbon city pilot policy shock, hence the need for a parallel trend test for the explanatory variables to be consistent with the use of the DID method to assess the effectiveness of the policy. Therefore, this paper draws on the event study approach proposed by Jacobson et al. (1993) [52] to conduct parallel trend tests. In this paper, the pre-policy period is chosen as the base period. The results of the parallel trend test shown in Figure 4 indicate that none of the estimated coefficients before the implementation of the low-carbon city pilot policy are significant, i.e., there is no significant difference between the energy efficiency of enterprises in the experimental and control groups, and the study sample passes the parallel trend test.

4.3. Robustness Test

4.3.1. Individual Placebo Test

We tested the degree to which the regression results are affected by random factors and omitted variables, and this section uses a placebo test with replacement experimental groups of firms to confirm whether the effect of the low-carbon city pilot policy on the energy efficiency of firms is coincidental. A random sample of 672 firms from a total sample of 2528 was selected as a pseudo-experimental group to match the number of firms in an actual pilot area. The individual placebo test was conducted and repeated 1000 times at random to re-estimate the interaction term with Timert. Figure 5 shows the p-value of the interaction coefficients and the kernel density distribution for the 1000 estimates.

As can be seen, the regression coefficients fall around the value of zero and follow a normal distribution. In contrast, most regression results are insignificant, indicating that the randomly sampled pseudo-experimental group does not significantly impact the company’s energy efficiency. Accordingly, baseline regression estimates are not the result of chance and are unlikely to be influenced by other random factors and omitted variables; moreover, the core findings remain robust.

4.3.2. Time Placebo Test

The previous study found that implementing the low-carbon city pilot policy had a significant effect on the energy efficiency of local firms, but this could also be due to other contemporaneous policy factors that were not considered. To further demonstrate the reliability of the previous estimation results, we adopt a counter-fact-checking method, a temporal placebo, to examine whether the core explanatory variables remain significant when the low-carbon city pilot policy is not implemented. If it remains significant, it indicates that unobserved factors contribute to the firms’ energy efficiency. At the same time, if it is not significant, it indicates that the positive effect of implementing the low-carbon pilot city policy on local firms’ energy efficiency is robust and reliable. In this paper, all three batches of low-carbon city pilot policy are advanced by two and three years, then regressed again, with the results shown in Table 3. From Table 3, it can be seen that the policy is insignificant regardless of whether it is two or three years ahead of the interaction term, so the model can be judged to be consistent with the counterfactual assumption through the time placebo test.

4.3.3. PSM-DID Method

In order to reduce the impact of possible systematic differences between firms on the model estimation, this paper uses the nearest neighbor 1:2 matching method to match the sample. Then, it uses the matched sample to run a baseline regression for robustness validation. Table 4 shows the results of the equilibrium test after the nearest neighbor matching method. Smith and Todd concluded that the absolute value of the covariate deviation before and after matching should be small, and the smaller the value, the better the matching effect. Less than 10% is a good match and less than 5% is a great match. As can be seen from Table 4, the deviations after matching by both matching methods decreased significantly and the absolute values were less than 5%. According to T-test results, there was no significant difference between the experimental group and control groups after most covariates were matched, indicating that the matching effect was good by balance test.

Figure 6 shows the kernel density before and after the tendency matching. By comparing the density curves of the experimental and control groups in the before and after plots, we found that the curves of the experimental and control groups were tighter after the matching, indicating that the difference was significantly reduced after the matching.

The results of the DID model regression with the matched samples are shown in Table 5 (1). The interaction term regression coefficients are consistent and significant with the baseline regression coefficients at the level of conformity. It can be seen that the PSM-DID regression results are not significantly different from the baseline regression results and the model can be judged as robust.

4.3.4. Replacing Explained Variable

Regarding the measurement of energy efficiency, we use the firm’s energy efficiency measured by the non-radial, non-oriented slack-based model with directional distance function (SBM-DDF), a production frontier analysis tool, in the robustness check section and regress it again, as presented by column (2) in Table 5. The table shows that the coefficient of the interaction term after replacing the variable remains positive at the 1% significance level. This indicates that the low-carbon city pilot policy has shown the same positive effect on the energy efficiency of enterprises as the baseline regression, and the study’s findings have not changed.

4.3.5. Excluding Special Years

Table 5 (3) excludes special years from the sample. In this section, we consider environmental changes caused by special events such as the sub-prime mortgage crisis, the European debt crisis, and the COVID-19 pandemic, and exclude enterprises’ data in the corresponding years to avoid the impact of special events. As we can see from column (3), the regression results after excluding the special year sample are consistent with the baseline regression in terms of coefficient compliance and significance level, indicating that the conclusions of this paper are still robust after excluding the interference of special events.

4.3.6. Introducing New Control Variables

Column (4) introduces city-level control variables as well as control industries. To further verify the reliability of the results, we select the GDP of the city in which the firm is located, the share of the added value of the secondary industry in the GDP of the city in which the firm is located, and the total year-end population of the city in which the firm is located as city-level control variables. Additionally, considering the difference in manufacturing industries, we control for the industry fixed effect in the following regressions. According to column (4) in Table 5, the coefficient of DID is positive and significant at the 1% significance level, consistent with the previous results and indicating that the baseline regression results are robust.

5. Mechanism Analysis

The results of the DID model and a series of robustness tests in the previous section confirm that the low-carbon city pilot policy can significantly improve the energy efficiency of enterprises; but how is this effect achieved? This requires an in-depth investigation into the underlying mechanisms of influence. The above theoretical analysis and research hypothesis have already led to the theoretical hypothesis that the low-carbon city pilot policy can improve the energy efficiency of enterprises through innovation and agglomeration effects.

Table 6 shows the mechanism analysis of the green innovation effect. It can be found that the coefficient is positive at 5% significance level, indicating that the effect of the low-carbon city pilot policy on the improvement of the green energy efficiency of enterprises is positively influenced by green innovation. In particular, the positive effect of green innovation input on energy efficiency is the largest, and the coefficient of green innovation efficiency is the lowest among the three. This is probably because the incentive of the low-carbon city pilot policy on enterprises’ green innovation lies in mobilizing and guiding more diversified resource endowments into innovation behavior. Its impact mechanism mainly lies in increasing enterprises’ innovation input, which has a smaller impact on innovation output and innovation efficiency. Hence, the green innovation input has the most substantial contribution to energy efficiency.

Table 7 shows the mechanism analysis of the agglomeration effect, and it can be found that the coefficient is positive at the 10% significance level, which indicates that the effect of the low-carbon city pilot policy on the improvement of the energy efficiency of enterprises is positively influenced by the agglomeration effect. As analyzed above, the low-carbon city pilot policy provides necessary elements for industrial agglomeration and creates policy space for manufacturing agglomeration, productive services agglomeration, and synergistic agglomeration through resource allocation. Specifically, the three agglomeration patterns are, in order of magnitude: manufacturing, synergistic, and productive services agglomeration. This is because the industrial agglomeration currently taking place in China’s urban area is dominated by manufacturing agglomeration, while synergistic agglomeration has been around for a while and is still at a stage where manufacturing agglomeration is the main force. The total energy consumption and energy costs of the manufacturing sector are much higher than those of the production services, so the manufacturing sector has a stronger will and incentive to optimize energy efficiency. In addition, traditional manufacturing industry is characterized by high energy consumption, high emissions, and low energy efficiency. In contrast, the energy use pattern of productive services is relatively intensive and refined, and energy efficiency is initially higher than that of the manufacturing industry, resulting in more room for the manufacturing industry to start and optimize its energy efficiency.

6. Heterogeneity Analysis

6.1. Heterogeneity Analysis of Firm Size

Considering that the enterprises of different sizes may respond to environmental policies at different degrees, this paper analyzes the sensitivity of different enterprise sizes to environmental policies by looking at two categories: (1) large enterprises and (2) small and medium enterprises, i.e., examining whether there are differences in the impact of low-carbon city pilot policy on the energy efficiency of enterprises of different sizes. Table 8 (1) and (2) show the estimated results of the impact of low-carbon city pilot policy on the energy efficiency of enterprises of different sizes. In the group of large enterprises, the interaction term’s estimated coefficient was 0.009, which is significant at the 1% level; in the group of middle and small-sized firms, the interaction term’s estimated coefficient was 0.005, but is insignificant. This indicates that: (1) the low-carbon city pilot policy has a more significant impact on the energy efficiency of large enterprises; (2) there is a significant scale effect on the energy efficiency of lager enterprises; and (3) that large enterprises are more vulnerable to environmental policies. Possible reasons for the heterogeneity in the contribution of the low-carbon city pilot policy toward the energy efficiency of firms in terms of firm size include the relatively high cost of acquiring advanced technological equipment that is highly energy-efficient, which only large firms can afford, and the financial constraints of middle and small-sized firms that usually have difficulty in doing so. In addition, large enterprises can further improve their energy efficiency by introducing and developing waste heat recovery technologies and equipment, for example, high energy-consuming industries such as steel and cement can use waste heat to generate electricity. In contrast, middle and small-sized enterprises need more investment in research and development in this technological area and more technical reserves and equipment to recover the waste heat and energy available, resulting in significant energy losses. Overall, large enterprises have a higher capacity to promote energy efficiency as a factor input and can not only increase investment in the introduction of advanced technology and equipment, in independent technology research, and in the development to promote structural energy conservation, but also promote factor substitution and improve energy efficiency. Moreover, based on their size, large enterprises can effectively share the cost pressure per input unit, improve overall energy efficiency, and reduce the energy consumption required for their production activities. The energy costs saved can be invested in a new round of energy efficiency improvement activities, forming a virtuous cycle.

6.2. Heterogeneity Analysis of Firm Property

This section examines whether there are differences in the impact of low-carbon city pilot policy on the energy efficiency of enterprises under different ownership. Table 8 (3) and (4) show the estimated results of the impact of the low-carbon city pilot policy on the energy efficiency of state-owned and non-state-owned firms. For state-owned firms, the estimated coefficient of the interaction term DID was 0.011, tested for significance at the 1% level; in the non-state-owned firms, the estimated coefficient of the interaction term DID was 0.006, tested for significance at the 5% level. As the coefficients were significant in both groups, we determined whether there was a difference between two groups by using Fisher’s method to combine p-values; and the empirical p-value of 0.062 rejected the original hypothesis that there was a significant difference between the coefficients of the differential interaction term for state-owned firms and non-state-owned firms. Comparing the coefficients of two groups shows that the low-carbon city pilot policy has a more significant impact on the energy efficiency of state-owned firms and that state-owned firms are more vulnerable to environmental policies. This may be attributed to the fact that key industries are leading in policy implementation and are receiving high government attention. In addition, state-owned firms have more critical responsibilities in local economic development and are generally subject to stronger environmental regulations. Particularly in recent years, the central government has placed a high priority on climate governance and the achievement of dual carbon targets, and state-owned firms are naturally required to meet the political targets and tasks set by the central government and are therefore more motivated to improve their energy efficiency in the face of environmental constraints. Moreover, as the distribution and pricing of energy factors in China are not yet fully marketed, state-owned firms have more political connections than non-state-owned firms and can obtain more energy resources at lower prices, allowing them to invest more resources in technological research and development and management innovation to optimize energy efficiency, with less budgetary constraints and more significant incentives to innovate.

6.3. Heterogeneity Analysis of Geographic Region

Different regions with different energy structures, factor endowments, infrastructure, innovation, and technological capabilities may show different energy efficiency improvement results under the same policy. This section examines whether there are differences in the impact of the low-carbon city pilot policy on energy efficiency in different regions, and the regression results are shown in Table 9. Regarding spatial segmentation, energy efficiency trends in all sub-regions are similar to those at the national level. However, there is variability in the impact of low-carbon city pilot policy on energy efficiency at the spatial level. In particular, the policy effect is more robust in the eastern region than in the central and western regions, and the pilot policy does not significantly impact energy efficiency in the central region. In contrast, it has a significant negative impact on the western region. Possible reasons for this are that the eastern region is China’s main capital concentration and energy consumption area, with a larger share of state-owned capital and developed factor markets, resulting in a greater capital and energy substitution rate in the eastern region. In contrast, the developed region is more attractive to highly qualified labor. The entry of highly qualified labor is conducive to further promoting technological innovation for energy efficiency improvement and increasing energy efficiency.

In addition, based on the existing advantages of the eastern region, the policy incentives of low-carbon city pilot policy can further accelerate the upgrading of industrial structure and the effectiveness of green innovation in the eastern region, thus enhancing energy efficiency. As for the central and western regions, they have a relatively weak economic and social foundation, and in the process of economic development, they have taken over many high energy-consuming and high-emission industries transferred from the eastern regions. They need more education and research resources, so they are at a natural disadvantage in terms of talent cultivation. The attractiveness of highly skilled labor is also weaker than in the East, resulting in a software–hardware environment that lags behind that of the East, limiting the improvement of energy efficiency in the central and western regions. Studies have also found significant spatial heterogeneity in the structure of energy consumption across regions in China [53], particularly in the proportion of fossil energy sources such as coal and oil versus clean energy sources such as water, light, wind, and heat. The eastern region has a more developed industrial system, a more diversified industrial structure, and differences in the demand for different types of energy by different industries. Therefore, a diversified industrial structure will result in a diversified energy mix, reducing the share of fossil energy and empowering more industries to develop with a given amount of paraffin equivalent. This will also result in a cleaner and more efficient energy consumption system. The above analysis is in line with Ervural (2018) [54], Li et al. (2019) [55], and Zhao et al. (2021) [56], who infer that a diversified and low-carbon energy mix is conducive to energy efficiency.

7. Conclusions and Suggestion

Energy is the vital material foundation and driving force for human civilization’s progress and the important support for economic development and social progress. Under the guidance of development concepts characterized by innovation, green, and sustainability, China’s economic and energy development has made remarkable achievements. However, the transition from high-speed economic growth to high-quality growth has also put forward higher requirements to achieve green and low-carbon developments as well as energy efficiency. In-depth analysis is beneficial to stimulate the green innovation drive of enterprises and improve the energy efficiency of enterprises. As a quasi-natural experiment based on the national low-carbon city pilot policy, this paper systematically assesses the impact of low-carbon construction-driven energy efficiency in a China-based scenario using a time-varying DID model with panel data from listed manufacturing firms in 230 prefecture-level cities in China from 2007–2020. The study found that: first, there is a significant positive correlation between the low-carbon city pilot policy and the improvement of green total factor energy efficiency, which can improve the energy efficiency of enterprises in the policy pilot area to a certain extent, and the policy coefficient remains significant after the inclusion of control variables and fixed effects for testing. This finding holds after a placebo test and a series of robustness tests. Compared with previous studies, this paper is the first to measure energy efficiency at the enterprise level and determine the impact of low-carbon city pilot policy within this level [3]. Second, the low-carbon city pilot policy improves energy efficiency through two action channels of promoting corporate green innovation and enhancing the industrial agglomeration effect, which is roughly the same as previous studies. The innovation of this paper is that it examines the impact of low-carbon city pilot policies on energy efficiency not from the perspective of industrial structure, but from the perspective of industrial agglomeration, and divides green technology innovation and industrial agglomeration effects into innovation input, innovation output, innovation efficiency, and manufacturing agglomeration; and producer service agglomeration and collaborative agglomeration, respectively. The impact mechanism of the policy is examined in more detail. Among them, the low-carbon city pilot policy is the most significant in promoting energy efficiency through enterprise green innovation input and manufacturing agglomeration effect. Third, through heterogeneity analysis, the pilot policy has a more significant impact on the energy efficiency of large enterprises, and the improvement effect on the energy efficiency of state-owned enterprises is more prominent. From a geographical perspective, the low-carbon city pilot policy significantly improves energy efficiency in the eastern region with a higher factor market development. However, it has yet to produce the expected effect on improving energy efficiency in the central and western regions.

Based on the above conclusions, the policy implications of this article are as follows. Firstly, the research results of this article show that the low-carbon city pilot policy has a sustained positive effect on improving enterprise energy efficiency, which helps deal with the relationships among economic development and energy conservation and emission reduction in the short and long term. Therefore, the central government should continue to: strengthen the top-level design of low-carbon development; formulate the medium- and long-term strategy of urban low-carbon development; strengthen energy structure optimization, renewable energy utilization, and public auxiliary facilities transformation for key industries; strengthen the whole chain, full dimension, and entire process energy management for high-energy-consuming regions and industries; strengthen standard leading and energy-saving services; synergistically improve the energy efficiency levels of enterprises of all sizes in the eastern, central, and western regions; optimize the industrial energy-use structure; and promote energy-saving technology to comprehensively improve energy efficiency.

Secondly, in terms of promoting green innovation in enterprises, this paper suggests establishing and improving the assessment system for green technology standards and the evaluation criteria for green technology innovation enterprises, encouraging local governments to provide priority support to green enterprises. Through financial resources and green finance, the government should support market-oriented green technologies and related innovation activities to increase the proportion of green innovation projects supported by enterprises in relation to national special projects and national key R&D programs within the field of science and technology, as well as play the leading role of enterprises in green technology innovation. In terms of strengthening the industrial agglomeration effect, this paper suggests promoting the optimization of the industrial structure in key energy-using regions, focusing on green, low-carbon development, the efficient transformation of industries, and on upgrading the regional industrial development capacity. Additionally, we suggest vigorously developing productive services and guide the transformation of manufacturing industries into services. Based on high-tech industrial parks, local governments could build low-carbon industrial agglomerations, construct several low-carbon industrial carrier platforms, give rise to the industrial agglomeration effect and its positive externalities, and improve the overall energy efficiency level of enterprises in the agglomerations.

Thirdly, this paper suggests providing a more comprehensive policy support for middle- and small-sized and private enterprises to promote energy efficiency. Around the goal of creating a resource-saving and environmentally friendly enterprise, this paper suggests, with regard to middle- and small-sized firms, to further strengthen the supervision and management of energy conservation and emission reduction, to actively promote technological progress in energy conservation and emission reduction, and to establish a sound policy incentive and restraint mechanism to promote energy conservation and emission reduction. Financial funds such as middle and small-size firms development funds, special funds for technological transformation, and funds for eliminating backward production capacity will play a guiding and driving role for middle and small-sized firms, increasing financial support for their energy conservation and clean production in terms of technological research and development, technological transformation, and training and education.

Fourthly, different policies are implemented for cities with different energy types and varying degrees of factor marketization. For the central and western regions, we recommend that the government carry out the following recommendations: accelerate the elimination of backward production capacity; shut down and ban or renovate high-emission, high-pollution, and high-energy-consuming enterprises; and encourage the establishment of low-carbon industrial parks to introduce industrial enterprises with clean energy and efficient energy use. Supporting large enterprises to make use of their industrial advantages and technological reserves to integrate green innovation forces under the law, drives, assists, and funds social entrepreneurial and innovative teams and various local enterprises to invest in green technological innovation, raising the overall level of green innovation and industrial agglomeration in the region and thus promotes the advancement of energy-using technologies and energy efficiency. For the eastern region, the government should continue to: strengthen the industrial foundation for energy-saving and efficiency improvement; focus on improving the supply of energy-saving technology and equipment products; vigorously develop energy-saving services; actively build green growth sources; and cultivate new competitive advantages in green manufacturing. At the same time, the government should promote the digital upgrade of energy efficiency optimization, encourage the empowering effect of digital technology on the energy efficiency improvement of enterprises, build an energy control system with state perception, real-time analysis, scientific decision-making, and precise execution, and accelerate the digital and green upgrading of production methods.

Energy efficiency improvement is critical for energy conservation, emission reduction, and industrial green transformation. This paper theoretically analyzes and quantitatively evaluates the impact of low-carbon city pilot policies on the energy efficiency of enterprises, providing valuable references for follow-up, with a better implementation of China’s low-carbon development strategy and optimization of related policies. However, what still needs more profound attention is how to increase the enthusiasm of energy consumers to participate in improving energy efficiency to a greater extent, and how to overcome the rebound effect of energy efficiency while improving energy efficiency, all of which are vital theoretical and practical issues worth exploring in the future.

Author Contributions

X.X. was responsible for conceptualization and topic design. G.H. was responsible for the design of the empirical methods, as well as the interpretation of the results. S.Z. (Shuo Zhang) was responsible for the establishment of research hypothesis and theoretical analysis framework. S.Z. (Shuo Zhang) wrote the paper and was supported by other members of the team. S.Z. (Simeng Zhang) was responsible for data collection and collation. All authors have read and agreed to the published version of the manuscript.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Low-carbon pilot cities’ approval process.

Figure 2. Spatiotemporal fractal map of low-carbon pilot cities.

Figure 3. Spatiotemporal fractal map of energy efficiency ((a) 2007, (b) 2020).

Figure 4. Parallel trend test.

Figure 5. Individual placebo test.

Figure 6. Kernel density.

Table 1

Descriptive statistic.

Variable	N	Mean	SD	Min	Max
Gml	19,309	1.026	1.015	0.081	0.681
Size	19,309	21.881	21.725	1.177	17.641
Lev	19,309	0.393	0.383	0.200	0.007
ROA	19,309	0.048	0.045	0.072	−0.999
ATO	19,309	0.708	0.616	0.460	0.000
Cashflow	19,309	0.050	0.048	0.073	−1.938
FIXED	19,309	0.231	0.204	0.141	0.000
Growth	19,309	0.279	0.113	4.127	−0.985
Loss	19,309	0.092	0.000	0.288	0.000
Board	19,309	2.128	2.197	0.196	0.693
Dual	19,309	0.297	0.000	0.457	0.000
Top1	19,309	0.345	0.327	0.144	0.029
SOE	19,309	0.320	0.000	0.466	0.000
ListAge	19,309	1.896	2.079	0.922	0.000

Table 2

Regression results of low-carbon city pilot policy and energy efficiency of enterprises.

Variable	(1)	(2)	(3)
DID	0.036 ***	0.017 ***	0.008 ***
	(31.525)	(6.809)	(3.737)
Constant term	1.007 ***	1.0657 ***	1.027 ***
	(1224.870)	(11.760)	(31.988)
Control variables	No	Yes	Yes
Individual fixed effect	No	No	Yes
Year fixed effect	No	No	Yes
observations	19,309	19,309	19,309
adj.R2	0.549	0.548	0.524

Tips: (1) *** indicates two-tailed tests at 1%, statistical significance levels; (2) T-value in brackets, standard errors adjusted for robust standard error of unban clustering, same below.

Table 3

Results of changing the timing of policy implementation.

Variable	(1)	(3)
	Two Years Ahead of Schedule	Three Years Ahead of Schedule
DID1	0.0001
	(0.036)
DID2		−0.0024
		(−0.875)
Constant term	1.0268 ***	1.0276 ***
	(31.968)	(31.981)
Control variables	Yes	Yes
Individual fixed effect	Yes	Yes
Year fixed effect	Yes	Yes
Industry fixed effect	Yes	Yes

Tips: t statistics in parentheses, *** p < 0.01.

Table 4

Result of balance test.

Variable	Unmatched	Nearest Neighbor Matching Method (1:2)
	Matched	%Bias	%Reduced |Bias|	p > |t|
Size	U	−1.6	56.0	0.311
	M	0.7		0.560
Lev	U	−6.3	59.9	0.000
	M	−2.5		0.040
ROA	U	2.2	−0.3	0.155
	M	2.2		0.075
ATO	U	−6.9	99.5	0.000
	M	−0.0		0.976
Cashflow	U	−4.7	80.0	0.003
	M	−0.9		0.436
FIXED	U	−29.8	93.5	0.000
	M	−1.9		0.096
Growth	U	0.7	80.3	0.665
	M	0.1		0.893
Loss	U	−0.2	−551.1	0.900
	M	−1.3		0.293
Board	U	−9.5	86.4	0.000
	M	−1.3		0.291
Dual	U	12.7	94.6	0.000
	M	0.7		0.582
Top1	U	5.9	60.7	0.000
	M	−2.3		0.058
SOE	U	−8.5	80.3	0.000
	M	−1.7		0.163
ListAge	U	−17.3	89.2	0.000
	M	1.9		0.142

Table 5

Robustness test regression results.

Variable	(1)	(2)	(3)	(4)
	PSM-1:2 Match	Replacing Explained Variable	Excluding Special Years	Introducing New Control Variables
DID	0.008 ***	0.004 ***	0.008 ***	0.006 ***
	(3.757)	(4.677)	(2.759)	(2.994)
Constant term	1.025 ***	1.013 ***	1.049 ***	1.371 ***
	(31.903)	(83.251)	(25.275)	(38.283)
Control variables	Yes	Yes	Yes	Yes
Individual fixed effect	Yes	Yes	Yes	Yes
Year fixed effect	Yes	Yes	Yes	Yes
Industry fixed effect	No	No	No	Yes
N	19,293	19,309	14,422	19,265
adj.R2	−0.024	−0.012	−0.042	0.010

Tips: t statistics in parentheses, *** p < 0.01.

Table 6

Mechanism analysis of green innovation.

Variable	Tec1	Tec2	Tec3
	Green Innovation Input	Green Innovation Output	Green Innovation Efficiency
Treat × Time × M	0.012 **	0.006 ***	0.003 *
	(1.976)	(3.086)	(1.690)
Treat × Time	0.007 ***	0.007 ***	0.008 ***
	(3.233)	(3.233)	(3.232)
Tec	0.005 *	0.005 *	0.012 *
	(1.757)	(1.758)	(1.655)
Constant terms	1.091 ***	1.028 ***	1.084 ***
	(12.984)	(12.004)	(12.7764)
Control variables	Yes	Yes	Yes
Individual fixed effect	Yes	Yes	Yes
Year fixed effect	Yes	Yes	Yes
N	19,309	19,309	19,309
adj.R2	0.521	0.553	0.572

Tips: t statistics in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01.

Table 7

Mechanism analysis of agglomeration effect.

Variable	Agg1	Agg2	Agg3
	Manufacturing Agglomeration	Productive Services Agglomeration	Synergistic Agglomeration
Treat × Time × Agg	0.054 **	0.003 *	0.032 *
	(3.087)	(1.670)	(1.828)
Treat × Time	0.006 ***	0.007 ***	0.007 ***
	(3.112)	(3.087)	(3.098)
Agg	0.067 **	0.005	0.025 *
	(2.029)	(1.209)	(1.770)
Constant terms	1.176 ***	1.980 ***	1.568 ***
	(10.980)	(10.124)	(10.087)
Control variables	Yes	Yes	Yes
Individual fixed effect	Yes	Yes	Yes
Year fixed effect	Yes	Yes	Yes
N	19309	19309	19309
adj.R2	0.624	0.602	0.593

Tips: t statistics in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01.

Table 8

Mechanism heterogeneity regression results of firm size and property.

Variable	(1)	(2)	(3)	(4)
	Large Firms	Middle and Small Firms	State-Owned Firms	Non-State-Owned Firms
DID	0.009 ***	0.005	0.011 ***	0.006 **
	(3.104)	(1.413)	(3.237)	(1.987)
Constant terms	1.081 ***	0.985 ***	1.028 ***	1.042 ***
	(19.443)	(13.464)	(19.327)	(24.113)
Control variables	Yes	Yes	Yes	Yes
Two fixed effects	Yes	Yes	Yes	Yes
N	9629	9680	6177	13,132
adj.R2	0.534	0.525	0.511	0.556

Tips: t statistics in parentheses, ** p < 0.05, *** p < 0.01.

Table 9

Heterogeneity regression results of each geographical region.

Variable	(1)	(2)	(3)
	Eastern Region	Central Region	Western Region
DID	0.015 ***	0.008	−0.011 **
	(3.544)	(1.503)	(2.037)
Constant terms	2.430 ***	0.645 ***	2.076 ***
	(12.443)	(12.862)	(14.537)
Control variables	Yes	Yes	Yes
Individual fixed effect	Yes	Yes	Yes
Year fixed effect	Yes	Yes	Yes
N	7680	6335	5294
adj.R2	0.560	0.558	0.532

Tips: t statistics in parentheses, ** p < 0.05, *** p < 0.01.

References

1. Shi, D.; Wang, J.J. Measurement and Evaluation of China’s Ecological Pressure and Ecological Efficiency Based on Ecological Footprint. China Ind. Econ.; 2016; 5, pp. 5-21.

2. Fan, F.; Dai, S.; Yang, B. Urban density, directed technological change, and carbon intensity: An empirical study based on Chinese cities. Technol. Soc.; 2023; 72, 102151. [DOI: https://dx.doi.org/10.1016/j.techsoc.2022.102151]

3. Fan, D.; Liu, T.T. Impact mechanism and Heterogeneity of Low-carbon Cities on Total Factor Energy Efficiency. Rev. Ind. Econ.; 2022; 49, pp. 93-111.

4. Xu, J.; Cui, J.B. Low-carbon Cities and Firms’ Green Technological Innovation. China Ind. Econ.; 2020; 393, pp. 178-196.

5. Wang, B.B.; Qi, S.Z. The Effect of Market-Oriented and Command-and-Control Policy Tools on Emissions Reduction Innovation—An Empirical Analysis Based on China’s Industrial Patents Data. China Ind. Econ.; 2016; 6, pp. 91-108.

6. Tang, M.; Li, X.; Zhang, Y.; Wu, Y.; Wu, B. From Command-and-Control to Market-Based Environmental Policies: Optimal Transition Timing and China’s Heterogeneous Environmental Effectiveness. Econ. Model.; 2020; 90, pp. 1-10. [DOI: https://dx.doi.org/10.1016/j.econmod.2020.04.021]

7. Cui, J.; Zhang, J.; Zhang, Y. Carbon Pricing Induces Innovation: Evidence from China’s Regional Carbon Market Pilots. AEA Pap. Proceeding; 2018; 108, pp. 453-457. [DOI: https://dx.doi.org/10.1257/pandp.20181027]

8. Xie, R.H.; Yuan, Y.J.; Huang, J.J. Different Types of Environmental Regulations and Heterogeneous Influence on Green Productivity: Evidence from China. Ecol. Econ.; 2017; 132, pp. 104-112. [DOI: https://dx.doi.org/10.1016/j.ecolecon.2016.10.019]

9. Cui, Q.; Ma, X.Y.; Zhang, S.S. Study on Evaluation and Influencing Factors of Green Total Factor Energy Efficiency Analysis: Based on Data from China’s Eight Economic Regions. J. Technol. Econ. Manag.; 2022; 3, pp. 94-99.

10. You, J.H.; Gao, Z.G. Can Government Environmental Regulations be Good for Energy Efficiency? An Empirical Study in Xinjiang. Resour. Sci.; 2013; 35, pp. 1211-1219.

11. Dirckinck-Holmfeld, K. The options of local authorities for addressing climate change and energy efficiency through environmental regulation of companies. J. Clean. Prod.; 2015; 98, pp. 175-184. [DOI: https://dx.doi.org/10.1016/j.jclepro.2014.12.067]

12. Hancevic, P.I. Environmental Regulation and Productivity: The Case of Electricity Generation under the CAAA-1990. Energy Econ.; 2016; 60, pp. 131-143. [DOI: https://dx.doi.org/10.1016/j.eneco.2016.09.022]

13. Curtis, E.M.; Lee, J.M. When do environmental regulations backfire? Onsite industrial electricity generation, energy efficiency and policy instruments. J. Environ. Econ. Manag.; 2019; 96, pp. 174-194. [DOI: https://dx.doi.org/10.1016/j.jeem.2019.04.004]

14. Tao, C.Q.; Li, C.; Wang, X.H. Suitability of Environmental Regulation Effect on Total-Factor Energy Efficiency and Relation to Energy Consumption Structure Evolution. China Popul. Resour. Environ.; 2018; 28, pp. 98-108.

15. Pan, X.; Ai, B.; Li, C. Dynamic relationship among environmental regulation, technological innovation and energy efficiency based on large scale provincial panel data in China. Technol. Forecast. Soc. Chang.; 2019; 144, pp. 428-435. [DOI: https://dx.doi.org/10.1016/j.techfore.2017.12.012]

16. Wu, H.T.; Hao, Y.; Ren, S.Y. How do environmental regulation and environmental decentralization affect green total factor energy efficiency: Evidence from China. Energy Econ.; 2020; 91, 104880. [DOI: https://dx.doi.org/10.1016/j.eneco.2020.104880]

17. Wang, T.; Yan, L.; He, J.H. An empirical study on the effect of environmental regulation on total factor energy efficiency—Decomposition verification based on Potter Hypothesis. China Environ. Sci.; 2017; 37, pp. 1571-1578.

18. Gehrsitz, M. The Effect of Low Emission Zones on Air Pollution and Infant Healh. J. Environ. Econ. Manag.; 2017; 83, pp. 121-144. [DOI: https://dx.doi.org/10.1016/j.jeem.2017.02.003]

19. Song, H.; Sun, Y.J.; Chen, D.K. Assesment for the Effect of Government Air Pollution Control Policy:Empirical Evidence from “Low-carbon City” Construction in China. J. Manag. World; 2019; 35, 95–108+195

20. Zhang, H. Can Low-carbon City Construction Reduce Carbon Emissions? Evidence from a Quasi-natural Experiment. Bus. Manag. J.; 2020; 42, pp. 25-41.

21. Yu, X.; Chen, N.; Li, M.Q. Research on Carbon Emission Characteristics and Reduction Pathways of Low-carbon Pilot Cities in China. China Popul. Resour. Environ.; 2020; 30, pp. 1-9.

22. Wang, L.; Shao, J. The impact of foreign direct investment on China’s carbon emission efficiency through energy intensity and low-carbon city pilot policy. Energy Environ.; 2022; 97, 105212. [DOI: https://dx.doi.org/10.1177/0958305X221100524]

23. Ma, D.Y.; Sun, H. Low-carbon City Pilot Policy and Strategic Emerging Enterprise Growth: Based on the Empirical Research of A-Share Listed Companies. East China Econ. Manag.; 2023; 37, pp. 84-94.

24. Zhu, C.; Lee, C.C. The effects of low-carbon pilot policy on technological innovation: Evidence from prefecture-level data in China. Technol. Forecast. Soc. Chang.; 2022; 183, 121955. [DOI: https://dx.doi.org/10.1016/j.techfore.2022.121955]

25. Zheng, J.; Shao, X.; Liu, W. The impact of the pilot program on industrial structure upgrading in low-carbon cities. J. Clean. Prod.; 2021; 290, 125868. [DOI: https://dx.doi.org/10.1016/j.jclepro.2021.125868]

26. Zhang, Y.S.; Zhang, S.P.; Wang, X.H. The Impact of Low-carbon City Construction on Urban-High-Quality Development under the Double Carbon Coal: Based on a Quasi-Natural Experiment of the Pilot Policy for Low-carbon Cities. J. Xi’an Jiaotong Univ. (Soc. Sci.); 2022; 42, pp. 39-48.

27. Mu, X.Z.; Zhou, W.T.; Hu, G.W. Impacts of Different Types of Environment Regulations’ on Total Factor Energy Efficiency of China. J. Beijing Inst. Technol. (Soc. Sci. Ed.); 2022; 24, pp. 56-74.

28. Zhang, B.B.; Zhou, J.T.; Yan, Z.J. Low-carbon City Pilot Policies and Total Factor Energy Efficiency Improvement: Quasi-natural Experiment from the Implementation of Three Batches of Pilot Policies. Econ. Rev.; 2021; 5, pp. 32-49.

29. Song, Q.; Qin, M.; Wang, R.; Qi, Y. How does the nested structure affect policy innovation?: Empirical research on China’s low carbon pilot cities. Energy Policy; 2020; 144, 111695. [DOI: https://dx.doi.org/10.1016/j.enpol.2020.111695]

30. Zhuang, G.Y. Policy Design Logic of Low-carbon City Pilots in China. China Popul. Resour. Environ.; 2020; 30, pp. 19-28.

31. Yang, S.; Jahanger, A.; Hossain, M.R. How effective has the low-carbon city pilot policy been as an environmental intervention in curbing pollution? Evidence from Chinese industrial enterprises. Energy Econ.; 2023; 118, 106523. [DOI: https://dx.doi.org/10.1016/j.eneco.2023.106523]

32. Wen, H.; Chen, S.; Lee, C.C. Impact of low-carbon city construction on financing, investment, and total factor productivity of energy-intensive enterprises. Energy J.; 2023; 44, [DOI: https://dx.doi.org/10.5547/01956574.44.2.hwen]

33. Chen, H.; Guo, W.; Feng, X. The impact of low-carbon city pilot policy on the total factor productivity of listed enterprises in China. Resour. Conserv. Recycl.; 2021; 169, 105457. [DOI: https://dx.doi.org/10.1016/j.resconrec.2021.105457]

34. Fisher, V.K.; Jefferson, G.H.; Liu, H.; Tao, Q. What Is Driving China’s Decline in Energy Intensity. Resour. Energy Econ.; 2004; 26, pp. 77-97. [DOI: https://dx.doi.org/10.1016/j.reseneeco.2003.07.002]

35. Pan, X.; Guo, S.; Han, C. Influence of FDI quality on energy efficiency in China based on seemingly unrelated regression method. Energy; 2020; 192, 116463. [DOI: https://dx.doi.org/10.1016/j.energy.2019.116463]

36. Zhou, L.; Li, J.; Li, F. Energy consumption model and energy efficiency of machine tools: A comprehensive literature review. J. Clean. Prod.; 2016; 112, pp. 3721-3734. [DOI: https://dx.doi.org/10.1016/j.jclepro.2015.05.093]

37. Yasmeen, R.; Zhang, X.; Tao, R. The impact of green technology, environmental tax and natural resources on energy efficiency and productivity: Perspective of OECD Rule of Law. Energy Rep.; 2023; 9, pp. 1308-1319. [DOI: https://dx.doi.org/10.1016/j.egyr.2022.12.067]

38. Yang, Y.; Xue, R.; Zhang, X. Can the marketization of urban land transfer improve energy efficiency?. J. Environ. Manag.; 2023; 329, 117126. [DOI: https://dx.doi.org/10.1016/j.jenvman.2022.117126]

39. Chen, Z.; Chen, Q.Y. Energy Efficiency of Chinese Firms: Heterogeneity, Influencing Factors and Policy Implications. China Ind. Econ.; 2019; 12, pp. 78-95.

40. Xing, F.; Wang, H.J. Firm Size, Market Competition and Implementation Performance of R&D Subsidy. Sci. Res. Manag.; 2018; 39, pp. 43-49.

41. Williams, B.; Brian, M. Real Effects of Financial Reporting on Innovation:Evidence from Tax Law and Accounting Standards. Account. Rev.; 2021; 96, pp. 397-425. [DOI: https://dx.doi.org/10.2308/TAR-2018-0582]

42. Bossink, B. A Dutch Public-Private Strategy for Innovation in Sustainable Construction. Constr. Manag. Econ.; 2002; 20, pp. 633-642. [DOI: https://dx.doi.org/10.1080/01446190210163534]

43. Song, X.L.; Lee, J.Y. Industrial Collaborative Agglomeration, Institutional Environment and Industrial Green Innovation Efficiency. Sci. Technol. Prog. Policy; 2023; 40, pp. 56-65.

44. Lee, Z.; Yang, S.Y. Has the Pilot Project of Innovative Cities Increased the Level of Innocation. Econ. Perspect.; 2019; 38, pp. 70-85.

45. Shi, D.Q.; Ding, H.; Wei, P.; Liu, J.J. Can Smart City Construction Reduce Environmental Pollution. China Ind. Econ.; 2018; 6, pp. 117-135.

46. Scott, A.J. Triumph of the City: How Our Greatest Invention Makes Us Richer, Smarter, Greener, Healthier, and Happier—By Edward Glaeser. Econ. Geogr.; 2012; 88, pp. 97-100. [DOI: https://dx.doi.org/10.1111/j.1944-8287.2011.01134.x]

47. Yu, H.; Ke, H.; Ye, Y. Agglomeration and flow of innovation elements and the impact on regional innovation efficiency. Int. J. Technol. Manag.; 2023; 92, pp. 229-254. [DOI: https://dx.doi.org/10.1504/IJTM.2023.128840]

48. Freeman, C. Networks of Innovators: A Synthesis of Research Issues. Res. Policy; 1991; 20, pp. 499-514. [DOI: https://dx.doi.org/10.1016/0048-7333(91)90072-X]

49. Maddison, A. American economic association growth and slowdown in advanced capitalist economies: Techniques of quantitative assessment. J. Econ. Lit.; 1987; 25, pp. 649-698.

50. Fowlie, M.; Greenstone, M.; Wolfram, C. Do energy efficiency investments deliver? Evidence from the weatherization assistance program. Q. J. Econ.; 2018; 133, pp. 1597-1644. [DOI: https://dx.doi.org/10.1093/qje/qjy005]

51. Liu, H.; Wang, C.; Zhang, M.; Wang, S. Evaluating the effects of air pollution control policies in China using a difference-in-differences approach. Sci. Total Environ.; 2022; 845, 157333. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2022.157333] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35842143]

52. Jacobson, L.S.; LaLonde, R.J.; Sullivan, D.G. Earnings Losses of Displaced Workers. Am. Econ. Rev.; 1993; 83, pp. 685-709.

53. Li, Y.; Hu, H.; Wang, L. How does mandatory energy conversion affect sustainable development: Perspectives of regional heterogeneity and efficiency decomposition. J. Environ. Manag.; 2023; 331, 117279. [DOI: https://dx.doi.org/10.1016/j.jenvman.2023.117279]

54. Ervural, B.C.; Zaim, S.; Delen, D. A two-stage analytical approach to assess sustainable energy efficiency. Energy; 2018; 164, pp. 822-836. [DOI: https://dx.doi.org/10.1016/j.energy.2018.08.213]

55. Lee, C.Y.; Wang, F.Z. Study on Endogenous Mechanism of Urban Land Resources Allocation and House Price Dynamicchange. Price Theory Pract.; 2019; 8, 56–59+63

56. Zhao, N.N.; Wang, Z.B.; Lee, H.M. Change of Energy Consumption Pattern and its Impact on Economic Development in China. Resour. Sci.; 2021; 43, pp. 122-133. [DOI: https://dx.doi.org/10.18402/resci.2021.01.10]