The Impact of “Dual-Control” Regulations on the Green Total Factor Efficiency of Shaoxing’s Industrial Sector

Abstract

Promoting the decoupling between economic growth and carbon emissions through government intervention is very crucial for China to achieve carbon neutrality. This paper evaluates the green development performance with the help of the green total factor efficiency index and explores the impact of “Dual-Control” regulations on the green development of the industrial sector in Shaoxing using the differences-in-differences model. The results show that there are great diversities in the green development of different industries and that the energy-intensive industries have relatively poorer performance. The “Dual-Control” regulations significantly narrow the differences and promote the green development of Shaoxing’s industry but result in profit erosion for industrial enterprises owing to direct energy-saving expenditure and other indirect policy execution costs. The results of the dynamic analysis reveal that the negative economic impact has hysteresis and persistence. Different from previous studies, this paper considers the cross effects of different policies and examines the comprehensive effect of the policy package under the “Dual-Control” regulations. The conclusion provides a supplement to revealing the relationship between government regulation and energy conservation and emission reduction.

1. Introduction

Climate issues have aroused widespread concern. In 2020, the Chinese government made a solemn pledge to achieve carbon peaking by 2030 and carbon neutrality by 2060 [1]. It poses a huge challenge to China. The burning of fossil fuels is the main cause of global warming. China is still in the process of industrialization and urbanization. There is an obvious contradiction between ensuring economic development and promoting energy conservation and carbon reduction in the short term [2]. Improving energy utilization efficiency and carbon emission efficiency is the key to solving this contradiction, which means promoting green development is an inevitable requirement for China to fulfill its carbon neutrality commitment.

The eastern region in China boasts four of China’s most important industrial bases and is a major contributor to China’s energy consumption and CO₂ emissions. Shaoxing, located in the eastern coastal province of Zhejiang, has a strong industrial foundation with a robust economy, ranking among the top 100 cities in China. During the period of the “12th Five-Year Plan”, Shaoxing, under the leadership of the Zhejiang Provincial Government, carried out the system of “Dual-Control” regulations. Under the system, the local government breaks down the targets and tasks of total energy consumption control and energy intensity control and distributes them to all its jurisdictions. The jurisdictions further assign tasks to various units and enterprises within their area. To ensure all targets are met, a series of policy measures, such as new energy consumption control, backward energy consumption elimination, energy-conservation technology transformation and renewable energy development have been implemented in industrial sectors. Shaoxing’s “Dual-Control” regulations can be considered an important microcosm of China’s energy conservation and emission reduction management over the past decade. Discussing the policy effect of the regulations is of historical and practical significance.

From the various policy documents issued by the Chinese central and local governments at different times, it is revealed that China has always taken the route of “policy packages” in promoting the green transformation of its economy and society. Take Shaoxing as an example: after the implementation of “Dual-Control” regulations, the municipal government launched a combination of policy instruments, which range from energy consumption management to energy supply management, from existing capacity management to new capacity management, and from compulsory administration means to market-oriented tools. Existing studies mostly focused on a specific policy instrument when analyzing the impact of government regulation on industrial green development [3,4,5]. It is conducive to understanding the characteristics and effects of each type of tool but may lead to a misjudgment because different instruments may conflict with each other. It is important to treat different policy measures as a whole. The discussion on the overall effect of a “policy package” can, on the one hand, accurately reflect the final effectiveness of all the government’s efforts and, on the other, may reveal the additional benefits or costs that arise from the combination of different policy instruments. To date, studies on “policy packages” are relatively limited, showing a weak, poor, scattered research status.

This study aims to make a quantitative analysis of the impact of the “policy package” under the “Dual-Control” system on the green development and transformation of the industrial sector in Shaoxing. The following are our potential marginal contributions. Firstly, it is the leading study to empirically investigate the impact and mechanism of the “Dual-Control” regulations on the green development transition of Shaoxing’s industrial sector, which enhances the evidence of the connection between environmental regulation and green development transition. Growing literature has extensively addressed the influence of “dual-control” regulations at the theoretical level, with limited attention given to empirical investigations. The reason is obvious. On the one hand, the term “Dual-Control” refers to all energy-related policy tools, on the other hand, the timetable of the adoption of “Dual-Control” regulations varies substantially, both at the local and industry levels. These make empirical analysis difficult. This paper employs the DID method to avoid the problem of selecting a proxy for the “Dual-Control” regulations and solves the problem of constructing treated and control groups utilizing the fact that the impact of the policy on energy consumption varies across industries. The empirical study is successfully developed. Secondly, we demonstrate the heterogeneity of the effects of “Dual-Control” regulations. According to our findings, the positive impact of “Dual-Control” regulations on the green development transition varies significantly between industries with high energy consumption and industries with low energy consumption. The effect is relatively pronounced in the high energy consumption sector. This makes us fully realize that policies should be more visionary, targeted and effective in order to reach the “dual carbon” goal. To sum up, we have made a further study base on the existing research and obtained a more comprehensive understanding of the effects of the policy package.

Further material is divided into several parts. A brief literature review is made in Section 2. Section 3 presents a theoretical analysis of the impact of “Dual-Control” regulations on the green development of industrial sectors. Section 4 introduces the methodology and variables used in our empirical study. Empirical results and discussions are presented in Section 5. Finally, in Section 6, conclusions and suggestions are drawn.

2. Literature Review

2.1. The Connotation and Measurement of Green Development

Economists and sociologists have long been concerned about the depletion of resources and the environmental damage caused by the excessive pursuit of material wealth. As early as 1989, Pearce et al. proposed the concept of green development, arguing that economic development should take into account the carrying capacity of the natural ecological environment and that the costs of economic activities that lead to resource depletion and environmental pollution should be included in economic accounting [6]. There is no definite definition of green development that has been established. In 2005, the United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP) formally introduced the concept of green growth and defined it as an environmentally sustainable economic process that promotes low-carbon development and meets the needs of all people in society. In 2007, the United Nations Environment Programme (UNEP) defined a green economy as “an economy that contributes to valuing people and nature and creating decent and well-paying jobs” and revised this definition in its 2011 report to “one that results in improved human well-being and social equity, while significantly reducing environmental risks and ecological scarcities” [7]. In 2009, green growth was defined by the Organization for Economic Cooperation and Development (OECD) as the pursuit of economic growth and development while preventing increased water scarcity, worsening resource bottlenecks, greater pollution, climate change, and unrecoverable biodiversity loss. In 2011, it was further revised as “fostering economic growth and development, while ensuring that natural assets continue to provide the resources and environmental services on which our well-being relies” [8]. In 2012, the World Bank defined green growth as “achieving efficient, clean and resilient production processes without a slowdown in economic growth” [9]. To sum up, green development means an economic development model based on ecological and environmental capacity and resource carrying capacity, which prioritizes resource conservation and ecological protection over economic development.

There are two primary approaches to evaluating green development. The first one is based on the performance of the green development index. This approach starts from the connotation and requirements of green development and then establishes a set of indicators encompassing economic development, social development, resource utilization, environmental protection, green innovation, green culture and so on [7,10,11,12]. It then assigns different weights to different indicators through the entropy method, hierarchical analysis, expert scoring and other methods, and finally calculates the weighted average performance of all indicators, so as to obtain the green development index performance. For example, Bui et al. (2017) selected 57 sustainable development indexes based on four criteria: economic development, environmental protection, social performance and technological development to evaluate the sustainable development of the mining sector in Asia-Pacific Economic Cooperation (APEC) economies [13]. Wang et al. (2018) selected 23 indicators in terms of economic growth, innovation potential, ecological efficiency, living environment and pollution disposal and evaluated the green development performance of nine cities in the Pearl River Delta (PRD) of China [14]. Yang et al. (2019) developed a green development index system with 19 indicators that refer to green economic development, green social development, and green environmental development, and assessed the green development of 119 mineral resource cities in China using the entropy weight method and hierarchical analysis method [15]. Long et al. (2021) constructed a green development index consisting of 29 indicators based on four dimensions: green economic development, green social development, green resource development and green environmental development, and applied the dynamic Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method to analyze the green development of coal-based cities in China [16]. Various requirements of green development are considered in this approach. However, it is more subjective in the selection and weighting of indicators and more demanding of data.

The second approach is based on the efficiency index performance of green development. This approach generally treats energy consumption and pollution emissions as inputs and outputs and completes the portrayal of green development efficiency by measuring total factor efficiencies such as energy input efficiency, desirable output efficiency and undesirable output efficiency [17,18]. This method is based on input-output relationships, and the difference between different articles lies in the choice of measurement models. Stochastic frontier analysis (SFA) and data envelopment analysis (DEA) are the two models that have been used more frequently in existing studies. The former is a parametric estimation method that requires prior assumptions about the functional form of the production technology [17,19]. The latter is a non-parametric method, based directly on actual data on inputs and outputs of all production decision units, measured by linear programming techniques [20,21]. Both methods have their advantages and disadvantages; SFA takes into account the influence of errors, but the choice of function form directly affects the results and is more demanding on the data; DEA avoids the influence of the subjective choice of function form, but is susceptible to individual outliers. In recent years, more and more studies have adopted the DEA method [17].

Scholars have conducted extensive research on the green transition of China in recent years. Most of these researches confirmed that the green development performance of China was on the rise in the past few decades while a few studies found that the green development performance of China was not improved with economic development in the sample time [22,23,24,25,26,27]. However, all existing studies agree that China has a huge space for energy conservation and emission reduction.

2.2. Environmental Regulation and the Green Development

Existing studies have shown that there are many factors that may affect the green development performance of one country, one region or one specific industry. Different studies may target different regions and industrial sectors, and their conclusions may vary slightly. Numerous factors, including but not limited to energy consumption structure, industrial structure, ownership structure, market concentration, fossil energy prices, foreign direct investment, technological progress, labor productivity, enterprise size, management effectiveness and government regulatory efforts have been shown to influence the green efficiency [25,26,28,29]. Among them, the power of government regulation has aroused widespread concern.

On the issue of government regulation and green development, a large number of scholars have turned their attention to China. This may be because China is the world’s largest source of energy consumption and carbon dioxide emissions. Since the 11th Five-Year Plan, the Chinese government has raised the requirements for energy conservation and emission reduction in the industrial sector, as well as implemented many policy initiatives [30]. Since then, more and more studies began to concentrate on the evaluation of the policy effects of various regulatory measures. These studies mostly focus on a certain policy instrument and quantitative evaluations of policy effects are obtained through panel econometric models, policy dummy variables, difference-in-difference models (DID), and other similar techniques. Both positive and negative evidence was uncovered. For example, Zhou and Tang (2021) [4], Zhou and Zhang (2022) [5], Chen and Kong (2022) [31] conducted empirical studies on the relationship between government regulation and green development in China’s industry based on different policies and different samples. Their findings showed that government regulation had a significant positive impact on industrial green development in China. However, Tang et al. (2020) analyzed the impact of command-and-control environmental regulation on the total factor productivity of industrial enterprises in China and found that the regulation hindered the productivity improvement of industrial enterprises [3].

There are also many studies on other countries besides China. Based on the data from 17 EU countries, Neves et al. (2020) analyzed the short-term and long-term impacts of environmental regulation policies and found that environmental regulation can effectively reduce carbon dioxide emissions [32]. Hamamoto (2006) and Yabar et al. (2013) all found that environmental regulation can promote technology innovation, thereby improving the productivity of industry in Japan [33,34]. Murty et al. (2006) analyzed the relationship between environmental regulation and industry productive efficiency and found that environmental regulation had a positive impact on the productive efficiency of the sugar industry in India [35]. Similar studies include Rubashkina et al. (2015), Park et al. (2020) [36,37]. Most studies support the idea that environmental regulation has a positive impact on the promotion of green development.

3. Analysis of the Impact of Using “Dual-Control” Regulations in the Chinese Economy

In 2007, the National Development and Reform Commission (NDRC) of China put forward the 11th Five-Year Plan for Energy Development for the first time, demonstrating the objectives of controlling total energy consumption amount and energy intensity. In 2013, in the 12th Five-Year Plan for Energy Development, the “Dual-Control” on total energy consumption and energy intensity was established, requiring the implementation of the targets and tasks to be incorporated into the regional comprehensive evaluation and assessment system for economic and social development. Since then, total energy consumption and energy intensity have become two essential binding indicators for measuring regional economic and social progress.

In 2021, the 14th Five-Year Plan for Energy Conservation and Optimal Allocation of Energy Resources of Zhejiang Province (hereinafter referred to as the “Plan”) was issued, which clarified that by 2025, the province’s energy consumption per unit of Gross Domestic Product (GDP) will be lowered by 15%, and total energy consumption will be kept around 269.1 million tons of standard coal. The Plan envisions a cumulative 18% reduction in energy consumption per unit of industrial-added value for the industrial sector between 2021 and 2025. In order to achieve this goal, the Plan also sets out a series of measures for each city. It requires Shaoxing to strictly control the manufacturing capacity of textile printing and dyeing, chemical fibers and plastic products; raise the entry standards for “Two High” projects (high energy consumption and high emissions), and strictly control new local projects in petrochemical, chemical fiber, cement, steel, data center and other high energy consumption programs; strengthen energy conservation monitoring and energy budget management, and strictly implement capacity and energy consumption reduction and replacement for new (reform and expansion) industrial construction projects; increase efforts to eliminate backward and excess production capacity, and fully complete the rectification of polluting enterprises; implement the total control of coal consumption, and promote “replacing coal with gas” policy in industrial sectors. The local government of Shaoxing has also introduced comprehensive use of resources and built green factories and green industrial parks; carried out market-based trading reforms for the paid use of energy rights and promoted the implementation of modern energy conservation management.

The “Dual-control” regulations consist of various policy measures. In terms of the form, content, and objectives, these regulatory policies are not significantly different from those of the past, as they all essentially aim to correct the negative externalities of energy consumption through policy coercion or market regulation. What is different from earlier initiatives is the governments’ determination and intensity in implementing “Dual-Control” regulations. Prior to the 12th Five-Year Plan, the Chinese central government had a relatively strong intent to reduce energy consumption and emissions, but the local governments prefer to boost the economy and tend to adopt the strategy of “bottom competition” in energy conservation and emission reduction. After the 12th Five-Year Plan, total energy consumption and energy intensity have become two key indicators for the central government to evaluate local governments. The local governments thereby place much more emphasis on energy conservation. The pressure of energy “dual-control” faced by governments at different levels is transformed into strict energy consumption constraints on enterprises within their jurisdictions through various regulatory means. More stringent environmental regulations have a more direct effect on the industrial sector’s green development.

Negative externalities, information asymmetries and unclear property rights determine the fact that the market cannot perfectly solve resource and environmental problems on its own. The implementation of the “Dual-Control” regulations, through the compulsory measures represented by laws, norms and standards (e.g., elimination of backward production capacity, and strict control of new energy consumption) and the market-oriented measures represented by prices and fees (e.g., green renovation subsidies, and energy consumption rights trading), can theoretically solve the problems of resource conservation and environmental protection in the case of market failure, which means boosting the green development of the economy and society.

The promotion of green development in the industrial sector by the energy “Dual-Control” regulations can be seen in both direct and indirect effects. Firstly, under the energy “Dual-Control” regulations, the government, on the one hand, gradually eliminates backward production capacity by controlling the approval of new energy consumption or making a number of enterprises close down, suspend, merge or switch to other lines of production, and, on the other hand, internalizes the part of the energy consumption costs previously burdened by whole society (negative externalities, such as the treatment of carbon emissions generated during energy combustion) through administrative requirements such as energy-conservation renovation, fossil energy consumption substitution and the purchase of energy rights, forcing industrial enterprises to pay more attention to energy conservation in their production and management process, which directly promotes the green development of the industry. Secondly, the “Dual-Control” regulations will encourage the upgrading of industrial structure and technological innovation in energy production and consumption, thereby further supporting the industrial sector’s green development. More specifically, the implementation of “Dual-Control” regulations will increase the producing costs and decrease profits, which will speed up the elimination of smaller and less efficient enterprises and stimulate the transfer of large-scale energy-consuming enterprises to other regions with weaker regulations, while low-energy-consuming industries, high-value-added industries, and technology-intensive industries will gain a greater competitive advantage. It will facilitate the upgrading of regional industrial structures, and indirectly increase the industrial sector’s overall green development level. Moreover, when enterprises are subject to stringent energy regulations, they are likely to reduce energy consumption by investing more in energy conservation activities, thereby promoting the research, development, and application of energy-saving technologies and enhancing the green development of the industry. The advancements in manufacturing methods and technology will also result in increased productivity and energy efficiency. This will boost the profitability of enterprises and help them achieve a higher level of development. Besides, the “Dual-Control” regulations will influence resource allocation. The financial resources released by the capacity phase-out process and the policy signals given to society and capital by the regulations would strengthen the financial market’s support for green companies and promote the green development of the industrial sector.

It is worth noting that regulations can also have a negative impact on the green development of industrial sectors. According to the “compliance cost” hypothesis, under strict energy regulations, companies must purchase more energy-efficient equipment, hire third parties or professionals to do energy management, adopt more expensive clean energy, etc., to meet government energy conservation requirements. These expenses may squeeze the enterprises’ investment in R&D, which is not conducive to technological progress. In addition, some local governments may take extreme actions to restrict production or electricity to enterprises in order to complete the tasks assigned by the central government. These measures directly affect the operation of enterprises, which may prevent them from seizing market opportunities to expand their production, or, at worst, result in their failure to deliver on time, bringing them direct losses for breach of contract, or even causing them to lose customers and markets. Forced shutdown may also bring about equipment usage and longevity issues. These explicit and implicit costs can erode the profitability of the enterprise and will probably lead to a reduction in energy conservation investment and R&D investment budget, which ultimately inhibits the green development of the enterprises.

Based on the analysis above, we propose the following hypotheses: the dual control system has a significant impact on the green development of Shaoxing’s industry; the direction of the impact depends on the relative size of the positive and negative impacts.

4. Methodology and Variables

4.1. Methodology

4.1.1. Model for Green Total Factor Efficiency Measurement

Green development means that economic development must be balanced with a guarantee of energy conservation and emission reduction. Most the traditional indicators for measuring green development, such as energy intensity or carbon emission intensity, are single-factor indicators that only reflect one aspect of green development. We use green total factor efficiency (GTFE) to evaluate the green development of Shaoxing’s industrial sector. It is a total factor indicator that incorporates energy utility and carbon dioxide emissions into performance measurement.

The GTFE is calculated based on the super-efficiency Slack-based-measure data envelopment analysis model (super-SBM-DEA). The model is proposed by Tone [38,39,40]. With this method, using the input and output data of the sample observations, we can draw the efficient frontier consisting of all effective points which could be approximately regarded as the minimum inputs and undesirable outputs under a given desirable output or the maximum desirable outputs under a given input and undesirable outputs. The efficiency of any DMU can subsequently be measured by the distance from its actual production point to the reference efficient point located on the efficiency frontier.

We choose this method based on the following two reasons. Firstly, DEA models regard the proportion of inputs (outputs) that can be reduced (expanded) as the degree of inefficiency. The traditional radial DEA model requires inputs (outputs) to change proportionally. However, Super-SBM-DEA allows different inputs and outputs to be reduced or expanded in different proportions. This is more suitable to reality. Secondly, when multiple decision-making units (DMUs) are effective at the same time under traditional DEA models, further comparison among the efficiency of different DMU is impossible. The super-SBM-DEA model overcomes the problem through two-step processing.

The super-SBM-DEA model is a combination of the standard SBM-DEA model and the super SBM-DEA model. Under the super-SBM-DEA model, we need to evaluate the green performance of different DMUs with a standard SBM-DEA model first. The standard SBM-DEA model under constant returns-to-scale assumption is as follows.

Suppose there are n DMUs. Each DMU has m inputs, s desirable outputs and r undesirable outputs. We denote the vectors of inputs, desirable outputs and undesirable outputs for $DM{U}_j$ by $x_j={\left(x_{j1},x_{j2},\cdots ,x_{jm}\right)}^T$, $y_j={\left(y_{j1},y_{j2},\cdots ,y_{js}\right)}^T$ and $z_j={\left(z_{j1},z_{j2},\cdots ,z_{jr}\right)}^T$, respectively. We define input, desirable output and undesirable output matrices X, Y and Z by X = $\left(x_1,x_2,\cdots ,x_n\right)ϵR^{m\times n}$, Y = $\left(y_1,y_2,\cdots ,y_n\right)ϵR^{s\times n}$ and Z = $\left(z_1,z_2,\cdots ,z_n\right)ϵR^{r\times n}$.

Let P(X) be the production feasible set which contains all possible production points. It can be expressed as follows.

$$P(X)=\left\{(X,Y,Z):X \mathrm{produce} (Y,Z)\right\}$$

We assume X, Y and Z are all greater than zero and satisfy the axiom: if (Y, Z) ∈ P(X) and Y’ ≤ Y, Z’ ≥ Z or X’ ≥ X, then there is (Y’, Z) ∈ P(X), (Y, Z’) ∈ P(X), (Y’, Z’) ∈ P(X) or P(X’) ∈ P(X). The axiom implies that production could be carried out in an ineffective way. It is very much in tune with reality. Evidently, the efficiency frontier is the envelope curve of the P(X).

According to Tong [38,39,40] and Cheng [41], evaluating the efficiency of $DM{U}_o$ ($x_o, y_o, u_o$) can be transformed into solving the following nonlinear program.

$$\begin{array}{c}{\rho }_o={\mathit{min}}_{\lambda ,S^x,S^y,S^z}\frac{1-\frac{1}{m}{\displaystyle {\sum }_{i=1}^m\frac{s_i^x}{x_{io}}}}{1+\frac{1}{\mathrm{s}+r}\left({\displaystyle {\sum }_{k=1}^s\frac{s_k^y}{y_{ko}}}+{\displaystyle {\sum }_{l=1}^r\frac{s_l^z}{z_{lo}}}\right)}\\ s.t.x_{io}={\displaystyle \sum _{j=1}^n{\lambda }_j}{x}_{ij}+s_i^x,(i=1,\cdots ,m);\\ { \mathrm{y}}_{ko}={\displaystyle \sum _{j=1}^n{\lambda }_j}{y}_{kj}-s_k^y,(k=1,\cdots ,s);\\ z_{lo}={\displaystyle \sum _{j=1}^n{\lambda }_j}{z}_{lj}+s_l^z,(l=1,\cdots ,r);\\ s_i^x\ge 0,s_k^y\ge 0,s_l^z\ge 0,{\lambda }_j\ge 0,\forall i,j,k,l\end{array}$$

(1)

where: j is the jth DMU; i is the ith input; k is the kth desirable output; l is lth undesirable output; λ represents the vector of weight coefficients; s^x and s^z are called input and undesirable output slacks which represent the potential for reducing inputs and undesirable outputs; s^y is called desirable output slack which can be seen as the potential for expanding desirable outputs; ${\rho }_0$ (0 ≤ ${\rho }_0$ ≤ 1), which is strictly monotonically decreasing with respect to s^x, s^z and s^y, represents the efficiency value of $DM{U}_o$.

The process above is repeated n times for o = (1, …, n). After collecting the data of the input and output variables in Section 4.2.1, the optimal solution of $\left({\lambda }^{*},s^{x*},s^{y*},s^{z*}\right)$ can be obtained by solving the nonlinear programs with the help of MATLAB. The objective function in the program can, on the one hand, find the efficient frontier by maximizing the slacks of inputs and outputs, and on the other hand, obtain the value of GTFE based on the fraction and the optimal solution.

As can be seen from Equation (1), ${\rho }_0$ is a weighted efficiency performance that reflects the average space for the reduction of inputs and undesirable outputs and the expansion of desirable outputs. If and only if ${\rho }_0$ equals to 1 (i.e., s^x = 0, s^z = 0 and s^y = 0), there is no room for efficiency improvement. $DM{U}_o$, therefore, is called SBM-efficient. When ${\rho }_0$ is smaller than 1, production is carried out in an ineffective way. $DM{U}_o$ is called SBM-inefficient.

In the first step, multiple DMUs may be found to be SBM-efficient. These DMUs cannot be compared with each other because their efficiency coefficient ($\rho$) equals to one. In order to rank these SBM-efficient DMUs, we move to the next step, that is, reevaluating the efficiency of SBM-efficient DMUs with the help of the super SBM-DEA model.

Suppose $DM{U}_t$ is an SBM-efficient DMU found in the last step, according to Cheng [41], the efficiency of $DM{U}_t$ can be reevaluated with the help of the super SBM-DEA model. The super SBM-DEA model under constant returns-to-scale assumption is as follows.

$$\begin{array}{c}\delta =min\frac{1+\frac{1}{m}{\displaystyle {\sum }_{i=1}^m\frac{s_i^x}{x_{it}}}}{1-\frac{1}{s+r}\left({\displaystyle {\sum }_{k=1}^s\frac{s_k^y}{y_{kt}}}+{\displaystyle {\sum }_{l=1}^r\frac{s_l^z}{z_{lt}}}\right)}\\ s.t.x_{it}\ge {\displaystyle \sum _{j=1,j\ne t}^n{\lambda }_j}{x}_{ij}-s_i^x,(i=1,\cdots ,m);\\ { \mathrm{y}}_{kt}\le {\displaystyle \sum _{j=1,j\ne t}^n{\lambda }_j}{y}_{kj}+s_k^y,(k=1,\cdots ,s);\\ z_{lt}\ge {\displaystyle \sum _{j=1,j\ne t}^n{\lambda }_j}{z}_{lj}-s_l^z,(l=1,\cdots ,r);\\ 1-\frac{1}{s+r}\left({\displaystyle \sum _{k=1}^s\frac{s_k^y}{y_{kt}}}+{\displaystyle \sum _{l=1}^r\frac{s_l^z}{z_{lt}}}\right)>0;\\ s_i^x\ge 0,s_k^y\ge 0,s_l^z\ge 0,{\lambda }_j\ge 0,\forall i,j,k,l\\ \end{array}$$

(2)

All symbols in the super SBM-DEA model above have the same meaning as in the standard SBM-DEA model. The efficiency of $DM{U}_t$ can be recalculated by δ.

As can be seen from the mathematical expression of Equation (2), the efficiency of SBM-efficient DMUs is similarly measured by the distance from their production point to the point closest to the efficient frontier. However, different from in the standard SBM-DEA model, the efficient frontier in the super SBM-DEA model is constructed with the input and output data of all DMUs, excluding that of the target SBM-efficient DMU. It means that the efficient frontiers used in the efficiency measurement of different SBM-efficient DMUs are different. These differences reflect the technical gap between the SBM-efficient DMUs and form the basis for further ranking.

In addition, the traditional DEA models adopt the current period method to construct the efficient frontier, i.e., the current efficient frontier is constructed based on current observations. This causes some issues. On the one hand, the current period method is susceptible to exogenous factors such as economic fluctuations, and spurious technical regressions may occur in dynamic analysis; on the other hand, the efficiency performance of the same DMU in different periods cannot be compared with each other due to the different frontiers. In this regard, we take reference from Pastor and Lovell (2005) [42], and adopt a global criterion function, i.e., assuming that the production techniques are fixed during the sample period and performing efficient frontier construction based on all observations.

4.1.2. Model for Effect Assessment of “Dual-Control” Regulations

The Differences-in-Differences model (DID) is used to analyze the Effects of the “Dual-Control” regulations.

In recent years, the DID model has been widely used in the field of policy effectiveness evaluation. The basic idea of DID is to identify the causal relationship between policy and dependent variables by comparing the differential effects of a Natural Experiment or a Quasi-experiment on the Treatment Group (the combination of samples affected by the experiment) and the Control Group (the combination of samples not affected by the experiment). Compared to traditional policy evaluation methods, DID can eliminate bias due to differences between treatment and control groups, and acquire more accurate and reliable results of policy effects [43].

Referring to Feng et al. (2021) [44], we build a DID model as follows.

$$y_{it}={\alpha }_0+{\alpha }_{1}pos{t}_t+{\alpha }_{2}trea{t}_i+{\alpha }_{3}pos{t}_t\ast trea{t}_i+{\alpha }_4{x}_{ijt}+u_i+{\lambda }_i+{\epsilon }_{it}$$

(3)

where: i (i = 1, …, N) represents the ith industrial sector; j (j = 1, …, M) is the jth control variable number; t stands for time; y represents the dependent variable; post is the policy dummy variable, taking 0 before the policy is implemented and 1 after the policy is implemented; treat is the group dummy variable, taking 0 for the control group and 1 for the treatment group; x is the control variable; α is the coefficient to be estimated; μ and λ represent the individual fixed effect and the time fixed effect; ε is the residual item, which represents all other factors that may affect the dependent variable but are not considered as control variables.

We focus on the coefficient α₃, which reflects the net effect of the policy. A significant non-zero α₃ indicates that policy has a significant effect on the explained variables.

The design of experimental and control groups is a key issue in DID model building. The “Dual-Control” regulations are applied to all industries, which means all regions and industries in Shaoxing are affected by the regulations. This makes it difficult to find experimental groups and control groups. Vig (2013) encountered a similar problem in his study of the effects of the Enhanced Mortgage Debt Protection Act, where theoretically there is no treatment or control group [45]. His solution was that, given that tangible assets such as land and plants are more likely to act as collateral than intangible assets, the Act would have a greater impact on firms with a higher proportion of tangible assets, so treatment and control groups could be constructed based on the proportion of tangible assets in the firm. This gives us an inspiration: energy-intensive industries are the focus of various government regulations and often have greater room for energy saving, the “Dual-Control” regulations thereby will have a greater impact on energy-intensive industries. In line with Vig (2013) [45], we construct the experimental group and control group based on the energy intensity performance of industrial sectors.

4.2. Variables

4.2.1. Variables Used in Green Total Factor Efficiency Measurement

We refer to most existing studies and select variables of inputs, desirable output and undesirable output as follows.

(1)

Desirable output (doutput). In this paper, the total industrial output value (current year prices), adjusted by the GDP deflator, is used to approximate the desirable output of the industry at constant prices.

(2)

Undesirable output (uoutput). In this paper, the total amount of carbon emission is used to measure the undesirable output. It is obtained by multiplying the consumption of each major energy species by the corresponding carbon emission factor.

(3)

Labor input (labor). In this paper, the annual average number of employees in each industry is used to approximate the labor input of the industry. When data on the number of employees in individual provinces are not available, the labor productivity of the industry is used for the projection. Where labor productivity data are missing, the average number of workers in the industry is used instead. If none of the data above are available, the number of employees in the industry is interpolated to approximate.

(4)

Capital input (capital). In this paper, capital stock is used to approximate the capital input of the industry. It is calculated by the perpetual inventory method and the formula is as follows:

$$K_t=K_{t-1}\left(1-{\delta }_t\right)+I_t$$

(4)

where: K_t is the capital stock in period t measured in constant base period prices; I_t is the amount of capital investment in year t measured in constant base period prices; δ_t is the capital depreciation rate in year t.

There are no direct statistics on capital stock, capital investment and capital depreciation rates for the base period. Referring to common practice [17], the net value of fixed assets for each industry in 2000 is used as the base period capital stock data for that industry. The current year’s depreciation is calculated by subtracting the previous year’s accumulated depreciation from the current year’s accumulated depreciation and compared to the previous year’s original value of fixed assets to obtain the current year’s depreciation rate for each industry. The difference between the current year’s original value of fixed assets and the previous year’s original value of fixed assets is used to obtain the current year’s fixed asset investment for each industry, which is then adjusted by the current year’s fixed asset investment price index to obtain the current year’s comparable price fixed asset investment for each industry.

(5)

Energy input (energy). In this paper, total energy consumption is used as energy input. It is obtained by converting the consumption of each major energy species into standard coal for each industry in the statistical yearbook and summing them up.

4.2.2. Variables Used in Effect Assessment of “Dual-Control” Regulations

(1)

Dependent variable (gtfe). The dependent variable in the effect assessment of “Dual-Control” regulations is the green total factor efficiency of the industrial sector calculated by the Super-SBM-DEA model.

(2)

Key independent variable (did). In the DID model, the key independent variable is the interaction term of the policy dummy variable (post) and the group dummy variable (treat). The policy dummy variable is zero before the implementation of “Dual-Control” regulations (before 2011) and one after the implementation of the regulations (2011 and after 2011). According to Vig (2013) [45], the value of the group dummy variable is determined by the performance of the kernel variable. In this paper, we group all industries using the average energy intensity in the two years prior to the formal implementation of the energy “Dual-Control” regulations, following the classical approach [45,46]. We first calculated the mean value of energy intensity for each industry between 2008 and 2009 and divided the sample into three groups based on this mean value: the highest 1/3, middle 1/3 and lowest 1/3, with the highest 1/3 defined as the treatment group and the lowest 1/3 as the control group.

(3)

Control variables. As there are many possible factors influencing the efficiency of green industrial development, this paper adds industry-level control variables in the model to avoid omitted variable errors as far as possible. The control variables include: opprofit, expressed as the ratio of operating profit to operating revenue; cost, expressed as the ratio of overheads to total industrial output; finanfacilities, expressed as the ratio of interest payments to total liabilities; tax, expressed as the ratio of business taxes and surcharges to total industrial output; scale, expressed as the ratio of average enterprise output size to total industrial output and estructure, expressed as the share of electricity consumption in energy consumption [5,25,26,27,28,29]. Some of the control variables for individual industries are missing and significantly abnormal in individual years and are corrected by linear interpolation as well.

5. Results and Discussion

5.1. Data Source and Descriptive Analysis

Considering the issue of data availability, the sample for this work consists of 30 industrial sectors in Shaoxing from 2000 to 2019. We used the performance of industrial enterprises above the scale to approximate the performance of the whole industry due to the lack of industry-wide caliber data. All of the data used in this study come from the Shaoxing Statistical Yearbook, Zhejiang Statistical Yearbook, and China Statistical Yearbook.

In the section on GTFE measurement, the data on the original value of fixed assets and energy consumption of individual industries are missing and apparently abnormal in individual years, we use linear interpolation to make supplements and corrections. The descriptive statistics for the input and output variables in this section are shown in

Table 1. Results of Descriptive Statistics for Input and Output Variables.

Variable	N	Mean	SD	Min	Max
doutput	570	661,705	1.134 × 106	1804	7.301 × 106
uoutput	570	1.806 × 106	8.864 × 106	1609	1.910 × 108
labor	570	23,249	47,752	135	327,909
capital	570	340,083	571,636	89.36	3.410 × 106
energy	570	750,721	3.813 × 106	783.6	8.400 × 107

.

After calculating the GTFE of different industrial sectors in different periods, we processed the data used in the section on the effect assessment of “Dual-Control” regulations. The results of the descriptive statistics for the explanatory and control variables in this section are shown in

Table 2. Results of Descriptive Statistics for Variables.

Variable	N	Mean	SD	Min	Max
gtfe	570	0.31	0.21	0.01	1.62
opprofit	570	0.05	0.23	−4.55	1.59
cost	570	0.05	0.06	0.00	1.03
finanfacilities	570	0.03	0.01	−0.01	0.10
tax	570	0.01	0.01	0.00	0.07
scale	570	8.42	0.82	6.17	11.06
estructure	570	0.63	0.28	0.00	1.00

. The performance of indicators shows large differences between different industries.

5.2. Green Total Factor Efficiency Performance of Shaoxing’s Industrial Sector

During the sample period, there is great heterogeneity in the green total factor efficiency of Shaoxing’s industrial sectors. We use the software of STATA to map the distribution of green total factor efficiency according to the kernel to visually examine the distribution of green total factor efficiency and convergence conditions.

Figure 1 depicts a kernel density plot of green total factor efficiency between 2000 and 2018 for the treatment group (top 30% energy intensity) and the control group (bottom 30% energy intensity). As seen in the graph, compared to the control group, the waveform of green total factor efficiency in the treatment group is to the left, with a lower crest, wider horizontal width, and longer right trailing, indicating that during the sample period, the green total factor efficiency in the treatment group is significantly lower than that in the control group, and the inter-industry difference of green total factor efficiency in the treatment group was significantly larger than in the control group.

The overall dynamics of green total factor efficiency in the industrial sector in Shaoxing over the sample period is complex. Using the year 2011, the point at which Shaoxing’s “Dual-Control” was formally included in the local government assessment as the dividing line, the kernel density of green total factor efficiency for the industrial sector in the two years before (2009), in the year of policy implementation (2011) and in the two years after (2013) are plotted as follows (Figure 2). It can be found that although the average performance of green total factor efficiency in Shaoxing’s industrial sector increased before the policy implementation, the variability within the industry gradually increased. After the implementation of the policy, the average performance of green total factor efficiency in Shaoxing’s industrial sector remained basically the same, but the variability within the industry appeared to narrow. This finding is different from most existing literature. Most existing studies generally believe that China’s green total factor productivity is increasing year by year [17,22,23,47], but our conclusion points out that the green total factor productivity of industry in Shaoxing is not increasing year by year as a whole but shows that the backward sectors are moving closer to the advanced sectors.

5.3. The Impact of the Energy “Dual-Control” Regulations on the Green Total Factor Efficiency of Shaoxing’s Industrial Sector

5.3.1. Baseline Regression

The paper conducts a baseline regression using the DID model, the results of which are presented in

Table 3. The Regression Results of DID Model.

	(1)	(2)	(3)	(4)	(5)	(6)
	gtfe	gtfe	gtfe	gtfe	gtfe	gtfe
post	−0.0507 **	−0.0507 **	−0.156 ***	−0.0777 ***	−0.0898 ***	−0.0757
	(0.0254)	(0.0254)	(0.0559)	(0.0251)	(0.0250)	(0.0589)
treat	−0.246 ***	0	0	−0.329 ***	0	0
	(0.0601)	(.)	(.)	(0.0582)	(.)	(.)
did	0.0764 **	0.0764 **	0.0764 **	0.117 ***	0.118 ***	0.114 ***
	(0.0359)	(0.0359)	(0.0349)	(0.0335)	(0.0331)	(0.0329)
opprofit				−0.0136	−0.00837	−0.0144
				(0.0325)	(0.0322)	(0.0322)
finanfacilities				−0.104	−0.316	1.460
				(0.676)	(0.671)	(1.008)
cost				−0.0466	0.261	0.300
				(0.185)	(0.199)	(0.204)
scale				0.151 ***	0.195 ***	0.200 ***
				(0.0203)	(0.0235)	(0.0269)
tax				0.584	0.837	−0.580
				(1.788)	(1.784)	(1.834)
estrucutre				0.0431	0.0677	0.177 **
				(0.0433)	(0.0446)	(0.0695)
_cons	0.464 ***	0.341 ***	0.376 ***	−0.794 ***	−1.355 ***	−1.475 ***
	(0.0425)	(0.0116)	(0.0376)	(0.180)	(0.209)	(0.254)
Time effects	NO	NO	YES	NO	NO	YES
Individual effects	NO	Yes	YES	NO	Yes	YES
N	380	380	380	380	380	380
R2		0.014	0.110		0.213	0.269

Standard errors in parentheses. ** p < 0.05, *** p < 0.01.

. Models (1)–(3) use different controls for time and individual effects without considering control variables, while models (4)–(6) use different controls for time and individual effects after incorporating the effects of control variables.

It can be seen that the net effect of “Dual-Control” regulations on the green total factor efficiency of the industrial sector is significantly positive without considering the control variables, and the coefficient estimate is consistent at 0.0764. After controlling for the effects of other possible influencing factors, the net effect remains considerable, and the estimated coefficient climbs to greater than 0.1. The coefficient of the interaction term of policy dummy and group dummy variables are significantly positive in all models. It implies that the energy “Dual-Control” regulations have considerably contributed to the green development of Shaoxing’s industrial sectors. This verifies the preceding analysis. On the one hand, the “Dual-Control” regulations actively regulated enterprise energy consumption and reduced energy consumption and CO₂ emissions per unit of industrial output value. On the other hand, the “Dual-Control” regulations force industrial enterprises to increase green innovation investment and carry out process technology upgrading, which indirectly promotes the improvement of enterprises’ green total factor efficiency. This finding is consistent with the results of most studies that are based on one specific policy [4,5,30]. However, the absolute value of policy impact in this paper is smaller than in some literature [5]. It shows that there is a possibility of a weakening effect between different policies in the policy package of “Dual-Control” regulations.

As for the control variables, the coefficient of scale is significantly positive, suggesting that a large production scale can help organizations acquire additional green development benefits via the scale effects of production, energy conservation, and emission reduction. The positive but unstable impact of the rising proportion of electricity consumption on the industrial sector’s green development implies that there is room for improvement in the industrial sector’s power use efficiency. In the survey of a number of industrial enterprises in Shaoxing, we determined that due to economic concerns, a substantial amount of industrial equipment is still in a state of secondary or even tertiary energy efficiency, and energy waste still exists. After adjusting for both time and individual effects, the coefficient of energy consumption structure is significant while that of the others is not significant. This shows that the energy conservation and emission reduction efforts of enterprises are driven more by external policies than by internal causes at present.

5.3.2. Robustness Test

(1)

Common Trend Test

An important prerequisite for the adoption of the DID model is that the treatment and control groups share similar trend features. In other words, in the absence of policy interventions, the trends in the explained variables should not be systematically different between the treatment group and control group.

In this regard we, referring to Moser and Voena (2012) [48], add interaction terms of group dummy variable and time trend to the baseline regression model, and test the significance of the coefficients of interaction terms before and after policy implementation. The model for the common trend test was set up as follows.

$$y_{it}={\beta }_0+{\beta }_{1}pos{t}_t+{\beta }_{2}trea{t}_i+{\displaystyle \sum _{j=1}^{11}{\gamma }_{j}tim{e}_j}\ast trea{t}_{it}+{\epsilon }_{it}$$

(5)

where: post and treat are the policy dummy variable and group dummy variable which are the same as that in Equation (3); β is the coefficient to be estimated; j represents the jth year and time_j is a time dummy variable. The time_j in the jth year equals 1 and the other years equals 0. For example, time₁ in 2000 equals 1 and the other years equals 0; time₂ in 2001 equals 1 and the other years equals 0 and so on; ε is the residual item, which represents all other influencing factors that are not considered as control variables.

If the treatment group shares a common trend with the control group, the coefficient on the interaction term should be insignificant.

Figure 3 reports the test results of the plateau trend before policy implementation. The point is the coefficient estimation result, and the vertical line is the confidence interval. If the confidence interval contains a value of 0, the coefficient estimation is not statistically significant. Otherwise, the coefficient estimation is statistically significant. As can be seen, the coefficients of the interaction term for both the time dummy variable and the group dummy variable are negative between 2000 and 2010, but not statistically significant. The hypothesis that the treatment group and control group had a consistent trend before the implementation of the “Dual-Control” cannot be rejected and the common trend hypothesis is passed. This result also demonstrates that our group treatment is appropriate and that the use of a DID model for policy effect assessment is reasonable.

(2)

Dosage Effect Test

As all industries should carry out the “Dual-Control”, we have constructed the treatment group and control group based on the performance of the kernel variable: energy intensity, setting the top third of industries as the treatment group and the bottom third of industries as the control group. If the test identification strategy is reasonable and robust, i.e., differences in energy consumption characteristics do lead to significantly different policy effects, then as the gap between the experimental and control groups on the kernel variable widens or narrows, the net effect of the policy should also widen or narrow. Therefore, we conducted a dosage effect test.

We, therefore, changed the identification strategy from the original tertile approach based on the two-year average energy intensity in 2008 and 2009 to the quartile approach. In other words, the former quartile of industries was set as the treatment group, and the latter quartile of industries was set as the control group. The new treatment group and control group were used to conduct DID analysis on the effect of “Dual-Control” regulations. The results are presented in

Table 4. Regression Results of DID Model under Quartile Approach.

	(7)	(8)	(9)	(10)	(11)	(12)
	gtfe	gtfe	gtfe	gtfe	gtfe	gtfe
post	−0.0341	−0.0341	−0.144 **	−0.0681 **	−0.0844 ***	−0.0361
	(0.0319)	(0.0319)	(0.0719)	(0.0317)	(0.0314)	(0.0742)
treat	−0.246 ***	0	0	−0.348 ***	0	0
	(0.0765)	(.)	(.)	(0.0799)	(.)	(.)
did	0.0850 *	0.0850 *	0.0850 *	0.130 ***	0.135 ***	0.124 ***
	(0.0467)	(0.0467)	(0.0453)	(0.0434)	(0.0426)	(0.0420)
opprofit				−0.0182	−0.0153	−0.0139
				(0.0381)	(0.0376)	(0.0370)
finanfacilities				0.0332	−0.247	2.165 *
				(0.887)	(0.876)	(1.270)
cost				−0.0197	0.315	0.425 *
				(0.220)	(0.232)	(0.237)
scale				0.173 ***	0.231 ***	0.245 ***
				(0.0256)	(0.0299)	(0.0343)
tax				0.854	0.768	−1.146
				(2.285)	(2.268)	(2.317)
estrucutre				0.0594	0.0888	0.208 **
				(0.0548)	(0.0560)	(0.0838)
_cons	0.471 ***	0.356 ***	0.392 ***	−0.984 ***	−1.660 ***	−1.878 ***
	(0.0523)	(0.0151)	(0.0486)	(0.228)	(0.265)	(0.322)
Time effects	NO	NO	YES	NO	NO	YES
Individual effects	NO	Yes	YES	NO	Yes	YES
N	285	285	285	285	285	285
R2		0.012	0.132		0.226	0.304

Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

.

Comparing models (1)–(3) in Table 3 and model (7)–(9) in Table 4, it can be found that without considering the impact of control variables, the coefficient of the DID term is significantly positive under both identification strategies, and the coefficient value increases from 0.0764 to 0.085 under the four-quartile approach. Comparing model (4)–(6) in Table 3 and model (10)–(12) in Table 4, it can be found that, considering the impact of control variables, the coefficient of the DID term is also significantly positive under both identification strategies, and the coefficient value under the four-quartile approach also increased to different degrees compared to the trisection method. This is consistent with the conclusion above. The net effect of the policy widens with the gap between the experimental and control groups widening. It is proved that our method is reasonable and our results are reliable.

(3)

The Placebo Test

The DID model can to some extent be affected by omitted variables, random factors, etc. To further validate the robustness and reliability of the results, we took reference from Cantoni et al. (2017) [49] and adopted a randomly generated experimental group for the placebo test.

Specifically, we randomly selected 10 of the 30 industries to form the treatment group, repeated the regression 500 times according to model (3) in Table 3, counted the t-value statistics of the “Dual-Control” reform (i.e., double difference term) in 500 regressions, and plotted the kernel density of the t-values. If the estimated coefficients of the DID term are distributed around zero under the randomization treatment, this indicates that the model is not designed to miss sufficiently important influences, i.e., the effects in the baseline analysis are indeed the result of the policy focus in this paper. The kernel density plot of Figure 4 demonstrates that the coefficients of the DID term for the randomly generated treatment group are dispersed around 0, indicating that there are no significant model setting issues and that the major findings are robust.

5.4. Further Analysis

We further quantified the impact of the energy “Dual-Control” regulations on the industrial sector’s profit performance. Using the ratio of total industry profit to total industry output, the net effect of the energy “Dual-Control” regulations on the profit performance of the industrial sector in Shaoxing was estimated. The results are shown in

Table 5. Impact of the “Dual-Control” regulations on Industry Profits.

	(13)	(14)	(15)	(16)	(17)	(18)
	Profit	Profit	Profit	Profit	Profit	Profit
t	−0.00621	−0.00621	−0.0119	−0.00395		−0.0491 ***
	(0.00708)	(0.00708)	(0.0161)	(0.00671)		(0.0149)
treat	−0.00535	0	0	−0.00789	0	0
	(0.0147)	(.)	(.)	(0.00790)	(.)	(.)
did	−0.0287 ***	−0.0287 ***	−0.0287 ***	−0.0143	−0.0190 ***	−0.00693
	(0.0100)	(0.0100)	(0.0100)	(0.00912)	(0.00632)	(0.00833)
opprofit				0.0370 ***	0.0271 ***	0.0246 ***
				(0.00867)	(0.00823)	(0.00814)
finanfacilities				−1.010 ***	−1.082 ***	−2.001 ***
				(0.180)	(0.170)	(0.255)
cost				−0.335 ***	−0.276 ***	−0.289 ***
				(0.0419)	(0.0506)	(0.0516)
scale				0.00420	0.0230 ***	0.0183 ***
				(0.00365)	(0.00592)	(0.00680)
tax				0.756	0.609	0.742
				(0.467)	(0.456)	(0.464)
estructure				−0.0164	−0.0110	−0.0227
				(0.0103)	(0.0107)	(0.0176)
_cons	0.0590 ***	0.0563 ***	0.0544 ***	0.0717 **	−0.0950 *	−0.0217
	(0.0104)	(0.00325)	(0.0108)	(0.0328)	(0.0528)	(0.0643)
Time effects	NO	NO	YES	NO	NO	YES
Individual effects	NO	Yes	YES	NO	Yes	YES
N	380	380	380	380	380	380
R2		0.066	0.107		0.371	0.430
adj. R2		0.011	0.007		0.325	0.355

Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

.

As can be seen from Table 5, the net impact of the energy “Dual-Control” regulations on industrial profits is estimated to be negative in all models, and the coefficients are significantly non-zero in all models except for models (16) and (18). This suggests that the energy “Dual-Control” regulations have indeed caused some erosion of profits in the industrial sector in Shaoxing.

In addition, the dynamic effects of the energy “Dual-Control” regulations on industrial profits are calculated using a year dummy (with a value of 1 for the current year and 0 for the other years) and an interaction term of group dummy variables. The results are presented in

Table 6. Analysis of the Dynamic Effect of the “Dual-Control” of Energy on Industry Profits.

	(19)	(20)	(21)
	Profit	Profit	Profit
2011	−0.00249	−0.00249	−0.00501
	(0.0158)	(0.0158)	(0.0225)
2012	0.0108	0.0108	0.00864
	(0.0158)	(0.0158)	(0.0225)
2013	−0.0134	−0.0134	−0.0101
	(0.0158)	(0.0158)	(0.0225)
2014	−0.0162	−0.0162	−0.0127
	(0.0158)	(0.0158)	(0.0225)
2015	−0.0155	−0.0155	−0.00822
	(0.0158)	(0.0158)	(0.0225)
2016	−0.0655 ***	−0.0655 ***	−0.0572 **
	(0.0158)	(0.0158)	(0.0225)
2017	−0.0540 ***	−0.0540 ***	−0.0462 **
	(0.0158)	(0.0158)	(0.0225)
2018	−0.0562 ***	−0.0562 ***	−0.0546 **
	(0.0158)	(0.0158)	(0.0225)
			(0.0153)
Time effects	NO	NO	YES
Individual effects	NO	Yes	YES
N	380	380	380
R2		0.099	0.125

Standard errors in parentheses. ** p < 0.05, *** p < 0.01.

.

From Table 6, it is found that, after applying various adjustments for individual and temporal effects and integrating the estimated performance of the coefficients of the cross-products for different years in all models, the impact of the energy “Dual-Control” regulations on industrial profitability has considerable lags. Four years following the policy’s adoption, the energy “Dual-Control” regulations have had a consistent and negative influence on industrial sector profitability. This indicates that following relatively easy access to energy conservations (such as management savings and relatively inexpensive equipment replacement), the need to conserve energy under the “Dual-Control” regulations has created a “cost of compliance” issue for the green development of organizations.

6. Conclusions and Suggestions

This paper adopts the super SBM-DEA model to study the change in green total factor efficiency in Shaoxing’s industries and analyze the impact of “Dual-Control” regulations on the green development of Shaoxing’s industries based on DID model. The main conclusions are as follows: First, there is room for further improvement of the green total factor efficiency in Shaoxing’s industrial sectors. The average green total factor efficiency performance of Shaoxing’s industry during 2000–2018 was only 0.31. This is consistent with the findings of most relevant studies on China [18,19,20]. Secondly, there is a significant difference between the green total factor efficiency of energy-intensive industries and non-energy-intensive industries in Shaoxing. During the sample period, the average green total factor efficiency performance of the non-energy-intensive industries was about 0.44, while that of the energy-intensive industries was only 0.23. From the perspective within, the difference in green total factor efficiency between different industries inside the energy-intensive industry is larger than that between different industries inside the non-energy-intensive industry. Third, the system of “Dual-Control” regulations has a significant and positive impact on the green total factor efficiency in Shaoxing’s industry. After the implementation of the mechanism, the green total factor efficiency of the industry has increased by around 0.1 on average. This is mostly because of that the backward sectors are moving closer to the advanced sectors. The conclusion extends and renews the relative fields [4,5,30,31]. Fourthly, the implementation of the energy “Dual-Control” regulations has a negative impact on the economic profits of Shaoxing’s industries, especially in the energy-intensive industries. Moreover, this negative impact shows hysteresis and continuity. It shows that the energy conservation costs that industrial enterprises need to pay will increase with the tightening of the “Dual-Control” regulations and the improvement of the difficulty of energy conservation. These costs, on the one hand, directly weaken their current profits, and on the other hand, may affect the enterprises’ investment, which is detrimental to their long-term profit performance. Unlike most studies that only focus on the effect of energy conservation and emission reduction [4,5,30,31,34,47], our conclusion emphasizes the economic cost of regulation policies. This reminds countries all over the world that are currently facing difficulties in economic development to be more cautious about climate policymaking.

The results of our investigation have substantial significance for deepening the green development of industries in developing countries such as China, which can be summarized as follows. (1) Although green development is the consensus of more and more enterprises, the pursuit of maximum economic profit is still one of the primary goals for industrial enterprises at the present stage. The cost of energy conservation will eat into industrial enterprises’ economic profit, which will dampen the enterprises’ enthusiasm for green transformation. The process of green development requires more participation from the market and the government. The regulation policies should be well coordinated to ensure maximizing of the whole effect. (2) The formulation of energy policies should be tailored to local conditions and take full account of the heterogeneity among industries. At present, in some places, there is no obvious difference in the requirements for different industries when breaking down energy conservation targets or formulating energy conservation standards and policies. In practice, due to the inherent production process characteristics, energy-intensive industries are more likely to become the targets of governments at all levels when taking actions for energy conservation and emission reduction. It does not, however, imply that energy-intensive industries should be eliminated. The non-energy-intensive industry has a relatively small share of energy consumption and higher energy intensity, as a consequence, the government’s guidance and stimulation for energy conservation are relatively insufficient. The difference in the government’s attention and the convergence of standard policies lead to an inefficient green transformation. A major challenge in promoting future green transformation is to introduce targeted and appropriate policies. (3) The regulations of resources and the environment are bound to be accompanied by corresponding economic costs. Countries around the world are currently witnessing a slowing down in economic growth, and the continued impact of COVID-19, the increasing uncertainty in the international trade environment, and the intensifying survival pressure on industrial enterprises. It is the primary task for governments to formulate appropriate policies that not only promote energy conservation, emission reduction, and green development transformation but, addtionally, ensure the stable growth of local economies. The government must take great caution in the choice of policy instruments and the construction of the policy package. The policy package should be conducive to advancing the dual carbon targets and providing enterprises with financing, tax, and other support, so as to minimize the cost pressure brought by resource and environmental regulations.

Due to the unavailability of data, this paper has some shortcomings. Firstly, there may be some missing control variables in the model of analyzing the effect of the policy package. Secondly, the heterogeneity of policy effects among industries is not fully considered. In our opinion, the research can be further supplemented and improved through sample selection (such as changing from industry samples to enterprise samples), data mining and other methods.