Do the Chinese Government’s Efforts to Make a Low-Carbon Industrial Transition Hinder or Promote the Economic Development? Evidence from Low-Carbon Industrial Parks Pilot Policy

Abstract

Under the background of “peak carbon dioxide emissions” and “carbon neutrality” strategy, it is urgent to explore whether China’s great efforts to continuously promote industrial low-carbon transition can promote high-quality economic development. Taking the implementation of low-carbon industrial parks pilot policy (LIPPP) as a “quasi-natural experiment”, this paper tries to answer this question. The results show that the LIPPP doesn’t significantly promote the sustainable-oriented high-quality development (SHD) of the economy, but mainly boosts the value-oriented high-quality development (VHD), which is characterized by being obviously biased towards the technological progress. The policy effect is more obvious in the central region and cities with a relatively poor natural resource endowment. The mechanism analysis shows that the LIPPP promotes the VHD of the economy through the innovation incentive effect and the capital deepening effect. The technological progress related to the VHD promoted by the LIPPP has a crowding out effect on that of the SHD. In addition, regional innovation, capital deepening, and energy transformation all play a certain role in promoting the SHD, but which is overshadowed by the effect of the VHD promoted by the LIPPP. This paper provides policy implications for China to promote high-quality economic development in the process of low-carbon transition.

1. Introduction

The quality of economic growth is the need for a higher level of social and economic growth after the economic development reaches a certain stage [1]. After more than 40 years of sustained and rapid growth, China’s economy has achieved leapfrog development, but it has also paid a heavy price in resources and the environment. China’s extensive and factor-driven development mode in the past is unsustainable, and it is necessary to shift to a high-quality model. However, as environmental problems become more serious, the meaning of high-quality economic development is also changing. Economic development should not only focus on the improvement of production efficiency and value creation, but also take into account the input of resources and the emission of pollution. This means that social production should take full account of the impact of environment and climate on the basis of striving to improve total factor productivity, that is to say, on the basis of pursuing VHD, we should try our best to realize the transition to SHD.

China is in the late stage of industrialization. The double pressure of economic growth and environmental governance makes it impossible for China to completely copy the industrialization process of western developed countries. The low-carbon transition of economic development has become a national strategy in China. However, steady economic growth is crucial for China at a critical stage of development and is the basis for stable development of the whole society. The transition to a low-carbon economy will come at a cost, and there is uncertainty about whether it will hinder or promote high-quality development of the Chinese economy. As the world’s second largest economy and largest carbon emitter, China has put forward the strategic goal of achieving peak carbon dioxide emissions by 2030 and carbon neutrality by 2060, which will play a vital role in the global joint response to climate change [2]. As a major source of carbon emissions, industry will inevitably become the focus of low-carbon transition. The LIPPP is an important attempt by the Chinese government to promote the low-carbon transformation of the industry across the country with key industrial parks as the carrier. The implementation of the pilot policy provides a unique “quasi-natural experiment” to assess the economic, environmental and social impacts of the low-carbon transition. Therefore, can the large-scale national pilot policies of industrial low-carbon transformation promoted by the Chinese government boost high-quality economic development? Which type of high-quality development is the pilot policy promoting? What is the mechanism of influence? The answers to these questions are critical to the sustainable development of China’s economy. However, existing research does not answer this question. We hope that the answers to these questions will provide valuable references for the high-quality development of China’s economy.

Based on the panel data of 284 prefecture-level cities in China from 2010 to 2017, this paper empirically studies the impact of the LIPPP on the high-quality development of urban economy in China by using a multi-period differences-in-differences model. In order to more clearly identify the impact of LIPPP on China’s high-quality economic development, this paper not only decomposed the indicators of high-quality economic development into technical progress and technical efficiency, but also investigated the impact of pilot policy on VHD and SHD, respectively. The main contributions of this paper are as follows: (1) The impact of the LIPPP on China’s high-quality economic development is investigated for the first time. (2) Based on the two dimensions of the VHD and the SHD, this paper makes a comprehensive study of the policy effects of the LIPPP, and performs heterogeneity analysis from the perspectives of regional characteristics and natural resource endowment, so as to present the specific effects of the pilot policies more clearly. (3) Based on the endogenous growth theory, the paper explores the internal mechanism of the LIPPP to promote high-quality development transformation. (4) The study in this paper theoretically expands the research on the impact of environmental regulation on sustainable economic development and provides policy enlightenment for China’s high-quality economic development.

The structure of the remaining parts is as follows: the second part is a literature review and mechanism analysis; the third part is the policy background; the fourth part is the research design; the fifth part is empirical analysis; and the sixth part is the research conclusion and policy implication.

2. Literature Review and Mechanism Analysis

2.1. Literature Review

The study of this paper mainly examines the impact of industrial low-carbon transition on high-quality economic development and takes the LIPPP as the research entry point. The literature related to the research topic of this paper mainly includes the following two aspects.

2.1.1. Research Progress on the Impact of Environmental Regulation on High-Quality Economic Development

There is abundant research on the impact of environmental regulation on the quality of economic development. However, some scholars believe that environmental regulation will force enterprises to internalize the pollution externality cost, increase the production cost of enterprises, and make enterprises have to change the optimal production decision, which will adversely affect the innovation ability, total factor productivity, and market competitiveness of enterprises [3,4,5,6,7,8,9,10]. However, some scholars, mainly represented by Porter, believe that the implementation of environmental regulations will promote the quality of economic development. Strict and appropriate environmental regulation can improve enterprises’ enthusiasm for technological innovation, thus generating an innovation incentive effect and promoting the improvement of enterprises’ total factor productivity. The innovation compensation effect brought by technological innovation can offset the cost effect generated by environmental regulation, which is conducive to high-quality economic development [11,12]. Subsequently, many scholars proved Porter’s conclusion empirically from different perspectives [13,14,15,16]. It can be seen that existing studies hold completely opposite views on the impact of environmental regulations on the quality of economic growth, and the impact of environmental regulations will show uncertainty in different environments and conditions.

2.1.2. Research on Low-Carbon Transformation of Industrial Parks

As an important part of China’s economy, industrial parks are also an important source of carbon emissions. The quality of the development of industrial parks will largely reflect the high-quality development level of a city. At present, there is some research on low-carbon transformation of industrial parks. Wang et al., took Suzhou Industrial Park as a case study and used scenario analysis to describe the emission trajectory under three different approaches. Under the low-carbon scenario, carbon intensity would fall by 38%, but it would still be difficult to meet the national emission reduction target [17]. Fang et al., established an embodied carbon accounting framework based on emergy analysis to identify the input-output structure and embodied carbon emission flow of industrial parks [18]. Zhang et al., took a national iron and steel industrial park in China as an example, and further conducted technical assessment on carbon emission reduction potential and unit emission reduction cost by Stochastic Frontier Analysis method (SFA) [19]. Xing et al., takes industrial parks as the main users of natural gas distributed energy and tries to optimize the design of distributed energy system and natural gas distribution in multiple industrial parks under the condition of natural gas shortage [20]. Existing studies on industrial parks mainly focus on the case analysis of specific parks or a certain type of park, but lack the overall analysis and macro research on the economic impact of industrial parks.

The LIPPP is a government-led incentive policy for institutional innovation and economic transformation. It aims to take high-quality development of industrial parks as the entry point to promote the transformation of Chinese cities and even the overall economy to low-carbon and high-quality development. At present, there is little research on the LIPPP, which mainly focuses on the study of performance assessing methods of pilot parks. Zhou took Huangjinshan low-carbon industrial park in Hubei Province as the research object, adopted the analytic hierarchy process (AHP), Delphi method, and other methods to build the evaluation index system of a low-carbon industrial park, and analyzed the problems generated in practical application [21]. Considering individual differences of pilot low-carbon industrial parks, Yu first conducted a classified study on 51 low-carbon industrial parks and pointed out that different types of parks should have different paths and emphases of low-carbon development [22]. Later, Yu et al., further applied the cluster analysis method to classify the data of 51 pilot low-carbon industrial parks, and then selected representative pilot parks for case analysis. The research results show that 3H (high carbon content, high coal consumption, high energy dependence) industrial enterprises should pay more attention to the upgrading of traditional heavy industry, and technological innovation and strengthening carbon management are effective ways to achieve the low-carbon development of 3L (low carbon content, low coal consumption, low energy dependence) industrial enterprises [23]. Through the literature review, it was found that there is a lack of research on the economic impact of the LIPPP. Existing studies mainly focus on qualitative analysis, and there is a lack of empirical research on the role of pilot policies in economic high-quality development and transformation.

Through the review of relevant literature, it can be found that scholars have inconsistent views on whether environmental regulation can promote high-quality economic development. Further, the research on LIPPP mainly focuses on qualitative analysis and lacks empirical research. Low-carbon transformation of industrial parks is an important means to achieve high-quality economic development, but unfortunately, there is still a lack of research on the impact of LIPPP on high-quality economic development.

2.2. Mechanism Analysis

For China in the late stage of industrialization, the quality of industrial development largely determines the overall development level of a city. At the same time, industry is the major source of carbon emissions in China, and its green and high-quality development is crucial for China’s economy to achieve green and sustainable development. Under the current institutional arrangement and technological level, low-carbon development and emission reduction transformation have obvious public attributes for enterprises and are still in the field of market failure. Therefore, China’s transition to high-quality development must be led by the government. According to Marshall’s industrial spatial agglomeration theory, enterprises in the agglomeration area will achieve efficient economic development within the region through technology spillover effect, sharing effect and competition effect [24]. The LIPPP aims to make use of the industrial agglomeration effect of industrial parks and promote the green and low-carbon transformation of industrial parks by providing certain subsidies to pilot parks and formulating relatively strict environmental regulations and emission assessment. However, the development condition of China’s industrial parks is not optimistic. No matter in terms of management, technology and policies, it lacks a complete low-carbon incentive system [21]. As a large-scale exploration of low-carbon transformation in the industrial field nationwide, the supervision and assessment mechanism of the LIPPP is still in the stage of continuous testing and improvement. If there are loopholes in the assessment mechanism, enterprises meet the assessment due to profit-seeking motives, or relevant regulatory authorities fail to perform their supervisory duties effectively, enterprises in the pilot park are likely to only develop their own productivity level and promote the rapid accumulation of capital under the guidance of seeking subsidies and profits, and do not pay much attention to the green and sustainable development of enterprises. The implementation effect of the pilot policy may be contrary to the original intention of the policy. In addition, due to the high cost of enterprise transformation, it is difficult for enterprises to realize a significant transformation of production mode through only one policy attempt. Therefore, the enterprises in the pilot low-carbon industrial parks may only promote the VHD in the area under the profit-seeking motive and cost pressure, while the promotion effect on the SHD is weak. In view of this, this paper proposes:

Hypothesis 1 (H1).

The LIPPP promotes high-quality economic development through policy incentives, but the pilot policy mainly promotes the VHD of cities and have a limited promoting effect on the transformation of cities to SHD.

The shift from factor-driven growth to innovation-driven growth is the core of high-quality development. The innovation vitality shown by all kinds of innovation elements of economic society as a whole determines the quality of economic development. The LIPPP will generate the innovation incentive effect to promote the technological progress of enterprises through a certain degree of subsidies and assessment, and then promote the high-quality development of the economy. However, due to the late start of China’s market economic reform, many industries are at a lower position in the global value chain, and there is still a considerable gap between China’s innovation capacity and that of developed countries. China’s overall innovation is characterized by focusing on quantity rather than quality, which is one of the important reasons why China’s overall industry is large but not strong [25]. In addition, innovation that takes into account green development has higher requirements for R&D investment and cost control than innovation in the traditional sense. Therefore, this paper proposes:

Hypothesis 2 (H2).

The LIPPP can significantly promote the improvement of the comprehensive innovation level of the city and will improve the VHD through the incentive effect of the innovation.

According to the endogenous growth theory, technological progress is determined by the endogenous economic system [26]. However, with the expansion of enterprise scale and the accumulation of capital, enterprises have the motivation to improve the organic composition of capital in order to achieve the improvement of production efficiency and produce more surplus value. Sufficient labor supply will improve the quality of human capital and job matching degree of enterprises, thus improving the production efficiency of enterprises [27,28]. In the stage of industrialization development, industry is the foundation and important component of a city’s economic development. As a city’s industrial enterprise agglomeration area, industrial park’s capital deepening level has an important influence on the economic development of the whole city. However, the deepening of capital will bring about the crowding out effect of employment, coupled with the downward pressure of macro economy after the 2008 financial crisis, it is difficult for the pilot to exert the promoting effect of employment growth. Therefore, this paper proposes:

Hypothesis 3 (H3).

The LIPPP will promote high-quality economic development through the capital deepening effect, while the mechanism of employment driving effect is difficult to generate.

The transformation of high-quality economic development mode must be accompanied by the adjustment of energy structure, and the energy consumption of economic development will be transformed to clean and low carbon. The transformation of the energy structure to an efficient and clean direction will also react to the transformation and upgrading of the industrial structure and promote the sustainable and high-quality development of the economy. The enterprises in the low-carbon industrial park are relatively advanced in the way of energy use and have the economic and technical conditions to improve the mode of energy use. The pilot policy may promote the transformation of the energy structure of the city. However, due to the restriction of resource endowment structure and the urgent demand of economic growth, China’s long-term dependence on coal power is difficult to change in the short term, which is one of the important reasons for China’s high carbon emissions. In addition, the adjustment of energy structure is a complicated process, and it is difficult to have a significant impact on the quality of economic development through only one policy incentive. Therefore, this paper proposes:

Hypothesis 4 (H4).

The LIPPP could promote the transformation of regional energy structure, but it is difficult for pilot policy to promote high-quality economic development through the boost effect of energy transformation.

3. Policy Background

Due to the public nature of environmental governance, only relying on the spontaneous regulation of market economy is prone to market failure, which requires the government’s macro-policy incentive and guidance. The construction of industrial parks plays an important role in the rapid development of China’s industrialization and is an important cornerstone for the rapid development of China’s economy. At the same time, industrial parks are also an important source of carbon emissions. Guiding industrial parks to transform to a green and low-carbon development model is bound to become an important breakthrough in carbon emission and pollution control. The LIPPP led by the National Development and Reform Commission (NDRC) and the Ministry of Industry and Information Technology (MIIT) is not only a useful attempt and an important approach in the process of low-carbon transformation of China’s economy, but also an important means to promote enterprise innovation and enhance industrial competitiveness. In 2013, a total of 55 industrial parks were nominated for the pilot program. Subsequently, the NDRC and the MIIT approved 51 industrial parks as pilot low-carbon industrial parks in two batches in 2014 and 2015. The pilot parks are distributed in most provinces and key cities in mainland China (see Figure 1), and the policy effect observation period is 2–3 years. The test of the policy effect of the pilot low-carbon industrial parks in the process of urban high-quality development and transformation in China is helpful to clarify the successful experience and shortcomings of the government’s macro policies in promoting high-quality economic development and transformation, which is crucial for China to further promote high-quality economic development and transformation.

4. Study Design

4.1. Model Setting

In this paper, the multi-period differences-in-differences method is used to establish the econometric regression model (1) to empirically test the impact of the LIPPP on high-quality economic development.

$${\mathrm{Y}}_{\mathrm{it}}{=\mathsf{\beta }}_0{+\mathsf{\beta }}_1{\mathrm{DID}+\mathsf{\theta }\mathbf{Control}+\mathsf{\delta }}_{\mathrm{i}}{+\mathsf{\gamma }}_{\mathrm{t}}{+\mathsf{\epsilon }}_{\mathrm{it}}$$

(1)

Y_it represents the high-quality development of i city in year t; β₀ is a constant term; DID is the dummy variable, that is, when the sample city is the city where the pilot low-carbon industrial park is located and is in the year or years after the implementation of the policy, if yes, the value is 1. β₁ is the coefficient of DID. Control represents a column vector composed of a series of control variables, with specific elements including the level of foreign investment, the level of urban economic development, industrial structure, infrastructure level and human capital quality. θ represents the coefficient row vector formed by the coefficients of each control variable; δ_i represents the individual fixed effect, γ_t represents the time fixed effect, and ε_it represents the random error term.

4.2. Indicator Measurement and Data Description

4.2.1. Explained Variable: High-Quality Economic Development

The steady improvement of productivity is the primary goal of China’s economic development. TFP is the most widely used intuitive index to evaluate the level of sustainable development of social productivity. In this paper, TFP and GTFP indexes are selected as proxy variables for the VHD and the SHD, respectively. The Data Envelopment Analysis (DEA) method can consider both agreed and unagreed outputs, and it does not need to set the function form and dimensionless treatment of variables, so it has been widely used in the measurement of GTFP. The sustainability of economic growth is an important connotation of high-quality economic development. Therefore, it is more appropriate to choose the growth rate of TFP and GTFP as quantitative indicators to measure the high-quality development transformation. In this paper, Lin and Li ‘s methods are used for reference, and the Malmquist–Luenberger(ML) index of non-radial super-efficiency DEA method is used to measure the growth rate of TFP and GTFP [29,30], which are, respectively the proxy variables of the VHD and the SHD. This paper further decomposed the measured results into two aspects: technical progress and technical efficiency. The subdivided measurement indicators are as follows, and the value data of each year are uniformly reduced to the price level of 2010.

• Capital stock measurement: Based on the capital stock measurement results of Ke and Xiang, annual capital stock data of prefecture-level cities in the sample period are calculated by using the sustainable inventory method [31,32]. Given that the implementation cycle of fixed asset investment is usually longer than one year, this paper defines fixed asset investment as the average value of fixed asset investment in the current year, the previous year, and the second year.
• Labor input: It is expressed by the number of employees at the end of the year in units of each city district.
• Energy input: Electric energy is the main method of energy use in cities and industrial parks, and China’s long-term dependence on coal power determines that the consumption of electric energy in China has a relatively stable relationship with carbon emissions. Therefore, electric power is a representative energy input when evaluating the promoting effect of the LIPPP on the green and high-quality development of Chinese cities. This paper uses the annual electricity consumption of each city as the energy input.
• Desirable output: The annual GDP value of each city.
• Undesired output: The annual discharge of industrial wastewater, industrial sulfur dioxide, and industrial smoke and dust in the city.

The measure of the explained variable is as follows:

We treat each city in China as a production decision making unit (DMU) to construct the optimal production technology boundary. Suppose that each city uses N inputs to obtain D desired outputs and I non-desired outputs. The production process can be expressed as:

$$p^t\left(x^t\right)=\left\{\left(x^t,y^t,b^t\right)|:x^t\ge X\lambda ,y^t\le Y\lambda ,b^t\ge B\lambda ,\lambda \ge 0\right\}$$

(2)

The SBM model containing the undesired output can be expressed as:

$$min{\rho }_k=\frac{1-\frac{1}{N}{{\displaystyle \sum }}_{n=1}^M\frac{s_n^{x-}}{x_{nk}}}{1+\frac{1}{D+I}\left({{\displaystyle \sum }}_{d=1}^D\frac{y_d}{y_{dk}}+\frac{b_i}{b_{ik}}\right)}$$

(3)

$$s.t.X\lambda +s^{x-}=x_k$$

$$Y\lambda -s^{y+}=y_k$$

$$B\lambda +s^{b-}=b_k$$

$$\lambda \ge 0,s^{x-},s^{y+},s^{b-}\ge 0$$

where, s^x−, s^y+ and s^b−, respectively, represent the relaxation values of factor input, expected output and non-expected output. x_nk, y_dk and b_ik, respectively, represent the n-th input, the d-th expected output and the i-th non-expected output of the k-th DMU. ρ_k is the variable between 0 and 1 representing the environmental efficiency of the k-th DMU, and when it is less than 1, it means that the k-th DMU is low efficiency.

Assume that ${\rho }_k^t$(x^t+1, y^t+1, b^t+1) and ${\rho }_k^{t+1}$(x^t+1, y^t+1, b^t+1) are the efficiency values of the k-th DMU during the period t to t + 1. The ML index of SHP is:

$$SH{P}_k^{t,t+1}={\left[\frac{{\rho }_k^t\left(x^{t+1},y^{t+1},b^{t+1}\right)}{{\rho }_k^t\left(x^t,y^t,b^t\right)}\times \frac{{\rho }_k^{t+1}\left(x^{t+1},y^{t+1},b^{t+1}\right)}{{\rho }_k^{t+1}\left(x^t,y^t,b^t\right)}\right]}^{\frac{1}{2}}$$

(4)

When $SH{P}_k^{t,t+1}>1$, it means that SHP increases from t period to t + 1 period. When $SH{P}_k^{t,t+1}<1$, it means that SHP decreases from t to t + 1. According to the decomposition method of ML index, the SHP growth rate is decomposed into SHP-P and SHP-E.

$$\begin{gathered} SH{P}_k^{t,t+1}=SHP-P_k^{t,t+1}\times SHP-E_k^{t,t+1} \\ ={\left[\frac{{\rho }_k^t\left(x^t,y^t,b^t\right)}{{\rho }_k^{t+1}\left(x^t,y^t,b^t\right)}\times \frac{{\rho }_k^t\left(x^{t+1},y^{t+1},b^{t+1}\right)}{{\rho }_k^{t+1}\left(x^{t+1},y^{t+1},b^{t+1}\right)}\right]}^{\frac{1}{2}}\times \frac{{\rho }_k^{t+1}\left(x^{t+1},y^{t+1},b^{t+1}\right)}{{\rho }_k^t\left(x^t,y^t,b^t\right)} \end{gathered}$$

(5)

$SHP-P_k^{t,t+1}$ and $SHP-E_k^{t,t+1}$, respectively, represent the technological progress and technical efficiency change of the k-th DMU during the period t to t + 1. The ML measure of VHP, VHP-P and VHP-E does not consider the undesired output and energy input, and the calculation process is consistent with the above process. SHP-P and SHP-E represent the two internal factors of SHP growth, and they, respectively, represent the change of technical progress and the change of technical efficiency of SHP growth. VHP-P and VHP-E represent the two internal factors of VHP growth, and they represent the change of technical progress and technical efficiency of VHP growth, respectively. We have included the calculation process of these indicators in the paper.

4.2.2. Core Explanatory Variable

The LIPPP includes 51 industrial parks, distributed in 47 cities. Due to the availability of data, Golmud, Yixing, and Yanji are excluded in this paper. The implementation time of the pilot policy for each park is determined according to the approval years of the two batches of pilot parks, namely, the first batch is 2014, and the second batch is 2015.

4.2.3. Other Control Variables

In order to make the estimation of policy effects clearer, this paper controls for other characteristics inherent to each city itself and which may affect the quality of urban development. Specifically, the city’s own economic development advantages, economic operating environment, and human resources endowment will all have an impact on the high-quality development of the city. Considering the influence of these three factors, this paper introduces the corresponding control variables of each city, including the level of foreign investment, the level of economic development, industrial structure, infrastructure level and human capital quality.

• Level of foreign investment (FDI), expressed as the annual foreign direct investment amount of each prefecture-level city.
• Economic development level (edlevel), expressed as per capita GDP of each prefecture-level city.
• Industrial structure (industruc), expressed as the proportion of tertiary industry added value in GDP of prefecture-level cities.
• Infrastructure level (infrust), expressed as the per capita urban road area of each prefecture-level city.
• Human Capital Quality (qhc), expressed as the number of students in colleges and universities in each prefecture-level city.

4.2.4. Data Description

The study period of pilot policies involved in this paper is 2–3 years. In order to avoid the impact of the 2008 financial crisis and other relevant policies on the empirical analysis of this paper, the panel data of 284 prefecture-level cities in China from 2010 to 2017 is selected as the research sample. The data involved in this paper are from the China Urban Statistical Yearbook, Wind Database, and China Urban Construction Database. In this paper, samples of prefecture-level cities that were reduced to county-level cities during the sample period and some samples of prefecture-level cities that are seriously missing in data are excluded. The Variance Inflation Factor (VIF) of all variables is far less than 10, indicating that there is no significant multicollinearity problem between control variables. The statistical description information is shown in

Table 1. Statistical description of variables.

Names of Variables	Variables	Observations	Means	Std	Minimum	Maximum
Value-oriented high-quality development	VHP	1988	0.9459	0.1014	0.2260	2.4090
Technological progress related to the VHP	VHP-P	1988	0.9043	0.0916	0.6090	1.0940
Technical efficiency related to the VHP	VHP-E	1988	1.0558	0.1550	0.3040	2.7370
Sustainability-oriented high-quality development	SHP	1988	1.0453	0.2849	0.2005	4.7132
Technological progress related to the SHP	SHP-P	1988	1.0217	0.2374	0.0361	3.7084
Technical efficiency related to the SHP	SHP-E	1988	1.0607	0.3382	0.2039	5.7079
Level of foreign investment	FDI	1988	9.3197	22.0912	0.0002	308.2563
Economic development level	edlevel	1988	4.9451	3.3263	0.6457	46.7749
Industrial structure	industruc	1988	44.8447	11.3970	10.1500	80.5600
Infrastructure level	infrust	1988	16.6507	7.0663	2.2500	60.0700
Human Capital Quality	qhc	1988	9.1828	16.3198	0.0000	106.7335

.

5. Empirical Analysis

5.1. Base Regression Results

The empirical results in

Table 2. Results of baseline regression.

Variables	(1) VHP	(2) VHP	(3) VHP-P	(4) VHP-P	(5) VHP-E	(6) VHP-E
DID	0.04331 *** (0.01228)	0.02793 *** (0.00963)	0.01635 *** (0.00597)	0.01668 ** (0.00763)	0.03499 ** (0.01392)	0.01357 (0.01845)
R-squared	0.1892	0.0825	0.0829	0.8564	0.1861	0.4455
Variables	(7) SHP	(8) SHP	(9) SHP-P	(10) SHP-P	(11) SHP-E	(12) SHP-E
DID	0.06691 ** (0.02956)	0.01438 (0.04000)	0.02306 (0.02076)	−0.03735 (0.02732)	0.04497 (0.03309)	0.05080 (0.04089)
Control	YES	YES	YES	YES	YES	YES
λi		YES		YES		YES
λt		YES		YES		YES
R-squared	0.0446	0.1403	0.1436	0.3789	0.0125	0.1374
Observations	1988	1988	1988	1988	1988	1988

Note: The regression coefficients are shown in the table, with the corresponding robust standard errors in parentheses. ** Regression coefficients significant at the 5% level. *** Regression coefficients significant at the 1% level.

show that the regression results in column (1) are significantly positive when the individual effects and time effects of cities are not considered. However, when the time and individual fixed effects are considered, the regression results of the VHD in column (2) are significantly positive, but the regression results of the SHD are not significant. This indicates that the enterprises in the pilot parks show the VHD and the SHD in terms of individual characteristics and time trend, but the effect of the pilot policy is mainly manifested in the VHD, which has no obvious effect on the SHD of the city. Hypothesis 1 has been verified. The regression results in columns (3)~(6) of Table 2 show that the effect of pilot policy is mainly reflected in the improvement of technical progress related to the VHD of cities, but has no obvious effect on the improvement of technical efficiency. This may be because the enterprises in the pilot park have already had a certain scale of development before the implementation of the policy, the infrastructure in the park has been relatively complete, and the flow of factor resources is relatively smooth. As a result, the scale effect and resource allocation effect caused by industrial agglomeration will not be significantly improved under the incentive of the pilot policy. With government subsidies and performance assessment, enterprises in the pilot park are more likely to increase R&D input due to the reduction of R&D costs, so as to further improve their own technological level of production. Firms will strive to produce more innovation with the incentive of subsidies and impunity for passing tests.

5.2. Parallel Trend Test

When using the difference-in-difference method to evaluate the policy effect, satisfying the parallel trend is a prerequisite for obtaining effective empirical regression results. In order to obtain rigorous and objective test results, this paper takes 2011 as the base period and conducts DID regression analysis consistent with the baseline regression for explained variables in the first two years and the last three years of the policy implementation. Figure 2a,b show the regression trend of the VHD and the SHD on the core explanatory variable DID. As can be seen from the results described in the figure, in the first 2 years of the policy implementation, the regression results of the VHD and the SHD do not significantly reject the zero value. In the year before the implementation of the policy, both regression results are negative. The results show that the experimental group and the control group are in the same trend before the implementation of the policy. In the year of policy implementation and subsequent years, the regression results of the VHD are almost all significantly greater than zero, while the regression results of the SHD do not significantly reject zero despite the trend change. This indicates that the pilot policy has a significant policy effect on the VHD of the city but has a weak effect on the SHD of the city.

5.3. Robustness Test

5.3.1. Deleting Samples of Provincial Capitals and Municipalities Directly under the Central Government

Compared with other prefecture-level cities, provincial capitals or municipalities usually have a higher level of economic development, better infrastructure conditions, and more abundant supply of factors. Therefore, provincial capitals and municipalities are excluded from the samples in this paper. The results in

Table 3. Test results of deleting some samples.

Variables	(1) VHP	(2) VHP	(3) VHP-P	(4) VHP-P	(5) VHP-E	(6) VHP-E
DID	0.04080 ** (0.01670)	0.02556 *** (0.00983)	0.01696 ** (0.00755)	0.01775 ** (0.00907)	0.02943 (0.01929)	0.00852 (0.02530)
R-squared	0.1204	0.0728	0.0773	0.8596	0.1046	0.4192
Variables	(7) SHP	(8) SHP	(9) SHP-P	(10) SHP-P	(11) SHP-E	(12) SHP-E
DID	0.04890 (0.03823)	−0.01213 (0.01590)	−0.00308 (0.01930)	−0.07355 *** (0.02825)	0.05036 (0.04705)	0.05949 (0.05587)
R-squared	0.0181	0.1255	0.0598	0.3814	0.0446	0.1249
Control	YES	YES	YES	YES	YES	YES
λi		YES		YES		YES
λt		YES		YES		YES
Observations	1778	1778	1778	1778	1778	1778

Note: ** Regression coefficients significant at the 5% level. *** Regression coefficients significant at the 1% level.

show that the effect of pilot policy is still mainly reflected in promoting the VHD of urban economy. It has no significant effect on the transformation of urban economy to the SHD, and even has a significant negative effect on the technological progress related to the SHD. This further indicates that the enterprises in the pilot parks have weak impetus for green innovation and are more inclined to promote the technological progress related to the VHD.

5.3.2. Propensity Score Matching (PSM-DID)

There are great differences in economic and social development among different regions in China. In order to better satisfy the randomness of the samples, this paper adopts the method of PSM to match the samples and eliminate the samples with large differences. Using Logit method, FDI, edlevel, industruc, infrust, and qhc are taken as covariables, and the one-to-one matching method by sampling with replacement is adopted to match the propensity score of the samples. After deleting a few non-matching items, the regression is conducted again according to model (1), and the results are shown in

Table 4. Test based on PSM-DID method.

Variables	(1) VHP	(2) VHP	(3) VHP-P	(4) VHP-P	(5) VHP-E	(6) VHP-E
DID	0.04277 *** (0.01246)	0.02816 ** (0.01158)	0.01596 *** (0.00611)	0.01729 ** (0.00768)	0.03497 ** (0.01415)	0.01334 (0.01903)
R-squared	0.1724	0.0824	0.0829	0.8577	0.1694	0.4455
Variables	(7) SHP	(8) SHP	(9) SHP-P	(10) SHP-P	(11) SHP-E	(12) SHP-E
DID	0.06306 ** (0.02985)	0.00924 (0.04119)	0.02282 (0.02090)	−0.03552 (0.02797)	0.04365 (0.03333)	0.04880 (0.04191)
R-squared	0.0443	0.1392	0.1766	0.3790	0.0774	0.1361
Control	YES	YES	YES	YES	YES	YES
λi		YES		YES		YES
λt		YES		YES		YES
Observations	1966	1966	1966	1966	1966	1966

Note: ** Regression coefficients significant at the 5% level. *** Regression coefficients significant at the 1% level.

. The results in Table 4 show that the above conclusions are still robust.

5.3.3. Counterfact Test

This paper assumes that the implementation years of the policies are 2012, 2013, and 2016, respectively, based on which a counterfact test is conducted. The results in

Table 5. Results of counterfact test.

Variables	Year	(1) SHP	(2) SHP-P	(3) SHP-E	(4) VHP	(5) VHP-P	(6) VHP-E
DID	2012	−0.03193 (0.04639)	−0.09375 * (0.05079)	0.04600 (0.05065)	0.00439 (0.01163)	0.01618 (0.00980)	−0.00905 (0.01221)
	2013	−0.00354 (0.03829)	−0.08129 ** (0.03665)	0.06533 (0.04213)	0.00901 (0.01209)	0.00149 (0.00419)	0.01391 (0.01231)
	2016	0.00404 (0.05901)	0.04195 (0.03646)	−0.00922 (0.06740)	0.01142 (0.01508)	0.01761 (0.01662)	0.00088 (0.01662)
Control		YES	YES	YES	YES	YES	YES
λi		YES	YES	YES	YES	YES	YES
λt		YES	YES	YES	YES	YES	YES
Observations		1988	1988	1988	1988	1988	1988

Note: * Regression coefficients significant at the 10% level. ** Regression coefficients significant at the 5% level.

show that the regression results of the SHD and the VHD are not significant under the condition of counterfact, which reflects the robustness of the above conclusions.

5.3.4. Test of Placebo

In order to exclude the influence of other non-relevant factors on the conclusion of this paper, 47 cities were randomly selected from the sample as the cities where the virtual low-carbon industrial park pilots are located. A total of 1000 samples were repeated and regression was conducted according to model (1). Figure 3a,b show the kernel density of regression results of the VHD and technological progress related to the VHD. The results show that the absolute T-value of most of the estimated coefficients is less than 2, and the p-value is less than 0.1. The above results indicate that the conclusions of this paper pass the placebo test, and the impact of pilot policy on urban high-quality development transformation is not related to other unknown factors.

5.3.5. Remove the Interference of Other Policies

With the rapid development of China’s economy, the natural resource endowment of many regions has changed from abundant to deficient, or even become resource-exhausted cities. In order to help resource-exhausted cities get rid of their dependence on the development path of natural resource-related industries, the Chinese government has formulated corresponding assistance policies for resource-exhausted cities. In order to avoid the interference of support policies for resource-depleted cities, this paper further eliminated the resource-depleted cities in the sample. The regression results in

Table 6. Results of removing the interference of other policies.

Variables	(1) SHP	(2) SHP-P	(3) SHP-E	(4) VHP	(5) VHP-P	(6) VHP-E
DID	−0.00026 (0.03637)	−0.03387 (0.02961)	0.02683 (0.03464)	0.02980 *** (0.00961)	0.01330 *** (0.00459)	0.02691 (0.03493)
Control	YES	YES	YES	YES	YES	YES
λi	YES	YES	YES	YES	YES	YES
λt	YES	YES	YES	YES	YES	YES
Observations	1820	1820	1820	1820	1820	1820
R-squared	0.1456	0.3746	0.1516	0.0849	0.8597	0.1517

Note: *** Regression coefficients significant at the 1% level.

show that the above conclusions are still robust.

5.4. Analysis of Heterogeneity

5.4.1. Regional Heterogeneity

In order to further observe the effect of the policy in different regions, the study sample is further decomposed into eastern, central and western regions. The results in

Table 7. Regional heterogeneity.

Variables	Region (Observations)	(1) SHP	(2) SHP-P	(3) SHP-E	(4) VHP	(5) VHP-E	(6) VHP-E
DID	Eastern region (699)	0.05272 (0.05510)	−0.01859 (0.05835)	0.03666 (0.05058)	−0.00056 (0.01510)	0.01014 (0.01059)	0.03666 (0.05058)
	Central region (700)	−0.04378 (0.04090)	−0.05282 (0.04625)	0.00580 (0.03308)	0.03454 *** (0.01114)	0.02998 *** (0.01034)	0.00580 (0.03308)
	Western region (588)	0.03846 (0.11859)	−0.02948 (0.02616)	0.11303 (0.12158)	0.06607 (0.05390)	0.00925 (0.01185)	0.11302 (0.12158)
Control		YES	YES	YES	YES	YES	YES
λi		YES	YES	YES	YES	YES	YES
λt		YES	YES	YES	YES	YES	YES

Note: *** Regression coefficients significant at the 1% level.

show that the effect of pilot policy is mainly in the central region, but not in the eastern and western regions. This may be mainly because the technological level and economic development level of the eastern region is in a leading position in China, and it is relatively difficult to further improve the level of productivity through policy incentives. The technological conditions of the western region are relatively backward, and the industry mainly focuses on the upstream of the value chain and the resource-based primary processing. Most of the western regions are still in the middle stage of industrialization, and the factor-driven effect is more obvious. The factor endowment and technical conditions of the central region are relatively superior, and the development gap between the central region and the eastern region is relatively small. In addition, the central region has obvious geographical advantages and can promote local technological progress by undertaking a large number of industrial transfers from the eastern region. Therefore, the central region has a larger space for technological improvement and is more sensitive to policy incentives.

5.4.2. The Heterogeneity of Natural Resource Endowments

Because of its unique endowment advantages, resource-based cities have gradually formed different industrial structures and development modes in the process of economic development. In order to further investigate the heterogeneity of the effect of pilot policy under natural resource endowment conditions, this paper divides the samples into resource-based cities and non-resource-based cities according to the Sustainable Development Plan for Resource-based Cities issued by The State Council in 2013. The results in column (4) and (5) of

Table 8. Heterogeneity of natural resource endowment.

Variables	Region (Observations)	(1) SHP	(2) SHP-P	(3) SHP-E	(4) VHP	(5) VHP-P	(6) VHP-E
DID	resource-based cities (791)	−0.08274 (0.10597)	−0.12012 *** (0.02189)	0.05102 (0.11315)	0.00847 ** (0.00403)	0.04399*** (0.00923)	−0.04595 (0.02871)
	Non-resource-based cities (1197)	0.04244 (0.03768)	−0.01524 (0.03513)	0.06025 (0.03812)	0.03401 ** (0.01558)	0.00763 (0.00551)	0.03274 ** (0.01309)
Control		YES	YES	YES	YES	YES	YES
λi		YES	YES	YES	YES	YES	YES
λt		YES	YES	YES	YES	YES	YES

Note: ** Regression coefficients significant at the 5% level. *** Regression coefficients significant at the 1% level.

show that resource-based cities are more inclined to promote technological progress related to the VHD urban economy due to their special industrial structure and development mode. The regression result of the pilot policy on the technological progress related to the SHD of resource-based cities is significantly negative, which indicates that enterprises may mainly use innovative resources to improve their ability to obtain value during the implementation of the pilot policy, thus forming the crowding out effect of technological progress related to the VHD on that of the SHD. In addition, the results in columns (4) and (6) of Table 8 show that the policy effect of pilot policy on non-resource cities also focuses on the VHD, and significantly improves the technical efficiency level and resource allocation efficiency of non-resource cities.

5.5. Analysis of Mechanism

In order to analyze the influence mechanism behind the policy effect, this paper further establishes the mediation mechanism test model. Among them, Media_it represents the mediating effect of i city in year t, including innovation incentive effect, capital deepening effect, employment driving effect and energy transformation boosting effect.

$${\mathrm{Media}}_{\mathrm{it}}{=\mathsf{\beta }}_0^{\prime }{+\mathsf{\beta }}_1^{\prime }{\mathrm{DID}+\mathsf{\theta }}^{\prime }{\mathbf{Control}+\mathsf{\delta }}_{\mathrm{i}}{+\mathsf{\gamma }}_{\mathrm{t}}{+\mathsf{\epsilon }}_{\mathrm{it}}$$

(6)

$${\mathrm{Y}}_{\mathrm{it}}{=\mathsf{\beta }}_0^{″}{+\mathsf{\beta }}_1^{″}{\mathrm{DID}+\mathrm{Media}}_{\mathrm{it}}{+{\mathsf{\theta }}^{″}}^{}{\mathbf{Control}+\mathsf{\delta }}_{\mathrm{i}}{+\mathsf{\gamma }}_{\mathrm{t}}{+\mathsf{\epsilon }}_{\mathrm{it}}$$

(7)

5.5.1. Innovation Incentive Effect

The Urban Innovation Index proposed by Professor Kou Zonglai in the China Urban and Industrial Innovation Report 2017 takes into full account the urban innovation, industrial innovation and enterprise innovation of Chinese cities and has a high practical reference value. In order to reflect the comprehensive innovation level of cities, this paper quotes the research results of this report, and uses the Urban Innovation Index as the proxy variable of the urban innovation level. The results in columns (5)~(8) of

Table 9. Mechanism test of innovation incentive effect.

I	Variables	(1)	(2)	(3)	(4)
		Innovation	SHP	SHP-P	SHP-E
SHP	DID	24.5148 *** (3.20476)	0.00897 (0.03453)	−0.04617 * (0.02454)	0.05904 (0.04212)
	Innovation		0.00022 (0.00026)	0.00036 ** (0.00018)	−0.00034 (0.00031)
II	Variables	(5)	(6)	(7)	(8)
		Innovation	VHP	VHP-P	VHP-E
VHP	DID	24.5148 *** (3.20476)	0.02230 *** (0.00768)	0.01610 *** (0.00477)	0.00750 (0.01535)
	Innovation		0.00020 ** (0.00009)	0.00002 ** (0.00014)	0.00025 ** (0.00011)
Control		YES	YES	YES	YES
λi		YES	YES	YES	YES
λt		YES	YES	YES	YES
Observations		1988	1988	1988	1988

Note: * Regression coefficients significant at the 10% level. ** Regression coefficients significant at the 5% level. *** Regression coefficients significant at the 1% level.

show that the pilot policy have significantly improved the urban innovation level, and the improvement of the urban innovation level is an important mechanism for the pilot policy to promote the VHD and technological progress related to the VHD of urban economy. The results in column (3) of Table 9 show that the regression result of technological progress related to the SHD on pilot policy is significantly negative, while the regression result on urban innovation level is significantly positive. This indicates that the pilot policy does not promote the SHD of the economy through the mechanism of improving the level of urban innovation, and the innovation incentive effect of the pilot policy on technological progress related to the VHD has a certain crowding out effect on that of the SHD. It can be seen that the LIPPP does not significantly promote urban green and sustainable development through the innovation incentive effect, which mainly acts on the VHD of cities, thus Hypothesis 2 is verified.

5.5.2. Capital Deepening Effect

This paper uses capital stock per capita as the proxy variable of capital deepening effect. The regression results of (1) and (5) in

Table 10. Mechanism test of capital deepening effect.

I	Variables	(1)	(2)	(3)	(4)
		Percapita	SHP	SHP-P	SHP-E
SHP	DID	10.7307 *** (3.27388)	−0.01500 (0.03815)	−0.03491 (0.02801)	0.01435 (0.03665)
	percapita		0.00274 *** (0.00072)	−0.00023 (0.00033)	0.00340 *** (0.00080)
II	Variables	(5)	(6)	(7)	(8)
		percapita	VHP	VHP-P	VHP-E
VHP	DID	10.7307 *** (3.27388)	0.00431 (0.01532)	0.00879 ** (0.00457)	−0.00666 (0.01987)
	percapita		0.00220 *** (0.00045)	0.00074 *** (0.00006)	0.00188 *** (0.00056)
Control		YES	YES	YES	YES
λi		YES	YES	YES	YES
λt		YES	YES	YES	YES
Observations		1988	1988	1988	1988

Note: ** Regression coefficients significant at the 5% level. *** Regression coefficients significant at the 1% level.

are significantly positive, indicating that the pilot policy has significantly promoted the capital deepening level of the city. The regression results of the per capita capital in columns (2), (4) and (6)~(8) are all significant at the 1% significance level, indicating that the deepening of urban capital has significantly improved the SHD and the VHD of urban economy. However, the pilot policy only promotes the VHD and technological progress related to VHD of urban economy through the capital deepening effect. The capital deepening effect is not an intermediary mechanism for pilot policy to promote the SHD. The results in column (3) are not significant and negative, while the results in column (7) are significantly positive, indicating that the pilot policy has significantly promoted the VHD of the city through the capital deepening effect, and the technological progress related to the VHD promoted by capital deepening effect crowds out that of the SHD, which further confirms the above conclusions.

5.5.3. Employment Driving Effect

This paper uses the average annual growth rate of labor force as the proxy variable of employment pull effect. The results in

Table 11. Mechanism test of employment driving effect.

I	Variables	(1)	(2)	(3)	(4)
		Labor Increase	SHP	SHP-P	SHP-E
SHP	DID	−1.28781 (1.41187)	0.01008 (0.03943)	−0.03719 (0.02721)	0.04558 (0.03946)
	laborincrease		−0.00334 *** (0.00104)	0.00013 (0.00057)	−0.00405 *** (0.00130)
II	Variables	(5)	(6)	(7)	(8)
		Labor increase	VHP	VHP-P	VHP-E
VHP	DID	−1.28781 (1.41187)	0.02260 (0.01598)	0.01661 ** (0.00759)	0.00665 (0.02062)
	laborincrease		−0.00414 *** (0.00025)	−0.00005 (0.00010)	−0.00537 *** (0.00035)
Control		YES	YES	YES	YES
λi		YES	YES	YES	YES
λt		YES	YES	YES	YES
Observations		1988	1988	1988	1988

Note: ** Regression coefficients significant at the 5% level. *** Regression coefficients significant at the 1% level.

(1) or (5) show that there is no mechanism path for pilot policy to promote economic SHD and VHD through the employment-driven effect. In addition, the results in columns (2), (4), (6) and (8) show that labor force is no longer the main driving force for high-quality economic development, and the mode of high-quality economic development has shifted to capital-driven and innovation-driven. At this point, Hypothesis 3 is verified.

5.5.4. Energy Transformation Boosting Effect

Compared with coal, oil, and other fossil fuels, natural gas is cleaner. This paper uses the natural gas consumption of urban industry published by the National Bureau of Statistics of China as the proxy variable of energy transformation. The regression results in

Table 12. Mechanism test of energy transformation boost effect.

I	Variables	(1)	(2)	(3)	(4)
		Gasconsume	SHP	SHP-P	SHP-E
SHP	DID	1.87409 ** (0.95033)	0.01065 (0.04022)	−0.04525 * (0.02727)	0.05672 (0.04023)
	gasconsume		0.00231 (0.00184)	0.00396 ** (0.00176)	−0.00255 (0.00252)
II	Variables	(5)	(6)	(7)	(8)
		gasconsume	VHP	VHP-P	VHP-E
VHP	DID	1.87409 ** (0.95033)	0.02760 * (0.01652)	0.01600 ** (0.00778)	0.01336 (0.01932)
	gasconsume		−0.00021 (0.00097)	0.00006 (0.00046)	−0.00009 (0.00130)
Control		YES	YES	YES	YES
λi		YES	YES	YES	YES
λt		YES	YES	YES	YES
Observations		1988	1988	1988	1988

Note: * Regression coefficients significant at the 10% level. ** Regression coefficients significant at the 5% level.

show that the LIPPP significantly promotes the urban energy transformation. However, in terms of both technological progress and technological efficiency, the boost effect of the energy transformation generated by the pilot policy does not play an obvious mechanism role in the process of promoting high-quality economic development. Hypothesis 4 is verified.

According to the test results of the above mechanism, the level of urban innovation, the degree of capital deepening, and the condition of energy transformation all play a certain role in promoting the SHD. However, in the implementation process of the LIPPP, those promoting effects are overshadowed by the policy effect of the pilot policy to promote the VHD, which results in the insignificant promoting effect of the pilot policy on the SHD.

6. Research Conclusions and Policy Implications

6.1. Research Conclusions

After 40 years of reform and opening-up, China has achieved great success in socio-economic development and improving the quality of people’s lives. However, extensive development has caused great damage to resources and environment. How to achieve sustainable social and economic development is an urgent problem for China to solve. At present, China is in the late stage of industrialization. Under the dual pressure of economic development and ecological environment, China must realize the VHD and the SHD at the same time. To this end, using panel data from 284 prefecture-level cities in China from 2010 to 2017, this paper examines the impact of the LIPPP on high-quality economic development. The results show that the LIPPP mainly promotes the VHD and technological progress related to the VHD of urban economy, but have no significant impact on the SHD that takes into account both energy input and pollution emission. The effect of the pilot policy is characterized by partial technological progress and has no obvious effect on the improvement of urban technical efficiency. The LIPPP mainly promotes the VHD and technological progress related to the VHD of urban economy through the innovation incentive effect and the capital deepening effect. The technological progress related to the VHD has a crowding out effect on that of the SHD, which is the main reason that the policy effect of the LIPPP on the SHD of urban economy is not significant. Although the level of regional innovation, the degree of capital deepening and the condition of energy transformation have certain promoting effects on the SHD of urban economy, they are overshadowed by the effect of the pilot policy in promoting the VHD.

Due to the lack of data of micro-enterprises in the industrial park, this paper cannot investigate the micro-impact of LIPPP. With the further promotion and improvement of data statistics in the future, it is worth considering studying the impact of green transformation on economic development based on low-carbon industrial parks at the micro enterprise level.

6.2. Policy Implications

The research of this paper has the following policy implications for the high-quality development of China’s economy:

• Due to the public nature of green innovation, the government should increase the incentive and guidance at the macro level and impose scientific and effective management and supervision. We should take an objective view of the low quality of overall green innovation in China. When formulating relevant policies, we should correctly treat the relationship between the government and the market, that means we should not only achieve effective supervision, but also give full play to the basic role of the market in the process of economic operation.
• The improvement of technical efficiency also plays a key fundamental role in the high-quality development of urban economy, while we should not only focus on the promotion of technological progress.
• China’s factor endowment structure has undergone profound changes, and capital elements have been relatively abundant. It is necessary to guide the movement direction of capital correctly. The profit-seeking nature of capital makes enterprises pay more attention to the realization of short-term interests in the period of rapid development. The government should guide enterprises to actively realize green transformation from the demand side, such as policy support for green energy investment, strengthening public awareness of environmental protection and green consumption guidance and so on.
• The government should increase investment in education and infrastructure, promote the flow of talent and information, and optimize the allocation of resources. The key to the accumulation of human capital lies in the development of education, and the high quality and efficient flow of information is crucial to the optimal allocation of human capital. Therefore, it is necessary to further strengthen the construction of transportation and digital infrastructure to promote the cross-regional flow of factors and the efficiency of information dissemination, which is conducive to the quality improvement and rational allocation of human capital.
• The optimization of energy structure is an important aspect for China to achieve high-quality development transformation. China should make efforts to develop renewable energy technologies and reduce its dependence on fossil fuels, especially coal. This is not only the general trend of global economic development, but also the inevitable course for China’s economy to achieve high-quality development and transformation in the new era.