A Study on the Evaluation and Determinants of Green Economic Efficiency: Empirical Evidence from Provincial Panel Data in China

Abstract

In the past decades, China has continued to pursue the quantitative expansion of economic development at the expense of the environmental quality resulting in high environmental degradation. Hence, this study evaluates the economic efficiency under the constraints of energy and environment, as well as unravels the determinants of economic efficiency in 30 provinces in China during 2005 to 2019. It deploys the improved super-efficiency Slack-Based Measure (SBM) model and Tobit regression model. The statistical results show that economic growth efficiency displays a declining trend in recent years in China. However, there is a regional heterogeneity of green economic efficiency across the provinces, with the efficiency values exhibiting a clear downward stepwise distribution from the eastern to western region. Moreover, the regression results indicate that energy consumption and GDP growth have significant adverse effects on green economic efficiency. This implies that China’s economic growth is excessively reliant on energy consumption which exceeds the environmental carrying capacity, causing a decline in green economic efficiency. The study of China’s economic efficiency is fundamental because it explores the potential space for sustainable growth as well as provides clarity on the current status and prospects of economic development with a view to avoiding economic stagnation and providing policy support for sustainable economic development. Therefore, policy makers should proactively take measures to improve energy efficiency and shift economic development targets from quantitative expansion to qualitative improvement that facilitates green economic efficiency. Based on the empirical results of the different regions, this study also provides some respective policy recommendations.

Keywords

Energy consumption environmental quality green economy efficiency super-efficiency SBM model sustainable development

Introduction

Economic development and environmental protection are some of the major concerns of several countries because changes in global climatic parameters influence the wellbeing of all individuals, organizations and nations on earth directly or indirectly (Cetin et al., 2023). The neglect of environmental protection has brought serious disasters to human society. The continuous rise in industrial pollutant or discharge is damaging the environment and gravely threatening people’s health and social stability. The International Energy Agency (2021) states that global carbon emissions from fuel combustion and industrial processes increased by nearly 2.1 Gt in 2021 compared to 2020, setting a new record for annual emissions. People are beginning to realize the imperative of considering the limited carrying capacity of the environment while pursuing economic development. Given the reality, more countries and regions are moving away from focusing on the speed of economic growth to exploring the quality and efficiency of economic growth. Economists and policymakers expressed their commitment and determination to mitigate climate change and began to adopt green strategies to achieve higher levels of green economic efficiency (Yao, 2021).

The green economy is identified as a “low-carbon, resource-efficient, and socially inclusive” economy by United Nations Environment Programme (2011). In essence, the purpose of efficiency is to obtain maximum welfare output with minimum ecological input (Hou et al., 2020). In the economic activities, more output with a given input or less input with a given output reflects more efficient economic growth, and vice versa. Based on this, the green economic growth efficiency directly manifests the comprehensive ability of a region such as resource allocation, market competitiveness and sustainable development. The World Commission on Environment and Development (WCED) has introduced the concept of sustainable development in 1987, which emphasizes the importance of environmental and resource sustainability for socio-economic and human life. It is expected for sustainable economic growth to place emphasis on the quality of development as well as environmental consequences. The green economy efficiency is considered as a multidimensional and multifaceted concept that attracts countries around the world to strive toward green economy transition (Naseer et al., 2022). Optimizing the eco-efficiency of economic development will be the most effective path to mitigate the greenhouse effect and achieve sustainable development (Liu et al., 2019). Due to the environmental, resources and population problems, it is optimal for countries to actively change the model of attaining economic growth.

The transition to a green economy and achieving more green economic efficiency are on government agenda around the world. This study focuses on China because the country is currently experiencing significant resource and environmental problems such as the continuous reduction of arable land, the over-exploitation and inefficient usage of energy, the aggravating desertification and soil erosion, energy shortages and various natural disasters. These allow the economic growth efficiency to decline even as China’s economy is becoming more globalized and experiencing great international influence and competitiveness. For instance, China’s energy consumption reached 5.41 billion tons in 2022, an increase of 2.9% from the previous year, and ranking first in the world (China Energy Statistical Yearbook), which critically affects the country’s sustainable development. Inappropriate environmental policies are also responsible for problems such as misallocation and inefficient utilization of energy, which in turn adversely affect the economic efficiency. Therefore, the study of China’s economic efficiency is fundamental because it explores the potential space for sustainable growth as well as provides clarity on the current status and prospects of economic development, with a view to avoiding economic stagnation and providing policy support for sustainable economic development.

Moreover, the problem of regional disparity is quite significant in China. Due to the vast territory of China, there are remarkable discrepancies in the degree of economic development and industrial layout of different regions (Su et al., 2021). Disparities in local economic growth targets may also cause regional heterogeneity of economic efficiency. Over the decades, China prioritized the development of the eastern provinces such as Beijing, Shanghai, Tianjin, Jiangsu, Zhejiang, and Guangdong, as these provinces are more culturally open, technologically advanced, better located and their economic development patterns are more compatible with sustainable development requirements. While plenty of surplus labor force from the mid-western region migrated to the eastern region, providing sufficient manpower for local economic development (Liu & Dong, 2021). This enables the eastern region to develop rapidly under the national economic strategies, reaping higher levels of openness, industrialization and innovation (Dong et al., 2019). These disparities also resulted in regional conflicts, educational inequality and other social problems in China. The factors influencing the economic efficiency under the constraints of energy and environment are multiple and may depend on the specific details of economic development, the development model, the economic structure and the implementation of environmental regulations (Yao, 2021). As the second largest economy, China has embarked on a journey to achieve green economic efficiency and is destined to overcome many challenges.

Within this context, this research investigates the economic efficiency under the constraints of energy and environment with respect to the current reality in China. The main contributions are reflected in the following areas. Firstly, the conceptual distinction between economic growth and economic efficiency is barely made in some studies. This research incorporates energy inputs and pollution outputs into the economic efficiency evaluation model, which enable us to objectively evaluate the real level of China’s economic efficiency, as well as provide policy recommendations for environmental governance. Secondly, although some scholars have been keen to explore the economic growth efficiency, there are few studies on the provincial economic efficiency in China under the constraints of energy and environment. This research analyzes and compares the economic efficiency of different regions in China based on provincial data. The establishment of the regional evaluation system is conducive to scientifically reflect the relationship between economic growth and environment in different regions, thus, providing targeted counter-measures for regional ecological management and industrial transformation.

From the perspective of methodological development, as for the efficiency evaluation model, the traditional Data Envelopment Analysis (DEA) analysis presumes that inputs or outputs vary in proportion to a specific objective and failed to effectively solve the problem of slack. Therefore, this study employs an improved super-efficiency Slack-Based Measure (SBM) model (Tone, 2001) that handles unexpected outputs more accurately, and allows efficiency values larger than one, thereby enabling further comparison between effective Decision-Making Units (DMUs). In other words, the SBM makes it possible to evaluate the efficiency when there is undesirable output, as well as solves the problem of the slackness of input and output. This study also considers the effects of different pollutants, such as wastewater, waste gas, and solid waste, as the undesired output indicators, which is conducive to evaluating the efficiency of economic growth more effectively and optimizing the related evaluation methods.

Furthermore, this research reveals that China’s economic growth efficiency tends to decline in recent years under the constraints of energy and environment, especially in the relatively developed eastern region. In response to this fact, it is imperative to examine the driving factors of economic efficiency to achieve sustainable economic growth. This research explores the relevant factors affecting the changes in economic efficiency in different regions according to the significant regional disparities in China, thereby providing more specific and targeted suggestions for regional high-technology transfer and industrial structure upgrading. The Tobit regression model is utilized to explore the driving factors of the economic growth efficiency in China, which makes a certain supplement for the relative research.

The remainder of this research is organized as follows. After this introduction, the literature review section reviews the related studies, while the methodology section introduces the economic efficiency evaluation model and Tobit model. The fourth section displays the empirical findings while the discussion and policy implications section provide some policy suggestions. The final section presents the conclusions and future research.

Literature Review

Research on Green Economic Efficiency

Efficiency evaluation methods are constantly evolving and have unlimited potential for efficiency applications. Data Envelopment Analysis (DEA) is a non-parametric and multi-factorial efficiency evaluation tool proposed by Charnes and Cooper (1985) for assessing the relative efficiency of Decision-Making Units (DMUs). This method offers the benefits of fewer model assumptions, the ability to handle multiple inputs and multiple outputs problems concurrently, and has developed into a significant tool for assessing relative efficiency (Gao et al., 2021). Moreover, it can handle cross-sectional data and time series data simultaneously, and can present the input-output efficiency in the form of linear programming.

However, the traditional Data Envelopment Analysis (DEA) presumes that inputs or outputs vary in proportion to a specific objective and failed to effectively solve the problem of slack. As one of the models of DEA, the Slack-Based Measure (SBM) model proposed by Tone (2001) is superior to the traditional DEA model in introducing the slack variable into the objective function. It makes it possible to evaluate the efficiency when there is undesirable output, and it also solves the problem of the slackness of input and output (Yang & Fang, 2020). The SBM model has been widely used in recent years. For example, the SBM model was applied by Kazemzadeh et al. (2022) to estimate the energy efficiency of 16 emerging countries from 1990 to 2014. The results reveal that Turkey and Hungary have high energy efficiency while China and India have the worst performance in energy efficiency. Shang et al. (2020) measured the energy efficiency in China’s regions based on SBM model. Zheng et al. (2019) also measured the eco-efficiency in China during 2000 to 2015 using the SBM model, and the results exposed the regional heterogeneity of China’s economic efficiency. Similarly, Zhang and Li (2022) highlighted the regional disparities in China’s economic efficiency and revealed that the eastern coastal region has the geographical advantage to develop the green economy.

Moreover, the comprehensive technical efficiency index of traditional DEA is 1, which cannot support further tracking and comparison of efficiency changes. Therefore, the super-efficiency SBM model (Tone, 2001) is more frequently adopted by scholars. Yuan et al. (2020) applied the non-radial super-efficiency SBM model and dynamic spatial panel Durbin model to investigate the efficiency change in China’s 287 cities during 2003 and 2016. The findings of Geng et al. (2023) revealed the spatial heterogeneity of green economic efficiency in China by conducting the two-stage super-efficiency SBM model and the Tobit model. Zhang and Wang (2023) used a super-efficiency SBM model to investigate the total factor energy efficiency of the Regional Comprehensive Economic Partnership (RCEP). Yan et al. (2022) also adopted the super-efficiency SBM model to measure the green innovation efficiency of 29 manufacturing industries in China from 2010 to 2019. Li and Ianenko (2023) examined the evolution of innovation efficiency in 31 Chinese provinces from 2010 to 2020 by employing a super-efficiency SBM model. Hou et al. (2020) analyzed 30 provinces in China from 2006 to 2017 by utilizing a super-efficiency SBM model. They found that the eastern provinces had better geographic advantages and more employment opportunities, which positively impacted the local economy, as well as education and health care. Shuai and Fan (2020) as well as Chen and Yao (2021) also adopted the super-efficiency SBM model to reveal the spatial heterogeneity of green economic efficiency and forest eco-efficiency in China, respectively.

Research on the Influencing Factors of Green Economic Efficiency

The structural imbalances of energy consumption and economic growth have considerable impacts on economic efficiency. Therefore, optimizing the economic structure plays a crucial role in promoting economic efficiency. The consumption of fossil fuels exerts a remarkable negative impact on economic growth efficiency (Shang et al., 2023). Geng et al. (2023) advocated that the negative impact of natural resource consumption on green economic efficiency is long-term and persistent. The energy consumption has also been noticed by Zhang and Li (2022) and Yuan et al. (2020). In particularly, the results of Yuan et al. (2020) demonstrated that economic efficiency decreases with the growth of energy consumption, reflecting China’s excessive reliance on fossil energy. For instance, the concept of green productivity is drawn from two vital development strategies namely productivity improvement and environmental protection. Ahmed (2019) integrated innovation and climate with economic growth to provide a framework for continuous improvement and sustainable development. The framework measures green productivity and its contributions to sustainable development. The study argued that enhancement of productivity will continue to drive high and sustained economic growth. Therefore, it is necessary to embrace technological progress that integrate three dimensions of sustainable development (i.e., economic, environmental, and social).

The findings of Geng et al. (2023) indicated that the development of digital economy is positively correlated with green economic efficiency. Nevertheless, the opposite opinion has been verified by Su et al. (2021), who analyzed the data in China during 2000 to 2017. Their results pointed out that economic growth inhibits green economic efficiency by distorting marketization and emphasized that significant regional heterogeneity exists in this effect. In other words, economic growth has a greater disincentive effect on green efficiency in the eastern regions than the central and western regions.

Furthermore, some other factors also affect economic growth efficiency. For example, structural imbalance between urban and rural areas may cause social instability. Liu and Dong (2021) scrutinized statistical data of 278 Chinese cities from 2003 to 2017. The results verified that technological innovation is conducive to improving resource utilization, thereby increasing the green economic efficiency. Their findings also affirmed that urbanization is negatively correlated with economic efficiency in the central region. The econometric results of Su et al. (2021) supported that urbanization, industrial structure and technological innovation are influential factors of economic growth efficiency. Technological innovation is not only a pivotal driver of economic growth, but also an important factor in improving the green economic efficiency. However, if technological innovation only aims to expand production scale and increase economic volume, it will significantly inhibit green economic efficiency (Yuan et al., 2020). The research of Zhang and Li (2022) pointed out that innovation level may reduce the green economic efficiency. They also mentioned the impact of industrial development on green economic efficiency. The imbalance of industrial structure gives rise to problems such as over-capacity and economic recession. The rapid growth of industrialization increases energy consumption and thus pollutant emissions. Excessive reliance on energy-consuming industrial model significantly depresses green economic efficiency (Yuan et al., 2020). In addition, the accumulation of physical and human capital also significantly influence the economic growth efficiency (Yuan et al., 2020).

In spite of the conclusions obtained from the above literature, conflicting research perspectives on the influencing factors of economic growth efficiency still exist. The empirical findings of these studies may differ due to the various research objectives, sample periods and econometric approaches used. Therefore, it is of great practical significance and research value to augment the efficiency analysis model in line with China’s national conditions. In the process of efficiency analysis, some studies adopted traditional efficiency evaluation tools, thus ignoring the slack problem and the comparison of efficiency changes. This research avoids these potential errors by employing the super-efficiency SBM model. Moreover, although some scholars have been keen to explore the economic growth efficiency, most studies on efficiency evaluation models lack comparative analysis between regions. This research investigates the relevant factors influencing the economic efficiency in different regions based on the significant regional disparities in China, with a view to obtaining more specific insights for targeted policy formulations.

Methodology

Model Specification

After reviewing the prior literature in the field, this research is primarily divided into two stages. The first stage of the empirical tests shows an improved super-efficiency SBM model that measures the green economy efficiency of 30 provinces in China (Geng et al., 2023; Li & Ianenko, 2023; Shuai & Fan, 2020; Yan et al., 2022; Zhang & Wang, 2023). The traditional DEA analysis is conducted from the perspective of the most advantageous Decision-Making Unit (DMU), focusing on optimizing the DMU metrics and identifying the evolving direction. However, it presumes that inputs or outputs vary in proportion to a specific objective, and thus failed to effectively solve the problem of slack. Tone (2001) solved this slack problem by proposing a non-radial SBM model based on the existing DEA methods. On the other hand, the comprehensive technical efficiency index of traditional DEA is distributed in the (0,1) interval, and the efficiency value of an effective DMU is 1, which cannot support further tracking and comparison of efficiency changes when multiple effective DMUs exist. Therefore, Tone (2001) proposed an improved super-efficiency SBM model that handles unexpected outputs more accurately, as well as allows efficiency values larger than 1, which enables further comparison between effective DMUs. The form of the super-efficiency SBM model with undesirable outputs is as follows:

\min ρ^{*} = \frac{\frac{1}{m} \sum_{i = 1}^{m} (\bar{x} / x_{ik})}{1 / (r_{1} + r_{2}) * (\sum_{r = 1}^{r_{1}} (\bar{y^{d}} / y_{rk}^{d}) + \sum_{q = 1}^{r_{2}} (\bar{y^{u}} / y_{qk}^{u}))}

(1)

$Among them : \bar{x} \geq \sum_{j = 1 \neq k}^{n} x_{ij} λ_{j}, i = 1, \dots, m;$

\begin{matrix} \bar{y^{d}} \leq \sum_{j - 1, \neq k}^{n} y_{rj}^{d} λ_{j}, r = 1, \dots, s_{1}; \\ \bar{y^{d}} \geq \sum_{j - 1, \neq k}^{n} y_{qj}^{d} λ_{j}, q = 1, \dots, s_{2}; \\ λ_{y} \geq 0, j = 1, \dots, n; j \neq 0; \\ \bar{x} \geq x_{k}, k = 1, \dots, m; \\ \bar{y^{d}} \leq y_{k}^{d}, d = 1, \dots, r_{1}; \\ \bar{y^{u}} \leq y_{k}^{u}, b = 1, \dots, r_{2} . \end{matrix}

where $ρ^{*}$ is the green economic efficiency, λ is the constant vector, n is the Decision-Making Units. Each DMU has m inputs, $r_{1}$ expected outputs, and $r_{2}$ unexpected outputs. Let $X (X \in R^{m})$ , $Y^{d} (Y^{d} \in R^{s 1})$ and $Y^{u} (Y^{u} \in R^{s 2})$ be matrices, such that $X = [x_{1}, . . ., x_{n}] \in R^{m * n}$ and $Y = [y_{1}^{d}, \dots, y_{n}^{d}] \in R^{r_{1} * n}$ .

The second stage is to examine the factors influencing the economic efficiency based on the efficiency values derived in the previous stage. Tobit analysis is the model with limited dependent variable, and it is appropriate when the dependent variable is truncated or censored. This section adopts Tobit model to estimate the factors influencing the green economic efficiency. The Tobit model is specified as follows (Tobin, 1958):

\begin{matrix} Y_{i}^{*} = X_{i} β + ε_{i} \\ Y_{i} = Y_{i}^{*} {if Y}_{i}^{*} \geq 0 \\ Y_{i} = 0 {if Y}_{i}^{*} \leq 0 \end{matrix}

(2)

where $Y_{I}^{*}$ is the latent dependent variable, $Y_{i}$ is the observed variable, $X_{i}$ is the vector of independent variable, β is the vector of correlation coefficient, $ε_{i}$ is independent and $ε_{i} ~ N (0, σ)$ . Hence, $Y_{i}^{*} ~ N (X_{i} β, σ)$ .

This research employs the panel data of 30 provinces in China covering the 2005 to 2019 period. The annual data were collected from the (https://data.stats.gov.cn/) China Statistical Yearbook and (https://data.cnki.net/yearbook/Single/N2020120303) China Energy Statistical Yearbook and China Environmental Statistical Yearbook. MaxDea is utilized to achieve efficiency value results (Hou et al., 2020).

Economic Growth Efficiency Analysis

In the first stage, the super-efficiency SBM model is employed to achieve the green economic efficiency values. The input and output indicators need to be identified first. Energy consumption is typically considered to be the major source of pollutant emissions, and it plays an indispensable role in economic development and social progress as a production-oriented factor. Also, the labor force and physical capital are critical input indicators in industrial efficiency measurement. Therefore, energy consumption (Su et al., 2021; Yuan et al., 2019), labor force (Yuan et al., 2020; Zhang & Li, 2022; Zheng et al., 2019) and physical capital (Su et al., 2021; Zhang & Li, 2022) are selected as input factors in this research. The output is divided into desired output and undesired output. As the name implies, desired output is beneficial to efficiency value, thus, a larger value is better. The real GDP (Su et al., 2021; Zhang & Li, 2022; Zheng et al., 2019) is selected as the desired output indicator.

Conversely, a non-desired output is unfavorable to the efficiency value, therefore, a smaller value is better. In order to give comprehensive consideration to the impact of environmental factors on economic efficiency, the non-desired output variables selected in this research include industrial wastewater emissions (Hou et al., 2020; Yuan et al., 2020), industrial solid waste emissions (Su et al., 2021; Yuan et al., 2019; Zhang & Li, 2022) and CO2 emissions (Hou et al., 2020; Su et al., 2021). Among them, industrial wastewater and industrial solid waste are water and solid waste that cannot be reused in the industrial production process. As for CO2 emissions, it mainly arises from the combustion emissions of fossil energy. These indicators cover the main pollutants that affect environmental quality and contribute to a more accurate measurement of economic growth efficiency (As shown in Table 1).

Table 1.

The Description of Inputs and Output Indicators.

Indicator index	Variables	Sources
Input	Labor force	China Statistical Yearbook (2021)
	Physical capital	China Statistical Yearbook (2021)
	Energy consumption	China Energy Statistical Yearbook (2021)
Desirable output	Real GDP	China Statistical Yearbook (2021)
Undesired output	CO2 emissions	China Environmental Statistical Yearbook (2021)
	Industrial wastewater emissions
	Industrial solid waste emissions

Tobit Model

For the second stage, an econometric Tobit model is generated to relate economic efficiency scores to factors. The following model is utilized to examine the factors influencing economic efficiency in China (Chen & Yao, 2021; Shuai & Fan, 2020).

\begin{matrix} E E_{it} = β_{0} + β_{1} E C_{it} + β_{2} GD P_{it} + β_{3} U R_{it} + β_{4} IN V_{it} \\ + β_{5} I D_{it} + β_{6} H_{it} + β_{7} K_{it} + ε_{it} \end{matrix}

(3)

where EE = the economic efficiency; EC = energy consumption per capita; GDP = real GDP per capita. This study is aimed to strike a balance between energy, environment and economic development. Thus, EC and GDP are selected as the key independent variables in this model. The control variables included in the model are: UR = urbanization (the ratio of the permanent population in city to the total population in the region); INV = innovation; ID = industrial development; H = human capital (measured in average years of schooling); K = physical capital (measured in the ratio of regional gross fixed capital formation to GDP); t = time; i = province; and ε = error term. These variables are converted to natural logarithm before analysis to reduce possible heteroscedasticity problems.

Energy consumption is highly controversial in the issue of economic and productive efficiency. A development pattern that excessively relies on energy consumption is likely to aggravate the environmental burden, and thus diminish economic efficiency. Due to the significant regional heterogeneity in China in terms of population and economic scale, per capita energy consumption is adopted as the indicator in this research (Begum et al., 2015). Economic growth could elevate green economic efficiency by promoting innovation, as well as inhibit it by distorting marketization. Gross Domestic Product (GDP) reflects the economic development level of a country or region. The real GDP per capita avoids the bias of results due to population disparity. Therefore, following Chen and Yao (2021) and Ehigiamusoe and Lean (2018), this research adopts the real GDP per capita as the indicator of economic development. According to the previous literature (Geng et al., 2023; Shang et al., 2023; Yu et al., 2022), energy consumption is typically considered to be the major source of pollutant emissions. Also, economic growth is able to provide the financial basis for the development of science, technology, innovation, and the upgrading of the economic structure. Therefore, positive change in energy consumption and negative change in economic development are likely to depress economic efficiency. Thus, the expected signs of their coefficients are $β_{1} < 0 and β_{2} > 0$ .

In the process of establishing the efficiency evaluation model, some studies adopted traditional efficiency evaluation tools, thus ignoring the slack problem and the comparison of efficiency changes. This research avoids these potential errors by employing the super-efficiency SBM model, enabling further comparison between effective Decision-Making Units (DMUs). This research also considers the effects of different pollutants, such as wastewater, waste gas, and solid waste, as the undesired output indicators, which is conducive to evaluating the efficiency of economic growth more effectively and optimizing the related evaluation methods. Moreover, unlike studies that lack regional comparisons, this research analyzes and compares the economic efficiency of different regions in China based on the provincial data, thus providing targeted counter-measures for regional ecological management and industrial transformation.

Empirical findings

Results of Regional Green Economy Efficiency

The economic efficiency values of the 30 provinces in China from 2005 to 2019 are presented in Table 2. Higher efficiency value indicates more efficient economic development. According to the geographical distribution of the provinces, the 30 provinces in China are divided into three regions: western, central, and eastern. The western region includes Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang. The central region includes Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, and Hunan. The eastern region includes Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan.

Table 2.

Economic Efficiency of Considered Provinces in China.

Region	Province	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	Mean
Western	Inner Mongolia	0.823	0.773	0.744	0.665	0.646	0.608	0.554	0.499	0.433	0.46	0.497	0.494	0.487	0.504	0.486	0.578
	Guangxi	0.496	0.516	0.477	0.483	0.466	0.476	0.486	0.465	0.454	0.489	0.492	0.491	0.48	0.445	0.413	0.475
	Chongqing	0.692	0.713	0.689	0.696	0.681	0.706	0.672	0.645	0.64	0.661	0.647	0.651	0.646	0.634	0.617	0.666
	Sichuan	0.637	0.636	0.665	0.679	0.67	0.693	0.714	0.707	0.699	0.809	0.685	0.7	1.081	1.06	1.007	0.763
	Guizhou	0.581	0.595	0.6	0.639	0.633	0.63	0.604	0.58	0.558	0.581	0.583	0.573	0.572	0.565	0.528	0.588
	Yunnan	0.62	0.623	0.625	0.633	0.609	0.586	0.568	0.561	0.558	0.573	0.599	0.588	0.599	0.595	0.575	0.594
	Shaanxi	0.604	0.643	0.673	0.725	0.73	0.763	0.764	0.788	0.757	0.89	1.047	1.011	0.876	0.751	0.607	0.775
	Gansu	0.643	0.674	0.669	0.641	0.602	0.607	0.6	0.583	0.547	0.55	0.5	0.477	0.437	0.471	0.459	0.564
	Qinghai	0.349	0.365	0.398	0.458	0.44	0.454	0.446	0.423	0.435	0.467	0.514	0.444	0.414	0.421	0.398	0.428
	Ningxia	0.427	0.416	0.414	0.502	0.466	0.46	0.451	0.443	0.446	0.443	0.403	0.373	0.37	0.46	0.43	0.434
	Xinjiang	0.606	0.622	0.612	0.65	0.616	0.688	0.793	0.69	0.677	0.748	0.65	0.55	0.426	0.583	0.521	0.629
Central	Shanxi	1.047	0.764	0.725	0.753	0.702	0.693	0.734	0.617	0.55	0.552	0.524	0.483	0.551	0.547	0.521	0.651
	Jilin	0.59	0.613	0.614	0.562	0.539	0.496	0.475	0.463	0.436	0.464	0.437	0.431	0.398	0.388	0.367	0.485
	Heilongjiang	0.715	0.718	0.689	0.675	0.606	0.569	0.559	0.531	0.511	0.577	0.794	0.622	0.585	0.547	0.482	0.612
	Anhui	0.737	0.713	0.749	0.801	0.82	0.8	0.821	0.805	0.779	0.806	0.782	0.77	0.76	0.752	0.709	0.774
	Jiangxi	0.715	0.726	0.75	0.757	0.722	0.723	0.72	0.711	0.698	0.72	0.706	0.715	0.721	0.713	0.682	0.719
	Henan	1.019	1.023	1.034	1.044	0.864	0.828	0.759	0.725	0.903	0.913	0.947	0.82	0.757	0.739	0.648	0.868
	Hubei	0.681	0.704	0.731	0.74	0.725	0.747	0.72	0.69	0.656	0.665	0.669	0.687	0.692	0.696	0.673	0.698
	Hunan	0.706	0.729	0.776	0.812	0.813	0.784	0.747	0.741	0.73	0.745	0.797	1.127	1.072	1.013	1.026	0.841
Eastern	Beijing	1.09	1.097	1.138	1.162	1.169	1.183	1.203	1.23	1.947	1.213	2.101	1.231	1.238	1.3	1.413	1.314
	Tianjin	1.08	1.094	1.03	1.117	1.194	1.209	1.242	1.291	1.289	1.378	1.353	1.25	1.218	1.352	1.288	1.226
	Hebei	0.623	0.637	0.67	0.682	0.595	0.762	0.739	0.732	0.77	0.795	0.748	0.629	0.605	0.554	0.517	0.671
	Liaoning	0.608	0.608	0.607	0.607	0.549	0.514	0.505	0.491	0.481	0.498	0.52	0.547	0.535	0.529	0.503	0.54
	Shanghai	0.875	0.919	0.884	0.87	0.865	0.881	0.845	0.823	0.797	0.845	0.821	0.835	0.826	0.813	0.774	0.845
	Jiangsu	0.806	0.815	0.817	0.803	0.791	0.791	0.784	0.77	0.734	0.797	0.83	0.821	0.829	0.808	0.793	0.799
	Zhejiang	0.789	0.801	0.792	0.797	0.782	0.801	0.786	0.775	0.726	0.752	0.731	0.734	0.745	0.756	0.732	0.767
	Fujian	1.079	1.079	1.032	0.971	0.869	0.916	0.812	0.792	0.859	0.954	0.902	0.754	0.759	0.669	0.704	0.877
	Shandong	0.735	0.747	0.743	0.844	0.777	0.719	0.695	0.678	0.669	0.713	0.707	0.647	0.619	0.57	0.514	0.692
	Guangdong	1.316	1.31	1.081	1.066	1.037	1.037	1.018	0.896	0.852	0.856	0.844	0.815	0.799	0.762	0.724	0.961
	Hainan	0.77	0.773	0.71	0.772	0.728	0.744	0.716	0.689	0.615	0.66	0.648	0.67	0.664	0.63	0.609	0.693

The three worst performing provinces in terms of green economic efficiency are Guangxi, Qinghai and Ningxia, which are all located in the western region. This finding is consistent with Hou et al. (2020) who found the lowest economic efficiency in Xinjiang, Ningxia and Qinghai. Zhang and Li (2022) also pointed out that Ningxia’s green economic efficiency is lowest in China because of the weak economic foundation and poor environmental governance. These provinces have common characteristics of high energy consumption, significant environmental pollution problems, and flourishing heavy industries. Also, the economic development of most western provinces still relies on traditional industries with outdated equipment and lack of innovation. Therefore, most western provinces exhibit relatively inferior economic efficiency.

The three provinces with the best performance in green economic efficiency are Beijing, Tianjin, and Guangdong, which are located in the eastern region. Similarly, Hou et al. (2020) also unveiled the highest economic efficiency in Beijing. Typically, these provinces have more open cultures, higher levels of technology, and better geographical locations, and their economic development patterns are more compatible with sustainable development requirements. However, not all eastern provinces exhibit high levels of green economic efficiency. For instance, Shandong, Liaoning, and Hebei perform worse than other coastal provinces in terms of economic efficiency value. In the case of Liaoning, its economic development mainly relies on traditional industries, with outdated equipment, backward technology, incomplete management systems, excessive pollutant emissions, insufficient awareness of environmental protection, and lack of capital investment. These have led to the emergence of severe environmental quality problems in Liaoning. Similar challenges exist in Shandong and Hebei provinces. According to the data released by the China Bureau of Statistics, Shandong ranks first with 9% of China’s total carbon emissions in 2020. Under severe environmental pressure, it is imperative for these provinces to optimize the energy consumption structure, adjust the industrial structure, increase the investment in environmental governance, and aggressively promote the construction of eco-cities.

Moreover, with respect to the central region, Shanxi, Jilin, Heilongjiang, and Hubei have lower green economic efficiency. These provinces have a high proportion of agriculture in their economic structure, and their industries are mostly traditional energy-based, which undoubtedly suffer from high pollution emissions, outdated infrastructure, and a scarcity of high-tech.

The average level of economic efficiency of the provinces mentioned in this research is reflected in Figure 1. Darker colors represent higher green economic efficiency, and vice versa. Obviously, the green economic efficiency of the eastern region is higher than that of the western region. Based on the results of the efficiency values, Figure 2 presents the general trend of economic efficiency performance in different regions. The values of green economic efficiency exhibit a clear stepwise distribution from the eastern to western region. The fact that the eastern region has a higher level of economic development than other regions has been confirmed in many studies (Dong et al., 2019; Liu & Dong, 2021). Su et al. (2021) and Zhang and Li (2022) also substantiated the spatial heterogeneity of green economy efficiency in China.

Figure 1.

Economic efficiency values in different provinces in China.

Figure 2.

Economic efficiency values in different regions in China.

The plausible reasons for the significantly higher economic efficiency values in the eastern region are the abundant rainfall, higher forest cover, and richer tourism resources in the coastal region. In particular, energy and environmental issues are taken seriously earlier in some eastern provinces. They transfer some high energy-consuming industries and replace them by vigorously developing modern service industries such as finance and increasing the proportion of tertiary industries, thus improving green economic efficiency. Furthermore, plenty of talented employees from the mid-west are moving to eastern region and contributing to the economic efficiency.

The western region shows the lowest level of green economy efficiency. On the one hand, due to the abundance of energy resources, indiscriminate exploitation has occurred in some western provinces. On the other hand, with rapid economic development, most western provinces are suffering from outdated industrial equipment and backward technology, resulting in low levels of energy efficiency. Besides, some local governments blindly pursue the economic development, disregarding the excessive development of high energy-consuming and high-polluting industries, ignoring the environmental pollution, thus aggravating the pressure of environmental governance. Although the western region shows less green economic efficiency than other regions, its stability ranks at the top of the three. Particularly in the context of the decline of economic efficiency values over recent years, the decrease in the western region is insignificant and displays a greater development potential.

Results of Tobit Regression

The regression analysis of the entire China as well as the eastern, central, and western regions were conducted. The results are shown in Table 3. In terms of the key variables, the overall results of China indicate that energy consumption and GDP growth have significant negative impacts on green economic efficiency, implying that these variables are the main source of economic inefficiencies.

Table 3.

The Results of Tobit Regressions.

Variables	China	Western region	Central region	Eastern region
EC	−0.224** (−2.46)	0.226** (2.05)	−0.038*** (−3.27)	−0.855*** (−3.83)
GDP	−0.272*** (−2.69)	−0.571** (−1.94)	1.99 (1.30)	−0.408*** (−2.78)
UR	0.391** (2.30)	0.467 (0.76)	−0.015*** (−6.90)	1.471*** (3.11)
INV	0.051* (1.76)	0.242*** (3.36)	1.280*** (3.49)	0.059 (1.52)
ID	0.061 (0.67)	−0.308*** (−2.66)	0.003** (2.39)	0.241 (1.24)
H	2.094*** (4.67)	2.030*** (3.27)	0.087** (2.53)	2.436*** (2.88)
K	−0.024 (−0.59)	−0.202 (−1.49)	−0.002*** (−3.75)	0.290*** (3.24)

Notes. EC = energy consumption, GDP = real GDP per capita, UR = urbanization, INV = innovation, ID = industrial development, H = human capital, K = physical capital.

***

, **and * indicate the statistical significance at the 1%, 5% and 10%, respectively.

The empirical outcomes of the regional analysis show that energy consumption has a significant and negative impact on economic efficiency in the central and eastern regions. This outcome is consistent with the findings of Meng et al. (2018) as well as Shuai and Fan (2020). Yuan et al. (2020) also obtained similar results, demonstrating the excessive reliance on energy consumption in China. This negative result suggests that improving energy efficiency is an effective measure to facilitate economic efficiency. However, the western region demonstrates the opposite result, that is, energy consumption promotes green economic efficiency. This reflects the fact that the economic value derived from energy consumption far outweighs the environmental costs and is the primary economic driver in the western region.

On the other hand, GDP growth has a significant depressing effect on economic efficiency in the eastern and western regions, suggesting that economic growth in these regions is excessively reliant on energy consumption to the extent that it exceeds the environmental carrying capacity, resulting in a decline in green economic efficiency. This sloppy economic growth pattern is destined to be unsustainable. The findings of Shuai and Fan (2020) and Meng et al. (2018) also verified the negative relationship between GDP growth and economic efficiency. The adverse effect of GDP growth is spatially heterogeneous, as the impact is stronger in the western region compared to the entire China and eastern region. The disparities of results in different regions may stem from the distinct stages of development, and the different policy implementation.

In terms of the control variables, the effect of urbanization on green economic efficiency in China and eastern region is positive, indicating that urbanization promotes green economic efficiency. However, the coefficient of urbanization is insignificant in the western region, suggesting that urbanization does not enhance green economic efficiency in this region. The justification may be the current level of urbanization in the western region is relatively low and has not yet been able to have a significant impact on economic efficiency. While urban development is negatively correlated with green economic efficiency in the central region. This is consistent with the findings of Liu and Dong (2021) who emphasized the negative role of urbanization on economic efficiency in the central region. The main reason for this result is that urbanization is advancing rapidly in the central region, which is accompanied by energy consumption and over-exploitation of land, leading to a decrease in green economic efficiency.

The results also reveal that innovation has a positive impact on green economic efficiency in China, western and central regions, indicating that innovation is conducive to the economic efficiency. Similar to the results of Liu and Dong (2021) who proved that innovation was positively correlated with the green economy efficiency. The justification for this result is the progress in innovation contributes to technological advances, lower energy consumption and higher product efficiency.

Industrial development exerts a significant positive effect on green economic efficiency in the central region, whereas it exhibits a significant negative effect in the western region. However, industrial development has no significant impact on green economic efficiency in the entire China and eastern region. The opposite results in central region and western region suggests that the western region urgently needs to upgrade its industrial structure. The relentless pursuit of industrial output will reduce the economic efficiency in the western region.

Moreover, the empirical results further indicate that human capital facilitates economic efficiency in the entire China, western, central, and eastern regions. However, the impact of human capital on economic efficiency is stronger in the eastern and western regions compared to the central region. The plausible reason is that a developed economy enriches the skill level and assets of the workforce, thus increasing the influence of human capital on green economic efficiency. This result disagreed with Yuan et al. (2020) who argued that human capital reduces economic efficiency.

Finally, the physical capital has a significant and positive impact on green economic efficiency in the eastern region but has the opposite effect in the central region. However, physical capital has insignificant impact on green economic efficiency in the entire China and western region. This result may be due to the fact that the growth of physical capital in the central region is accompanied by energy consumption, which has a negative impact on green economic efficiency, while the eastern region has developed a more mature system to deal with this issue.

Discussion and Policy Implications

The overall regression results indicate that energy consumption and GDP growth have significant negative impact on green economic efficiency, implying that these variables are the main source of economic inefficiencies in China. Therefore, the following policy recommendations are provided to alleviate the constraints of energy and environment on economic efficiency: Firstly, it may be necessary for policymakers to shift the economic development objective from quantitative expansion to qualitative improvement. Secondly, a reduction in the consumption of fossil fuels will have a significantly positive effect on improving economic efficiency. Since nearly half of coal consumption is utilized for power generation in China, policymakers need to boost the energy efficiency of coal-fired power plants. This can be conducted by promoting technological innovation and setting up a competition mechanism to make inefficient coal-fired power plants obsolete to mitigate environmental pressure. The policies of energy diversification can also reduce dependence on fossil fuels. Consequently, the Chinese government should take active measures to encourage the utilization of clean energy such as wind, solar, natural gas, and nuclear.

Furthermore, the overall level of green economy efficiency in China displays a declining trend over recent years and has significant regional disparities. The values of green economic efficiency exhibit a clear downward stepwise distribution from the eastern to western region. China covers a vast territory, and there are large disparities in the industrial structures and development stages of different regions. Over the decades, the eastern region of China was prioritized in the layout of development strategies, making it more developed than the western region in all aspects such as openness, industrialization, urbanization, and education level. Therefore, it may be necessary for policy makers to take full account of regional disparities in formulating economic efficiency strategies and to integrate local characteristics in designing feasible development policies.

To reach the desired targets, the requisite recommendations are fundamental for the respective regions in China. In the western region, energy consumption, innovation, and human capital are positively associated with green economic efficiency, while GDP growth and industrial development are negatively associated with green economic efficiency. The economic development of the western region is characterized by serious problems of energy inefficiency and environmental pollution, and heavy industry accounts for a high proportion of its economic structure. This exacerbates the imbalance between economic development and environmental quality in the western region. In response to this situation, the local government is advised to develop region-specific environmental policies, increase environmental management and restoration funding, and strengthen the implementation of binding regulations such as energy conservation and emission reduction. The notable contribution of energy consumption to economic efficiency in western China does not signify that this region can mindlessly involve in energy-intensive projects. Instead, policymakers are expected to tactfully take advantage of resources to reduce the weight of industry in economic structure and prioritize the development of modern services. Moreover, the western region is suggested to introduce advanced production technologies from developing regions and encourage the development of environmental protection industries. Meanwhile, in order to establish a positive environment to undertake the industrial transfer from developed regions, the western region should raise the quality of talents and innovation level and improve the construction of financial and tax services.

In the eastern region, energy consumption and GDP growth have significant adverse effects on economic efficiency, while urbanization, human capital and physical capital play positive roles in promoting economic efficiency. The eastern region is the most developed region in China and is superior to other regions in terms of technology level, human capital, physical capital and service industry. Therefore, the eastern region is a pioneer in constructing a resource-saving and environment-friendly economy, and a practitioner in accelerating the transformation of the economic pattern, optimizing the industrial structure and upgrading the technology level in China. The priority of local development should be transferred from promoting GDP growth to aggressively introducing advanced technology. The development of new energy industries such as tidal energy, solar energy, and nuclear energy contributes to the reduction of carbon emissions in multiple ways. In addition, the eastern region needs to implement more stringent environmental management policies. Optimized policies are expected to be more effective in guiding the rapid development of resource-saving and environmental-friendly industries, as well as advocating the practice of low-carbon consumption by residents.

Finally, in the central region, energy consumption and urbanization decrease green economic efficiency, while the innovation, industrial development and human capital contribute to green economic efficiency. The central region has a greater potential to reduce carbon emissions than other regions since it is not only the major distribution area for heavy industries in China, but also the region with the highest proportion of coal in the energy mix. The economic development pattern of the central region is inclined to be rough, thus, the carbon reduction is more obligatory for economic development. In response to the empirical results for the central region, the policy emphasis is to improve energy efficiency and optimize energy structure by introducing advanced technologies, such as adopting advanced production processes to elevate the efficiency of coal utilization. Considering that the central region has significantly less geographical advantages than the eastern region, the government needs to design economic development and environmental management policies that are compatible with the local characteristics.

Conclusion

In the past decades, China has continued to pursue the speed of economic growth at the expense of the environmental quality, resulting in a sloppy development pattern. This research seeks to evaluate China’s economic efficiency under the constraints of energy and environment, as well as unravel the factors influencing economic efficiency. An improved super-efficiency SBM model is employed to evaluate the green economic efficiency of provinces in China, while the Tobit regression model is employed to explore the determinants of economic efficiency. This research uses the panel data of 30 provinces in China during the 2005 to 2019 period.

The statistical results unveil that the economic growth efficiency displays a declining trend over recent years in China. There is regional heterogeneity of green economic efficiency across provinces, that is, the efficiency values exhibit a clear downward stepwise distribution from the eastern to western region. The reason for this result is that most western provinces still rest on traditional industries with outdated equipment and lack of innovation. In contrast, the eastern region has a more open culture, higher levels of technology and better geographical locations, and their economic development patterns are more compatible with sustainable development requirements. Moreover, the overall regression results indicate that energy consumption and GDP growth have significant negative impacts on green economic efficiency, implying that they are the main source of economic inefficiencies in China. These adverse effects indicate that economic growth is excessively reliant on energy consumption, that it exceeds the environmental carrying capacity, resulting in a decline in green economic efficiency. Thus, an improvement in energy efficiency is an effective measure to facilitate economic efficiency. Also, the development of urbanization, innovation and human capital have significant contributions to economic efficiency.

The economic implication of this research is to introduce energy inputs and pollution outputs into the existing economic efficiency evaluation model, thereby objectively evaluating the real level of China’s economic growth efficiency. The practical significance is to investigate the relevant factors affecting the economic efficiency in different regions based on the significant regional disparities in China. This offers the opportunity to obtain more specific insights and proffers more targeted policy recommendations.

Though this study has succeeded in unravelling the determinants of green economic efficiency in China, there are some limitations which provide opportunities for future research. The main limitation of this research is that carbon emissions, wastewater and solid waste emissions are employed as undesired outputs, which may not provide a comprehensive picture of the overall economic efficiency. Therefore, it is recommended for future studies to add more pollutant gases (e.g., sulfur dioxide, nitrous dioxide, and methane) as the undesired outputs. Furthermore, though the greening of the city, forest cover, and the level of medical care contribute to the environmental quality (and benefit the residents), these variables were not considered in this study Hence, it is suggested for future studies to consider these variables as desired outputs. Finally, since China is a developing country, we recommend that future studies should conduct a comparative analysis of the determinants of green economic efficiency in both developing and developing countries for greater insights.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Xinxu Zhao

Kizito Uyi Ehigiamusoe

Data Availability Statement

The data underlying this study are openly available in China Statistical Yearbook (2021) at https://www.stats.gov.cn/sj/ndsj/2021/indexeh.htm, China Energy Statistical Yearbook (2021) at https://www.chinayearbooks.com/china-energy-statistical-yearbook-2021.html, and China Environmental Statistical Year book (2021) at

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