1. Introduction
The YRB has the most energy resources among the seven major river basins in China, and has formed a distribution pattern of upstream hydropower, midstream coal, and downstream oil. Relying on its abundant energy resources, the construction and spatial layout of heavy industries with high energy consumption in the YRB have been gradually formed. In addition, the industries in this basin, such as the iron and steel, automobile, and petroleum chemical industries, have an important influence on China. As an important energy and chemical base, the provinces of the YRB are not only an important part of China’s economic development pattern, but also an important typical area of global warming [
1]. The YRB is an important ecological corridor connecting the Qinghai–Tibet Plateau with the Loess Plateau and the North China Plain, in which the carbon emission from energy consumption has increased dramatically in recent years [
2]. Under the traditional development model of “High energy consumption, High emission, High pollution”, the contradiction between economic development and ecological protection is gradually emerging and sharp. In 2019, the energy consumption of the nine provinces in the YRB reached 1638.65 million tons of standard coal, accounting for about 33.65% of the national energy consumption. Compared with the upper reaches, the middle and lower reaches are the main sources of carbon emission in the YRB, and the proportion of carbon emission in the middle reaches has always remained above 40% [
3]. From 2000 to 2017, the CEE of the middle and lower reaches of the Yellow River (Shanxi, Inner Mongolia, Henan, and Shaanxi) was much lower than that of other economic regions in China [
4]. The inefficient use of energy restricts intensive economic development and seriously affects environmental security [
5].
In 2019, China regarded the ecological protection and high-quality development of the YRB as a major national strategy [
6] and issued the “Outline of the Plan for Ecological Protection and High-quality Development of the Yellow River Basin” in 2021. In the context of green development, the YRB is facing unprecedented challenges and opportunities. The improvement of the CEE in the YRB contributes to its ecological protection and high-quality development, and provides an important regional force for China’s implementation of the Paris Climate Agreement. Therefore, to promote the development of a green economy in the YRB, we must accurately measure the CEE of the provinces in the basin, and analyze the temporal and spatial differences in and driving factors of the increase in CEE, so that we can make policy recommendations accordingly.
Based on the above research background and purpose, the possible contributions of this study are as follows. Firstly, this study selects 21 energy sources to calculate carbon dioxide emissions more accurately, and then, calculates the CEE of nine provinces in the YRB from the perspective of the river basin, which is based on the stochastic frontier model. Secondly, the spatial and temporal characteristics of CEE from 2005 to 2019 are systematically analyzed at the provincial unit level. Thirdly, an SDM with two-way fixed effects is introduced to investigate the influencing factors of CEE. Finally, this study gives a new economic explanation of CEE in the YRB. These findings may provide some suggestions for policymakers to improve CEE.
The structure of the remainder of this paper is as follows: the second part presents a literature review, the third part covers the materials and methods, the fourth part presents an empirical analysis, the fifth part presents the conclusions, the sixth part presents a discussion, and the last part covers policy recommendations.
2. Literature Review
As an important indicator to measure the development of a green economy, CEE can effectively explain the relationship between economic development and carbon dioxide emissions, which is the key to achieving carbon emission reduction [
7]. In recent years, CEE has become a hot topic in the academic research field. There have been many relevant studies on CEE, and rich research results have been achieved. Previous research mainly focuses on efficiency evaluation, regional differences, and influencing factors.
(1) There are strong disputes among scholars on the definition and measurement of CEE. According to the number of input factors, CEE can be divided into single-factor CEE [
8,
9] and total-factor CEE [
10,
11]. Single-factor CEE is mainly expressed as the ratio of two-factor indicators, such as carbon productivity [
12], carbon intensity [
13,
14], and carbon index [
15]. The advantages of the single-factor CCE index are that it is convenient, simple, and easy to understand when evaluating regional CEE. However, the single-factor CCE index mainly measures the single proportional relationship between carbon emission and output (or input), ignoring the intrinsic correlation of capital, labor or other production inputs, and CEE [
16]. It can easily cause deviation and one-sided calculation results. In addition, the diversification of measurement indicators is also prone to controversy [
10]. Therefore, some scholars have carried out studies on CEE from the perspective of total factors. They considered that the measurement of carbon efficiency should be integrated into three frameworks: energy consumption, economic development, and carbon emissions [
17]. Currently, data envelopment analysis (DEA) [
18,
19], SFA [
20,
21], and their extended models are widely used to calculate the total-factor CEE. As a non-parametric method, DEA is a data-oriented method, which uses the operations research method to construct a frontier that enveloping surfaces. It is used to estimate the total factor efficiency of homogeneous decision-making units (DMUs). DEA can obtain the weights of a set of optimal input and output variables through optimization methods based on objective data of evaluation objects, and determine the efficiency level of DMUs n the form of the ratio of input to output. DEA is widely used in the study of CEE due to its characteristics, such as the lack of need to set a specific production function form and to perform dimensionless data processing. However, this method may overestimate the level of technical inefficiency, requires high data quality, and does not have statistical characteristics.
SFA is a typical representative of the parametric method in frontier analysis. Compared with the non-parametric method, since the influence of random factors on output is considered, it can not only separate the influence of random factors, but also be free from the interference of measurement errors or other random errors, so errors can be avoided as far as possible. In addition, due to the introduction of random disturbance terms in the SFA model and its statistical characteristics, the estimated parameters can be tested, and the model itself can be tested, so the measured results are more real. In this paper, we choose the SFA model to measure the total-factor CEE of the provinces in the YRB.
(2) Existing studies on factors affecting CEE can be roughly divided into two categories. The first category is decomposition analysis, which allows us to quantify the factors driving CO
2 emissions [
22]. The representative research methods include index decomposition analysis (IDA), structural decomposition analysis (SDA), and the logarithmic mean divisia index (LMDI) [
23]. IDA is a method that divides changes in the target variables into several combinations of influencing factors, screens out the factors with greater influence, decomposes and quantifies their output, and thus, objectively determines the different degrees of influence of each factor on the target variables [
24]. SDA is based on the input–output theory, in which the input–output table can connect multiple regions, and the research scope includes single and multiple economies [
25,
26]. LMDI can effectively solve the final residual value problem and prevent subjective randomness when estimating and determining parameters caused by the unexplained residual value [
27].
Another category is the econometric model method. Based on the different perspectives, scholars use the Granger causality test, cointegration analysis, and panel data regression to test the relationships between economic development [
28,
29], industrial structure upgrading [
30], technological progress [
31], urbanization [
32], international trade [
33], renewable energy development [
34], and CEE. Zhang et al. argued that factor mismatch has a significant inhibitory effect and a spatial spillover effect on CEE, which is an important reason for low carbon emission levels and the differences in CEE between different regions [
35]. Chu et al. further found that there is a significant inverted U-shaped relationship between improper energy allocation and CEE in China [
36]. For different categories and industries, the degree and direction of the impact of mismatched energy on CEE are also different [
37]. By using the spatial mediation model and the spatial adjustment model, Dong et al. found that environment-related green technology innovation can significantly improve regional CEE, and it can indirectly affect CEE by affecting economic development and urbanization [
38]. Yao et al. found that the development of digital finance can promote the efficiency of carbon emissions effectively. Its depth of use and degree of digitization play a promoting role, while the breadth of coverage plays a restraining role [
39]. Based on the spatial Durbin panel model, Li et al. found that improving the technical level is an important way to promote the growth of CEE in the Yangtze River Delta region [
40].
(3) Scholars also noticed that there may be regional differences and spatial effects in CEE, and gradually applied spatial econometrics to the study of CEE. Existing studies have shown that there are significant spatial autocorrelations and inter-provincial differences in China’s provincial CEE, and the average CEE of eastern coastal provinces is significantly higher than that of inland provinces [
41]. The CEE of the power sector in the eastern region is relatively high, and it has a positive spillover effect on surrounding provinces [
42]. The CEE of the construction industry is spatially high in the east and low in the west. The high–high (HH) agglomeration areas are mainly distributed in the coastal areas of the eastern Yangtze River delta, and the low–low (LL) agglomeration areas are mainly distributed in the northeast, southwest, and northwest regions [
43]. The CEE of transportation shows a decreasing trend from east to west, and there is a significant accumulation of space; it forms the HH agglomeration area composed of the eastern coastal provinces (including Hebei, Tianjin, Shandong, and Jiangsu), and the LL agglomeration area composed of Central and Southern China, South China, and Northeast China (including Guangdong, Jiangxi, Hunan, and Hubei) [
44].
To summarize, the existing literature on CEE has been relatively abundant and mainly focuses on industries, provinces, and economic zones. However, researchers have paid relatively insufficient attention to studying CEE from the perspective of watersheds, which play an important role in social and economic development. In 2016, China issued the “13th Five-Year Plan for Controlling Greenhouse Gas Emissions”, and clearly stated that strict control of carbon emissions in key river basins is one of the main goals and most important tasks in China’s greenhouse gas emission control from 2016 to 2021. Therefore, based on the defects of existing studies, this study selects 21 energy sources to calculate carbon dioxide emissions more accurately, calculates CEE based on the SFA model from the perspective of the river basin, and analyzes the spatial and temporal differences. Then, we investigate the influencing factors of CEE through a spatial model. Finally, we propose some countermeasures and suggestions for the improvement of the CEE of the YRB to promote the high-quality development of the basin.
5. Conclusions
This study adopted the stochastic frontier model to measure the CEE of nine provinces in the YRB from 2005 to 2019, and then, conducted kernel density estimation to visually analyze the overall state and spatiotemporal differences in the CEE of each province. Finally, this study selected an SDM with two-way fixed effects to investigate the influencing factors of provincial CEE in the YRB. The conclusions are as follows:
(1) The overall CEE of the YRB is on an upward trend, but there is still much room for improvement. There is a large gap in CEE among the nine provinces in the YRB. The lower reaches of the YRB have higher CEE, while the middle and upper reaches of the YRB are all at a low efficiency level.
(2) From the kernel density distribution curve, it can be found that the CEE levels of provinces in the YRB are relatively uniform and have a slow upward trend. The CEE of the nine provinces in the basin has shown little change, and the efficiency gap between the provinces has narrowed slightly at a slow pace. At the same time, the unbalanced phenomenon of “multi-level differentiation” does not exist in the CEE of the provinces in the YRB.
(3) There is a significantly negative spatial autocorrelation in the CEE of the provinces in the YRB. The CEE of the YRB is affected by a variety of factors, and each factor has a different effect. Technological innovation capability, energy consumption structure, population density, and urban greening level are the main significant factors affecting the CEE of the YRB. Both population density and urban greening level have a positive effect on the improvement of the CEE of the provinces themselves, and in the whole YRB. Among them, the sharing economy and low-carbon lifestyle brought about by an population density increase will also play a role in improving the CEE of neighboring provinces to a certain extent. Technological innovation capacity and energy-consumption structure had a negative impact on the overall CEE of the province and the basin during the research period.
6. Discussion
Improving CEE is a necessary way to achieve green and low-carbon economic development in China. Most of the relevant scholars conduct research on a macro- and meso-scale. The differences between Chinese regions are obvious, and the imbalance is serious. Therefore, it was necessary to carry out an in-depth study from a regional angle. This paper analyzes this issue from the perspective of the watershed, which can help mine more accurate characteristic information and enrich the existing research.
(1) China has a vast territory; different regions have different natural resource endowments, different levels of economic development, and different development modes. The overall CEE in the YBR is low, and is higher in the downstream provinces than in the middle and upper provinces. The spatial difference and imbalance in CEE are closely related to regional resource factor endowment and geographical advantage. The Western development strategy promotes the elimination of resource-based industries with high energy consumption and high emissions in the lower reaches of the Yellow River, and transfers them to the middle and upper reaches of the Yellow River with rich resources and few environmental constraints; this affects the improvement of CEE and the effect of green development in the upper reaches of the Yellow River [
69]. The middle reaches are mostly coal-resource-based cities, which rely heavily on coal resources and face great pressure to reduce carbon emissions, thus making the urban CEE low [
70].
Existing studies have studied the regional and industrial differences in CEE in China. From the perspective of the watershed, most studies focus on the exploration of a single watershed, and less attention is paid to the differentiation comparison among different basins. Jiang et al. [
71] analyzed the spatial evolution characteristics of CEE in the Yangtze River basin and the YRB, and found that CEE in the Yangtze River basin presents a spatial distribution pattern with low CEE in the middle and high CEE at both ends, while the YRB presents an increasing spatial pattern in the order of the top, middle, and bottom reaches. The Yangtze River basin and the YRB have typical north–south differences, with different development bases and conditions, but both emphasize green, low-carbon, sustainable, and high-quality development. It is of great significance to regional and national development to adopt policies based on local conditions and classification to effectively improve CEE and promote green development. Therefore, future studies could study the spatiotemporal evolution characteristics and influencing factors of CEE from the perspective of watershed comparison, accurately identifying the causes of differences and the path to improving CEE, in order to facilitate the government’s precise policies.
(2) It is necessary to clarify the influencing factors to improve CEE. Many factors, such as natural conditions, resource endowment, mode of production, institutional environment, and economic basis, will affect CEE. The influencing factors of CEE vary in different regions and different research objects. The existing literature selects different influencing factors according to their research aims, mostly focusing on property right structure, economic development level, industrial structure, technological innovation, urbanization, foreign investment, environmental policy, government intervention, energy consumption structure, energy prices, etc. [
72,
73]. On the basis of relevant studies, this paper selects seven factors: industrial structure upgrading, technological innovation ability, energy consumption structure, urbanization rate, population density, per capita green space, and environmental regulation intensity, considering the geographical characteristics, resource endowment, and economic development of the provinces in the YRB, especially as it is the main coal production and power supply base in China [
74]. Future studies could introduce more influencing factors for a comprehensive analysis.
(3) As the main methods of measuring CEE, SFA and DEA have achieved fruitful research results. Both methods have advantages and disadvantages. The advantages of SFA are obvious. Since the influence of random factors on output is considered and the influence of random factors can be separated when determining the efficiency frontier, the results will not be affected by measurement errors or other random errors, and the errors caused by the random bias of the deterministic model can be better overcome. Traditional SFA also has some disadvantages: it requires the assumption of production functions, which may cause multicollinearity problems. Setting the production function has certain subjectivity, and the results obtained using different settings are quite different. It cannot deal with the multi-input–multi-output problem. Researchers have made a series of improvements to SFA according to their specific research purposes. Herrala et al. [
75] investigated the CEE of 170 countries based on stochastic cost frontier analysis. Zhou et al. [
76] combined the Shepard distance function with SFA, so that the SFA model could simultaneously deal with the multi-input–multi-output problem. Kuosmanen adopted random non-parametric data envelope analysis (StoNED) by integrating SFA and DEA together, and found that it is more maneuverable, accurate, and flexible in efficiency measurement compared with SFA or DEA [
77]. Battese et al. [
78] introduced a meta-frontier model for different technology groups to solve the bias of efficiency evaluation results caused by heterogeneity among DMUs. Based on Zhang and Zhou’s [
79] two-step stochastic frontier model, Zhang and Tu [
80] made an improvement, taking into full consideration the technological heterogeneity between different industries and the unexpected output of enterprises, and calculated the total factor efficiency of the micro-enterprises in China.
This paper uses macro-level data, and some data are obtained through the estimation of basic data, which inevitably has noise; therefore, it is more appropriate to choose the random frontier boundary analysis method. In addition, under the framework of the stochastic frontier boundary, the definition of CEE in this paper is more intuitive, and it is more appropriate for evaluating the carbon dioxide emission performance in production activities, which is also an important reason for choosing the stochastic frontier boundary analysis method in this paper. In future studies, SFA could be improved or combined with other methods, and a comparative analysis could be carried out to select a suitable method that meets the authors’ research needs.
On this basis, our future research will mainly focus on two aspects. Firstly, different CEE measurement methods will be adopted for comparative analysis and further improvement to determine the most suitable and effective method for research objects and data acquisition problems, and to more accurately mine carbon emission characteristic information in specific regions. Secondly, the status, characteristics, and causes of CEE in various river basins will be compared and analyzed, and policies will be proposed that consider local, current, and appropriate circumstances to comprehensively promote the overall improvement of CEE in the whole country.