1. Introduction
Since the reform and opening up, China has created an amazing “Chinese miracle” by leveraging its demographic and resource dividends, and its economic achievements have attracted worldwide attention. However, resource constraints and environmental pressures have largely affected the sustainable development of the economy, which has emerged as a result of the transition of economic development stages. Therefore, in the face of increasingly severe environmental pressures, the 17th CPC National Congress for the first time included “ecological civilization” in the report of the Party Congress; the 18th CPC National Congress included “ecological civilization” in the overall layout of the “five in one”; the Fifth Plenary Session of the 18th CPC Central Committee included green development as one of the “five development concepts”; and the report of the 19th CPC National Congress emphasized the establishment of a sound economic system for green and low-carbon development. In response to the enormous pressure of carbon emissions, China proposed the goals and visions of “carbon peak” and “carbon neutrality” at the United Nations Climate Change Summit held in December 2020. It also promised to achieve peak carbon by 2030 and carbon neutrality by 2060. With the promotion of a series of policies, green development has increasingly become a new development path for achieving economic development and environmental protection. Therefore, unlike previous developments, the cost of green development in consideration of environmental protection is that the government needs to fully play its regulatory role in finance to ensure the orderly implementation of green development.
While local governments implement green development strategies, increasing fiscal pressure and performance expectations for officials further increase the borrowing level of local governments [
1]. Fiscal pressure is the main driving force for large-scale borrowing by local governments and is mainly reflected in the following two aspects [
2]. On the one hand, when the liquidity of local governments is insufficient to support existing fiscal expenditures, the fiscal gap expands, increasing fiscal pressure and prompting local governments to promote municipal construction through large-scale debt to support economic development and environmental protection. As a result, local government fiscal expenditures and future principal and interest repayment pressures significantly increase at the same time. On the other hand, local governments with better economic development will gain more development opportunities [
3]; this leads to regional development pressure, thereby promoting large-scale borrowing by local governments [
4]. At the same time, the promotion of local government officials may undermine the fiscal expenditure incentive mechanism of local governments, leading local governments to prefer to place economic development above environmental protection. In order to alleviate the fiscal budget dilemma, local governments may relax energy-saving regulations or energy consumption restrictions on high-energy-consuming enterprises to stimulate and help their rapid development, resulting in a decrease in green development efficiency (GDE). As an important financial tool to promote economic growth, government debt may have a considerable impact on local GDE.
Local government debt (LGD) can be divided into six categories, with the most important source being the acquisition of bank loans through the establishment of local investment and financing platform companies and the issuance of “city investment bonds”. Due to the “hidden” guarantee role of the government, the city investment bonds issued by local investment and financing platforms are usually regarded as “quasi-municipal bonds”, which are mainly used in municipal public facilities projects with relatively concentrated capital and long repayment periods, and play an important role in the green development and transformation of cities. In recent years, the financing scale of city investment bonds and the proportion of direct financing of the government have both increased rapidly. Therefore, in the context of achieving high-quality development in China, examining the impact of LGD, especially the city investment bonds established by local investment and financing platforms, on the efficiency of regional green development will further supplement the research on the impact of LGD on GDE.
Based on the above analysis, there are two main sources of direct impact of LGD on GDE. On the one hand, the pressure of official promotion has led to local governments prioritizing economic development while neglecting environmental protection. On the other hand, that pressure stems from budget constraints, which stimulate local governments to relax their supervision and constraints on high-energy-consuming enterprises.
Of course, achieving high-quality development is still a major task in China at present. In the past, most studies mainly considered how single economic factors, such as government policies [
5], urbanization [
6], foreign direct investment [
7], and green investment, affect local environmental protection [
8], but lacked consideration of the synergistic effects of economic development and environmental protection. It is noteworthy that when studying the impact of LGD on urban pollutant emissions, some scholars have paid more attention to sulfur dioxide emissions. For example, Qi et al. [
4] experimentally studied the impact of LGD on urban emission reduction using sulfur dioxide emissions in urban exhaust as an explanatory variable. Existing research has not considered the impact of LGD on GDE, and has also overlooked the possibility that LGD can indirectly affect GDE through other means. Therefore, while focusing on the relationship between LGD and GDE, this study also identified two mediating pathways of influence. In addition, empirical analysis was used to confirm the impact of local debt on GDE, and the mediating mechanism was explored.
The innovation of this article lies, first, in paying attention to the impact of LGD, which is mainly carried out by local investment and financing platforms, on the synergistic effect between environmental protection and economic development, and expressing this impact with GDE. This study regards LGD as a factor that affects green development and its specific level. Second, it explores the impact mechanism of LGD on GDE, providing a theoretical basis for subsequent related research. The research shows that the main ways in which LGD affects GDE are industrial structure, technological innovation, environmental governance investment, and government expenditure scale. Finally, this study combines the existing practical value and relevance of China’s green development to reveal appropriate and targeted policies. This study is divided into the following chapters: the second part is a literature review and research hypothesis; the third part explains the data and empirical methods; the fourth part discusses the empirical research and empirical results; and the fifth part contains the research conclusion and policy recommendations.
Author Contributions
Conceptualization, Y.G.; methodology, J.W.; software, J.W.; validation, Y.G.; formal analysis, J.W.; investigation, J.W.; resources, Y.G.; data curation, J.W.; writing—original draft preparation, J.W.; writing—review and editing, J.W.; visualization, J.W.; supervision, Y.H.; project administration, Y.H.; funding acquisition, Y.G. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the National Social Science Foundation (Approval number: 20FJYB029), the Graduate Education Teaching Reform Research Project of Jiangsu Province (Project number: JGKT22_C032), and the University Philosophy and Social Science Research Project of Jiangsu Province (Project number: 2023SJYB0266).
Data Availability Statement
This paper selects 30 provinces in China (omitting Xizang, Hong Kong, Macao, and Taiwan), and the time span is 2010–2020. All the original data are from the China Financial Yearbook, China Statistical Yearbook, China Environmental Yearbook, Wind Energy Database, CEIC database, EPS database, and statistical yearbooks of various provinces (autonomous regions, municipalities directly under the central government) over the years. The missing data for some provinces are supplemented by the average growth rate method.
Conflicts of Interest
The authors declare no conflict of interest.
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Table 1.
GDE measurement index.
Variable Type | Variable Name | Variable Measurement | Variable Unit |
---|
Input Variables | Capital (k) | Fixed assets investment volume | Billion Yuan |
Labor (l) | Number of Employees in the Whole Society at the End of the Year | Million |
Energy Source (e) | Electricity Consumption | Billion Kilowatt Hours |
Output Variables | Expected Output (g) | Real Gross Domestic Product | Billion Yuan |
Undesirable Output (b) | Industrial Wastewater Emissions | Million Tons |
Industrial Sulfur Dioxide Emissions | Million Tons |
Industrial Soot Emissions | Million Tons |
Table 2.
Descriptive statistics.
Variable | Sample Size | Average Value | Standard Deviation | Minimum Value | Median Value | Maximum Value |
---|
GDE | 330 | −0.9120 | 0.5565 | −1.61 | −1.12 | 0.00 |
LGD | 330 | 7.2662 | 1.6789 | 2.71 | 7.48 | 10.37 |
Ambient pressure | 330 | 0.0071 | 0.0081 | 0.00 | 0.00 | 0.05 |
Energy consumption structure | 330 | 0.9451 | 0.4441 | 0.02 | 0.86 | 2.46 |
Population density | 330 | 7.8754 | 0.4158 | 6.64 | 7.89 | 8.67 |
Infrastructure construction | 330 | 15.3258 | 4.6797 | 4.04 | 14.77 | 26.20 |
Table 3.
Basic regression results.
| (1) | (2) | (3) | (4) | (5) |
---|
| GDE | GDE | GDE | GDE | GDE |
---|
LGD | −0.036 * | −0.045 ** | −0.053 ** | −0.047 ** | −0.046 ** |
(−1.68) | (−2.01) | (−2.35) | (−2.07) | (−2.08) |
Ambient pressure | | −3.058 | −1.432 | −0.683 | −3.517 |
| (−1.34) | (−0.61) | (−0.29) | (−1.38) |
Infrastructure construction | | | 0.195 ** | 0.200 ** | 0.233 *** |
| | (2.32) | (2.41) | (2.81) |
Energy consumption structure | | | | 0.135 *** | 0.115 ** |
| | | (2.75) | (2.35) |
Population density | | | | | −0.017 *** |
| | | | (−2.74) |
Constant term | −0.660 *** | −0.583 *** | −0.750 *** | −1.852 *** | −1.482 *** |
(−6.11) | (−4.78) | (−5.33) | (−4.37) | (−3.37) |
Provincial fixed effect | YES | YES | YES | YES | YES |
Time fixed effect | YES | YES | YES | YES | YES |
N | 330 | 330 | 330 | 330 | 330 |
R-Square | 0.105 | 0.111 | 0.129 | 0.154 | 0.178 |
Table 4.
Results of the mechanism of industrialization.
| (1) | (2) | (3) |
---|
| GDE | Industrialization Degree | GDE |
---|
LGD | −0.046 ** | 0.010 ** | −0.038 * |
(−2.08) | (2.30) | (−1.71) |
Industrialization degree | | | −0.794 *** |
| | (−2.60) |
Control variable | YES | YES | YES |
Constant term | −1.482 *** | 0.615 *** | −0.993 ** |
(−3.37) | (6.91) | (−2.10) |
Provincial fixed effect | YES | YES | YES |
Time fixed effect | YES | YES | YES |
N | 330 | 330 | 330 |
R-Square | 0.146 | 0.663 | 0.152 |
Table 5.
The results of the mechanism of the urban population.
| (1) | (2) | (3) |
---|
| GDE | Urban Population | GDE |
---|
LGD | −0.046 ** | 0.033 *** | −0.001 |
(−2.08) | (5.59) | (−0.04) |
Urban population | | | −1.370 *** |
| | (−6.27) |
Control variable | YES | YES | YES |
Constant term | −1.482 *** | 7.693 *** | 9.056 *** |
(−3.37) | (65.64) | (5.23) |
Provincial fixed effect | YES | YES | YES |
Time fixed effect | YES | YES | YES |
N | 330 | 330 | 330 |
R-Square | 0.178 | 0.894 | 0.288 |
Table 6.
Robustness test.
| (1) Adding Control Variables | (2) Excluding Autonomous Regions | (3) Substitution Variables |
---|
| GDE | GDE | GDE |
---|
LGD | −0.045 ** | −0.050 ** | −0.096 *** |
(−2.03) | (−2.19) | (−4.34) |
Control variable | YES | YES | YES |
Constant term | −1.357 *** | −0.750 | 0.500 |
(−2.92) | (−2.43) | (0.28) |
N | 330 | 297 | 330 |
R-Square | 0.181 | 0.194 | 0.225 |
Table 7.
Heterogeneity analysis.
| (1) Eastern Region | (2) Central Region | (3) Western Region |
---|
| GDE | GDE | GDE |
---|
LGD | −0.066 * | 0.152 * | −0.052 ** |
(−1.75) | (1.80) | (−2.43) |
Control variable | YES | YES | YES |
Constant term | 1.967 * | −2.317 * | −3.096 *** |
(1.77) | (−1.83) | (−7.26) |
N | 132 | 99 | 99 |
R-Square | 0.154 | 0.320 | 0.753 |
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