The Assessment of Green Business Environments Using the Environmental–Economic Index: The Case of China
Abstract
:1. Introduction
2. Literature Review
2.1. Sustainable Development Goals (SDGs) Compliance
2.2. Green Economic Perspective
2.3. Environmental, Social, and Governance (ESG) Framework
2.4. Related Theories of Government Governance
2.5. Green Business Environment Evaluation Index (GBEEI)
3. Methodology
3.1. Principal Component Analysis
3.2. Spatial Econometrics
4. Empirical Result
4.1. Result of Principal Component Analysis
4.2. Spatial Correlation Test Results
4.3. Statistical Test Results for Model Selection
4.4. Spatial Regression Results
4.5. Spatial Effect Decomposition
4.6. Regional Heterogeneity Analysis
5. Conclusions
5.1. Provincial and Municipal Business Environment Evaluation
5.2. Effect of GBEEI on Sustainable Economic Development
5.3. Research Limitations and Future Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Dimensions | No. | Indicators | Indicator Description | Indicator Explanation |
---|---|---|---|---|
Economic Environment | X11 | Economic Development | Gross regional product index (Previous year = 100) | Econometric development level |
X12 | International Trade | Total amount of import and export of goods (USD 1000) | Level of foreign trade | |
X13 | Foreign Investment | Actual use of foreign direct investment (USD 10,000) | Increased confidence in the investment environment | |
X14 | Fixed Asset and Investment | Fixed asset investment price index (previous year = 100) | Whether the enterprise is optimistic about future economic development | |
X15 | Enterprise Digitization | The proportion of enterprises with e-commerce transaction activities (%) | The degree of digitalization of the enterprise | |
X16 | Capability to Acquire Capital | Number of listed companies | The quantity and quality of listed companies determine the economic scale and height of a province | |
X17 | Financing Capacity | Social financing scale | Economic attractiveness and financing capacity of a province | |
X18 | Transport Efficiency | Cargo turnover (billion ton-kilometers) | Logistics development status | |
Government Environment | X21 | Government Revenue Scale | The ratio of local general budget revenue to GDP (CNY ten billion) | The government’s ability to improve the quality of the provinces’ business environments via financial support |
X22 | Government Balance | Local government’s general budget revenue minus public service expenditure (%) | The ability of the government to coordinate the stable development of the economy | |
X23 | Tax | The ratio of tax revenue to GDP (%) | The economic status of each province | |
X24 | Land Cost | The ratio of land purchase cost to land purchase area (CNY/square meter) | The land cost of the enterprise | |
Social Environment | X31 | Population | Urban population density (person/square kilometer) | Provinces’ economic attractiveness |
X32 | Inflation | Consumer price index (previous year = 100) | Purchasing power | |
X33 | Disposable Income | Per capita disposable income of residents (CNY) | The wealth of the people | |
X34 | Employment | Urban registered unemployment rate (million people) | Measures slack labor capacity | |
X35 | Social Security Level | Number of participants in basic medical insurance (million people) | People’s living standards | |
X36 | Wage Level | Monthly minimum wage standard of each province (The highest grade, CNY) | Reflects social employment and income thresholds | |
Technical Environment | X41 | Input of Education | Education expenditure divided by local general public budget expenditure (CNY 100 million) | Related to the quality of citizens and the long-term development of the country |
X42 | Higher Education | The number of colleges and universities or institutions | Conducive to personnel training | |
X43 | Inventions and Patents | Number of effective invention patents (pieces) | Conducive to the progress and development of science and technology | |
X44 | Technology Input | Technology market turnover per unit of GDP (%) | Transformation and upgrading of economic structure | |
X45 | Technological Innovation | The number of new product development projects (pieces) | Promotion of social development | |
X46 | Cultural Atmosphere | Public library holdings per capita ((books per person)) | Improvement of humanistic quality | |
Green Environment | X51 | Power Consumption | Electricity consumption per CNY 100 million of GDP | Consumption of resources and the environment |
X52 | Environmental Protection Expenditure | Environmental protection expenditure in local government fiscal expenditure (CNY 100 Million) | Degree of protection of resources and environment | |
X53 | Waste Disposal | Harmless treatment capacity of municipal solid waste (10,000 tons) | Reduces environmental pollution and waste of resources | |
X54 | Air Pollution | Emissions of SO2, NO2, CO, O, PM10, and PM2.5 | Degree of pollution to the environment Chinese National Ambient Air Quality Standards (CNAAQS) | |
X55 | Living Environment | Parks and green areas per capita (square meters/per person) | Improvement of human living environment |
ID No. | Province (PR) | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | Beijing | BJ | 69.61 | 75.96 | 73.39 | 69.86 | 76.65 | 72.53 | 81.16 | 80.04 | 70.73 | 72.58 |
2 | Tianjin | TJ | 53.20 | 50.95 | 49.25 | 54.83 | 57.13 | 50.81 | 48.64 | 45.62 | 49.55 | 45.61 |
3 | Hebei | HEB | 42.70 | 44.38 | 43.29 | 41.04 | 41.19 | 41.52 | 42.97 | 44.28 | 46.28 | 46.99 |
4 | Shanxi | SX | 37.31 | 37.39 | 36.39 | 33.43 | 34.63 | 33.36 | 34.99 | 35.99 | 37.63 | 37.10 |
5 | Neimenggu | NM | 41.49 | 40.74 | 38.61 | 33.58 | 37.25 | 33.45 | 35.76 | 36.90 | 35.41 | 36.26 |
6 | Liaoning | LN | 56.30 | 57.89 | 50.68 | 49.16 | 44.11 | 39.79 | 38.58 | 43.95 | 41.34 | 39.10 |
7 | Jilin | JL | 36.26 | 36.24 | 33.82 | 33.94 | 35.72 | 32.06 | 33.15 | 35.38 | 32.83 | 32.58 |
8 | Heilongjiang | HL | 35.59 | 39.36 | 34.77 | 31.51 | 32.63 | 32.23 | 30.56 | 32.85 | 36.21 | 29.58 |
9 | Shanghai | SH | 77.55 | 76.35 | 72.91 | 82.16 | 81.91 | 85.18 | 80.91 | 79.13 | 84.57 | 82.12 |
10 | Jiangsu | JS | 75.16 | 75.18 | 78.64 | 79.70 | 73.13 | 72.38 | 72.12 | 74.92 | 69.99 | 76.73 |
11 | Zhejiang | ZJ | 67.33 | 60.35 | 71.02 | 72.06 | 66.97 | 68.50 | 68.95 | 71.18 | 68.79 | 73.72 |
12 | Anhui | AH | 43.67 | 41.57 | 41.71 | 42.71 | 42.08 | 44.18 | 44.80 | 42.33 | 41.02 | 42.03 |
13 | Fujian | FJ | 43.52 | 42.08 | 43.79 | 43.60 | 42.71 | 45.18 | 44.39 | 40.51 | 45.15 | 44.03 |
14 | Jiangxi | JX | 34.24 | 35.35 | 36.90 | 36.97 | 38.78 | 40.14 | 37.70 | 38.07 | 39.59 | 39.48 |
15 | Shandong | SD | 57.98 | 56.12 | 61.09 | 56.91 | 56.15 | 56.61 | 55.87 | 59.40 | 54.22 | 59.40 |
16 | Henan | HA | 41.52 | 42.77 | 41.78 | 40.98 | 42.14 | 43.30 | 41.50 | 43.71 | 44.97 | 44.68 |
17 | Hubei | HUB | 42.55 | 41.58 | 40.55 | 42.58 | 42.57 | 46.52 | 44.02 | 41.69 | 41.82 | 40.31 |
18 | Hunan | HUN | 39.46 | 38.20 | 39.08 | 40.26 | 39.48 | 41.96 | 38.64 | 38.71 | 39.92 | 40.02 |
19 | Guangdong | GD | 79.93 | 80.73 | 91.45 | 89.70 | 85.57 | 89.60 | 88.75 | 95.60 | 91.48 | 98.58 |
20 | Guangxi | GX | 33.59 | 33.62 | 33.10 | 35.33 | 32.87 | 34.10 | 33.27 | 33.15 | 33.43 | 31.82 |
21 | Hainan | HN | 34.79 | 34.73 | 31.00 | 34.91 | 34.35 | 40.87 | 37.35 | 33.33 | 35.40 | 34.67 |
22 | Chongqing | CQ | 38.82 | 39.42 | 37.98 | 37.31 | 36.64 | 37.66 | 38.77 | 36.29 | 36.40 | 36.64 |
23 | Sichuan | SC | 41.20 | 44.03 | 45.50 | 42.58 | 42.46 | 44.28 | 42.86 | 42.37 | 44.04 | 43.66 |
24 | Guizhou | GZ | 32.06 | 32.84 | 31.07 | 32.29 | 33.61 | 33.01 | 35.84 | 33.08 | 34.72 | 33.59 |
25 | Yunnan | YN | 34.27 | 35.36 | 35.23 | 36.06 | 36.36 | 35.68 | 34.33 | 32.64 | 34.85 | 31.36 |
26 | Shaanxi | SN | 35.42 | 37.49 | 37.04 | 36.35 | 36.80 | 36.55 | 38.52 | 36.55 | 39.60 | 37.59 |
27 | Gansu | GS | 27.95 | 28.67 | 28.25 | 27.97 | 32.11 | 28.93 | 31.42 | 30.27 | 30.79 | 30.92 |
28 | Qinghai | QH | 29.16 | 26.79 | 27.86 | 28.99 | 32.33 | 29.11 | 28.80 | 27.15 | 26.57 | 25.84 |
29 | Ningxia | NX | 33.13 | 27.50 | 29.92 | 29.67 | 29.85 | 28.52 | 33.36 | 32.10 | 29.02 | 30.84 |
30 | Xinjiang | XJ | 34.23 | 36.35 | 33.93 | 33.55 | 31.80 | 31.99 | 32.01 | 32.79 | 33.64 | 32.16 |
Type of Test | Null Hypothesis | Statistic | p-Value |
---|---|---|---|
Hausman test | The individual effect has no correlation with the regression variable | 36.59 | 0.000 |
LR test | Spatial fixed-effect nested within double fixed-effect | 68.82 | 0.000 |
Time fixed-effect nested within double fixed-effect | 651.55 | 0.000 | |
LM-Spatial error | No spatial correlation between error terms | 21.65 | 0.000 |
Robust LM-Spatial error | 22.81 | 0.000 | |
LM-Spatial lag | No spatial correlation between lag terms | 13.61 | 0.000 |
Robust LM-Spatial lag | 14.77 | 0.000 |
Statistical Tests | SLM vs. SDM | SEM vs. SDM | ||
---|---|---|---|---|
Z-Value | p-Value | Z-Value | p-Value | |
Wald test | 51.82 | 0.000 | 51.60 | 0.000 |
LR test | 48.01 | 0.000 | 47.39 | 0.000 |
Variable | OLS | Spatial Weight Matrix (W1) | Spatial Weight Matrix (W2) |
---|---|---|---|
Model I | Model II | Model III | |
lnGBE | 0.6573 *** (0.070) | 0.1026 *** (0.031) | 0.0717 ** (0.030) |
lnFC | 0.2766 *** (0.069) | 0.0655 *** (0.014) | 0.0892 *** (0.014) |
lnHC | 2.4981 *** (0.223) | −0.0039 (0.102) | 0.0946 (0.100) |
lnFDI | −0.0145 (0.017) | 0.0106 *** (0.004) | 0.0068 * (0.004) |
lnER | 0.0022 (0.018) | −0.0069 ** (0.003) | 0.0013 (0.003) |
lnIS | 0.4112 *** (0.112) | −0.1658 *** (0.039) | −0.1390 *** (0.037) |
W*lnGBE | 0.2362 *** (0.084) | 0.5582 *** (0.207) | |
W*lnFC | −0.0666 * (0.039) | 0.5013 *** (0.107) | |
W*lnHC | 0.6280 ** (0.278) | 1.8581 ** (0.769) | |
W*lnFDI | 0.0151 (0.011) | −0.0935 ** (0.037) | |
W*lnER | −0.0296 *** (0.008) | 0.0635 *** (0.023) | |
W*lnIS | 0.4800 *** (0.113) | −0.4939 (0.341) | |
ρ | 0.2095 ** (0.103) | 0.4866 *** (0.140) | |
N | 300 | 300 | 300 |
R² | 0.7166 | 0.7892 | 0.3194 |
Log-L | 674.8425 | 686.8714 |
Variable | Model II | Model III | ||||
---|---|---|---|---|---|---|
Direct Effect | Indirect Effect | Total Effect | Direct Effect | Indirect Effect | Total Effect | |
lnGBE | 0.1122 *** (0.031) | 0.3162 *** (0.108) | 0.4284 *** (0.117) | 0.0972 *** (0.032) | 1.1991 ** (0.532) | 1.2963 ** (0.544) |
lnFC | 0.0618 *** (0.012) | −0.0754 * (0.043) | −0.0136 (0.046) | 0.1105 *** (0.017) | 1.0723 *** (0.365) | 1.1828 *** (0.377) |
lnHC | 0.0248 (0.111) | 0.7588 ** (0.319) | 0.7836 ** (0.350) | 0.1794 (0.123) | 3.7181 ** (1.834) | 3.8975 ** (1.898) |
lnFDI | 0.0116 ** (0.005) | 0.0221 (0.016) | 0.0337 * (0.018) | 0.0035 (0.005) | −0.1754 ** (0.087) | −0.1719 * (0.091) |
lnER | −0.0086 ** (0.004) | −0.0376 *** (0.011) | −0.0462 *** (0.012) | 0.0036 (0.004) | 0.1326 * (0.073) | 0.1362 * (0.075) |
lnIS | −0.1470 *** (0.040) | 0.5514 *** (0.155) | 0.4044 ** (0.172) | −0.1598 *** (0.044) | −1.1191 (0.706) | −1.2789 * (0.734) |
Region | Variable | Direct Effect | Indirect Effect | Total Effect |
---|---|---|---|---|
Eastern Region | lnGBE | 0.1268 ** (0.051) | 0.1442 (0.108) | 0.2710 *** (0.100) |
lnFC | 0.0761 *** (0.028) | 0.2134 *** (0.062) | 0.2895 *** (0.065) | |
lnHC | 0.0572 (0.183) | 0.1390 (0.331) | 0.1963 (0.283) | |
lnFDI | 0.0041 (0.010) | −0.0582 ** (0.027) | −0.0541 * (0.030) | |
lnER | −0.0125 ** (0.006) | −0.0068 (0.011) | −0.0193 * (0.011) | |
lnIS | −0.0650 (0.097) | −0.0378 (0.257) | −0.1028 (0.223) | |
Central Region | lnGBE | 0.1629 *** (0.032) | 0.1902 ** (0.090) | 0.3532 *** (0.093) |
lnFC | 0.1525 *** (0.014) | −0.0641 (0.068) | 0.0884 (0.070) | |
lnHC | −0.0101 (0.121) | 0.2935 (0.240) | 0.2834 (0.277) | |
lnFDI | 0.0517 *** (0.009) | 0.1146 ** (0.047) | 0.1663 *** (0.054) | |
lnER | 0.0071 (0.005) | 0.0243 ** (0.010) | 0.0314 *** (0.011) | |
lnIS | −0.4155 *** (0.048) | −0.1687 (0.203) | −0.5842 ** (0.235) | |
Western Region | lnGBE | 0.1622 *** (0.055) | 0.4739 *** (0.124) | 0.6360 *** (0.148) |
lnFC | 0.0365 ** (0.016) | −0.1769 *** (0.045) | −0.1404 *** (0.049) | |
lnHC | −0.1521 (0.157) | 0.2284 (0.360) | 0.0763 (0.423) | |
lnFDI | −0.0002 (0.005) | 0.0314 *** (0.011) | 0.0312 ** (0.013) | |
lnER | 0.0051 (0.006) | 0.0126 (0.012) | 0.0177 (0.014) | |
lnIS | −0.1319 * (0.069) | −0.1429 (0.184) | −0.2747 (0.226) |
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Lee, C.-W.; Wang, C.-C.; Hsu, H.-H.; Kong, P. The Assessment of Green Business Environments Using the Environmental–Economic Index: The Case of China. Sustainability 2023, 15, 16419. https://doi.org/10.3390/su152316419
Lee C-W, Wang C-C, Hsu H-H, Kong P. The Assessment of Green Business Environments Using the Environmental–Economic Index: The Case of China. Sustainability. 2023; 15(23):16419. https://doi.org/10.3390/su152316419
Chicago/Turabian StyleLee, Cheng-Wen, Chin-Chuan Wang, Hui-Hsin Hsu, and Peiyi Kong. 2023. "The Assessment of Green Business Environments Using the Environmental–Economic Index: The Case of China" Sustainability 15, no. 23: 16419. https://doi.org/10.3390/su152316419