Research on China’s Carbon Emission Efficiency and Its Regional Differences
Abstract
:1. Introduction
2. Methodology
2.1. Introduction to the Research Methods
2.1.1. The First Stage of DEA
2.1.2. The Second Stage of DEA
2.1.3. The Third Stage of DEA
2.2. Data Description
2.2.1. Introduction of Related Variables
2.2.2. Data Source
3. Results
3.1. The First Stage of the Traditional DEA Analysis
3.2. The Second Stage of the SFA Analysis
3.3. Empirical Results of DEA after Adjustment in the Third Stage
4. Conclusions and Suggestions
4.1. Conclusions
4.2. Suggestions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Level 1 Indicators | Secondary Indicators | Level 3 Indicators |
---|---|---|
Input indexes | Size of population | Total population at the end of the year (/10,000) |
Stock of capital | Industrial energy investment (/RMB 100 million) | |
Energy use | Total regional energy consumption (/Million tons) | |
Output indexes | Carbon emission | Carbon emissions from each region (/Ton) |
GDP | Total GDP (/100 million yuan) | |
External environment variables | Economic development | Per capita GDP (/Ten thousand yuan) |
National consumption | Total social consumer goods (/100 million yuan) | |
Scientific and technological | Patent valid (/Piece) |
Area | Carbon Emission Efficiency | Pure Technical Efficiency | Scale Efficiency | Area | Carbon Emission Efficiency | Pure Technical Efficiency | Scale Efficiency |
---|---|---|---|---|---|---|---|
Beijing | 1.000 | 1.000 | 1.000 | Henan | 0.775 | 0.813 | 0.923 |
Tianjin | 0.992 | 0.998 | 0.990 | Hubei | 0.761 | 0.765 | 0.953 |
Hebei | 0.839 | 0.858 | 0.975 | Hunan | 0.693 | 0.708 | 0.934 |
Shanxi | 0.985 | 0.998 | 0.987 | Guangdong | 0.857 | 1.000 | 0.846 |
Nei Monggol | 1.000 | 1.000 | 1.000 | Guangxi | 0.715 | 0.720 | 0.946 |
Liaoning | 0.992 | 1.000 | 1.000 | Hainan | 0.975 | 1.000 | 0.983 |
Jilin | 0.877 | 0.878 | 0.980 | Chongqing | 0.696 | 0.675 | 0.974 |
Heilongjiang | 0.821 | 0.834 | 0.964 | Sichuan | 0.616 | 0.619 | 0.928 |
Shanghai | 0.992 | 1.000 | 1.000 | Guizhou | 0.738 | 0.756 | 0.947 |
Jiangsu | 0.986 | 1.000 | 0.988 | Yunnan | 0.608 | 0.609 | 0.944 |
Zhejiang | 0.891 | 0.916 | 0.969 | Shaanxi | 0.961 | 0.967 | 0.986 |
Anhui | 0.834 | 0.905 | 0.912 | Gansu | 0.714 | 0.712 | 0.971 |
Fujian | 0.865 | 0.869 | 0.972 | Qinghai | 0.524 | 1.000 | 0.477 |
Jiangxi | 0.819 | 0.838 | 0.955 | Ningxia | 0.999 | 1.000 | 0.997 |
Shandong | 0.965 | 1.000 | 0.973 | Xinjiang | 0.807 | 0.821 | 0.991 |
Average | Carbon emission efficiency: 0.843 | Pure technical efficiency: 0.875 | Scale efficiency: 0.949 |
Population | Coefficient | Standard Error | T-Ratio | p-Value |
---|---|---|---|---|
Cow distance item | −944 | 158 | −6 | 0 |
per capita GDP | 4 | 20 | 0 | 1 |
Total retail sales of consumer goods | 0 | 0 | 5 | 0 |
Number of valid patents | 0 | 0 | −3 | 0 |
sigmasq | 34,900,000 | 1 | 3,490,000 | 0 |
gamma | 1 | 0 | 524 | 0 |
Log- likelihood function | −2190 | |||
LR test of the one-sided error | 716 | |||
One-sided likelihood ratio test for p-values | 0 | |||
Energy Consumption | Coefficient | Standard Error | T-Ratio | p-Value |
Cow distance item | −17,000 | 417 | −4 | 0 |
per capita GDP | 92 | 56 | 2 | 0 |
Total retail sales of consumer goods | 0 | 0 | 4 | 0 |
Number of valid patents | 0 | 0 | −2 | 0 |
Sigmasq | 79,600,000 | 1 | 7,960,000 | 0 |
Gamma | 1 | 0 | 58 | 0 |
Log -likelihood function | −26,000 | |||
LR test of the one-sided error | 229 | |||
One-sided likelihood ratio test for p-values | 0.000 | |||
Energy Industry Investment | Coefficient | Standard Error | T-Ratio | p-Value |
Cow distance item | −177 | 50 | −4 | 0 |
per capita GDP | 11 | 7 | 2 | 0 |
Total retail sales of consumer goods | 0 | 0 | 3 | 0 |
Number of valid patents | 0 | 0 | −2 | 0 |
Sigmasq | 868,000 | 1 | 841,000 | 0 |
Gamma | 1 | 0 | 31 | 0 |
Log- likelihood function | −1974 | |||
LR test of the one-sided error | 143 | |||
One-sided likelihood ratio test for p-values | 0 |
Area | Population | Energy Consumption | Investment Amount in Industrial Energy Sources | Area | Population | Energy Consumption | Investment Amount in Industrial Energy Sources |
---|---|---|---|---|---|---|---|
Beijing | 3480 | 11,600 | 1184 | Henan | 10,100 | 25,000 | 2320 |
Tianjin | 2870 | 12,000 | 1220 | Hubei | 6600 | 19,200 | 1520 |
Hebei | 8300 | 33,900 | 2550 | Hunan | 7660 | 18,300 | 1444 |
Shanxi | 4890 | 24,600 | 1950 | Guangdong | 12,200 | 37,600 | 2320 |
NeiMonggol | 3680 | 27,300 | 2770 | Guangxi | 5990 | 14,300 | 1400 |
Liaoning | 5310 | 26,500 | 1510 | Hainan | 2330 | 6710 | 886 |
Jilin | 3820 | 11,100 | 1280 | Chongqing | 4260 | 12,000 | 1090 |
Heilongjiang | 4810 | 14,800 | 1360 | Sichuan | 9030 | 22,100 | 19,601 |
Shanghai | 3570 | 15,600 | 907 | Guizhou | 4890 | 13,700 | 1230 |
Jiangsu | 8850 | 35,600 | 2310 | Yunnan | 5960 | 14,800 | 1320 |
Zhejiang | 6680 | 24,700 | 1700 | Shaanxi | 4980 | 17,100 | 2190 |
Anhui | 7340 | 17,200 | 1670 | Gansu | 3950 | 11,700 | 1080 |
Fujian | 4910 | 16,400 | 1520 | Qinghai | 2040 | 890 | 1190 |
Jiangxi | 5792 | 13,100 | 1290 | Ningxia | 2120 | 11,700 | 1330 |
Shandong | 10,700 | 44,400 | 4100 | Xinjiang | 3820 | 21,200 | 1970 |
The First Stage | The Third Stage | ||||||
---|---|---|---|---|---|---|---|
Area | Carbon Emission Efficiency | Pure Technical Efficiency | Scale Efficiency | Area | Carbon Emission Efficiency | Pure Technical Efficiency | Scale Efficiency |
Beijing | 1.000 | 1.000 | 1.000 | Beijing | 0.959 | 1.000 | 0.959 |
Tianjin | 0.992 | 0.998 | 0.990 | Tianjin | 0.747 | 1.000 | 0.747 |
Hebei | 0.839 | 0.859 | 0.976 | Hebei | 0.883 | 0.909 | 0.972 |
Shanxi | 0.985 | 0.998 | 0.987 | Shanxi | 0.993 | 0.998 | 0.995 |
Nei Monggol | 1.000 | 1.000 | 1.000 | Nei Monggol | 1.000 | 1.000 | 1.000 |
Liaoning | 0.992 | 1.000 | 1.000 | Liaoning | 1.000 | 1.000 | 1.000 |
Jilin | 0.877 | 0.878 | 0.980 | Jilin | 0.713 | 0.957 | 0.745 |
Heilongjiang | 0.821 | 0.834 | 0.964 | Heilongjiang | 0.761 | 0.929 | 0.818 |
Shanghai | 0.992 | 1.000 | 1.000 | Shanghai | 0.928 | 1.000 | 0.928 |
Jiangsu | 0.986 | 1.000 | 0.988 | Jiangsu | 1.000 | 1.000 | 1.000 |
Zhejiang | 0.891 | 0.913 | 0.969 | Zhejiang | 0.913 | 0.952 | 0.959 |
Anhui | 0.834 | 0.905 | 0.912 | Anhui | 0.800 | 0.957 | 0.837 |
Fujian | 0.865 | 0.869 | 0.972 | Fujian | 0.817 | 0.946 | 0.864 |
Jiangxi | 0.819 | 0.838 | 0.955 | Jiangxi | 0.669 | 0.939 | 0.711 |
Shandong | 0.965 | 1.000 | 0.973 | Shandong | 1.000 | 1.000 | 1.000 |
Henan | 0.775 | 0.814 | 0.923 | Henan | 0.852 | 0.918 | 0.929 |
Hubei | 0.761 | 0.765 | 0.953 | Hubei | 0.958 | 0.899 | 0.844 |
Hunan | 0.693 | 0.708 | 0.934 | Hunan | 0.728 | 0.857 | 0.848 |
Guangdong | 0.857 | 1.000 | 0.846 | Guangdong | 1.000 | 1.000 | 1.000 |
Guangxi | 0.715 | 0.720 | 0.946 | Guangxi | 0.634 | 0.876 | 0.723 |
Hainan | 0.975 | 1.000 | 0.983 | Hainan | 0.361 | 1.000 | 0.361 |
Chongqing | 0.696 | 0.675 | 0.974 | Chongqing | 0.592 | 0.875 | 0.677 |
Sichuan | 0.616 | 0.619 | 0.928 | Sichuan | 0.669 | 0.755 | 0.887 |
Guizhou | 0.739 | 0.756 | 0.947 | Guizhou | 0.625 | 0.892 | 0.700 |
Yunnan | 0.608 | 0.609 | 0.944 | Yunnan | 0.578 | 0.801 | 0.720 |
Shaanxi | 0.961 | 0.967 | 0.986 | Shaanxi | 0.903 | 0.979 | 0.922 |
Gansu | 0.714 | 0.712 | 0.971 | Gansu | 0.569 | 0.883 | 0.645 |
Qinghai | 0.524 | 1.000 | 0.477 | Qinghai | 0.247 | 1.000 | 0.247 |
Ningxia | 0.999 | 1.000 | 0.997 | Ningxia | 0.588 | 1.000 | 0.588 |
Xinjiang | 0.807 | 0.821 | 0.991 | Xinjiang | 0.764 | 0.895 | 0.855 |
Average value | 0.843 | 0.875 | 0.949 | Average value | 0.775 | 0.941 | 0.816 |
High Efficiency Area | Beijing | Shanxi | Nei Monggol | Liaoning |
Shaanxi | Hubei | Zhejiang | Shandong | |
Shanghai | Guangdong | Jiangsu | ||
Medium Efficiency Area | Tianjin | Hebei | Jilin | Henan |
Jiangxi | Guangxi | Sichuan | Guizhou | |
Heilongjiang | Xinjiang | Anhui | Hunan | |
Low Efficiency Area | Hainan | Chongqing | Yunnan | Gansu |
Qinghai | Ningxia |
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Zhao, X.; Xu, H.; Sun, Q. Research on China’s Carbon Emission Efficiency and Its Regional Differences. Sustainability 2022, 14, 9731. https://doi.org/10.3390/su14159731
Zhao X, Xu H, Sun Q. Research on China’s Carbon Emission Efficiency and Its Regional Differences. Sustainability. 2022; 14(15):9731. https://doi.org/10.3390/su14159731
Chicago/Turabian StyleZhao, Xiaochun, Huixin Xu, and Qun Sun. 2022. "Research on China’s Carbon Emission Efficiency and Its Regional Differences" Sustainability 14, no. 15: 9731. https://doi.org/10.3390/su14159731