Abstract
China has become the world’s most carbon-emitting country, and the coal-fired power industry (CFPI) dominates China’s carbon emissions. Stimulating the carbon emission reduction potential of China’s CFPI is important for reducing global carbon emissions and mitigating global warming. To explore the potential for reducing carbon emissions in the CFPI, this study constructed a model based on the data envelopment analysis (DEA) method, considering profit motive and the cost of regulatory policy. To analyze the factors influencing carbon reduction potential (CRP), the Kaya-LMDI (Kaya Identity-Logarithmic Mean Divisia Index) method was also applied. Some policy implications for the regions in China came out. The results show that: (a) China’s coal power industry generation process has not yet reached its optimal profit. When China’s CFPI realizes the optimal profit, a CRP will also decrease industrial carbon emissions by 3.54%. (b) At the carbon costs ranging from 16.8 to 95.2 Yuan/ton caused by carbon regulation policy, the total CRP of China’s CFPI would be further enhanced to 4.32%. (c) The coal-fired power output rate and industry scale had a positive effect on CRP, while the labor productivity had a negative effect. Carbon costs caused by carbon regulation policies could promote the CFPI to realize a greater carbon emission reduction potential by adjusting labor productivity and the industry scale effect.
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Acknowledgements
We are grateful for financial support provided by the National Natural Science Foundation of China (Nos. 71922013 and 71834003), Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX19_0142), and Nanjing University of Aeronautics and Astronautics PhD short-term visiting scholar project (190608DF09).
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Appendices
Appendix A: optimal results
No. | Area | Labor (\(10^{3} \cdot {\text{persons}}\)) | Coal (\(10^{3} \cdot {\text{ton}}\)) | ||||
|---|---|---|---|---|---|---|---|
Actual value | Optimal value | Difference value | Actual value | Optimal value | Difference value | ||
1 | Beijing | 90.9 | 90.9 | 0 | 940 | 940 | 0 |
2 | Tianjin | 43.5 | 43.5 | 0 | 21,234 | 21,234 | 0 |
3 | Hebei | 187.9 | 187.9 | 0 | 89,303.9 | 89,303.9 | 0 |
4 | Shanxi | 125.4 | 116.5 | −8.9 | 103,239.1 | 102,035.7 | −1203.4 |
5 | Inner Mongolia | 145.5 | 140 | −5.4 | 190,377.1 | 188,687.6 | −1689.5 |
6 | Liaoning | 146.9 | 146.9 | 0 | 67,133 | 67,133 | 0 |
7 | Jilin | 123.5 | 93.2 | −30.2 | 39,560.6 | 34,544.7 | −5015.9 |
8 | Heilongjiang | 176 | 176 | 0 | 33,438.8 | 33,438.8 | 0 |
9 | Shanghai | 43.7 | 43.6 | −0.1 | 27,833.5 | 27,766.8 | −66.7 |
10 | Jiangsu | 145.1 | 145.1 | 0 | 162,754.7 | 162,754.7 | 0 |
11 | Zhejiang | 119.6 | 119.6 | 0 | 82,091.4 | 82,091.4 | 0 |
12 | Anhui | 104.5 | 110.9 | 6.4 | 110,545 | 86,455.9 | −24,089.1 |
13 | Fujian | 90.6 | 63.4 | −27.2 | 35,671.7 | 34,541.4 | −1130.3 |
14 | Jiangxi | 93.1 | 93.1 | 0 | 31,133.2 | 31,133.2 | 0 |
15 | Shandong | 230.9 | 302.4 | 71.5 | 201,795.3 | 187,046.5 | −14,748.8 |
16 | Henan | 261.8 | 261.8 | 0 | 107,655.4 | 107,655.4 | 0 |
17 | Hubei | 163.8 | 165.8 | 2.1 | 53,363.5 | 37,014.3 | −16,349.2 |
18 | Hunan | 166.2 | 166.2 | 0 | 27,049.2 | 27,049.2 | 0 |
19 | Guangdong | 312.7 | 312.7 | 0 | 100,638.2 | 100,638.2 | 0 |
20 | Guangxi | 137.4 | 118.2 | −19.3 | 20,802 | 20,376.4 | −425.6 |
21 | Hainan | 23.4 | 20.2 | −3.2 | 7587.5 | 7185.9 | −401.6 |
22 | Chongqing | 64.3 | 64.2 | −0.1 | 16,935.3 | 16,400.2 | −535.1 |
23 | Sichuan | 231.7 | 171.2 | −60.5 | 12,395.1 | 11,155.5 | −1239.6 |
24 | Guizhou | 122.1 | 78.3 | −43.8 | 60,211.6 | 58,566.8 | −1644.8 |
25 | Yunnan | 106.9 | 36.3 | −70.6 | 11,218.9 | 15,321.5 | 4102.6 |
26 | Shaanxi | 136.7 | 136.7 | 0 | 60,901.4 | 60,901.4 | 0 |
27 | Gansu | 121.9 | 69.7 | −52.2 | 30,607.9 | 33,874.4 | 3266.5 |
28 | Qinghai | 21.6 | 17 | −4.6 | 6924.7 | 6640.8 | −283.9 |
29 | Ningxia | 34.6 | 34.6 | 0 | 39,143.8 | 39,143.8 | 0 |
30 | Xinjiang | 96.5 | 115.3 | 18.8 | 103,801.4 | 99,530.1 | −4271.3 |
Sum | 3868.8 | 3641.3 | −227.5 | 1,856,287.2 | 1,790,561.5 | −65,725.7 | |
No. | Area | Auxiliary Power consumption (\(10^{6} \cdot {\text{kw}}\;{\text{h}}\)) | CO2 (\(10^{3} \cdot {\text{ton}}\)) | ||||
|---|---|---|---|---|---|---|---|
Actual value | Optimal value | Difference value | Actual value | Optimal value | Difference value | ||
1 | Beijing | 1194 | 1194 | 0 | 2462 | 2462 | 0 |
2 | Tianjin | 3617 | 3617 | 0 | 55,614 | 55,614 | 0 |
3 | Hebei | 13,835 | 13,835 | 0 | 233,895.8 | 233,895.8 | 0 |
4 | Shanxi | 17,387 | 17,387 | 0 | 270,393.5 | 267,241.7 | −3150 |
5 | Inner Mongolia | 24,638 | 24,638 | 0 | 498,616.7 | 494,191.8 | −4420 |
6 | Liaoning | 8776 | 8776 | 0 | 175,828 | 175,828 | 0 |
7 | Jilin | 4075 | 4643 | 568 | 103,613.2 | 90,475.5 | −13,140 |
8 | Heilongjiang | 5414 | 5414 | 0 | 87,579.6 | 87,579.6 | 0 |
9 | Shanghai | 3640 | 3644 | 4 | 72,898.7 | 72,724.1 | −170 |
10 | Jiangsu | 22,856 | 22,856 | 0 | 426,270.8 | 426,270.8 | 0 |
11 | Zhejiang | 11,735 | 11,735 | 0 | 215,005.6 | 215,005.6 | 0 |
12 | Anhui | 9885 | 9876 | −9 | 289,528.4 | 226,436.8 | −63,090 |
13 | Fujian | 4493 | 4406 | −87 | 93,427.7 | 90,467.1 | −2960 |
14 | Jiangxi | 4482 | 4482 | 0 | 81,541 | 81,541 | 0 |
15 | Shandong | 29,754 | 25,503 | −4251 | 528,522.1 | 489,893.5 | −38,630 |
16 | Henan | 14,077 | 14,077 | 0 | 281,960.3 | 281,960.3 | 0 |
17 | Hubei | 5418 | 5418 | 0 | 139,764.3 | 96,944.3 | −42,820 |
18 | Hunan | 4407 | 4407 | 0 | 70,844.6 | 70,844.6 | 0 |
19 | Guangdong | 15,818 | 15,818 | 0 | 263,581.5 | 263,581.5 | 0 |
20 | Guangxi | 3734 | 3484 | −250 | 54,482.5 | 53,368 | −1110 |
21 | Hainan | 1466 | 1466 | 0 | 19,872.4 | 18,820.5 | −1050 |
22 | Chongqing | 3185 | 3185 | 0 | 44,355.2 | 42,953.8 | −1400 |
23 | Sichuan | 1588 | 1753 | 165 | 32,464 | 29,217.6 | −3146 |
24 | Guizhou | 8829 | 7750 | −1079 | 157,700.2 | 153,392.3 | −4310 |
25 | Yunnan | 1915 | 1631 | −284 | 29,383.4 | 40,128.7 | 10,750 |
26 | Shaanxi | 9732 | 9732 | 0 | 159,506.9 | 159,506.9 | 0 |
27 | Gansu | 4131 | 4799 | 668 | 80,165.2 | 88,720.4 | 8560 |
28 | Qinghai | 1120 | 985 | −136 | 18,136.5 | 17,393 | −740 |
29 | Ningxia | 6661 | 6661 | 0 | 102,521.5 | 102,521.5 | 0 |
30 | Xinjiang | 14,004 | 7674 | −6330 | 271,866.2 | 260,679.4 | −11,190 |
Sum | 261,865 | 250,843 | −11,022 | 4,861,802 | 4,689,660.1 | −172,141.9 | |
Appendix B: optimal technology combination
There were 13 regions without the potential for carbon reductions, where the technology scale did not change in 2016 (Fig. 5). The other 17 regions, with carbon emission potential, could change their generation technology. We classified these 17 regions into 3 categories for analysis.
The optimal technology combination for the CFPI
The first category includes regions where technology scale was reduced, including Inner Mongolia, Fujian, Guangxi, Hainan, Chongqing, Sichuan, Yunnan, and Gansu. In 2016, the costs of the CFPI in these regions were lower than in the previous year, and the power generation was also lower than in the past. This is because the growth of electricity demand in China slowed (Yuan et al., 2016), affecting these areas. Therefore, these regions tended to select the most cost-effective technology and reduced their technology scale to achieve the optimization and CRP in 2016.
The second category includes regions where the technology scale was expanded, including Shanghai, Hubei, Anhui, Jilin, Guizhou, Xinjiang, Shandong, and Qinghai. The cost efficiency of coal-fired power in these regions in 2016 was lower than in the previous year, but the power demand increased. Selecting the most cost-effective technology and expanding its scale allowed the CFPI to achieve cost optimization and carbon reduction in 2016.
The third category includes the region where the production was unchanged: Shanxi. In 2015, the coal-fired power generation technology in the region was most cost-efficient, and the power generation in 2015 was almost equal to the demand in 2016. Therefore, given the generation technology and its scale in 2015, the optimization cost and emission reduction potential in Shanxi could be realized in 2016.
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An, Y., Zhou, D. & Wang, Q. Carbon emission reduction potential and its influencing factors in China’s coal-fired power industry: a cost optimization and decomposition analysis. Environ Dev Sustain 24, 3619–3639 (2022). https://doi.org/10.1007/s10668-021-01579-7
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DOI: https://doi.org/10.1007/s10668-021-01579-7