Identification of the key factors affecting Chinese carbon intensity and their historical trends using random forest algorithm

Zhipeng TANG; Ziao MEI; Weidong LIU; Yan XIA

doi:10.1007/s11442-020-1753-4

Journals >Journal of Geographical Sciences >Volume 30 >Issue 5 >Page 743 > Article

Journal of Geographical Sciences
Vol. 30, Issue 5, 743 (2020)

Identification of the key factors affecting Chinese carbon intensity and their historical trends using random forest algorithm

Zhipeng TANG^1,2, Ziao MEI^1,2, Weidong LIU^1,2, and Yan XIA^3,*

Author Affiliations

¹Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China

²College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

³Institutes of Science and Development, CAS, Beijing 100190, China

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DOI: 10.1007/s11442-020-1753-4 Cite this Article

Zhipeng TANG, Ziao MEI, Weidong LIU, Yan XIA. Identification of the key factors affecting Chinese carbon intensity and their historical trends using random forest algorithm[J]. Journal of Geographical Sciences, 2020, 30(5): 743 Copy Citation Text

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Average reductions in Gini coefficient and the corresponding cumulative percentage importance as a function of carbon intensity index number

Fig. 1. Average reductions in Gini coefficient and the corresponding cumulative percentage importance as a function of carbon intensity index number

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Fig. 2. Percentages of factors affecting Chinese carbon intensity in different categories between 1980 and 2017

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Table 1.

Categorization of factors influencing carbon intensity in China

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Carbon intensityindex number	1	2	3	4	5	6	7	8
Gini coefficient reduction	0.701	0.613	0.577	0.576	0.572	0.571	0.562	0.560
Carbon intensityindex number	9	10	11	12	13	14	15	16
Gini coefficient reductions	0.504	0.462	0.449	0.380	0.376	0.372	0.371	0.365
Carbon intensityindex number	17	18	19	20	21	22	23	24
Gini coefficient reductions	0.362	0.341	0.327	0.283	0.241	0.185	0.177	0.176
Carbon intensityindex number	25	26	27	28	29	30	31	32
Gini coefficient reductions	0.170	0.165	0.156	0.152	0.151	0.151	0.140	0.103
Carbon intensityindex number	33	34	35	36	37	38	39	40
Gini coefficient reductions	0.075	0.069	0.069	0.067	0.067	0.066	0.064	0.050
Carbon intensityindex number	41	42	43	44	45	46	47	48
Gini coefficient reductions	0.050	0.049	0.035	0.034	0.034	0.034	0.034	0.033
Carbon intensityindex number	49	50	51	52	53	54	55	56
Gini coefficient reductions	0.031	0.029	0.028	0.027	0.026	0.025	0.020	0.011

Table 2.

Carbon intensity indicator numbers and corresponding average reductions in Gini coefficient

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Category/Year	1980	...	2000	...	2010	...	2016	2017
Proportion of fossil energy	3	...	0	...	1	...	1	2
Price of fossil energy	0	...	0	...	0	...	0	0
Proportion of renewable energy (hydropower and biogas)	0	...	0	...	0	...	0	1
Proportion of new energy	0	...	0	...	1	...	3	2
Scale or proportion ofenergy-intensive industry	8	...	7	...	6	...	7	4
Proportion of service industry	0	...	1	...	2	...	2	2
Technological progress	8	...	6	...	4	...	6	5
Traditional consumption of residents	3	...	8	...	6	...	2	4
New consumption of residents	0	...	0	...	2	...	1	2
Total	22	...	22	...	22	...	22	22

Table 3.

Numbers of key factors affecting Chinese carbon intensity per category by year between 1980 and 2017 ¹(¹Note: Based on length limitations, Table 3 only lists the statistics for 1980, 2000, 2010, 2016, and 2017; please contact the author for additional data.)