中国碳强度关键影响因子的机器学习识别及其演进

doi:10.11821/dlxb201912012

Abstract

Abstract:

As the Chinese government ratified the Paris Climate Agreement in 2016, the goal of reducing carbon dioxide emissions per unit of gross domestic product (carbon intensity) from 60% to 65% of 2005 levels must now be achieved by 2030. However, as numerous factors influence Chinese carbon intensity, it is key to assess their relative importance in order to determine which are most important. As traditional methods are inadequate for identifying key factors from a range acting simultaneously, machine learning is applied in this research. The random forest (RF) algorithm based on decision tree theory was proposed by Breiman (2001); this algorithm is one of the most appropriate because it is insensitive to multicollinearity, robust to missing and unbalanced data, and provides reasonable predictive results. We therefore identified the key factors influencing Chinese carbon intensity using the RF algorithm and analyzed their evolution between 1980 and 2014. The results of this analysis reveal that dominant factors include the scale and proportion of energy-intensive industries as well as fossil energy proportion and technical progress between 1980 and 1991. As the Chinese economy developed rapidly between 1992 and 2007, effects on carbon intensity were enhanced by service industry proportion and the fossil fuel price such that the influence of traditional residential consumption also increased. The Chinese economy then entered a period of deep structural adjustment subsequent to the 2008 global financial crisis; energy-saving emission reductions were greatly enhanced over this period and effects on carbon intensity were also rapidly boosted by the increasing availability of new energy and its residential consumption. Optimization of energy and industrial structures, promotion of technical progress, green consumption, and the reduction and management of emissions will be key to cutting future carbon intensity levels within China. These approaches will all help to achieve the 2030 goal of reducing carbon emission intensity from 60% to 65% of 2005 levels.

Key words: machine learning, random forest, carbon intensity, key factor, China

LIU Weidong, TANG Zhipeng, XIA Yan, HAN Mengyao, JIANG Wanbei. Identifying the key factors influencing Chinese carbon intensity using machine learning, the random forest algorithm, and evolutionary analysis[J].Acta Geographica Sinica, 2019, 74(12): 2592-2603.

Figures/Tables 5

Tab. 1

Types and indexes of factors influencing Chinese carbon intensity

类别	具体因子指标(单位)	类别	具体因子指标(单位)
化石能源占比	煤炭占比(%)	高能耗产业规模及占比	纯碱(万t)
	石油占比(%)		烧碱(万t)
	天然气占比(%)		乙烯(万t)
化石能源价格	煤炭行业出厂价格指数		合成氨(万t)
化石能源价格	石油和天然气行业出厂价格指数		水泥(万t)
非化石能源占比	水电占比(%)		平板玻璃(万重量箱)
非化石能源占比	沼气占比(%)		粗钢(万t)
新能源占比	风电占比(%)		成品钢(万t)
	核能占比(%)		建筑业占比(%)
	光伏占比(%)		交通运输、仓储和邮政业占比(%)
	光热占比(%)	技术进步	全员劳动生产率(万元/人)
	地热占比(%)		发电及电站供热加工转换效率(%)
	液体生物燃料占比(%)		炼焦加工转换效率(%)
服务业占比	金融业占比(%)		炼油加工转换效率(%)
	信息传输、计算机服务和软件业占比(%)		发电标准煤耗(克/千瓦时)
	教育业占比(%)		供电标准煤耗(克/千瓦时)
	卫生和社会保障社会福利占比(%)		发电厂线路损失率(%)
	文化体育娱乐业占比(%)		粗钢行业单位综合耗能(吨标煤/t)
	科学研究、技术服务和地质勘查业占比(%)		水泥行业单位综合耗能(千克标煤/t)
居民传统消费	城镇每百户私人汽车(辆)		乙烯行业单位综合耗能(吨标煤/t)
	城镇每百户摩托车(辆)		合成氨行业单位综合耗能(吨标煤/t)
	农村每百户摩托车(辆)		科技拨款占财政总支出的占比(%)
	城镇每百户电冰箱(台)		公共建筑单位面积耗能(千克标煤/m²)
	农村每百户电冰箱(台)		城镇居住建筑单位面积耗能(千克标煤/m²)
	城镇每百户电视机(台)		农村居住建筑单位面积耗能(千克标煤/m²)
	农村每百户电视机(台)	居民新兴消费	互联网宽带接入用户(万户)
	城镇每百户洗衣机(台)	居民新兴消费	高铁营业里程(km)
	农村每百户洗衣机(台)		电子商务交易额(万亿)

Tab. 1

Tab. 2

Fig. 1

Tab. 3

Fig. 2

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碳强度因子指标数量	1	2	3	4	5	6	7	8
Gini系数减小值	0.784	0.655	0.525	0.449	0.407	0.390	0.380	0.371
碳强度因子指标数量	9	10	11	12	13	14	15	16
Gini系数减小值	0.360	0.350	0.342	0.335	0.328	0.321	0.310	0.302
碳强度因子指标数量	17	18	19	20	21	22	23	24
Gini系数减小值	0.292	0.287	0.278	0.268	0.263	0.250	0.229	0.201
碳强度因子指标数量	25	26	27	28	29	30	31	32
Gini系数减小值	0.194	0.189	0.184	0.180	0.175	0.170	0.166	0.160
碳强度因子指标数量	33	34	35	36	37	38	39	40
Gini系数减小值	0.154	0.147	0.142	0.135	0.127	0.115	0.106	0.085
碳强度因子指标数量	41	42	43	44	45	46	47	48
Gini系数减小值	0.078	0.072	0.067	0.060	0.054	0.046	0.037	0.032
碳强度因子指标数量	49	50	51	52	53	54	55	56
Gini系数减小值	0.028	0.027	0.024	0.021	0.018	0.014	0.010	0.005

类别\年份	1980	1981	…	2000	…	2010	…	2013	2014
化石能源占比	3	3	…	0	…	1	…	2	1
化石能源价格	0	0	…	0	…	0	…	2	2
可再生能源(水电和沼气)占比	0	0	…	0	…	0	…	1	1
新能源占比	0	0	…	0	…	1	…	1	2
高耗能产业规模及占比	8	8	…	7	…	6	…	4	4
服务业占比	0	0	…	1	…	2	…	2	2
技术进步	8	8	…	6	…	4	…	3	5
居民传统消费	3	3	…	8	…	6	…	5	4
居民新兴消费	0	0	…	0	…	2	…	2	1
合计	22	22	…	22	…	22	…	22	22

Identifying the key factors influencing Chinese carbon intensity using machine learning, the random forest algorithm, and evolutionary analysis

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