京津冀地区工业CO2排放测度及其影响因素研究

doi:10.3969/j.issn.1004-9479.2020.01.2018453

摘要/Abstract

摘要：

为探讨京津冀地区工业CO₂排放测度及其影响因素，基于2005—2016年数据对北京、天津、河北和京津冀地区工业能耗CO₂排放的时间变化趋势进行了对比分析，运用地理探测器技术分析能源结构、能耗强度、固定资产投资、轻重工业比值和规模以上工业平均用工人数对京津冀地区工业能耗CO₂排放量的影响作用大小。结果表明：①研究期内京津冀工业碳排放强度均为递减趋势。其中，河北最大，天津次之，北京最小；人均碳排放北京呈递减之势，天津和河北在波动中上升。②京津冀工业煤炭类CO₂排放占总排放量的主导地位是由河北省以煤炭为主的能源结构决定的。③能源结构对京津冀、天津和河北的CO₂排放影响最大，其次是轻重工业比值；北京市规模以上工业从业人数对CO₂排放的影响最大，其次是固定资产投资。

关键词: 京津冀, 工业CO₂排放, 影响因素, 地理探测器技术

Abstract:

In order to explore the measurement of industrial CO₂ emissions in Beijing-Tianjin-Hebei region and its influencing factors, the paper analyzed them based on 2005-2016 data. Firstly, the time trends of industrial energy CO₂ emissions in Beijing, Tianjin, Hebei and Beijing-Tianjin-Hebei region were compared and analyzed. And then the geographical detector technology was used to analyze the influence on the CO₂ emissions from industrial energy consumption with energy structure, energy intensity, fixed asset investment, light and heavy industry ratio and the number of employees of industries above designated size in Beijing-Tianjin-Hebei region. The result showed that: ① During the study period, the industrial carbon emission intensity of Beijing-Tianjin-Hebei was a decreasing trend. Among them, Hebei is the largest, Tianjin is the second, and Beijing is the smallest; per capita carbon emissions are declining in Beijing, and Tianjin and Hebei are rising in fluctuations.② The dominant position of CO₂ emissions from coal types fuel was determined by the energy structure of main coal types for Hebei Province in Beijing, Tianjin and Hebei region. ③ The energy structure had the greatest impact on the CO₂ emissions of Beijing-Tianjin-Hebei, Tianjin and Hebei, followed by the ratio of light and heavy industry; the number of employees of industries above designated size in Beijing had the greatest impact on CO₂ emissions, followed by fixed-asset investment. Therefore, it was necessary to adopt differentiated measures for energy conservation and CO₂ reduction for different regions.

Key words: Beijing-Tianjin-Hebei, industrial CO₂ emissions, influencing factors, geographical detector technology

韩媛媛, 皮荷杰, 时泽楠, 吕儒云, 廖剑宇. 京津冀地区工业CO₂排放测度及其影响因素研究[J]. 世界地理研究, 2020, 29(1): 140-147.

Yuanyuan HAN, Hejie PI, Zenan SHI, Ruyun LV, Jianyu LIAO. Study on measurement and influencing factors of industrial CO₂ emission in Beijing-Tianjin-Hebei Region[J]. World Regional Studies, 2020, 29(1): 140-147.

图/表 6

参考文献 12

1	王喜, 张艳, 秦耀辰, 等. 我国碳排放变化影响因素的时空分异与调控. 经济地理, 2016, 36(08): 158-165.
	Wang X, Zhang Y, Qin Y, et al. The Spatial-Temporal Differentiation of the Influence Factors of Carbon Emissions and the Regulation in China. Economic Geography, 2016, 36(08): 158-165.
2	孙艳芝,沈镭,钟帅,等.中国碳排放变化的驱动力效应分析.资源科学,2017,39(12):2265-2274.
	Sun Y, Shen L, Zhong S, et al. Driving force analysis of carbon emission changes in China.Resources Science, 2017, 39(12): 2265-2274.
3	赵巧芝, 闫庆友, 赵海蕊. 中国省域碳排放的空间特征及影响因素. 北京理工大学学报：社会科学版, 2018, 20(01): 9-16.
	Zhao Q, Yan Q, Zhao H. Research on Spatial Characteristics and Influencing Factors of Provincial Carbon Emissions in China. Journal of Beijing Institute of Technology：Social Sciences Edition, 2018, 20(01): 9-16.
4	何永贵, 于江浩. 河北省碳排放及其影响因素变化趋势研究. 环境科学与技术, 2018, 41(01): 184-191.
	He Y, Yu J. Study on the change trend of carbon emissions and its influencing factors in Hebei Province. Environmental Science & Technology, 2018, 41(01): 184-191.
5	邓荣荣. 湖南省工业碳排放变化、影响因素及减排对策研究——基于22个工业行业的实证. 南华大学学报：社会科学版, 2016, 17(01): 55-62.
	Deng R. The Change, Affecting Factors and Countermeasures of Hunan’s Industrial Carbon Emissions-Based on analysis of 22 industrial sectors. Journal of University of South China, 2016, 17(01): 55-62.
6	韩媛媛, 廖剑宇, 张旺, 等.长沙市规模以上工业能耗碳排放测算与预测研究. 国土与自然资源研究, 2017(05):46-50.
	Han Y, Liao J, Zhang W, et al. Calculation and Prediction of Carbon Emission from Industrial Scale Energy Consumption in Changsha. Territory & Natural Resources Study, 2017(05):46-50.
7	孙艳伟, 李加林, 李伟芳, 等. 海岛城市碳排放测度及其影响因素分析——以浙江省舟山市为例. 地理研究, 2018, 37(05): 1023-1033.
	Sun Y, Li J, Li W, et al. Estimating the carbon emissions and influencing factors of island city: A case study in Zhoushan Islands, Zhejiang province. Geographical Research, 2018, 37(05): 1023-1033.
8	李雪梅, 郝光菊, 张庆. 天津市高碳排放行业碳排放影响因素研究. 干旱区地理, 2017,40(5): 1089-1096.
	Li X, Hao G, Zhang Q. Influencing factors of high-emission for industrial sub-sectors in Tianjin. Arid Land Geography, 2017, 40(05): 1089-1096.
9	Eggleston H.S., Buendia L., Miwa K., et al. Chapter 2: Energy. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Japan: IGES, 2006.
10	Wu R N, Zhang J Q, Bao Y H, et al. 2016. Geographical detector model for influencing factors of industrial sector carbon dioxide emissions in Inner Mongolia, China. Sustainability, 8(2): 149-160.
11	王劲峰, 徐成东. 地理探测器: 原理与展望. 地理学报,2017, 72(1): 116- 134.
	Wang J, Xu C. Geodetector: Principle and prospective. Acta Geographica Sinica, 2017, 72（1）: 116- 134.
12	Wang J，Li X, Christakos G,et al. Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun Region，China.International Journal of Geographical Information Science，2010,24(1):107-127.

	Y	X₁	X₂	X₃	X₄	X₅
Y	1
X₁	-0.304	1
X₂	0.904^**	0.333	1
X₃	0.899^**	0.386	0.992^**	1
X₄	-0.325	-0.574	-0.612^*	0.661^*	1
X₅	0.987^**	-0.316	0.940^**	0.941^**	0.426	1

	Y	X₁	X₂	X₃	X₄	X₅
Y	1
X₁	-0.304	1
X₂	0.904^**	0.333	1
X₃	0.899^**	0.386	0.992^**	1
X₄	-0.325	-0.574	-0.612^*	0.661^*	1
X₅	0.987^**	-0.316	0.940^**	0.941^**	0.426	1

年份	碳排放量 /万吨	能源结构	能耗强度	固定资产投资 /亿元	轻重工业比值	平均用工人数 /万人
2005	55328.94	0.8131	1.6753	2741.87	0.2525	1048.38
2006	62458.94	0.8204	1.6348	4156.28	0.2317	1078.22
2007	68385.34	0.8190	1.5181	5836.11	0.2315	1132.94
2008	68618.78	0.8328	1.3870	7353.86	0.2274	1161.38
2009	72495.03	0.8412	1.2831	9511.28	0.2269	1186.01
2010	77556.71	0.8237	1.1862	11554.18	0.2156	1224.68
2011	87148.20	0.8141	1.1422	13075.16	0.2237	1289.37
2012	89044.12	0.8109	1.0466	15546.58	0.2380	1338.05
2013	90866.00	0.8171	0.9915	17668.16	0.2546	1369.57
2014	89105.51	0.8230	0.9095	20039.93	0.2589	1358.58
2015	84886.19	0.8101	0.7904	21546.75	0.2827	1329.35
2016	84793.38	0.8084	0.7794	21416.41	0.3028	1313.34

年份	碳排放量 /万吨	能源结构	能耗强度	固定资产投资 /亿元	轻重工业比值	平均用工人数 /万人
2005	55328.94	0.8131	1.6753	2741.87	0.2525	1048.38
2006	62458.94	0.8204	1.6348	4156.28	0.2317	1078.22
2007	68385.34	0.8190	1.5181	5836.11	0.2315	1132.94
2008	68618.78	0.8328	1.3870	7353.86	0.2274	1161.38
2009	72495.03	0.8412	1.2831	9511.28	0.2269	1186.01
2010	77556.71	0.8237	1.1862	11554.18	0.2156	1224.68
2011	87148.20	0.8141	1.1422	13075.16	0.2237	1289.37
2012	89044.12	0.8109	1.0466	15546.58	0.2380	1338.05
2013	90866.00	0.8171	0.9915	17668.16	0.2546	1369.57
2014	89105.51	0.8230	0.9095	20039.93	0.2589	1358.58
2015	84886.19	0.8101	0.7904	21546.75	0.2827	1329.35
2016	84793.38	0.8084	0.7794	21416.41	0.3028	1313.34

区域	X₁	X₂	X₃	X₄	X₅
京津冀	0.9997	0.0138	0.0099	0.1802	0.0086
北京	0.0013	0.0024	0.1816	0.1531	0.1974
天津	0.9550	0.0533	0.0088	0.2701	0.0148
河北	0.9998	0.0126	0.0073	0.6630	0.0065

京津冀地区工业CO₂排放测度及其影响因素研究

Study on measurement and influencing factors of industrial CO₂ emission in Beijing-Tianjin-Hebei Region

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