1990—2014年典型国家技术变革与结构调整的碳排放驱动效应测度

doi:10.18402/resci.2018.11.17

摘要/Abstract

摘要：

全球碳减排背景下不同发展水平国家根据共同但有区别的原则,不断采取技术变革和结构调整等碳减排策略,以便达到追求经济发展与实现碳减排目标的良好契合。本文以1990—2014年间世界上34个不同收入水平的典型国家为例,在VAR模型基础上,利用脉冲响应及方差分解分析来探究以碳排放强度、能源强度表征的技术变革和以可再生能源占比、工业化率表征的结构调整对碳排放的驱动效应,以便为各国经济社会发展过程中的碳减排实践提供参考。结果表明,高收入国家技术变革和结构调整皆有促进碳减排作用,其中碳排放变动的贡献度受结构调整主导,平均贡献度为22.71%;而中高等收入国家和中低等收入国家的技术变革对碳排放的正负影响效果不一,工业化率绝大部分对碳排放具有促进作用,可再生能源占比除了OPEC国家皆具有明显减排效果,其中碳排放变动的贡献度受技术变革主导,平均贡献度分别为21.41%和25.60%。

关键词: 碳排放, 技术变革, 结构调整, 驱动效应

Abstract:

In the context of global carbon emission reduction, countries with different levels of development, based on the principle of common but differentiated responsibility (CBDR), continue to adopt carbon emission reduction strategies such as technological change and structural adjustment in order to achieve a good fit between pursuing economic development and achieving carbon emission reduction targets. Taking 34 typical countries in the world with different income levels from 1990 to 2014 as an example, this study implemented an impulse response analysis and variance decomposition analysis to explore the driving effect of technological changes characterized by carbon intensity and energy intensity, structural adjustment characterized by proportion of renewable energy and industrialization rate on carbon emissions based on VAR model, in order to provide a reference for the practice of carbon emission reduction in the economic and social development in various countries. The results demonstrated that technological change and structural adjustment in high-income countries all play a major role in promoting carbon emission reduction. The contribution of carbon emission changes is dominated by structural adjustment with an average contribution of 22.71%, while technological changes in upper-middle-income countries and lower-middle-income countries have different effects on the carbon emissions. The vast majority of industrialization rate plays a most important role in promoting carbon emissions. The proportion of renewable energy except for OPEC countries have significant emission reduction effects, of which the contribution of changes in carbon emissions dominated by technological change, with an average contribution of 21.41% and 25.60%, respectively. According to the actual situation of different income countries, this study puts forward relevant countermeasures and suggestions from the aspects of energy structure, industrial structure and technology.

Key words: carbon emissions, technological change, structural adjustment, drive effect

王宪恩, 段志远, 王培博, 宋俊年, 王硕, 段海燕. 1990—2014年典型国家技术变革与结构调整的碳排放驱动效应测度[J]. 资源科学, 2018, 40(11): 2317-2327.

Xianen WANG, Zhiyuan DUAN, Peibo WANG, Junnian SONG, Shuo WANG, Haiyan DUAN. Study on measurement of carbon-driving effects from technological change and structural adjustment in typical countries from 1990 to 2014[J]. Resources Science, 2018, 40(11): 2317-2327.

图/表 7

表1

图1

图2

表2

图3

表3

表4

参考文献 23

[1]	联合国. 联合国气候变化框架公约[R]. 里约热内卢: 联合国环境与发展大会, 1992.
	[ United Nations.United Nations Framework Convention on Climate Change [R]. Rio de Janeiro: United Nations Conference on Environment and Development, 1992. ]
[2]	联合国. 《联合国气候变化框架公约》京都议定书[EB/OL]. (1997-12-11)[2018-03-02].
	[United Nations. Kyoto Protocol to the United Nations Framework Convention on Climate Change[EB/OL]. (1997-12-11)[2018-03-02]. . ]
[3]	黄宏纯. 巴黎气候大会开启气候治理新时代[J]. 生态经济, 2016, 32(2): 2-5.
	[ Huang H C.The Paris Climate Conference opens a new era of climate governance[J]. Eco-Economy, 2016, 32(2): 2-5. ]
[4]	Karmellos M, Kopidou D, Diakoulaki D.A decomposition analysis of the driving factors of CO 2, (Carbon dioxide) emissions from the power sector in the European Union countries[J]. Energy, 2016, 94(9): 680-692.
[5]	韩梦瑶, 刘卫东, 唐志鹏, 等. 世界主要国家碳排放影响因素分析-基于变系数面板模型[J]. 资源科学, 2017, 39(12): 2420-2429.
	[Han M Y, Liu W D, Tang Z P, et al. Carbon emission impact factor analysis of major countries based on varying coefficient panel modeling[J]. Resources Science, 2017, 39(12): 2420-2429. ]
[6]	梁大鹏, 刘天森, 李一军. 基于LMDI模型的金砖五国二氧化碳排放成本及其影响因素比较研究[J]. 资源科学, 2015, 37(12): 2319-2329.
	[ Liang D P, Liu T S, Li Y J.Comparative study of BRICS' CO2 emission cost and its influential factors based on LMDI model[J]. Resources Science, 2015, 37(12): 2319-2329. ]
[7]	Wei J, Huang K, Yang S, et al. Driving forces analysis of energy-related carbon dioxide (CO 2 ) emissions in Beijing: an input-output structural decomposition analysis[J]. Journal of Cleaner Production, 2016, 163(10): 58-68.
[8]	孙艳芝, 沈镭, 钟帅, 等. 中国碳排放变化的驱动力效应分析[J]. 资源科学, 2017, 39(12): 2265-2274.
	[Sun Y Z, Shen L, Zhong S, et al. Driving force analysis of carbon emission changes in China[J]. Resources Science, 2017, 39(12): 2265-2274. ]
[9]	原嫄, 席强敏, 孙铁山, 等. 产业结构对区域碳排放的影响-基于多国数据的实证分析[J]. 地理研究, 2016, 35(1): 82-94.
	[Yuan Y, Xi Q M, Sun T S, et al. The impact of the industrial structure on regional carbon emissions-empirical evidence across countries[J]. Geographical Research, 2016, 35(1): 82-94. ]
[10]	Wang M, Feng C.Decomposition of energy-related CO2 emissions in China: an empirical analysis based on provincial panel data of three sectors[J]. Applied Energy, 2017, 190: 772-787.
[11]	Shuai C, Shen L, Jiao L, et al. Identifying key impact factors on carbon emission: evidences from panel and time-series data of 125 countries from 1990 to 2011[J]. Applied Energy, 2017, 187: 310-325.
[12]	Wang C, Wang F, Zhang X, et al. Examining the driving factors of energy related carbon emissions using the extended STIRPAT model based on IPAT identity in Xinjiang[J]. Renewable & Sustainable Energy Reviews, 2017, 67: 51-61.
[13]	Wang S, Fang C, Wang Y.Spatiotemporal variations of energy-related CO 2, emissions in China and its influencing factors: an empirical analysis based on provincial panel data[J]. Renewable & Sustainable Energy Reviews, 2016, 55: 505-515.
[14]	蒋青嬗, 韩兆洲. 面板半参数空间自回归模型的变量选择-基于STIRPAT模型的碳排放影响因素分析[J]. 数理统计与管理, 2017, 36(5): 821-832.
	[Jiang Q S, Han Z Z.The variable selection for panel semiparametric spatial autoregressive model-the analysis for carbon emissions based on STIRPAT model[J]. Mathematical Statistics and Management, 2017, 36(5): 821-832. ]
[15]	王泳璇, 王宪恩, 史记, 等. 典型国家特定碳排放阶段影响因素研究[J]. 资源科学, 2013, 35(10): 1945-1952.
	[Wang Y X, Wang X E, Shi J, et al. Impact factors in the specific stage of carbon emissions of typical countries[J]. Resources Science, 2013, 35(10): 1945-1952. ]
[16]	顾阿伦, 何崇恺, 吕志强. 基于LMDI方法分析中国产业结构变动对碳排放的影响[J]. 资源科学, 2016, 38(10): 1861-1870.
	[Gu A L, He C K, Lu Z Q.Industrial structure changes impacts on carbon emissions in China based on LMDI method[J]. Resources Science, 2016, 38(10): 1861-1870. ]
[17]	董锋, 杨庆亮, 龙如银, 等. 中国碳排放分解与动态模拟[J]. 中国人口·资源与环境, 2015, 25(4): 1-8.
	[Dong F, Yang Q L, Long R Y, et al. Factor decomposition and dynamic simulation of China's carbon emissions[J]. China Population, Resources and Environment, 2015, 25(4): 1-8. ]
[18]	郭朝先. 中国二氧化碳排放增长因素分析-基于SDA分解技术[J]. 中国工业经济, 2010, (12): 47-56.
	[ Guo C X.An analysis of the increase of CO2 emission in China-based on SDA technique[J]. China Industrial Economics, 2010, (12): 47-56. ]
[19]	刘广为, 赵涛. 中国碳排放强度影响因素的动态效应分析[J]. 资源科学, 2012, 34(11): 2106-2114.
	[Liu G W, Zhao T.Influencing factors and dynamic effect analysis of China's carbon emission intensity[J]. Resources Science, 2012, 34(11): 2106-2114. ]
[20]	Xu B, Lin B.Carbon dioxide emissions reduction in China's transport sector: a dynamic VAR (vector autoregression) approach[J]. Energy, 2015, 83: 486-495.
[21]	World Bank Open Data [EB/OL](2017-08-15)[2017-12-28].
[22]	Mi Z F, Wei Y M, Wang B, et al. Socioeconomic impact assessment of China's CO 2, emissions peak prior to 2030[J]. Journal of Cleaner Production, 2017, 142: 2227-2236.
[23]	王亮, 赵涛. 中国可再生能源消费、经济增长与碳排放的动态关系[J]. 技术经济, 2013, 32(11): 99-104.
	[Wang L, Zhao T.Dynamic relationship between renewable energy consumption, economic growth and carbon emissions in China[J]. Technical Economy, 2013, 32(11): 99-104. ]

碳排放水平/亿t			中高等收入国家	中低等收入国家	低收入国家	合计
>10	碳排放量/亿t 占比/% 国家数/个	64.68 19 2	119.97 35 2	22.38 7 1	0 0 0	207.03 61 5
1~10	碳排放量/亿t 占比/% 国家数/个	48.56 14 13	40.06 12 12	14.37 4 7	0 0 0	102.99 30 32
0.1~1	碳排放量/亿t 占比/% 国家数/个	11.92 4 24	6.86 2 15	5.28 2 17	0.76 0 4	24.82 7 60
<0.1	碳排放量/亿t 占比/% 国家数/个	0.64 0 27	0.79 0 26	0.94 0 26	0.78 0 27	3.16 1 106
合计	碳排放量/亿t 占比/% 国家数/个	125.81 37 66	67.68 50 55	42.98 13 51	1.54 0 31	338.01 100 203

收入水平		碳排放量	碳排放强度	能源强度	可再生能源占比	工业化率
高收入国家	美国	65.14	6.40	6.59	9.59	12.28
	日本	70.58	2.25	8.83	3.57	14.77
	德国	66.57	0.61	11.03	21.17	0.63
	韩国	87.79	1.25	2.12	1.74	7.11
	加拿大	80.73	2.60	2.43	7.21	7.04
	英国	72.62	3.18	9.83	10.52	3.85
	澳大利亚	77.92	15.27	0.16	4.24	2.41
	意大利	62.16	6.54	2.12	9.81	19.36
	法国	57.36	11.90	8.49	10.14	12.11
	波兰	29.68	10.01	40.25	11.10	8.95
	西班牙	62.29	3.54	2.35	13.18	18.65
	荷兰	62.23	7.53	2.43	9.14	18.66
中高等收入国家	中国	68.29	14.16	5.47	7.90	4.17
	俄罗斯联邦	53.53	18.36	10.76	3.74	13.61
	伊朗	80.60	5.83	8.20	1.06	4.30
	巴西	80.96	7.72	4.63	2.88	3.81
	南非	47.97	19.95	13.60	5.68	12.80
	墨西哥	56.81	20.00	11.87	2.65	8.66
	土耳其	80.01	4.73	9.04	4.93	1.29
	泰国	67.40	3.14	22.03	6.20	1.23
	哈萨克斯坦	47.51	11.23	19.50	14.95	6.80
	马来西亚	82.55	7.58	4.84	3.43	1.59
	阿根廷	76.08	4.06	10.44	1.52	7.90
	委内瑞拉	82.62	8.60	1.51	3.01	4.27
	伊拉克	58.92	13.18	19.91	2.23	5.77
	阿尔及利亚	70.77	7.00	12.38	6.90	2.95
中低等收入国家	印度	66.62	10.96	9.59	7.54	5.28
	印度尼西亚	59.63	15.53	11.32	3.14	10.39
	乌克兰	58.61	12.72	9.49	2.76	16.43
	埃及	53.68	22.54	14.46	3.94	5.38
	越南	58.56	14.12	4.13	10.57	12.62
	巴基斯坦	58.47	7.67	19.60	10.68	3.59
	菲律宾	65.57	14.26	6.96	10.35	2.86
	乌兹别克斯坦	60.01	17.59	13.83	2.11	6.44

收入水平	碳排放强度		能源强度
收入水平	历史均值/（kg/美元）	年均变化率/%	历史均值/（kgoe /美元）	年均变化率/%
高收入国家	0.33	-2.01	0.14	-1.75
中高等收入国家	0.96	-1.25	0.33	-1.31
中低等收入国家	1.61	-2.21	0.71	-2.22

	碳排放量		技术因素		结构因素
	分布区间	平均值	分布区间	平均值	分布区间	平均值
高收入国家	60~80	66.25	10~20	13.98	20~30	22.17
中高等收入国家	60~90	71.55	10~30	21.41	0~10	10.45
中低等收入国家	50~70	60.14	20~30	25.60	10~20	14.26