中国省域能源消耗碳排放安全评价

doi:10.18306/dlkxjz.2016.04.009

摘要/Abstract

摘要：

碳排放安全评价和预测在应对全球气候变化、实现区域可持续发展方面有重要意义。根据1996-2012年统计数据,基于压力—响应模型,从经济、社会、环境3个层面构建省域能源消费碳排放安全评价指标体系,分析了中国能源消费碳排放安全的时空分异,并运用GM(1, 1)方法进行预测。结果表明：1996-2012年,中国碳排放安全综合指数在临界安全区间内呈现波动下降趋势。其中,压力系统指数与综合指数变化态势趋于一致,响应系统指数表现为上升趋势;多数省区处于临界安全状态,亚安全状态省区逐渐消失,安全等级差的省区出现南移现象;大部分省区压力系统恶化的同时响应系统好转;区域间碳排放安全差距逐渐缩小,高压力低响应的区域大量减少。预测发现,2020年全国大部分省区处于临界安全或以下状态,碳排放安全情况不容乐观。

关键词: 碳排放, 安全评价, 压力—响应模型, 时空分异, 中国

Abstract:

Facing the great challenge of climate change, carbon emission security-an important part of ecological security-has become a prominent issue. This study used the 1996-2012 statistics of 30 provinces (except Tibet) of the Chinese mainland to analyze the spatial and temporal differences of energy-related carbon emission security, with the aim to provide some guidance for regional carbon emission reduction strategy and sustainable development. The assessment indicator system of energy consumption-related carbon emission security was based on the pressure-response model, which consists of three main aspects: economic, social, and environmental. Then carbon emission security situation was projected for 2013 to 2020 by using the GM (1,1) method. The results show that: from 1996 to 2012, energy consumption-related carbon emission security of China was at a critical level (0.45-0.65) and showed a downward trend since 2000. The response system played an increasingly more important role in the security state of energy-related carbon emissions; carbon emission security of various provinces is very different, its spatial pattern underwent some changes, and the provinces of low security level shifted southward; most provinces face more pressure while the situation of the response system has improved; disparity of regional CO2 emission security gradually narrowed, provinces of high pressure and low response reduced substantially; energy intensity, per capita disposable income of urban residents, proportion of coal, per capita carbon emission, proportion of the secondary industry have great influence on carbon emission security in the majority of provinces. The forecasting results show that carbon emission security situation is not optimistic. Most provinces will be in the critical and unsafe state with regard to carbon emission.

Key words: carbon emissions, security state, stress-response model, spatiotemporal difference, China

荣培君, 杨群涛, 秦耀辰, 李旭, 张天宁, 张帅帅. 中国省域能源消耗碳排放安全评价[J]. 地理科学进展, 2016, 35(4): 487-495.

Peijun RONG, Quntao YANG, Yaochen QIN, Xu LI, Tianning ZHANG, Shuaishuai ZHANG. Energy-related carbon emission security at the provincial level in China[J]. PROGRESS IN GEOGRAPHY, 2016, 35(4): 487-495.

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参考文献 29

[1]	陈星, 周成虎. 2005. 生态安全: 国内外研究综述[J]. 地理科学进展, 24(6): 8-20. doi: 10.3969/j.issn.1007-6301.2005.06.002
	[Chen X, Zhou C H.2005. Review of the studies on ecological security[J]. Progress in Geography, 24(6): 8-20.] doi: 10.3969/j.issn.1007-6301.2005.06.002
[2]	程叶青, 王哲野, 张守志, 等. 2013. 中国能源消费碳排放强度及其影响因素的空间计量[J]. 地理学报, 68(10): 1418-1431. doi: 10.11821/dlxb201310011
	[Cheng Y Q, Wang Z Y, Zhang S Z, et al.2013. Spatial econometric analysis of carbon emission intensity and its driving factors from energy consumption in China[J]. Acta Geographica Sinica, 68(10): 1418-1431.] doi: 10.11821/dlxb201310011
[3]	樊杰, 王亚飞, 汤青, 等. 2015. 全国资源环境承载能力监测预警(2014版)学术思路与总体技术流程[J]. 地理科学, 35(1): 1-10.
	[Fan J, Wang Y F, Tang Q, et al.2015. Academic thought and technical progress of monitoring and early-warning of the national resources and environment carrying capacity (V 2014)[J]. Scientia Geographica Sinica, 35(1): 1-10.]
[4]	胡艳兴, 潘竟虎, 王怡睿. 2015. 基于ESDA-GWR的1997-2012年中国省域能源消费碳排放时空演变特征[J]. 环境科学学报, 35(6): 1896-1906.
	[Hu Y X, Pan J H, Wang Y R.2015. Spatial-temporal evolution of provincial carbon emission in China from 1997 to 2012 based on ESDA and GWR model[J]. Acta Scientiae Circumstantiae, 35(6): 1896-1906.]
[5]	刘竹, 耿涌, 薛冰, 等. 2011. 城市能源消费碳排放核算方法[J]. 资源科学, 33(7): 1325-1330.
	[Liu Z, Geng Y, Xue B, et al.2011. A calculation method of CO2 emission from urban energy consumption[J]. Resources Science, 33(7): 1325-1330.]
[6]	鲁丰先, 张艳, 秦耀辰, 等. 2013. 中国省级区域碳源汇空间格局研究[J]. 地理科学进展, 32(12): 1751-1759. doi: 10.11820/dlkxjz.2013.12.004
	[Lu F X, Zhang Y, Qin Y C, et al.2013. Spatial patterns of provincial carbon source and sink in China[J]. Progress in Geography, 32(12): 1751-1759.] doi: 10.11820/dlkxjz.2013.12.004
[7]	陆大道, 樊杰. 2012. 区域可持续发展研究的兴起与作用[J]. 中国科学院院刊, 27(3): 290-300, 319.
	[Lu D D, Fan J.2012. The rise and effects of regional sustainable development studies in China[J]. Bulletin of the Chinese Academy of Sciences, 27(3): 290-300, 319.]
[8]	秦大河, Stocker T.2014. IPCC第五次评估报告第一工作组报告的亮点结论[J]. 气候变化研究进展, 10(1): 1-6.
	[Qin D H, Stocker T.2014. Highlights of the IPCC working group I fifth assessment report[J]. Progressus Inquisitiones de Mutatione Climatis, 10(1): 1-6.]
[9]	孙昌龙, 靳诺, 张小雷, 等. 2013. 城市化不同演化阶段对碳排放的影响差异[J]. 地理科学, 33(3): 266-272.
	[Sun C L, Jin N, Zhang X L, et al.2013. The impact of urbanization on the CO2 emission in the various development stages[J]. Scientia Geographica Sinica, 33(3): 266-272.]
[10]	王莉雯, 卫亚星. 2014. 沈阳市经济发展演变与碳排放效应研究[J]. 自然资源学报, 29(1): 27-38. doi: 10.11849/zrzyxb.2014.01.003
	[Wang L W, Wei Y X.2014. The research on economic development and carbon emission effect of Shenyang[J]. Journal of Natural Resources, 29(1): 27-38.] doi: 10.11849/zrzyxb.2014.01.003
[11]	王铮, 朱永彬, 刘昌新, 等. 2010. 最优增长路径下的中国碳排放估计[J]. 地理学报, 65(12): 1559-1568. doi: 10.11821/xb201012011
	[Wang Z, Zhu Y B, Liu C X, et al.2010. Integrated projection of carbon emission for China under the optimal economic growth path[J]. Acta Geographica Sinica, 65(12): 1559-1568.] doi: 10.11821/xb201012011
[12]	魏晓华, 郑吉, 刘国华, 等. 2015. 人工林碳汇潜力新概念及应用[J]. 生态学报, 35(12): 3881-3885. doi: 10.5846/stxb201309252358
	[Wei X H, Zheng J, Liu G H, et al.2015. The concept and application of carbon sequestration potentials in plantation forests[J]. Acta Ecologica Sinica, 35(12): 3881-3885.] doi: 10.5846/stxb201309252358
[13]	徐安. 2011. 我国城市化与能源消费和碳排放的关系研究[D]. 武汉: 华中科技大学.
	[Xu A. 2011. Study of the relation of urbanization and energy consumption and carbon emissions in China[D]. Wuhan, China: Huazhong University of Science and Technology.]
[14]	徐美, 朱翔, 刘春腊. 2012. 基于RBF的湖南省土地生态安全动态预警[J]. 地理学报, 67(10): 1411-1422. doi: 10.11821/xb201210011
	[Xu M, Zhu X, Liu C L.2012. Early-warning of land ecological security in Hunan Province based on RBF[J]. Acta Geographica Sinica, 67(10): 1411-1422.] doi: 10.11821/xb201210011
[15]	张金萍, 闫卫阳, 孙玮, 等. 2014. 中国低碳发展的类型及空间分异[J]. 资源科学, 36(12): 2491-2499.
	[Zhang J P, Yan W Y, Sun W, et al.Types and spatial differentiation of low-carbon development in China[J]. Resources Science, 36(12): 2491-2499.]
[16]	张梦婕, 官冬杰, 苏维词. 2015. 基于系统动力学的重庆三峡库区生态安全情景模拟及指标阈值确定[J]. 生态学报, 35(14): 4880-4890. doi: 10.5846/stxb201311122717
	[Zhang M J, Guan D J, Su W C.2015. Scenarios simulation and indices thresholds determination of ecological security in Three Gorges Reservoir based on system dynamics[J]. Acta Ecologica Sinica, 35(14): 4880-4890.] doi: 10.5846/stxb201311122717
[17]	张强, 薛惠锋, 张明军, 等. 2010. 基于可拓分析的区域生态安全预警模型及应用: 以陕西省为例[J]. 生态学报, 30(16): 4277-4286.
	[Zhang Q, Xue H F, Zhang M J, et al.2010. Early-warning model based on extension analysis for ecological security and its application: Case study of Shaanxi Province[J]. Acta Ecologica Sinica, 30(16): 4277-4286.]
[18]	赵宏波, 马延吉. 2014. 基于变权—物元分析模型的老工业基地区域生态安全动态预警研究: 以吉林省为例[J]. 生态学报, 34(16): 4720-4733.
	[Zhao H B, Ma Y J.2014. Study on early-warning model based on variable weight-matter element analysis for ecological security in old industrial bases: A case study of Jilin Province[J]. Acta Ecologica Sinica, 34(16): 4720-4733.]
[19]	周健, 刘占才. 2011. 基于GM(1,1)预测模型的兰州市生态安全预警与调控研究[J]. 干旱区资源与环境, 25(1): 15-19.
	[Zhou J, Liu Z C.2011. The research on the control and warning of ecological security based on GM (1, 1) model[J]. Journal of Arid Land Resources and Environment, 25(1): 15-19.]
[20]	周葵, 戴小文. 2013. 中国城市化进程与碳排放量关系的实证研究[J]. 中国人口·资源与环境, 23(4): 41-48.
	[Zhou K, Dai X W.2013. An empirical study on the relationship between urbanization and carbon emission in China[J]. China Population, Resources and Environment, 23(4): 41-48.]
[21]	朱永彬, 王铮, 庞丽, 等. 2009. 基于经济模拟的中国能源消费与碳排放高峰预测[J]. 地理学报, 64(8): 935-944. doi: 10.3321/j.issn:0375-5444.2009.08.004
	[Zhu Y B, Wang Z, Pang L, et al.2009. Simulation on China's economy and prediction on energy consumption and carbon emission under optimal growth path[J]. Acta Geographica Sinica, 64(8): 935-944.] doi: 10.3321/j.issn:0375-5444.2009.08.004
[22]	Al-mulali U, Sab C N B C, Fereidouni H G.2012. Exploring the bi-directional long run relationship between urbanization, energy consumption, and carbon dioxide emission[J]. Energy, 46(1): 156-167. doi: 10.1016/j.energy.2012.08.043
[23]	Gari S R, Newton A, Icely J D.2015. A review of the application and evolution of the DPSIR framework with an emphasis on coastal social ecological systems[J]. Ocean & Coastal Management, 103: 63-77.
[24]	Lin B Q, Ouyang X L.2014. Energy demand in China: Comparison of characteristics between the US and China in rapid urbanization stage[J]. Energy Conversion and Management, 79: 128-139. doi: 10.1016/j.enconman.2013.12.016
[25]	Liu Z, Guan D B, Crawford-Brown D, et al.2013. A low-carbon road map for China[J]. Nature, 500: 143-145.
[26]	Martínez-Zarzoso I, Maruotti A.2011. The impact of urbanization on CO2 emissions: Evidence from developing countries[J]. Ecological Economics, 70(7): 1344-1353. doi: 10.1016/j.ecolecon.2011.02.009
[27]	Rapport D J, Whitford W G.1999. How ecosystems respond to stress common properties of arid and aquatic systems[J]. BioScience, 49(3): 193-203. doi: 10.2307/1313509
[28]	York R.2007. Demographic trends and energy consumption in European Union Nations, 1960-2025[J]. Social Science Research, 36(3): 855-872. doi: 10.1016/j.ssresearch.2006.06.007
[29]	Zha D L, Zhou D Q, Zhou P.2010. Driving forces of residential CO2 emissions in urban and rural China: An index decomposition analysis[J]. Energy Policy, 38(7): 3377-3383. doi: 10.1016/j.enpol.2010.02.011

目标层	指标层	单元指标层	子指标层	影响
能源消费碳排放安全	压力系统	经济压力	人均GDP(I₁)/(万元/人)	-
			第二产业所占比重(I₂)/%	-
			城镇居民人均可支配收入(I₃)/(元/人)	-
			农村居民家庭人均纯收入(I₄)/(元/人)	-
		社会压力	人口密度(I₅)/(人/km²)	-
			人口自然增长率(I₆)/%	-
			城镇化水平(I₇)/%	-
			能源强度(I₈)/(t标准煤/万元)	-
		环境压力	碳排放年均增长率(I₉)/%	-
			煤炭比重(I₁₀)/%	-
			脱钩指数(I₁₁)/%	-
			人均碳排(I₁₂)/( kg/人)	-
	响应系统	经济响应	工业污染治理投资占GDP比重(I₁₃)/%	+
		经济响应	林业投资占GDP比重(I₁₄)/%	+
		社会响应	R & D人员比重(I₁₅)/%	+
		社会响应	零碳能源比重(I₁₆)/%	+
		环境响应	建成区绿化覆盖率(I₁₇)/%	+
		环境响应	森林覆盖率(I₁₈)/%	+

区间	安全评价	警情特征
[0~0.25)	病态	碳排放增速极快且处于无序增长状态,人口压力巨大,能源消耗过度,环境污染严重,碳排放与经济发展严重不协调,碳排放调控及碳减排效果极不理想,人口、资源、环境、经济、社会系统无序发展。
[0.25~0.45)	不安全	碳排放增速较快且伴随一定的无序增长状态,人口压力较大,能源消耗与环境污染较严重,碳排放与经济发展比较不协调,碳排放调控及减排效果不理想,阻碍了人口、资源、环境、经济、社会系统的协调发展。
[0.45~0.65)	临界安全	碳排放增速稍快,存在一定的人口压力,能源消耗与环境污染较明显,经济发展态势一般,碳排放与经济发展处于协调边缘,碳排放调控及碳减排虽不理想但有初步效果,人口、资源、环境、经济、社会系统协调发展状况一般。
[0.65~0.85)	亚安全	碳排放增速放缓,人口增长处于比较稳定状态,资源能源消耗与环境污染得到一定程度改善,经济发展态势较好,碳排放与经济发展处于初级协调状态,碳排放调控及减排效果较好,人口、资源、环境、经济、社会系统协调发展状况较好。
[0.85~1.0]	安全	碳排放增速明显下降,人口增速较低且稳定,资源能源消耗与环境污染得到极大改善,经济运行良好,碳排放与经济增长处于高度耦合协调发展状态,碳排放调控及减排效果理想,实现人口、资源、环境、经济、社会系统的协调发展。

指标	省区数量	省区简称
能源强度	28	京、津、冀、晋、蒙、辽、吉、黑、沪、苏、浙、皖、闽、赣、鲁、豫、湘、粤、桂、渝、川、贵、滇、陕、甘、青、宁、新
城镇居民人均可支配收入	19	津、冀、晋、辽、黑、皖、闽、赣、豫、鄂、湘、桂、渝、川、贵、滇、陕、甘、新
煤炭比重	17	京、津、冀、吉、沪、浙、闽、赣、豫、湘、粤、琼、川、滇、甘、宁、新
人均碳排放	17	津、冀、蒙、辽、吉、苏、浙、闽、赣、鲁、豫、湘、桂、琼、川、滇、陕
第二产业比重	16	京、蒙、辽、黑、苏、闽、赣、鲁、豫、鄂、湘、桂、琼、渝、陕、青

省区	评价值	安全评价	省区	评价值	安全评价
京	0.7604	亚安全	豫	0.4030	不安全
津	0.4649	临界安全	鄂	0.2840	不安全
冀	0.3224	不安全	湘	0.3614	不安全
晋	0.6126	临界安全	粤	0.2585	不安全
蒙	0.4455	不安全	桂	0.4167	不安全
辽	0.5215	临界安全	琼	0.4526	不安全
吉	0.4561	临界安全	渝	0.5274	临界安全
黑	0.7414	亚安全	川	0.4338	不安全
沪	0.4286	不安全	贵	0.5476	临界安全
苏	0.5394	临界安全	滇	0.5333	临界安全
浙	0.4468	不安全	陕	0.4267	不安全
皖	0.5739	临界安全	甘	0.5503	临界安全
闽	0.3034	不安全	青	0.5906	临界安全
赣	0.4098	不安全	宁	0.6462	临界安全
鲁	0.4045	不安全	新	0.4963	临界安全