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基于IO-SDA模型的新疆能源消费碳排放影响机理分析

本站小编 Free考研考试/2021-12-29

王长建¹^,, 张小雷², 张虹鸥¹, 汪菲³

1. 广州地理研究所广东省地理空间信息技术与应用公共实验室,广州 510070

2. 中国科学院新疆生态与地理研究所,乌鲁木齐 830011

3. 新疆师范大学地理科学与旅游学院,乌鲁木齐 830054

Influencing mechanism of energy-related carbon emissions in Xinjiang based on IO-SDA model

WANGChangjian¹^,, ZHANGXiaolei², ZHANGHongou¹, WANGFei³

1. Guangzhou Institute of Geography, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou 510070, China

2. Xinjiang Institute of Ecology and Geography, CAS, Urumqi 830011, China

3. College of Geography Science and Tourism, Xinjiang Normal University, Urumqi 830054, China

收稿日期:2015-09-25
修回日期:2016-03-18
网络出版日期:2016-07-25
版权声明:2016《地理学报》编辑部本文是开放获取期刊文献，在以下情况下可以自由使用：学术研究、学术交流、科研教学等，但不允许用于商业目的.
基金资助:

国家自然科学基金青年基金(41501144)

广东省科学院引进高层次领军人才专项资金项目(2016GDASRC-0101)

广东省科学院平台环境与能力建设专项资金项目(2016GDASPT-0210)

作者简介:
-->作者简介：王长建(1986-), 男, 河南南阳人, 博士, 助理研究员, 中国地理学会会员(S110010114M), 主要从事能源地理、区域可持续发展研究。E-mail: wwwangcj@126.com

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摘要
基于区域视角的能源消费碳排放影响机理分析,是有效实现节能降耗减排的重要研究议题。本文基于投入产出理论,通过构建“能源—经济—碳排放”混合型投入产出分析框架,利用扩展的IO-SDA模型,对新疆维吾尔自治区（简称新疆）1997-2007年能源消费碳排放的影响因素进行结构分解分析。结果显示：① 新疆能源消费碳排放从1997年的2070.08万t增长到2007年的4034.33万t,碳排放的增长主要集中在能源资源生产与加工业和矿产资源开采与加工业。② 碳排放影响因素的直接效应分析,人均GDP、最终需求结构、人口规模和生产结构的变化是引起碳排放增长的重要影响因素,碳排放强度的降低是这一时期遏制碳排放增长的重要影响因素,说明在经济规模和人口数量不断增长的同时,经济结构未得到有效优化,生产技术未得到有效的提升,导致新疆能源消费碳排放的快速增长。③ 碳排放影响因素的间接效应分析,省域间调出、固定资本形成总额和城镇居民消费对于新疆能源消费碳排放的变化影响显著。④ 碳密集产业部门的固定资产投资增加,能源资源型产品的省域间调出增长,使得区域间“隐含碳”转移效应十分显著。

关键词：碳排放;IO-SDA模型;影响因素;新疆
Abstract
Global warming and climate change are issues that have aroused widespread attention, and the need for a transition to a low-carbon economy has become the consensus of the international community. China has become one of the world's largest energy consumers, as well as one of the biggest emitters of greenhouse gases. This further highlights the importance and urgency of research on carbon emissions from energy consumption. Based on regional perspectives of the impacts of carbon emissions, the analysis of mechanisms responsible for carbon emissions has become an important research topic. Xinjiang, an important Chinese energy production base, is currently going through a period of strategic opportunities for rapid development. It is critical to ensure stable socioeconomic development as well as to achieve energy savings and meeting emission reductions targets, thus the harmonious development of "society-economy-energy-environment," is the key issue currently facing the region. This study, based on the input-output theory, presents a structural decomposition analysis of the factors affecting energy consumption and carbon emissions in Xinjiang from 1997-2007. This analysis employs a hybrid input-output analysis framework of "energy-economy-carbon emissions," and uses an extended IO-SDA model. The data for this study come from the Xinjiang input-output table for 1997-2002-2007. Population, economic, and energy source data are derived from the Statistical Yearbook of the Xinjiang Uygur Autonomous Region. (1) Xinjiang's carbon emissions from energy consumption increased from 20.70 million tons in 1997 to 40.34 million tons in 2007; carbon emissions growth was mainly concentrated in the production and processing of energy resources, the mining of mineral resources, and the processing industry. (2) The analysis of the direct effects of the influencing factors on carbon emissions shows that the change in per capita GDP, final demand structure, population scale, and production structure were the important factors causing an increase in carbon emissions, while the decrease in carbon emission intensity during this period was an important factor in stopping the growth of carbon emissions. This shows that while Xinjiang's economy and population were growing, the economic structure had not been effectively optimized and production technology had not been improved, which results in a rapid growth of carbon emissions from energy consumption. (3) An analysis of the indirect effects of the factors influencing carbon emissions shows that inter-provincial transfers, gross fixed capital formation, and consumption by urban residents had significant influence on the changes in carbon emissions from energy consumption in Xinjiang. (4) The growth of investments in fixed assets of carbon-intensive industry sectors, as well as the growth of inter-provincial transfers of energy resource products, makes the transfer effect of inter-area "implicit carbon" very significant.

Keywords：carbon emissions;input-output-structural decomposition analysis;influencing factors;Xinjiang

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王长建, 张小雷, 张虹鸥, 汪菲. 基于IO-SDA模型的新疆能源消费碳排放影响机理分析[J]. , 2016, 71(7): 1105-1118 https://doi.org/10.11821/dlxb201607002
WANG Changjian, ZHANG Xiaolei, ZHANG Hongou, WANG Fei. Influencing mechanism of energy-related carbon emissions in Xinjiang based on IO-SDA model[J]. 地理学报, 2016, 71(7): 1105-1118 https://doi.org/10.11821/dlxb201607002

1 引言

以全球变暖为主要特征的气候变化问题被持续广泛地关注,经济的低碳转型已经成为国际社会的共识。当前,中国已经成为能源消费大国和温室气体排放大国。国际气候谈判的舆论压力和国内节能减排的资源环境约束,使得碳排放问题被政策制定者、产业制造者和科研工作者等广泛而又持续地关注^[1-6]。为此,中国政府在2009年哥本哈根气候大会上承诺2020年单位GDP二氧化碳排放强度比2005年下降40%~45%,非化石能源消费比重达到15%^[5];“十二五”规划（2011-2015年）中提出,2015年非化石能源占一次能源消费比重达到11.4%,单位GDP能源消耗降低16%,单位GDP二氧化碳排放降低17%^[7];2013年,国务院划定2015年能源消费红线,总量控制在40亿吨标准煤^[8];2014年,中美发布应对气候变化的联合声明,中国承诺到2030年前实现碳排放的峰值^[9]。随着经济的持续增长,工业化和城镇化进程的不断推进,中国能否兑现极具约束性的碳减排承诺,同时保持社会经济的稳定快速发展,深入探讨能源消费碳排放的影响要素,科学筛选重点节能产业和关键减碳行业,积极促进能源、经济、社会与环境的和谐发展,进一步突显能源消费碳排放研究的重要性与迫切性。
目前关于碳排放的研究大致分为,碳排放总量估算与核算^[10-14]、碳排放影响因素及其作用机理^[15-18]、碳排放情景分析及预测^[19-21]、碳减排技术及政策模拟^[22-25]等几个方面,其中碳排放影响要素及驱动因素解析是制定减排政策和实施情景模拟的关键。大量的研究成果表明,能源消费碳排放的影响因素众多。能源消费的快速增长、社会经济的快速发展对碳排放的增加起着决定性的作用,成为导致碳排放量上升的最主要因素^[26-29]。Usama Al-mulali等^[26]采用面板数据模型对30个撒哈拉以南非洲国家进行研究,结果表明能源消费对CO₂排放和经济增长有较强的推动作用。Usama Al-mulali^[27]利用面板数据模型对12个中东国家的CO₂排放的影响因素进行实证研究,结论表明能源消费、外商直接投资、国内生产总值依次为最重要的影响因素。Li等^[28]利用Path-STIRPAT模型对中国CO₂排放的影响因素进行研究,结果表明人均GDP增长是其最主要影响因素。Zhu等^[29]利用投入产出模型对中国1992-2005年居民能源消费的碳排放研究,表明居民能源消费水平的不断提升是导致居民直接碳排放增长的主要因素。同时,能源结构和产业结构的低碳化优化有助于减缓碳排放的增长^[30-31]。张雷^[30]通过对比发达国家和发展中国家长期发展的对比研究发现,经济结构的多元化发展导致国家能源消费需求增长的减缓,并通过建立产业-能源关联和能源—碳排放关联评价模型,分析产业结构演进和能源结构变化对中国碳排放总量增长和空间格局变化的影响^[31]。Wu等^[32]利用DEA模型研究表明中国能源效率的不断提升主要是靠技术进步的驱动。Li等^[33]利用STIRPAT模型对中国省级层面的能源消费碳排放的影响因素进行实证分析,研究表明在大多数省级区域内提高技术水平都产生了CO₂排放的降低。随着经济全球化进程的不断渗透,国际贸易和区际贸易对于碳排放的影响引起不断的关注,中国等发展中国家除了提高自身的生产技术水平和能源利用效率外,还应加强进出口贸易中隐含碳排放的相关研究^[34-35]。综上所述,碳排放影响因素涉及人口、经济、能源、产业、技术、政策等等。
以往的大量研究都集中在全球、洲际和国家等宏观区域层面,对于省级、城市等小尺度的研究相对缺乏。从地理学的研究视角分析,对于中国来说,东、中、西三大区域以及各个省、市、自治区之间都存在显著的人口增长规模、居民消费水平、社会经济发展模式、能源资源禀赋、技术水平等区域差异^[6]。同时,以往的能源碳排放影响机制研究大多借助计量模型关注各种影响因素的变化对于碳排放的直接影响,较少从最终需求层面关注其对于碳排放的间接影响。国内已经涌现的省级层面和市域层面的碳排放影响要素分析研究,对于揭示隐藏在区域差异中的多要素碳排放影响机理有较强的启示意义,对于各区域制定更具针对性和操作性的碳排放政策有较强的指导意义^{[15, 36-42]}。Liu等^[37]利用指数分解模型对1995-2009年北京、上海、天津和重庆4个直辖市的碳排放影响因素进行对比研究。Wang^[42]等采用LMDI模型对山东省1990-2009年能源消费碳排放的影响因素进行分解分析,经济增长和人口规模是碳排放增加的最主要影响因素。Wang等^[39]利用STIRPAT模型对广东省1980-2010年碳排放影响因素进行解析,人口规模、城镇化水平、人均GDP和工业化水平是其主要影响因素。Wang等^[38]借助IDA模型对苏州市2005-2010年能源消费碳排放进行分解分析,能源结构和产业结构的调整引致的能源消费强度降低有助于遏制碳排放的增长。Xi等^[43]对于沈阳市碳排放的研究表明,能源生产与加工业、制造业和建筑业是实现城市低碳发展的关键性减碳部门。Liang等^[41]利用SDA模型对东部沿海制造业中心江苏省的碳排放影响因素分解分析,江苏省的低碳发展不仅应该关注能源消费强度的降低和能源消费结构的优化,而且更应该注重国际出口贸易中隐含碳的下降。Geng等^[15]对于东北地区老工业基地辽宁省的碳排放影响因素分解分析,省域间调出贸易对于碳排放增长的影响显著。因此,省级区域甚至城市尺度的碳排放影响因素研究亟待深入而又广泛开展,以期从省级区域积极应对国家层面的减排承诺。新疆,作为中国重要的能源综合生产基地、向西开发的重要门户、丝绸之路经济带的核心区,当前正处于跨越式发展的重要战略机遇期,如何有效地完成节能降耗减排的约束指标,同时保障社会经济的持续稳定发展,将是新疆实现社会—经济—能源—环境和谐发展的重要现实命题。

2 研究方法与数据来源

2.1 IO-SDA模型构建

如何科学评价与定量分析碳排放的主要影响因素,指数分解分析（Index decomposition analysis, IDA）和结构分解分析（Structural decomposition analysis, SDA）是目前最为广泛和常用的研究方法。IDA模型采用数据聚合的形式分析人口因素、经济因素、结构因素等多种影响因素的变化对于碳排放产生的直接影响^[44]。SDA模型基于经典的投入产出理论^[45-46],因其数据更加完备、分析更为细致,并且弥补了IDA模型无法分析最终需求部门变化对碳排放产生间接影响的缺陷^{[35, 47-49]},日益成为国内外****用于分析经济、能源与碳排放问题的常用研究方法。Peters等^[48]利用SDA模型对1992-2002年中国碳排放增长的影响因素进行分析,表明经济结构、技术水平、城镇居民消费对碳排放增长影响显著。Minx等^[49]拓展早期Peters的关于中国碳排放的研究,进一步揭示2002-2007年生产部门的技术进步在很大程度上抵消了最终需求部门所引起的碳排放增长。Guan等借助IO-SDA模型,从最终需求角度发现城镇居民消费、固定资产投资和出口贸易对于中国2002-2007年碳排放增长的作用显著^[50-51]。
以投入产出分析（Input-output analysis）为基础,SDA模型将包含在投入产出表中的部门信息及其相互联系进行深度解析。本文通过构建IO-SDA模型分析新疆维吾尔自治区（以下简称新疆）能源消费碳排放的影响因素及其作用机理,其分析框架如下^{[15, 48-52]}：
将能源与环境要素（碳排放）纳入投入产出表（表1）,分别构建能源投入产出表和碳排放投入产出表,投入产出表以价值型为基础,能源要素和碳排放要素以实物型为基础,以此构建“能源—经济—碳排放”混合型投入产出模型（Hybrid Input-output table）。
Tab. 1
表1
表1能源—经济—碳排放投入产出模型
Tab. 1Energy-economy-carbon emission input-output model

		中间使用	最终需求		总产出
		1, 2, …, n	消费	资本形成		出口	调出
中间投入	1	$\begin{matrix} X_{ij} \end{matrix}$	$\begin{matrix} Y_{i} \end{matrix}$		$\begin{matrix} X_{i} \end{matrix}$
	2
	…
	n
增加值		$\begin{matrix} V_{j} \end{matrix}$
总投入		$\begin{matrix} X_{j} \end{matrix}$
能源投入	1	$\begin{matrix} E_{kj} \end{matrix}$	$\begin{matrix} E_{ky} \end{matrix}$		$\begin{matrix} E_{k} \end{matrix}$
	2
	…
	m
碳排放	1	$\begin{matrix} C_{kj} \end{matrix}$	$\begin{matrix} C_{ky} \end{matrix}$		$\begin{matrix} C_{k} \end{matrix}$
	2
	…
	k

新窗口打开
基于投入产出理论,构建能源消费碳排放影响因素的结构分解分析模型。

\begin{matrix} C = E {(I - A)}^{- 1} y \end{matrix}

（1）
式中：C表示碳排放总量;E为行业碳排放强度向量;A为直接消耗系数矩阵;

\begin{matrix} {(I - A)}^{- 1} \end{matrix}

为

\begin{matrix} n \times n \end{matrix}

的列昂揭夫逆矩阵;y表示最终需求部分,包括投入产出表中第Ⅱ象限的最终消费（政府消费、城镇居民消费、农村居民消费）、资本形成总额（固定资本形成总额和存贷增加）和进出口总额（进口、出口、调进和调出）。
其中,

\begin{matrix} n \times 1 \end{matrix}

的最终需求列向量y可以进一步分解为最终需求结构y_s和最终需求总量,最终需求总量可以进一步分解为人口规模P和人均最终需求量y_v（即人均GDP）^[52-53]。
因此,

\begin{matrix} y = P y_{s} y_{v} \end{matrix}

（2）
那么,能源消费碳排放的SDA模型推导如下：

\begin{matrix} C = E \times L \times y_{s} \times y_{v} \times P \end{matrix}

（3）
即：

\begin{matrix} ΔC = ΔEL y_{s} y_{v} P + EΔL y_{s} y_{v} P + ELΔ y_{s} y_{v} P + EL y_{s} Δ y_{v} P + E_{s} E_{i} L y_{s} y_{v} ΔP \end{matrix}

（4）
进而,

\begin{matrix} ΔC = f (Δ E) + f (Δ L) + f (Δ y_{s}) + f (Δ y_{v}) + f (Δ P) \end{matrix}

（5）
由此,碳排放量的变化（ΔC）被分解为五大影响因素：人口规模（P）;碳排放强度（E）;生产结构（L）,

\begin{matrix} L = {(I - A)}^{- 1} \end{matrix}

为

\begin{matrix} n \times n \end{matrix}

的列昂揭夫逆矩阵;最终需求结构（y_s）和人均最终需求量（y_v）。
IO-SDA模型的优势还在于能够刻画最终需求部门变化对碳排放产生的间接影响^{[15, 41, 49, 54]}。依据投入产出表中最终需求部门的类别,将

\begin{matrix} n \times 1 \end{matrix}

的最终需求列向量y进行对角化处理^{[15, 48-49, 52]},得到公式如下：

\begin{matrix} C_{k} = E {(I - A)}^{- 1} y_{k} \end{matrix}

（6）
式中：y_k为第k类别的最终需求;C_k为第k类别的最终需求变化引起的间接碳排放量。

2.2 数据来源与整理

数据资料主要基于1997年、2002年、2007年的新疆投入产出表,人口数据、经济数据和能源数据主要来自于《新疆维吾尔自治区统计年鉴》。为了与统计年鉴中分行业能源消费数据保持一致,特别将40部门的新疆1997年投入产出表和42部门的新疆2002年、2007年投入产出表按照一定的产业合并原则^{[15, 41]},将3张表格分别合并为28部门投入产出表（表2）。进而采用双重缩减法（Double deflation method）^{[15, 41, 48, 52, 55]}将新疆2002年和2007年投入产出表转换为1997年不变价,以期增强数据的可比性。
Tab. 2
表2
表2新疆28部门投入产出表
Tab. 2Input-output of 28 industries in Xinjiang

代码	产业	代码	产业
1	农业	15	金属制品业
2	煤炭采选业	16	通用、专用设备制造业
3	石油和天然气开采业	17	交通运输设备制造业
4	金属矿采选业	18	电气、机械及器材制造业
5	非金属矿采选业	19	通信设备、计算机及其它电子设备制造业
6	食品制造及烟草加工业	20	仪器仪表及文化办公用品制造业
7	纺织业	21	其他制造业
8	服装皮革羽绒及其他纤维制品制造业	22	电力热力生产和供应业
9	木材加工及家具制造业	23	燃气生产和供应业
10	造纸印刷及文教用品制造业	24	水的生产和供应业
11	石油加工及炼焦业	25	建筑业
12	化学原料及化学制品制造业	26	交通运输、仓储及邮电通信业
13	非金属矿物制品业	27	批发和零售贸易业、餐饮业
14	金属冶炼及压延加工业	28	其他行业

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2.3 碳排放量核算

依据IPCC碳排放计算指南,参照IPCC提供的一般性缺省值和相关能源消费碳排放研究,确定主要的碳排放系数用以碳排放的核算^{[15, 40-42]},计算公式如下：

\begin{matrix} C_{t} = \sum^{} E_{t}^{i} \times LC V_{i} \times C F_{t}^{i} \times O_{i} \end{matrix}

（7）
式中：t表征时间;i代表不同种类的能源;C_t表示t时间的碳排放总量（万t）;

\begin{matrix} E_{t}^{i} \end{matrix}

表示t时间第i种能源的消费总量（万t）;LCV_i（Lower calorific value）为第i种能源的燃料低热值;

\begin{matrix} C F_{t}^{i} \end{matrix}

为第i种能源的碳排放系数;O_i为第i种能源的燃烧氧化率（表3）。
Tab. 3
表3
表3能源消费碳排放转换因子
Tab. 3Carbon emission conversion factors of energy sources

	碳排放因子^a	转换因子^b	氧化率^a	低热值(LCV)^c
原煤	25.800	0.7143 tce/t	0.918	20.908
焦炭	29.410	0.9714 tce/t	0.928	28.435
洗煤	25.800	0.2857 tce/t	0.918	8.363
精煤	27.680	0.9000 tce/t	0.918	26.344
原油	20.080	1.4286 tce/t	0.979	41.816
柴油	20.170	1.4571 tce/t	0.982	42.652
煤油	19.600	1.4714 tce/t	0.980	43.070
汽油	18.900	1.4714 tce/t	0.986	43.070
燃料油	21.090	1.4286 tce/t	0.985	41.816
天然气	17.200	1.3300 tce/10³m³	0.990	38.931
炼油气	18.200	1.5714 tce/t	0.989	46.055
液化石油气	17.200	1.7143 tce/t	0.989	50.179

注：a来源于[15];b来源于[41];c来源于[15, 43]。

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3 实证分析

3.1 不同影响因素对碳排放增长的结构分解分析

基于IPCC能源消费碳排放核算体系,新疆能源消费碳排放从1997年的2070.08万t增长到2002年的2408.06万t、2007年的4034.33万t,11年间增长了94.88%,尤其是2002-2007年的碳排放增长量占1997-2007年碳排放增长总量的82.79%（图1）。1997-2007年,新疆28个产业部门碳排放量的增长主要由能源生产与加工业、电力热力生产和供应业和矿产资源开采与加工业引起,碳排放量的增长集中在石油加工及炼焦业（1466.68万t）、电力热力生产和供应业（786.54万t）、金属冶炼及压延加工业（155.67万t）、非金属矿物制品业（100.84万t）、化学原料及化学制品制造业（66.39万t）,这些能源密集部门以及高碳行业,将是当前乃至中远期新疆产业发展与产业布局应当特别关注的重点节能产业和关键减碳行业。

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图11997-2007年新疆碳排放不同影响因素的结构分解分析
-->Fig. 1Structure decomposition analysis of various influencing factors in Xinjiang during 1997-2007
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依据计算结果（图1）：1997-2002年,人口规模、人均GDP、最终需求结构和生产结构的变化分别导致228.12万t、470.62万t、471.73万t和64.28万t的碳排放增长量,碳排放强度的变化引起896.77万t的碳排放减少量。2002-2007年,人口规模、人均GDP、最终需求结构和生产结构的变化分别导致243.33万t、1363.13万t、317.14万t、264.31万t的碳排放增长量,碳排放强度的变化在这一阶段引起561.65万t的碳排放减少量。
人均GDP的快速增长对碳排放增长的贡献作用在2002-2007年这一发展阶段明显高于1997-2002年。人均GDP由1997年的6052.39元/人增长到2002年的8464.51元/人进而增长到2007年的16815.47元/人,以可比价计算1997-2002年的年均增长速度为6.87%,2002-2007年的年均增长速度高达12.35%。经济快速增长的规模效应,使得经济产出效应成为1997-2007年新疆碳排放快速增长的最重要影响因素且贡献作用持续增强。碳排放强度的变化对碳排放降低的贡献作用在2002-2007年这一发展阶段明显低于1997-2002年,其主要原因是碳排放强度的降低速度有所放缓。新疆碳排放强度由1997年的1.99 t碳/万元GDP下降到2002年的1.49 t碳/万元GDP进而下降到2007年的1.14 t碳/万元GDP,以可比价计算1997-2002年的年均下降速度为5.85%,2002-2007年的年均下降速度仅为3.28%。人口规模的变化对碳排放总量增长的作用程度在1997-2002年和2002-2007年这两个时期没有显著变化,均呈现促进碳排放总量增长的正效应。2002-2007年相比较于1997-2007年,最终需求结构的改变导致碳排放总量增长的变化相对弱化,生产结构的改变导致碳排放总量增长的变化明显增强,其主要原因是第二产业比重由1997年的37.1%增长到2007年的46.8%,第三产业和第一产业比重均有不同程度下降。1997-2007年,“九五”时期新疆确立的优势资源转换战略持续推进,依托能矿资源优势加速新型工业的发展;“西部大开发”战略的全面实施,固定资产投资水平显著增长,新疆能矿资源的勘探开发进一步升级,能源重化工企业规模不断扩大,造成这一时期新疆产业结构的演进以第二产业的快速增长为主要特征且能矿资源重化工趋势明显。虽然新型工业化的持续推进使得新疆的能源消费强度由1997年的3.01 t标准煤/万元GDP下降到2002年的2.24 t标准煤/万元GDP进而下降到2007年的1.86 t标准煤/万元GDP,但是距离全国平均水平还有一定的差距,进一步突显了新疆生产技术的低碳化程度亟待提升。
1997-2007年,人均GDP、最终需求结构、人口规模和生产结构的变化引起的碳排放增长量分别占这一时期碳排放总增长量的127.04%、28.78%、27.63%和25.48%（表4）,说明在经济规模（人均GDP）和人口数量（人口规模）不断增长的同时,经济结构（最终需求结构）未得到有效优化,生产技术（生产结构）未得到有效的提升,导致新疆能源消费碳排放的快速增长。1997-2007年,碳排放强度的降低引起的碳排放减少量占这一时期碳排放总量变化的绝对值为108.92%,碳排放强度是这一时期唯一的遏制碳排放增长的影响因素。
Tab. 4
表4
表41997-2007年新疆不同影响因素对碳排放增长的贡献率(%)
Tab. 4Structure decomposition analysis of contributions of various influencing factors in Xinjiang

影响因素	1997-2002年	2002-2007年	1997-2007年
人口规模(Population size)	67.49	14.96	27.63
人均GDP(GDP per capita)	139.24	83.82	127.04
最终需求结构(Final demand structure)	139.57	19.50	28.78
生产结构(Production structure)	19.02	16.25	25.48
碳排放强度(Carbon emission intensity)	-265.33	-34.54	-108.92
总效应(Total change)	100.00	100.00	100.00

3.2 不同最终需求类别对碳排放增长的结构分解分析

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依据公式（6）的计算结果,分析不同最终需求类别的变化对新疆能源消费碳排放产生的间接影响（图2,表5）。同时分析不同最终需求类别的变化对新疆产业部门碳排放产生的间接影响（表6）。

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图2不同类别最终需求对新疆1997-2007年碳排放的结构分解分析
-->Fig. 2Increment of carbon emissions from different finaldemands in Xinjiang
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Tab. 5
表5
表51997年-2007年新疆不同最终需求类别变化对碳排放增长的贡献率(%)
Tab. 5Contribution to carbon emissions of different final demands in Xinjiang

最终需求	1997-2002年	2002-2007年	1997-2007年
农村居民消费(Rural household consumption)	9.31	6.72	7.10
城镇居民消费(Urban household consumption)	-10.90	24.59	19.32
政府消费(Government consumption)	-1.36	12.51	10.45
固定资本形成总额(Fixed capital formation)	76.41	78.45	78.15
存货增加(Inventory increase)	-47.64	7.67	-0.54
调出(Inter-provincial export)	28.20	90.52	81.27
出口(International export)	16.55	5.22	6.90
调进(Inter-provincial import)	63.09	-128.10	-99.72
进口(International import)	-33.65	2.42	-2.93
总效应(Total change)	100.00	100.00	100.00

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Tab. 6
表6
表6不同最终需求对1997-2007年新疆产业部门碳排放增长的结构分解分析(万t)
Tab. 6Carbon emission changes caused by different final demands from different sectors in Xinjiang (10⁴ t)

代码	农村居民消费	城镇居民消费	政府消费	固定资本形成总额	存货增加	调出	出口	调进	进口
1	18.73	-6.69	3.13	-8.68	-81.00	166.55	88.20	15.52	3.21
2	5.37	-2.01	0.00	0.00	-3.18	0.38	-0.02	28.69	-0.08
3	-0.19	1.17	0.00	0.00	-22.39	-18.39	2.92	6.23	-18.64
4	0.00	0.00	0.00	0.00	2.13	24.10	-0.07	-0.05	-9.69
5	0.00	0.00	0.00	0.00	-0.13	-3.75	0.52	14.72	0.03
6	-0.58	33.53	0.00	0.00	-8.55	-16.50	13.44	12.63	-0.10
7	2.69	0.97	0.00	0.00	6.80	-46.03	-12.37	-0.93	0.22
8	3.86	10.53	0.00	0.00	0.38	-11.12	-3.01	-4.49	-3.79
9	1.62	1.25	0.00	9.70	2.66	-0.49	9.25	-28.57	-2.61
10	0.14	-4.93	0.00	0.00	0.50	-2.75	-1.01	-34.15	2.99
11	11.11	-1.18	0.00	0.00	16.41	1028.67	-6.80	24.90	-0.58
12	6.26	29.82	0.00	0.00	16.75	173.26	-1.34	-169.70	-5.59
13	19.60	12.56	0.00	0.00	4.77	-2.65	3.85	-84.60	-0.38
14	0.00	-8.64	0.00	0.00	2.45	181.33	19.83	-313.60	32.63
15	0.15	1.33	0.00	133.95	21.81	1.53	-1.83	-194.61	-16.37
16	-0.02	5.45	0.00	191.04	25.53	-13.99	-0.97	-316.38	-12.18
17	5.90	6.14	0.00	-16.49	-0.67	-28.30	5.06	91.86	0.95
18	0.60	1.76	0.00	48.71	6.25	43.47	0.78	-46.61	-4.28
19	12.16	62.17	0.00	206.80	5.99	31.34	4.10	-390.85	-13.27
20	0.28	-1.42	0.00	-1.12	4.66	-0.08	-0.13	-8.19	-4.96
21	0.24	5.64	0.00	0.00	-1.15	-8.74	0.34	-11.43	3.23
22	18.93	104.03	0.00	0.00	-0.22	-8.23	0.00	15.89	0.00
23	0.15	2.55	0.00	0.00	-0.52	-5.96	0.00	0.31	0.00
24	1.20	2.12	0.00	0.00	0.00	0.00	0.00	0.37	0.00
25	7.00	0.00	0.00	944.13	0.00	0.13	0.00	-235.83	0.00
26	17.19	65.26	25.87	-29.54	-6.05	162.85	1.97	-88.19	-4.28
27	-14.95	-24.58	0.04	0.60	-3.92	-50.82	12.78	-80.33	-4.08
28	22.13	82.68	176.28	55.93	0.06	0.55	0.00	-161.42	0.00
碳排放增量	139.55	379.52	205.32	1535.04	-10.63	1596.37	135.50	-1958.81	-57.61

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从最终需求的研究视角,省域间调进、调出和固定资本形成总额对于新疆能源消费碳排放的变化影响显著。同时,居民消费对于新疆能源消费碳排放的增长作用逐渐显著,尤其是城镇居民消费对于碳排放增长的影响更为明显。
1997-2007年,新疆对外贸易中的省域间调进与调出对碳排放的作用程度远远高于国际进口与出口对碳排放的影响,并且出口引起的碳排放增长要高于进口引起的碳排放减少,调出引起的碳排放增长要低于调进引起的碳排放减少。1997-2007年调出（即省域间出口）使新疆的碳排放量增长1596.37万t,占碳排放增长总量的比重为81.27%,使得调出成为这段时间内影响碳排放增长的最重要贡献因子;1997-2007年调进（即省域间进口）使新疆的碳排放量降低1958.81万t,占碳排放总量变化的比重为99.72%（以绝对值表征）,使得调进成为这段时间内遏制碳排放增长的最重要贡献因子。1997-2007年调出引起的碳排放增长为1596.37万t,调出引起碳排放的增长量主要集中在石油加工及炼焦业（1028.67万t）、金属冶炼及压延加工业（181.33万t）、化学原料及化学制品制造业（173.26万t）和农业（166.55万t）;调进引起的碳排放降低为1958.81万t,调进引起的碳排放减少量主要来自于通信设备、计算机及其他电子设备制造业（390.85万t）、机械工业（316.38万t）和金属冶炼及压延加工业（313.60万t）,调出与调进的平衡差额为-362.44万t。1997-2007年出口引起的碳排放增长为135.50万t,出口引起碳排放的增长量主要集中在农业（88.20万t）、金属冶炼及压延加工业（19.83万t）和食品制造及烟草加工业（13.44万t）;进口引起的碳排放降低为57.61万t,进口引起的碳排放减少量主要来自石油和天然气开采业（18.64万t）和金属制品业（16.37万t）,出口与进口的平衡差额为77.89万t,占1997-2007年的碳排放增量的3.97%。1997-2007年,新疆的进出口贸易方式主要以边境小额贸易为主,2007年新疆边境小额贸易的进出口总额为94.17亿美元,占海关进出口总额的68.65%,且贸易对象主要是以哈萨克斯坦和吉尔吉斯斯坦为主的中亚国家,出口商品主要为机电产品、鞋类、纺织服装、钢铁和农产品,进口商品主要为石油、天然气和矿产资源。
1997-2007年,固定资本形成总额使新疆的碳排放量增长1535.04万t,占碳排放增长总量的比重为78.15%,使得固定资产投资成为新疆碳排放增长的又一显著影响因素。固定资本形成总额引起碳排放的增长量主要集中在建筑业（944.13万t）、通信设备制造业（206.80万t）、机械工业（191.04万t）和金属制品业（133.95万t）。新疆固定资本形成总额由1997年的461.41亿元增长到2002年的856.70亿元进而快速增长到2007年的2004.99亿元,且固定资本形成总额主要集中在建筑业、设备制造业和金属制品业等高耗能产业部门。那么注重建筑业的低碳节能,提升设备制造业和金属制品业的生产工艺水平对于新疆的产业低碳发展将显得尤为重要。
1997-2007年,居民消费引起的碳排放增长为519.07万t,其中,城镇居民消费引起碳排放量的增长为379.52万t;然而,农村居民消费引起碳排放量的增长为139.55万t。城镇居民消费引起的碳排放增长主要集中在电力热力生产和供应业（104.03万t）、第三产业中的其他行业（82.68万t）和交通运输、仓储及邮电通信业（65.26万t）。农村居民消费引起的碳排放增长主要集中在第三产业中的其他行业（22.13万t）、非金属矿物制品业（19.60万t）和电力热力生产和供应业（18.93万t）。城镇化水平的快速提升,大量农村人口涌入城镇,新疆城镇化水平由1997年的35.20%增长到2002年的35.91%进而快速提升到2007年的39.15%。城镇居民人均消费水平由1997年的3328元/人快速增长到2007年的8986元/人,同期农村居民人均消费水平由1997年的1891元/人增长到2007年的2320元/人。城乡居民人均消费水平差距的不断扩大,进一步解释了城镇居民消费对碳排放增长的作用程度远高于农村居民消费对碳排放增长的作用程度。随着新型城镇化进程的推进和城镇化水平的稳步增长,城镇居民的消费需求和消费水平将会不断提升,将对电力、热力的生产与供应产生持续不断的增长需求,以及以第三产业为主的其他行业,尤其交通运输、仓储及邮电通信业的增长需求,那么改善以火电为主的电力生产结构和以煤为主的冬季供暖系统,注重交通运输业的低碳节能都将显得更为重要。
省域间调出对于新疆碳排放的间接影响显著增强,其对碳排放增长的间接影响远高于出口对于新疆碳排放的间接影响,主要集中在石油加工及炼焦产品、金属冶炼及压延加工制品、化工产品等能源资源密集型行业部门以及农产品的省域间调出贸易。新疆能源资源密集型产业的快速发展是资源优势、成本优势和市场需求等共同作用的结果,新疆煤炭资源、油气资源和矿产资源储量丰富,劳动力、土地等要素成本相对较低,资源优势效应和成本驱动效应使得能源资源密集型行业的生产和投资显著增加。新疆全社会固定资产投资总额由1997年的446.81亿元增长到2002年的813.02亿元进而快速增长到2007年的1850.84亿元,并且主要集中在能源密集型的采矿业、制造业以及电力热力生产和供应业。2007年石油和天然气开采业的固定资产投资占全社会固定资产投资总额的19.49%,石油加工及炼焦业占7.63%,电力热力生产和供应业占5.69%。原油加工量由1997年的850.59万t增长到2007年的1705.11万t,汽油产量由1997年的208.43万t增长到2007年的277.34万t,柴油产量由1997年的304.41万t增长到2007年的819.96万t,水泥产量由1997年的628万t增长到2007年的1537万t,钢材产量由1997年的99.89万t增长到2007年的470.94万t。新疆能源资源型产品的调出,使得区域间“隐含碳”转移显著。区域间相互依赖性^[56-58]的存在背景下,新疆作为国家其他省区的能源资源产品供应地在一定程度上强化了新疆对国内市场的依赖,使得省域间调出引致碳排放增长的间接效应十分明显。
除此之外,无论是能源资源型产品调出引致的区域“隐含碳”转移,还是能源资源的直接调出,对于新疆的区域低碳发展都将产生不利的影响。2007年以来,新疆能源消费量迅速增长,尤其是高碳能源—煤炭的增长迅速,相对低碳能源—石油的增长较为平缓（图3a）。从能源的调出分析（图3b）,2007年以来,新疆的能源调出快速增长,石油的调出保持持续增长,几乎本区的石油生产量全部用于调出;煤炭的调出量在2007年之后也呈现出迅速增长趋势。“十二五”规划中确定的山西、鄂尔多斯盆地、内蒙古东部地区、西南地区和新疆等五大国家综合能源基地,新疆基地是距离中国主要能源消费区运输距离最远的能源综合生产基地。那么考虑到一部分的碳排放增长量由最终消费区域引致,以及能源资源以及能源资源型产品长距离外运的巨大运输成本,新疆在当前以及未来的跨越式发展机遇期,应适当争取执行差别化的节能减排降耗指标。

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图32000-2011年新疆能源消费总量与能源调出总量
-->Fig. 3Energy consumption and energy outflow in Xinjiang from 2000 to 2011
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4 结论与讨论

4.1 结论

借鉴经典的投入产出理论,通过构建“能源—经济—碳排放”的混合型投入产出分析框架,利用IO-SDA结构分解模型,对新疆1997-2007年能源消费碳排放的影响因素进行结构分解分析。主要结论如下：
（1）1997-2007年,新疆的能源消费碳排放从1997年的2070.08万t增长到2002年的2408.06万t、2007年的4034.33万t,11年间增长了94.88%,碳排放的增长主要集中在能源资源生产与加工业和矿产资源开采与加工业。
（2）碳排放影响因素的直接效应分析,其中人均GDP、最终需求结构、生产结构和人口规模的变化是引起碳排放增长的重要影响因素;碳排放强度的降低是这一时期遏制碳排放增长的重要影响因素,说明在经济规模（人均GDP）和人口数量（人口规模）不断增长的同时,经济结构（最终需求结构）未得到有效优化,生产技术（生产结构）未得到有效的提升,导致新疆能源消费碳排放的快速增长。
（3）碳排放影响因素的间接效应分析,从最终需求的研究视角,省域间调进、调出和固定资本形成总额对于新疆能源消费碳排放的变化影响显著。同时,居民消费对于新疆能源消费碳排放的增长作用逐渐显著,尤其是城镇居民消费对于碳排放增长的影响程度。碳密集产业部门的固定资产投资增加,能源资源型产品的省域间调出增长,使得区域间“隐含碳”转移效应十分显著。

4.2 讨论

随着新疆新型城镇化和新型工业化进程的推进,19省市“援疆政策”的实施,“丝绸之路经济带”的全面建设,新疆的城镇化水平、工业化水平、固定资产投资总量都将得到进一步的增长。在西部大开发和跨越式发展过程中,综合分析新疆各地州确立的主导产业,大多是依托能源资源优势和成本优势发展煤炭产业、煤化工产业、火电产业、石油化工产业、装备制造业、钢铁产业、水泥产业、电解铝产业、多晶硅产业、轻工纺织业等,能源矿产资源优势能否转化为产业优势,以及转化为产业优势的过程中东部及中部发达地区的技术扩散和知识溢出能否协同促使能源资源密集型产业的技术升级和低碳发展,将是新疆乃至西部地区优势资源转换战略能否低碳可持续发展的关键问题。同时,如何发挥产业关联效应,促使能源资源原材料工业发展带动依托本地市场的更具区域竞争性优势的加工制造业发展,将对新疆区域经济的结构转型和低碳发展产生深远的影响。大量投资进驻新疆的同时,应该升级生产工艺和淘汰落后产能,尤其在国家级工业园区以及新兴工业园区的低碳技术变革与节能降耗改造,自治区政府和能源资源型企业应更加关注高端人才的引进,尤其是发电、新能源、新型化工、新材料、高端装备制造等行业的技术研发人才。集约利用化石能源的同时,应加大可再生能源利用,统筹规划太阳能和风能在新疆的布局和发展,尤其是在规模较大的工业园区和人口集中的住宅区域规划建设可再生能源发电系统。
无论是能源资源型产品调出引致的区域“隐含碳”转移,还是能源资源的直接调出,对于新疆的区域低碳发展都将产生不利的影响。在技术进步、新能源替代、重点行业节能减排进程缓慢的同时,新疆碳减排和节能降耗的工作重心应该重点思考：如何建设能源综合生产基地,如何协调本地能源消费与区外能源调出,如何争取更多的相对低碳能源（石油和天然气）。新疆能源综合生产基地是距离中国主要能源消费区距离最远的能源基地,考虑到能源外运长距离的巨大运输成本,以及持续增长的能源调出量,应适当考虑区域间相互依赖性的影响下,新疆争取执行差别化的节能减排降耗指标。
在西北干旱区,水资源是制约社会经济发展影响生态安全的关键要素,对未来经济社会可持续发展起着至关重要的作用。新疆的高碳行业煤炭生产及煤化工、石油加工及炼焦业、金属冶炼及压延加工业等都是高耗水产业,干旱区能源资源密集型产业的布局与发展应着重关注水资源的支撑作用和约束作用。
The authors have declared that no competing interests exist.

参考文献原文顺序
文献年度倒序
文中引用次数倒序
被引期刊影响因子

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[10]	Guan Dabo, Liu Zhu, Geng Yong, et al.The gigatonne gap in China's carbon dioxide inventories. Nature Clim. Change, 2012, 2(9): 672-675.https://doi.org/10.1038/nclimate1560 URL [本文引用: 1]摘要 Reliable statistics are important for both climate science and international negotiations about emission-reduction targets. However, China is often questioned in terms of its data transparency and accuracy. Now researchers have compiled the carbon dioxide emission inventories for China and its 30 provinces for the period 1997-2010, and found a 1.4 gigatonne discrepancy between national and provincial inventories in 2010.
[11]	Tao Yuguo, Huang Zhenfang, Wu Limin, et al.Measuring carbon dioxide emissions for regional tourism and its factor decomposition: A case study of Jiangsu province. Acta Geographica Sinica, 2014, 69(10): 1438-1448.https://doi.org/10.11821/dlxb201410004 URL Magsci摘要区域旅游业碳排放测度是分解旅游业减碳任务的需要。依托1997年、2002年和2007年的投入产出表和旅游终端收入,以江苏省为案例地,测度了旅游业各部门包括直接和间接碳排放的旅游业碳排放总量,并利用LMDI分解了影响因素的作用机理。结果显示,旅游业碳排放总量增长较快,较均衡分布于各部门,绝大多数来自间接层面;国内游客的碳排放总量显著高于入境游客,但前者的每人次碳排放远低于后者,也远低于发达国家,还低于发展中国家;省内各地区的碳排放总量和每人次碳排放均存在着显著差异;游客规模不断扩大和旅游消费水平持续提高是碳排放增长的主要驱动力,能源强度下降和能源结构调整则对碳排放具有一定的抑制作用,收入结构变动作用具有一定的阶段波动性特征。结果表明,旅游业减碳不仅需各部门共同分担,更依赖向其提供中间产品的关联产业的大力联动;国内游客是主要碳源,需要大量排放空间;游客每人次碳排放高的地区应承担较大的减排责任;降低能源利用强度和引导旅游消费低碳发展,是旅游业碳减排的主要方向。 [陶玉国, 黄震方, 吴丽敏, 等. 江苏省区域旅游业碳排放测度及其因素分解 . 地理学报, 2014, 69(10): 1438-1448.]https://doi.org/10.11821/dlxb201410004 URL Magsci摘要区域旅游业碳排放测度是分解旅游业减碳任务的需要。依托1997年、2002年和2007年的投入产出表和旅游终端收入,以江苏省为案例地,测度了旅游业各部门包括直接和间接碳排放的旅游业碳排放总量,并利用LMDI分解了影响因素的作用机理。结果显示,旅游业碳排放总量增长较快,较均衡分布于各部门,绝大多数来自间接层面;国内游客的碳排放总量显著高于入境游客,但前者的每人次碳排放远低于后者,也远低于发达国家,还低于发展中国家;省内各地区的碳排放总量和每人次碳排放均存在着显著差异;游客规模不断扩大和旅游消费水平持续提高是碳排放增长的主要驱动力,能源强度下降和能源结构调整则对碳排放具有一定的抑制作用,收入结构变动作用具有一定的阶段波动性特征。结果表明,旅游业减碳不仅需各部门共同分担,更依赖向其提供中间产品的关联产业的大力联动;国内游客是主要碳源,需要大量排放空间;游客每人次碳排放高的地区应承担较大的减排责任;降低能源利用强度和引导旅游消费低碳发展,是旅游业碳减排的主要方向。
[12]	Zhao Rongqin, Zhang Shuai, Huang Xianjin, et al.Spatial variation of carbon budget and carbon balance zoning of Central Plains Economic Region at county-level. Acta Geographica Sinica, 2014, 69(10): 1425-1437.https://doi.org/10.11821/dlxb201410003 URL Magsci摘要区域碳收支核算是当前全球气候变化与碳排放研究的核心内容之一。开展县域空间碳收支与碳平衡研究不仅有助于从理论上构建县级尺度碳效率和碳生态压力评估的方法,而且对于县域空间碳补偿及低碳发展策略的制定也具有重要的现实意义。本文采用2009年中原经济区县域单元的各种统计数据及土地利用数据,对县域空间的碳收支状况进行了核算分析,并在碳平衡分区的基础上提出了中原经济区主体功能区优化的思路和政策建议。主要结论:1中原经济区2009年碳吸收和碳排放总量分别为1.3亿t和2.1亿t。碳排放量基本呈现"从市辖区到周边县(市)逐渐降低"的规律;碳吸收量的分布具有"西北低、东南高"的特点;2县域空间碳补偿率的分布具有显著的区域差异,人均GDP越高的地区,其碳补偿率往往越低;反之,碳补偿率越高;3由于县域单元碳源/汇具有较大的空间差异,中原经济区县域空间的碳排放经济贡献率和碳生态容量存在明显不匹配现象;4基于碳平衡分析,本文将中原经济区县域空间划分为碳强度控制区、碳收支平衡区、碳汇功能区、碳总量控制区及低碳优化区等5类区域,并在此基础上提出了中原经济区主体功能区优化的思路及低碳发展策略。 [赵荣钦, 张帅, 黄贤金, 等. 中原经济区县域碳收支空间分异及碳平衡分区 . 地理学报, 2014, 69(10): 1425-1437.]https://doi.org/10.11821/dlxb201410003 URL Magsci摘要区域碳收支核算是当前全球气候变化与碳排放研究的核心内容之一。开展县域空间碳收支与碳平衡研究不仅有助于从理论上构建县级尺度碳效率和碳生态压力评估的方法,而且对于县域空间碳补偿及低碳发展策略的制定也具有重要的现实意义。本文采用2009年中原经济区县域单元的各种统计数据及土地利用数据,对县域空间的碳收支状况进行了核算分析,并在碳平衡分区的基础上提出了中原经济区主体功能区优化的思路和政策建议。主要结论:1中原经济区2009年碳吸收和碳排放总量分别为1.3亿t和2.1亿t。碳排放量基本呈现"从市辖区到周边县(市)逐渐降低"的规律;碳吸收量的分布具有"西北低、东南高"的特点;2县域空间碳补偿率的分布具有显著的区域差异,人均GDP越高的地区,其碳补偿率往往越低;反之,碳补偿率越高;3由于县域单元碳源/汇具有较大的空间差异,中原经济区县域空间的碳排放经济贡献率和碳生态容量存在明显不匹配现象;4基于碳平衡分析,本文将中原经济区县域空间划分为碳强度控制区、碳收支平衡区、碳汇功能区、碳总量控制区及低碳优化区等5类区域,并在此基础上提出了中原经济区主体功能区优化的思路及低碳发展策略。
[13]	Zhao Rongqin, Huang Xianjin, Zhong Taiyang.Research on carbon emission intensity and carbon footprint of different industrial spaces in China. Acta Geographica Sinica, 2010, 65(9): 1048-1057.https://doi.org/10.11821/xb201009002 URL Magsci摘要 <p>采用2007 年中国各省区不同产业各种能源消费等数据，通过构建能源消费碳排放和碳足迹模型，对各省区化石能源和农村生物质能源的碳排放量进行了估算；建立了不同产业空间与能源消费碳排放的对应关系，将产业活动空间分为农业空间、生活与工商业空间、交通产业空间、渔业与水利业空间、其他产业空间等五大类；对各省区不同产业空间碳排放强度和碳足迹进行了对比分析。主要结论如下：(1) 中国2007 年能源消费碳排放总量为1.65 GtC，其中化石能源碳排放占89%；(2) 2007 年中国产业空间碳排放强度为1.98 t/hm<sup>2</sup>，其中，生活及工商业空间、交通产业空间的碳排放强度较高，分别为55.16 t/hm<sup>2</sup>和49.65 t/hm<sup>2</sup>；(3) 2007 年中国产业空间碳足迹为522.34×10<sup>6</sup> hm<sup>2</sup>，由此造成的生态赤字为28.69×10<sup>6</sup> hm<sup>2</sup>，这说明我国的生产性土地面积不足以补偿产业空间的碳排放，补偿率约为94.5%。各地区碳足迹差异明显，不少省份甚至存在生态盈余。总体而言，从产业活动空间的角度来看，中国目前的碳赤字不大；(4) 全国产业空间单位面积碳足迹为0.63 hm<sup>2</sup>/hm<sup>2</sup>，其中生活与工商业空间的碳足迹最大，为17.5 hm<sup>2</sup>/hm<sup>2</sup>。不同产业空间单位面积碳足迹大都呈现从东到西逐渐下降的趋势。</p> [赵荣钦, 黄贤金, 钟太洋. 中国不同产业空间的碳排放强度与碳足迹分析 . 地理学报, 2010, 65(9): 1048-1057.]https://doi.org/10.11821/xb201009002 URL Magsci摘要 <p>采用2007 年中国各省区不同产业各种能源消费等数据，通过构建能源消费碳排放和碳足迹模型，对各省区化石能源和农村生物质能源的碳排放量进行了估算；建立了不同产业空间与能源消费碳排放的对应关系，将产业活动空间分为农业空间、生活与工商业空间、交通产业空间、渔业与水利业空间、其他产业空间等五大类；对各省区不同产业空间碳排放强度和碳足迹进行了对比分析。主要结论如下：(1) 中国2007 年能源消费碳排放总量为1.65 GtC，其中化石能源碳排放占89%；(2) 2007 年中国产业空间碳排放强度为1.98 t/hm<sup>2</sup>，其中，生活及工商业空间、交通产业空间的碳排放强度较高，分别为55.16 t/hm<sup>2</sup>和49.65 t/hm<sup>2</sup>；(3) 2007 年中国产业空间碳足迹为522.34×10<sup>6</sup> hm<sup>2</sup>，由此造成的生态赤字为28.69×10<sup>6</sup> hm<sup>2</sup>，这说明我国的生产性土地面积不足以补偿产业空间的碳排放，补偿率约为94.5%。各地区碳足迹差异明显，不少省份甚至存在生态盈余。总体而言，从产业活动空间的角度来看，中国目前的碳赤字不大；(4) 全国产业空间单位面积碳足迹为0.63 hm<sup>2</sup>/hm<sup>2</sup>，其中生活与工商业空间的碳足迹最大，为17.5 hm<sup>2</sup>/hm<sup>2</sup>。不同产业空间单位面积碳足迹大都呈现从东到西逐渐下降的趋势。</p>
[14]	Zhao Rongqin, Huang Xianjin, Peng Buzhuo.Research on carbon cycle and carbon balance of Nanjing urban system. Acta Geographica Sinica, 2012, 67(6): 758-770.https://doi.org/10.11821/xb201206004 URL Magsci [本文引用: 1]摘要城市是人类能源活动和碳排放的集中地,开展城市系统碳循环研究有助于深入了解城市在区域碳循环过程中的地位和作用。本文集成了城市碳储量和碳通量的核算方法,并以南京市为例开展了城市系统碳循环与碳平衡的实证研究。结论如下:① 南京市城市碳储量呈缓慢上升趋势,2009 年为6937 万t,其中自然碳储量占88%,且总量基本保持稳定;人为碳储量(特别是城市绿地和建筑物碳库) 呈大幅增长趋势;② 垂直碳输入通量以植物光合作用和水域碳吸收为主,历年来基本稳定;水平碳输入通量大幅增长,2009 年为3043 万t,其中能源和木材碳输入呈增长趋势,而食物碳输入则呈下降趋势;③ 垂直碳输出通量呈增长趋势,2009年为3295 万t,其中化石能源碳排放占近80%,自然过程仅占6%;水平碳输出通量以能源制品、水产品和含碳废弃物为主,其总量呈明显下降趋势;④ 南京市历年城市碳输出均高于碳输入,且两者的差额呈现扩大趋势。总体而言,“隐流碳和加工需求碳”的比重有所下降,说明碳的利用率有所提升;⑤ 南京市碳补偿率明显下降,这表明自然生态系统的碳吸收能力不足以补偿人为活动的碳排放,城市碳循环压力在不断加大。 [赵荣钦, 黄贤金, 彭补拙. 南京城市系统碳循环与碳平衡分析 . 地理学报, 2012, 67(6): 758-770.]https://doi.org/10.11821/xb201206004 URL Magsci [本文引用: 1]摘要城市是人类能源活动和碳排放的集中地,开展城市系统碳循环研究有助于深入了解城市在区域碳循环过程中的地位和作用。本文集成了城市碳储量和碳通量的核算方法,并以南京市为例开展了城市系统碳循环与碳平衡的实证研究。结论如下:① 南京市城市碳储量呈缓慢上升趋势,2009 年为6937 万t,其中自然碳储量占88%,且总量基本保持稳定;人为碳储量(特别是城市绿地和建筑物碳库) 呈大幅增长趋势;② 垂直碳输入通量以植物光合作用和水域碳吸收为主,历年来基本稳定;水平碳输入通量大幅增长,2009 年为3043 万t,其中能源和木材碳输入呈增长趋势,而食物碳输入则呈下降趋势;③ 垂直碳输出通量呈增长趋势,2009年为3295 万t,其中化石能源碳排放占近80%,自然过程仅占6%;水平碳输出通量以能源制品、水产品和含碳废弃物为主,其总量呈明显下降趋势;④ 南京市历年城市碳输出均高于碳输入,且两者的差额呈现扩大趋势。总体而言,“隐流碳和加工需求碳”的比重有所下降,说明碳的利用率有所提升;⑤ 南京市碳补偿率明显下降,这表明自然生态系统的碳吸收能力不足以补偿人为活动的碳排放,城市碳循环压力在不断加大。
[15]	Geng Yong, Zhao Hongyan, Liu Zhu, et al.Exploring driving factors of energy-related CO₂ emissions in Chinese provinces: A case of Liaoning. Energy Policy, 2013, 60: 820-826.https://doi.org/10.1016/j.enpol.2013.05.054 URL [本文引用: 9]摘要 In order to uncover driving forces for provincial CO 2 emission in China, a case study was undertaken to shed light on the CO 2 emission growth in such a region. Liaoning province was selected due to its typical features as one industrial province. The environmental input鈥搊utput analysis and structure decomposing analysis have been conducted in order to provide a holistic picture on Liaoning's CO 2 emissions during 1997鈥2007. Research outcomes indicate that rapid increase of per capita consumption activities is the main driver for Liaoning to have a significant CO 2 emission growth, followed by consumption structure, production structure and population size. Energy intensity and energy structure partly offset the CO 2 emission increase. Electricity power and heat supply and construction sectors caused the most CO 2 emission, indicating that more specific mitigation policies for these two sectors should be prepared. From final demand point of view, it is clear that trade plays a leading role in regional CO 2 emission, followed by fixed capital investment and urban household consumption which become increasingly important over time. Consequently, in order to realize low carbon development, local governments should consider all these factors so that appropriate mitigation policies can be raised by considering the local realities.
[16]	Jotzo F, Burke P J, Wood P J, et al.Decomposing the 2010 global carbon dioxide emissions rebound. Nature Climate Change, 2012, 2(4): 213-214.https://doi.org/10.1038/nclimate1450 URL摘要 Not Available
[17]	Cheng Yeqing, Wang Zheye, Zhang Shouzhi, et al.Spatial econometric analysis of carbon emission intensity and its driving factors from energy consumption in China. Acta Geographica Sinica, 2013, 68(10): 1418-1431.https://doi.org/10.11821/dlxb201310011 URL Magsci摘要碳排放所引起的全球气候变化对人类经济社会发展带来了严峻的挑战。中国政府承诺到2020 年GDP碳排放强度较2005 年降低40%~45%,这一目标的实现有赖于全国层面社会经济和产业结构的实质性转型,更有赖于省区层面节能减排的具体行动。基于联合国政府间气候变化专门委员会(IPCC) 提供的方法,本文估算了全国30 个省区1997-2010 年碳排放强度,采用空间自相关分析方法和空间面板计量模型,探讨了中国省级尺度碳排放强度的时空格局特征及其主要影响因素,旨在为政府制定差异化节能减排的政策和发展低碳经济提供科学依据。研究结果表明:① 1997-2010 年,中国能能源消费CO<sub>2</sub>排放总量从4.16 Gt 增加到11.29Gt,年均增长率为7.15%,而同期GDP年均增长率达11.72%,碳排放强度总体上呈逐年下降的态势;② 1997-2010 年,碳排放强度的Moran's I 指数呈波动型增长,说明中国能源消费碳排放强度在省区尺度上具有明显的空间集聚特征,且集聚程度有不断增强的态势,同时,碳排放强度高值集聚区和低值集聚区表现出一定程度的路径依赖或空间锁定;③ 空间面板计量模型分析结果表明,能源强度、能源结构、产业结构和城市化率对中国能源消费碳排放强度时空格局演变具有重要影响;④ 提高能源利用效率,优化能源结构和产业结构,走低碳城市化道路,以及实行节能减排省区联动策略是推动中国实现节能减排目标的重要途径。 [程叶青, 王哲野, 张守志, 等. 中国能源消费碳排放强度及其影响因素的空间计量 . 地理学报, 2013, 68(10): 1418-1431.]https://doi.org/10.11821/dlxb201310011 URL Magsci摘要碳排放所引起的全球气候变化对人类经济社会发展带来了严峻的挑战。中国政府承诺到2020 年GDP碳排放强度较2005 年降低40%~45%,这一目标的实现有赖于全国层面社会经济和产业结构的实质性转型,更有赖于省区层面节能减排的具体行动。基于联合国政府间气候变化专门委员会(IPCC) 提供的方法,本文估算了全国30 个省区1997-2010 年碳排放强度,采用空间自相关分析方法和空间面板计量模型,探讨了中国省级尺度碳排放强度的时空格局特征及其主要影响因素,旨在为政府制定差异化节能减排的政策和发展低碳经济提供科学依据。研究结果表明:① 1997-2010 年,中国能能源消费CO<sub>2</sub>排放总量从4.16 Gt 增加到11.29Gt,年均增长率为7.15%,而同期GDP年均增长率达11.72%,碳排放强度总体上呈逐年下降的态势;② 1997-2010 年,碳排放强度的Moran's I 指数呈波动型增长,说明中国能源消费碳排放强度在省区尺度上具有明显的空间集聚特征,且集聚程度有不断增强的态势,同时,碳排放强度高值集聚区和低值集聚区表现出一定程度的路径依赖或空间锁定;③ 空间面板计量模型分析结果表明,能源强度、能源结构、产业结构和城市化率对中国能源消费碳排放强度时空格局演变具有重要影响;④ 提高能源利用效率,优化能源结构和产业结构,走低碳城市化道路,以及实行节能减排省区联动策略是推动中国实现节能减排目标的重要途径。
[18]	Su Yongxian, Chen Xiuzhi, Ye Yuyao, et al.The characteristics and mechanisms of carbon emissions from energy consumption in China using DMSP/OLS night light imageries. Acta Geographica Sinica, 2013, 68(11): 1513-1526.https://doi.org/10.11821/dlxb201311007 URL Magsci [本文引用: 1]摘要本研究基于DMSP/OLS夜间灯光影像实现了1992-2010年以市级为基础单元的我国碳排放估算,弥补了统计数据不全、统计口径不一的缺点。从全国、4个经济区和6大城市群3个层面的碳排放分析结果显示,我国CO2排放总量持续增长,各地区、省市增速各不相同,空间聚集程度越来越明显,基本形成了"东部沿海城市高高集聚,西部欠发达城市低低集聚"的格局。人均碳排放强度基本呈现为"东部东北部西部中部",单位GDP碳排放强度则呈现为"东北部和西部较高"、"东部和中部较低"。 GDP增长是决定CO2排放总量增长的主导因素,而能源结构、能源利用效率、产业结构是影响碳排放强度的主要原因。对于西部和东北部等以能源和重工业为主导产业的城市,其减排策略应着重能源结构优化和能源利用效率的提高。对于东部和中部等以技术、劳动密集型和轻工业为主导产业的城市,其减排策略应侧重于产业结构调整和转型升级。 [苏泳娴, 陈修治, 叶玉瑶, 等. 基于夜间灯光数据的中国能源消费碳排放特征及机理 . 地理学报, 2013, 68(11): 1513-1526.]https://doi.org/10.11821/dlxb201311007 URL Magsci [本文引用: 1]摘要本研究基于DMSP/OLS夜间灯光影像实现了1992-2010年以市级为基础单元的我国碳排放估算,弥补了统计数据不全、统计口径不一的缺点。从全国、4个经济区和6大城市群3个层面的碳排放分析结果显示,我国CO2排放总量持续增长,各地区、省市增速各不相同,空间聚集程度越来越明显,基本形成了"东部沿海城市高高集聚,西部欠发达城市低低集聚"的格局。人均碳排放强度基本呈现为"东部东北部西部中部",单位GDP碳排放强度则呈现为"东北部和西部较高"、"东部和中部较低"。 GDP增长是决定CO2排放总量增长的主导因素,而能源结构、能源利用效率、产业结构是影响碳排放强度的主要原因。对于西部和东北部等以能源和重工业为主导产业的城市,其减排策略应着重能源结构优化和能源利用效率的提高。对于东部和中部等以技术、劳动密集型和轻工业为主导产业的城市,其减排策略应侧重于产业结构调整和转型升级。
[19]	Zhou N, Fridley D, Khanna N Z, et al.China's energy and emissions outlook to 2050: Perspectives from bottom-up energy end-use model. Energy Policy, 2013, 53: 51-62.https://doi.org/10.1016/j.enpol.2012.09.065 URL [本文引用: 1]摘要 Although China became the world's largest CO2 emitter in 2007, the country has also taken serious actions to reduce its energy and carbon intensity. This study uses the bottom-up LBNL China End-Use Energy Model to assess the role of energy efficiency policies in transitioning China to a lower emission trajectory and meeting its 2020 intensity reduction goals. Two scenarios 鈥 Continued Improvement and Accelerated Improvement 鈥 were developed to assess the impact of actions already taken by the Chinese government as well as planned and potential actions, and to evaluate the potential for China to reduce energy demand and emissions. This scenario analysis presents an important modeling approach based in the diffusion of end-use technologies and physical drivers of energy demand and thereby help illuminate China's complex and dynamic drivers of energy consumption and implications of energy efficiency policies. The findings suggest that China's CO2 emissions will not likely continue growing throughout this century because of saturation effects in appliances, residential and commercial floor area, roadways, fertilizer use; and population peak around 2030 with slowing urban population growth. The scenarios also underscore the significant role that policy-driven efficiency improvements will play in meeting 2020 carbon mitigation goals along with a decarbonized power supply.
[20]	Wang Ke, Zhang Xian, Wei Yiming, et al.Regional allocation of CO₂ emissions allowance over provinces in China by 2020. Energy Policy, 2013, 54: 214-229.https://doi.org/10.1016/j.enpol.2012.11.030 URL摘要 The mitigation efforts of China are increasingly important for meeting global climate target since the rapid economic growth of China has led to an increasing share in the world's total CO 2 emissions. This paper sets out to explore the approach for realizing China's national mitigation targets submitted to the UNFCCC as part of the Copenhagen Accord; that is, to reduce the intensity of CO 2 emissions per unit of GDP by 40鈥45% by 2020, as well as reducing the energy intensity and increasing the share of non-fossil fuel consumption, through regional allocation of emission allowance over China's provinces. Since the realization of China's mitigation target essentially represents a total amount emission allowance allocation problem, an improved zero sum gains data envelopment analysis optimization model, which could deal with the constant total amount resources allocation, is proposed in this study. By utilizing this model and based on several scenarios of China's economic growth, CO 2 emissions, and energy consumption, a new efficient emission allowance allocation scheme on provincial level for China by 2020 is proposed. The allocation results indicate that different provinces have to shoulder different mitigation burdens in terms of emission intensity reduction, energy intensity reduction, and share of non-fossil fuels increase.
[21]	Yan Hua, Guo Yungong, Lin Fengchun.Analyzing the developing model of Chinese cities under the control of CO₂ emissions using the STIRPAT model: A case study of Shanghai. Acta Geographica Sinica, 2010, 65(8): 983-990.https://doi.org/10.11821/xb201008009 URL [本文引用: 1]摘要随着经济的快速发展,中国 CO2排放量不断增加,研究中国各大城市采取何种发展模式,减缓CO2的排放量,成为当前研究热点。利用STIRPAT模型,定量分析了CO2排放量与人口、富裕度、城市化水平和技术进步之间的关系,并经岭回归拟合发现人口数量、人均GDP、城市化水平和技术进步每发生1%的变化,将引起CO2排放总量相应发生0.618%、(0.178+0.009lnA)%、0.816%和0.264%的变化。在上述研究的基础上,以上海市为例,通过设置10种不同的发展情景,分析了在何种情景下最有利于减缓CO2的排放。结果表明,当经济、人口保持中速增长,城市化率进程放缓而节能减排技术取得较大进步时,上海市最有利于减缓CO2排放量,此时上海市2010年、2015年和2020年CO2排放量分别为17053.57万t、19286.64万t和 20885.69万t。 [燕华, 郭运功, 林逢春. 基于STIRPAT模型分析CO₂控制下上海城市发展模式 . 地理学报, 2010, 65(8): 983-990.]https://doi.org/10.11821/xb201008009 URL [本文引用: 1]摘要随着经济的快速发展,中国 CO2排放量不断增加,研究中国各大城市采取何种发展模式,减缓CO2的排放量,成为当前研究热点。利用STIRPAT模型,定量分析了CO2排放量与人口、富裕度、城市化水平和技术进步之间的关系,并经岭回归拟合发现人口数量、人均GDP、城市化水平和技术进步每发生1%的变化,将引起CO2排放总量相应发生0.618%、(0.178+0.009lnA)%、0.816%和0.264%的变化。在上述研究的基础上,以上海市为例,通过设置10种不同的发展情景,分析了在何种情景下最有利于减缓CO2的排放。结果表明,当经济、人口保持中速增长,城市化率进程放缓而节能减排技术取得较大进步时,上海市最有利于减缓CO2排放量,此时上海市2010年、2015年和2020年CO2排放量分别为17053.57万t、19286.64万t和 20885.69万t。
[22]	Jin W.Can technological innovation help China take on its climate responsibility? An intertemporal general equilibrium analysis. Energy Policy, 2012, 49: 629-641.https://doi.org/10.1016/j.enpol.2012.07.007 URL [本文引用: 1]摘要 This paper examines the effectiveness of China’s indigenous R&D and technological innovation to curb its carbon emissions. The mechanism of endogenous technical change (TC) is incorporated an intertemporal computable general equilibrium (CGE) model. R&D investments and knowledge creations are modeled as the endogenous behaviors of private firms. The accumulated stocks of knowledge are applied in the production process to affect the rate and bias of TC. Simulation results show that: (1) while China’s indigenous R&D efforts play a significant role to curb carbon emissions, sole dependence on R&D may be far from sufficient to achieve pledged climate target, with complementary policies being required to reinforce existing climate actions; (2) innovation policies can strengthen R&D investment and cut emissions further, but the complementary effect is relatively minor; (3) carbon taxation can generate significant carbon-saving benefits and fulfill climate target, but this achievement is at the cost of economic losses. The induced technical improvement, however, can partially mitigate the deadweight loss incurred by carbon tax distortion.
[23]	Maisonnave H, Pycroft J, Saveyn B, et al.Does climate policy make the EU economy more resilient to oil price rises? A CGE analysis. Energy Policy, 2012, 47: 172-179.https://doi.org/10.1016/j.enpol.2012.04.053 URL摘要 Unilateral EU climate policy implies a cost on the EU of around 1.0% of GDP. An oil price rise in the presence of EU climate policy does imply an additional cost on the EU of 1.5% of GDP (making a total loss of 2.5% of GDP), but this is less than the 2.2% of GDP that the EU would lose from the oil price rise in the absence of climate policy. This is evidence that even unilateral climate policy does offer some economic protection for the EU.
[24]	Wang Changjian, Wang Fei, Zhang Hongou, et al.China's carbon trading scheme is a priority. Environmental Science & Technology, 2014, 48(23): 13559-13559.https://doi.org/10.1021/es505198t URLPMID:25412119摘要 Not Available
[25]	Li Na, Shi Minjun, Yuan Yongna.Impacts of carbon tax policy on regional development in China: A dynamic simulation based on a multi-regional CGE model. Acta Geographica Sinica, 2010, 65(12): 1569-1580.Magsci [本文引用: 1]摘要利用中国动态多区域可计算一般均衡(CGE) 模型，模拟了低碳经济时代实施碳税政策对中国区域发展格局演进的影响。模拟结果显示，如果各地区实施同一碳税政策，对区域经济的影响存在着区域差异，能源富集地区尤其是欠发达地区的经济损失较大，对发达地区则产生正面的影响，因而将扩大区域经济差异。如果实施差别碳税，对能源富集地区和欠发达地区的影响有所减轻，有利于缩小区域经济差异。针对不同区域制定差异化的低碳经济发展政策，有利于兼顾公平和效率，使中国走上低碳发展和区域经济协调的双赢之路。 [李娜, 石敏俊, 袁永娜. 低碳经济政策对区域发展格局演进的影响: 基于动态多区域CGE模型的模拟分析 . 地理学报, 2010, 65(12): 1569-1580.]Magsci [本文引用: 1]摘要利用中国动态多区域可计算一般均衡(CGE) 模型，模拟了低碳经济时代实施碳税政策对中国区域发展格局演进的影响。模拟结果显示，如果各地区实施同一碳税政策，对区域经济的影响存在着区域差异，能源富集地区尤其是欠发达地区的经济损失较大，对发达地区则产生正面的影响，因而将扩大区域经济差异。如果实施差别碳税，对能源富集地区和欠发达地区的影响有所减轻，有利于缩小区域经济差异。针对不同区域制定差异化的低碳经济发展政策，有利于兼顾公平和效率，使中国走上低碳发展和区域经济协调的双赢之路。
[26]	Al-mulali U, Che N B C S. The impact of energy consumption and CO₂ emission on the economic growth and financial development in the Sub-Saharan African countries. Energy, 2012, 39(1): 180-186.https://doi.org/10.1016/j.energy.2012.01.032 URL [本文引用: 2]摘要 Downloadable (with restrictions)! Author(s): Al-mulali, Usama & Binti Che Sab, Che Normee. 2012 Abstract: This study investigated the impact of energy consumption and CO2 emission on GDP (gross domestic product) growth and the financial development in thirty Sub Saharan African Countries. The panel model was used in this study from the period 1980 to 2008. The results showed that energy consumption had played an important role to increase both economic growth and the financial development in the investigated economies but with the consequence of high po llution. This study recommended that these countries should increase energy productivity by increasing energy efficiency, implementation of energy savings projects, energy conservation, and energy infrastructure outsourcing to achieve its financial development and GDP growth and to increase their investment on energy projects to achieve the full energy potential.
[27]	Al-mulali U. Factors affecting CO₂ emission in the Middle East: A panel data analysis. Energy, 2012, 44(1): 564-569.https://doi.org/10.1016/j.energy.2012.05.045 URL [本文引用: 1]摘要 This study investigated the major factors that influence the CO 2 emission in 12 Middle Eastern countries, namely, Bahrain, Egypt, Iran, Jordan, Kuwait, Lebanon, Oman, Qatar, Saudi Arabia, Syria, the UAE, and Yemen. A panel model was used in this study and the period 1990鈥2009 was considered. The results showed that the total primary energy consumption, foreign direct investment net inflows, GDP, and total trade were important factors in increasing CO 2 emission in the investigated countries. Thus, it is important for these counties to examine the requirements for foreign investment to promote environmental protection and increase the technological transfer through foreign companies to reduce the environmental damage. It is also important for them to adopt trade-related measures and policies to increase environmental protection since total trade increases CO 2 emission. It is also crucial for these countries to increase energy productivity to achieve their GDP growth.
[28]	Li Huanan, Mu Hailin, Zhang Ming, et al.Analysis on influence factors of China's CO₂ emissions based on Path-STIRPAT model. Energy Policy, 2011, 39(11): 6906-6911.https://doi.org/10.1016/j.enpol.2011.08.056 Magsci [本文引用: 1]摘要 With the intensification of global warming and continued growth in energy consumption, China is facing increasing pressure to cut its CO2 (carbon dioxide) emissions down. This paper discusses the driving forces influencing China's CO2 emissions based on Path-STIRPAT model-a method combining Path analysis with STIRPAT (stochastic impacts by regression on population, affluence and technology) model. The analysis shows that GDP per capita (A), industrial structure (IS), population (P), urbanization level (R) and technology level (T) are the main factors influencing China's CO2 emissions, which exert an influence interactively and collaboratively. The sequence of the size of factors' direct influence on China's CO2 emission is A>T>P>R>IS, while that of factors' total influence is A>R>P>T>IS. One percent increase in A, IS, P, R and T leads to 0.44, 1.58, 1.31, 1.12 and -1.09 percentage change in CO2 emission totally, where their direct contribution is 0.45, 0.07, 0.63, 0.08, 0.92, respectively. Improving T is the most important way for CO2 reduction in China. (C) 2011 Elsevier Ltd. All rights reserved.
[29]	Zhu Q, Peng X Z, Wu K Y.Calculation and decomposition of indirect carbon emissions from residential consumption in China based on the input-output model. Energy Policy, 2012, 48: 618-626.https://doi.org/10.1016/j.enpol.2012.05.068 URL [本文引用: 2]摘要 Based on the input–output model and the comparable price input–output tables, the current paper investigates the indirect carbon emissions from residential consumption in China in 1992–2005, and examines the impacts on the emissions using the structural decomposition method. The results demonstrate that the rise of the residential consumption level played a dominant role in the growth of residential indirect emissions. The persistent decline of the carbon emission intensity of industrial sectors presented a significant negative effect on the emissions. The change in the intermediate demand of industrial sectors resulted in an overall positive effect, except in the initial years. The increase in population prompted the indirect emissions to a certain extent; however, population size is no longer the main reason for the growth of the emissions. The change in the consumption structure showed a weak positive effect, demonstrating the importance for China to control and slow down the increase in the emissions while in the process of optimizing the residential consumption structure. The results imply that the means for restructuring the economy and improving efficiency, rather than for lowering the consumption scale, should be adopted by China to achieve the targets of energy conservation and emission reduction.
[30]	Zhang Lei.Economic development and its bearing on CO₂ emissions. Acta Geographica Sinica, 2003, 58(4): 629-637.https://doi.org/10.3321/j.issn:0375-5444.2003.04.019 URL Magsci [本文引用: 2]摘要探讨国家经济发展对碳排放的影响,采用具体评价模式对发达国家和发展中国家长期发展的对比研究.结果表明:第一,经济结构多元化的发展导致国家能源消费需求增长的减缓;第二,能源消费结构的多元化发展则导致国家碳排放水平的下降;第三,经济和能源消费的两者结构多元化的演进最终促使国家发展完成从高碳燃料为主向低碳为主的转变. [张雷. 经济发展对碳排放的影响 . 地理学报, 2003, 58(4): 629-637.]https://doi.org/10.3321/j.issn:0375-5444.2003.04.019 URL Magsci [本文引用: 2]摘要探讨国家经济发展对碳排放的影响,采用具体评价模式对发达国家和发展中国家长期发展的对比研究.结果表明:第一,经济结构多元化的发展导致国家能源消费需求增长的减缓;第二,能源消费结构的多元化发展则导致国家碳排放水平的下降;第三,经济和能源消费的两者结构多元化的演进最终促使国家发展完成从高碳燃料为主向低碳为主的转变.
[31]	Zhang Lei, Huang Yuanxi, Li Yanmei, et al.An investigation on spatial changing pattern of CO₂ emissions in China. Resources Science, 2010, 32(2): 211-217.URL Magsci [本文引用: 2]摘要作为世界能源消费大国，中国的碳排放问题不仅体现在总量增长方面，而且也体现在碳排放的空间格局变化方面。从大区地域系统变化来看：东部地区的碳排放始终在全国占据着主导地位；中部地区碳排放在全国的比重表现出稳中有降的态势；西部地区比重虽较小，但基本保持着上升趋势。从省（区、市）级变化来看：1953年以来，碳排放的区域差异不断增大，并且其变化可以分为3个阶段：1952年为起始阶段、1953年-1980年为初级分化阶段、1981年-2005年为快速演进阶段。本文试图通过产业-能源关联和能源-碳排放关联两个基本评价模型，解析中国碳排放区域格局变化的原因。分析结果表明：①产业结构的演进决定着一次能源消费的基本空间格局；②地区产业结构多元化程度越是走向成熟，其一次能源消费的增速也就越是减缓；③缓慢的一次能源消费结构变化是难以降低地区碳排放增长的关键所在。因此，加快产业结构演进速率以逐步减缓地区一次能源消费总量增长，以及最大限度地改善各地区、特别是东部地区的一次能源供应结构，是有效控制区域碳排放增长的关键。 [张雷, 黄园淅, 李艳梅, 等. 中国碳排放区域格局变化与减排途径分析 . 资源科学, 2010, 32(2): 211-217.]URL Magsci [本文引用: 2]摘要作为世界能源消费大国，中国的碳排放问题不仅体现在总量增长方面，而且也体现在碳排放的空间格局变化方面。从大区地域系统变化来看：东部地区的碳排放始终在全国占据着主导地位；中部地区碳排放在全国的比重表现出稳中有降的态势；西部地区比重虽较小，但基本保持着上升趋势。从省（区、市）级变化来看：1953年以来，碳排放的区域差异不断增大，并且其变化可以分为3个阶段：1952年为起始阶段、1953年-1980年为初级分化阶段、1981年-2005年为快速演进阶段。本文试图通过产业-能源关联和能源-碳排放关联两个基本评价模型，解析中国碳排放区域格局变化的原因。分析结果表明：①产业结构的演进决定着一次能源消费的基本空间格局；②地区产业结构多元化程度越是走向成熟，其一次能源消费的增速也就越是减缓；③缓慢的一次能源消费结构变化是难以降低地区碳排放增长的关键所在。因此，加快产业结构演进速率以逐步减缓地区一次能源消费总量增长，以及最大限度地改善各地区、特别是东部地区的一次能源供应结构，是有效控制区域碳排放增长的关键。
[32]	Wu F, Fan L W, Zhou P, et al.Industrial energy efficiency with CO₂ emissions in China: A nonparametric analysis. Energy Policy, 2012, 49: 164-172.https://doi.org/10.1016/j.enpol.2012.05.035 URL [本文引用: 1]摘要 Global awareness on energy security and climate change has created much interest in assessing economy-wide energy efficiency performance. A number of previous studies have contributed to evaluate energy efficiency performance using different analytical techniques among which data envelopment analysis (DEA) has recently received increasing attention. Most of DEA-related energy efficiency studies do not consider undesirable outputs such as CO2 emissions in their modeling framework, which may lead to biased energy efficiency values. Within a joint production framework of desirable and undesirable outputs, in this paper we construct both static and dynamic energy efficiency performance indexes for measuring industrial energy efficiency performance by using several environmental DEA models with CO2 emissions. The dynamic energy efficiency performance indexes have further been decomposed into two contributing components. We finally apply the indexes proposed to assess the industrial energy efficiency performance of different provinces in China over time. Our empirical study shows that the energy efficiency improvement in China's industrial sector was mainly driven by technological improvement.
[33]	Li H N, Mu H L, Zhang M, et al.Analysis of regional difference on impact factors of China's energy-related CO₂ emissions. Energy, 2012, 39(1): 319-326.https://doi.org/10.1016/j.energy.2012.01.008 URL [本文引用: 1]摘要 With the intensification of global warming, the issue of carbon emissions causes more and more attention in recent years. In this paper, China’s 30 provincial-level administrative units are divided into five emission regions according to the annual average value of provincial CO2 emissions per capita during 1990 and 2010. The regional differences in impact factors on CO2 emissions are discussed using STIRPAT (stochastic impacts by regression on population, affluence, and technology) model. The results indicate that although GDP (Gross domestic product) per capita, industrial structure, population, urbanization and technology level have different impacts on CO2 emissions in different emission regions, they are almost always the main factors in all emission regions. In most emission regions, urbanization and GDP per capita has a bigger impact on CO2 emissions than other factors. Improving technology level produces a small reduction in CO2 emissions in most emission regions, but it is still a primary way for CO2 reduction in China. It’s noteworthy that industrial structure isn’t the main factor and improving technology level increases CO2 emissions in high emission region. Different measures should be adopted for CO2 reductions according to local conditions in different regions.
[34]	Du Huibin, Guo Jianghong, Mao Guozhu, et al.CO₂ emissions embodied in China-US trade: Input-output analysis based on the emergy/dollar ratio. Energy Policy, 2011, 39(10): 5980-5987.https://doi.org/10.1016/j.enpol.2011.06.060 Magsci [本文引用: 1]摘要 To gain insight into changes in CO2 emissions embodied in China-US trade, an input-output analysis based on the emergy/dollar ratio (EDR) is used to estimate embodied CO2 emissions; a structural decomposition analysis (SDA) is employed to analyze the driving factors for changes in CO2 emissions embodied in China's exports to the US during 2002-2007. The results of the input-output analysis show that net export of CO2 emissions increased quickly from 2002 to 2005 but decreased from 2005 to 2007. These trends are due to a reduction in total CO2 emission intensity, a decrease in the exchange rate, and small imports of embodied CO(2)emissions. The results of the SDA demonstrate that total export volume was the largest driving factor for the increase in embodied CO2 emissions during 2002-2007, followed by intermediate input structure. Direct CO(2)emissions intensity had a negative effect on changes in embodied CO2 emissions. The results suggest that China should establish a framework for allocating emission responsibilities, enhance energy efficiency, and improve intermediate input structure. (C) 2011 Elsevier Ltd. All rights reserved.
[35]	Su Bin, Ang B W, Low M.Input-output analysis of CO₂ emissions embodied in trade and the driving forces: Processing and normal exports. Ecological Economics, 2013, 88: 119-125.https://doi.org/10.1016/j.ecolecon.2013.01.017 URL Magsci [本文引用: 2]摘要 In recent years, energy-related CO2 emissions embodied in international trade and the driving forces have been widely studied by researchers using the environmental input-output framework. Most previous studies however, do not differentiate different input structures in manufacturing processing exports and normal exports. Using China as an example, this paper exemplifies how implications of results obtained using different export assumptions differ. The study posits that the utilization of traditional I-O model results in an overestimation of emissions embodied in processing exports and an underestimation in normal exports. The estimate of CO2 emissions embodied in China's exports drops by 32% when the extended I-O model is used. The choice of export assumption has more impact on the decomposition results for processing exports. The study further highlights that for a country with an export structure similar to China, it is meaningful to look into the impact of export assumption in embodied emission studies. (C) 2013 Elsevier B.V. All rights reserved.
[36]	Zhao Min, Tan Lirong, Zhang Weiguo, et al.Decomposing the influencing factors of industrial carbon emissions in Shanghai using the LMDI method. Energy, 2010, 35(6): 2505-2510.https://doi.org/10.1016/j.energy.2010.02.049 URL Magsci [本文引用: 1]摘要 Knowledge of influencing factors of industrial carbon emissions (ICE) is crucial to the efforts of reducing anthropogenic greenhouse gas emissions. In this paper, main factors responsible for the ICE in Shanghai between 1996 and 2007 were identified and quantitatively analyzed using the Log-Mean Divisia Index method. It was found that the industrial output was the main driving force of ICE. The decline in energy intensity and the adjustment of energy and industrial structure are major determinants for reduction of ICE, with the former alone accounting for 90% of the reduction. To better investigate the relative contribution of different industrial sectors and their changes over time, we divided the study period into two equal time intervals and analyzed some high-carbon emission sectors. The results suggested that the intensity of energy use should be reduced further, for it was far higher than the world average. Adjustment of industrial structure by developing low-carbon emission industries is more crucial than energy mix.
[37]	Liu Zhu, Liang Sai, Geng Yong, et al.Features, trajectories and driving forces for energy-related GHG emissions from Chinese mega cites: The case of Beijing, Tianjin, Shanghai and Chongqing. Energy, 2012, 37(1): 245-254.https://doi.org/10.1016/j.energy.2011.11.040 URL [本文引用: 1]摘要 With China’s rapid economic development and urbanization process, cities are facing great challenges for tackling anthropogenic climate change. In this paper we present features, trajectories and driving forces for energy-related greenhouse gas (GHG) emissions from four Chinese mega-cities (Beijing, Tianjin, Shanghai and Chongqing) during 1995–2009. First, top-down GHG inventories of these four cities, including direct emissions (scope 1) and emissions from imported electricity (scope 2) are presented. Then, the driving forces for the GHG emission changes are uncovered by adopting a time serial LMDI decomposition analysis. Results indicate that annual GHG emission in these four cities exceeds more than 500 million tons and such an amount is still rapidly growing. GHG emissions are mainly generated from energy use in industrial sector and coal-burning thermal power plants. The growth of GHG emissions in four mega-cities during 1995–2009 is mainly due to economic activity effect, partially offset by improvements in carbon intensity. Besides, the proportion of indirect GHG emission from imported energy use (scope 2) keeps growing, implying that big cities are further dependent on energy/material supplies from neighboring regions. Therefore, a comprehensive consideration on various perspectives is needed so that different stakeholders can better understand their responsibilities on reducing total GHG emissions.
[38]	Wang Hongsheng, Wang Yunxia, Wang Haikun, et al.Mitigating greenhouse gas emissions from China's cities: Case study of Suzhou. Energy Policy, 2014, 68: 482-489.https://doi.org/10.1016/j.enpol.2013.12.066 URL [本文引用: 1]摘要 Knowledge of the factors driving greenhouse gas (GHG) emissions from cities is crucial to mitigating China's anthropogenic emissions. In this paper, the main drivers increasing GHG emissions from the Chinese city of Suzhou between 2005 and 2010 were identified and quantitatively analyzed using the Kaya identity and the log-mean Divisia index method. We found that economy and population were the major drivers of GHG emissions in Suzhou, having contributed 162.20% and 109.04%, respectively, to the increase in emissions. A decline in carbon intensity, which was caused by the declining energy intensity and an adjustment to the mixture of power and industrial structures, was the major determinant and accounted for a reduction of 171.24% in GHG emissions. Slowing and maintaining healthy growth rates of economy and population could be the primary and most effective means if Suzhou tries to curb the total emissions over the short term. It may be more realistic for Suzhou to control emissions by optimizing the economic structure for low-carbon industrial development because of the city's relative high energy requirements and low potential to mitigate GHGs by adjusting the energy mixture.
[39]	Wang Ping, Wu Wanshui, Zhu Bangzhu, et al.Examining the impact factors of energy-related CO₂ emissions using the STIRPAT model in Guangdong Province, China. Applied Energy, 2013, 106: 65-71.https://doi.org/10.1016/j.apenergy.2013.01.036 URL [本文引用: 1]摘要 To find the key impact factors of CO 2 emissions to realize the carbon intensity target, this paper examined the impact factors of population, economic level, technology level, urbanization level, industrialization level, service level, energy consumption structure and foreign trade degree on the energy-related CO 2 emissions in Guangdong Province, China from 1980 to 2010 using an extended STIRPAT model. We employed ridge regression to fit the extended STIRPAT model. Empirical results indicate that factors such as population, urbanization level, GDP per capita, industrialization level and service level, can cause an increase in CO 2 emissions. However, technology level, energy consumption structure and foreign trade degree can lead to a decrease in CO 2 emissions. The estimated elastic coefficients suggest that population is the most important impact factor of CO 2 emissions. Industrialization level, urbanization level, energy consumption structure, service level and GDP per capita are also significant impact factors, but the other factors such as technology level and foreign trade degree are less important impact factors. Some policy recommendations are also given on how to mitigate the growth of CO 2 emissions.
[40]	Wang Changjian, Zhang Xiaolei, Wang Fei, et al.Decomposition of energy-related carbon emissions in Xinjiang and relative mitigation policy recommendations. Front Earth Sciense, 2015, 9(1): 65-76.https://doi.org/10.1007/s11707-014-0442-y URL [本文引用: 1]
[41]	Liang Sai, Zhang Tianzhu.What is driving CO₂ emissions in a typical manufacturing center of South China? The case of Jiangsu Province. Energy Policy, 2011, 39(11): 7078-7083.https://doi.org/10.1016/j.enpol.2011.08.014 URL [本文引用: 4]摘要 Investigating CO 2 emissions of China's manufacturing centers contributes to local and global CO 2 mitigation targets. This study considers Jiangsu Province as a representation of manufacturing centers in South China. Effects of material efficiency improvements, technology development, consumption structure changes and consumption volume growth in Jiangsu Province on its CO 2 emissions during 1997鈥2007 are investigated using structural decomposition analysis based on environmental input鈥搊utput table. In order to reduce CO 2 emissions, Jiangsu Province should not only rely on material efficiency improvements and technology development, but also rely on consumption structure changes. For consumption structure changes in detail, Jiangsu Province should not only focus on fixed capital formation and urban residential consumption, but also focus on international and intranational imports and exports. For the implementation of material efficiency improvements and technology development, Jiangsu Province should focus on technology innovation and international technology transfer. For the implementation of consumption structure changes, Jiangsu Province should mainly focus on identified sectors for each separate final demand category: five sectors for urban residential consumption, three sectors for fixed capital formation, four sectors for international exports, five sectors for intranational exports, three sectors for international imports and four sectors for intranational imports.
[42]	Wang Changjian, Wang Fei, Zhang Hongou, et al.Carbon emissions decomposition and environmental mitigation policy recommendations for sustainable development in Shandong province. Sustainability, 2014, 6(11): 8164-8179.https://doi.org/10.3390/su6118164 URL [本文引用: 3]摘要 Provincial carbon emissions research is necessary for China to realize emissions reduction targets. Two-level decomposition model based on the Kaya identity was applied to uncover the main driving forces for the energy related carbon emissions in Shandong province from 1995 to 2011, an important energy base in China. Coal consumption is still the biggest contributor to the increased carbon emissions in Shandong. Decomposition results show that the affluence effect is the most important contributors to the carbon emissions increments. The energy intensity effect is the dominant factor in curbing carbon emissions. The emission coefficient effect plays an important negative but relatively minor effect on carbon emissions. Based on the local realities, a series of environment-friendly mitigation policies are raised by fully considering all of these influencing factors. Sustainable mitigation policies will pay more attention to the low-carbon economic development along with the significant energy intensity reduction in Shangdong province.
[43]	Xi Fengming, Geng Yong, Chen Xudong, et al.Contributing to local policy making on GHG emission reduction through inventorying and attribution: A case study of Shenyang, China. Energy Policy, 2011, 39(10): 5999-6010.https://doi.org/10.1016/j.enpol.2011.06.063 Magsci [本文引用: 1]摘要 Cities consumed 84% of commercial energy in China, which indicates cities should be the main areas for GHG emissions reduction. Our case study of Shenyang in this paper shows how a clear inventory analysis on GHG emissions at city level can help to identify the major industries and societal sectors for reduction efforts so as to facilitate low-carbon policy-making. The results showed total carbon emission in 2007 was 57 Mt CO(2) equivalents (CO(2)e), of which 41 Mt CO(2)e was in-boundary emissions and 16 Mt CO(2)e was out-of-boundary emissions. The energy sector was dominant in the emission inventory, accounting for 93.1% of total emissions. Within energy sector, emissions from energy production industry, manufacturing and construction industry accounted for 88.4% of this sector. Our analysis showed that comparing with geographical boundary, setting system boundary based on single process standard could provide better information to decision makers for carbon emission reduction. After attributing electricity and heating consumption to final users, the resident and commercial sector became the largest emitter, accounting for 28.5% of total emissions. Spatial analysis of emissions showed that industrial districts such as Shenbei and Tiexi had the large potential to reduce their carbon emissions. Implications of results are finally discussed. (C) 2011 Elsevier Ltd. All rights reserved.
[44]	Ang B W.Decomposition methodology in industrial energy demand analysis. Energy, 1995, 20(11): 1081-1095.https://doi.org/10.1016/0360-5442(95)00068-R URL [本文引用: 1]摘要 We discuss some methodological and application issues related to decomposing national industrial energy consumption into changes associated with aggregate industrial production level, production structure and sectoral energy intensity. Past studies are classified and reviewed with respect to study scope and decomposition technique. A framework for decomposition method formulation which incorporates three different approaches is presented. Several specific methods are described and their applications are illustrated with an example. Relevant application issues, such as method selection, periodwise vs time-series decomposition, significance of levels of sector disaggregation, and result interpretation are discussed.
[45]	Rose A, Casler S.Input-output structural decomposition analysis: A critical appraisal. Economic Systems Research, 1996, 8(1): 33-62.URL [本文引用: 1]
[46]	Casler S D, Rose A.Carbon dioxide emissions in the U.S. economy: A structural decomposition analysis. Environ Resource Econ, 1998, 11(3/4): 349-363.https://doi.org/10.1023/A:1008224101980 URL [本文引用: 1]摘要 This paper provides an empirical analysis of the impact of various influences on carbon dioxide emissions. It incorporates methodological refinements of input-output structural decomposition analysis, which is the examination of economic change by means of a set of comparative static variations in key parameters of I-O tables. The analysis is performed using a two-tiered KLEM model, which allows for estimation of substitution and technological change effects within and between input aggregates. The model is used to decompose the sources of change in CO 2 emissions in the U.S. over the 1972鈥82 timeframe using hybrid energy/value tables for the initial and terminal years. Results show the significant effect of substitution within the energy sector and between energy and other inputs as the leading causes of the decline in carbon dioxide emissions. Copyright Kluwer Academic Publishers 1998
[47]	Su Bin, Ang B W.Structural decomposition analysis applied to energy and emissions: Some methodological developments. Energy Econ, 2012, 34(1): 177-188.https://doi.org/10.1016/j.eneco.2011.10.009 URL [本文引用: 1]摘要 The only comprehensive study comparing structural decomposition analysis (SDA) and index decomposition analysis (IDA) was conducted around 2000. There have since been new developments in both techniques in energy and emission studies. These developments have been studied systematically for IDA but similar studies for SDA are lacking. In this paper, we fill the gap by examining the new methodological developments in SDA. A new development is a shift towards using decomposition methods that are ideal. We compare four such SDA methods analytically and empirically through decomposing changes in China's CO 2 emissions. We then provide guidelines on method selection. Finally, we discuss the similarities and differences between SDA and IDA based on the latest available information.
[48]	Peters G P, Weber C L, Guan D, et al.China's growing CO₂ emissions: A race between increasing consumption and efficiency gains. Environmental Science & Technology, 2007, 41(17): 5939-5944.https://doi.org/10.1021/es070108f URLPMID:17937264 [本文引用: 4]摘要 China's rapidly growing economy and energy consumption are creating serious environmental problems on both local and global scales. Understanding the key drivers behind China's growing energy consumption and the associated CO2 emissions is critical for the development of global climate policies and provides insight into how other emerging economies may develop a low emissions future. Using recently released Chinese economic input-output data and structural decomposition analysis we analyze how changes in China's technology, economic structure, urbanization, and lifestyles affect CO2 emissions. We find that infrastructure construction and urban household consumption, both in turn driven by urbanization and lifestyle changes, have outpaced efficiency improvements in the growth of CO2 emissions. Net trade had a small effect on total emissions due to equal, but significant, growth in emissions from the production of exports and emissions avoided by imports. Technology and efficiency improvements have only partially offset consumption growth, but there remains considerable untapped potential to reduce emissions by improving both production and consumption systems. As China continues to rapidly develop there is an opportunity to further implement and extend policies, such as the Circular Economy, that will help China avoid the high emissions path taken by today's developed countries.
[49]	Minx J C, Baiocchi Giovanni, Peters Glen P, et al.A "Carbonizing Dragon": China's fast growing CO₂ emissions revisited. Environmental Science & Technology, 2011, 45(21): 9144-9153.URL [本文引用: 4]
[50]	Guan D, Peters G P, Weber C L, et al.Journey to world top emitter: An analysis of the driving forces of China's recent CO₂ emissions surge. Geophysical Research Letters, 2009, 36(4): L04709.https://doi.org/10.1029/2008GL036540 URL [本文引用: 1]摘要 Abstract Top of page Abstract 1.Introduction 2.Method and Data 3.Results 4.Conclusions References Supporting Information [1] China's economy has been growing at an accelerated rate from 2002 to 2005 and with it China's carbon emissions. It is easier to understand the growth in China's carbon emissions by considering which consumption activities - households and government, capital investments, and international trade - drive Chinese production and hence emissions. This paper adopts structural decomposition analysis, a macro-economic approach using data from national statistical offices, to investigate the drivers of China's recent CO 2 emissions surge. The speed of efficiency gains in production sectors cannot cope with the growth in emissions due to growth in final consumption and associated production processes. More specifically, Chinese export production is responsible for one-half of the emission increase. Capital formation contributes to one-third of the emission increase. A fast growing component is carbon emissions related to consumption of services by urban households and governmental institutions, which are responsible for most of the remaining emissions.
[51]	Guan D, Hubacek K, Weber C L, et al.The drivers of Chinese CO₂ emissions from 1980 to 2030. Global Environmental Change, 2008, 18(4): 626-634.https://doi.org/10.1016/j.gloenvcha.2008.08.001 URL [本文引用: 1]摘要 China's energy consumption doubled within the first 25 years of economic reforms initiated at the end of the 1970s, and doubled again in the past 5 years. It has resulted of a threefold CO 2 emissions increase since early of 1980s. China's heavy reliance on coal will make it the largest emitter of CO 2 in the world. By combining structural decomposition and input鈥搊utput analysis we seek to assess the driving forces of China's CO 2 emissions from 1980 to 2030. In our reference scenario, production-related CO 2 emissions will increase another three times by 2030. Household consumption, capital investment and growth in exports will largely drive the increase in CO 2 emissions. Efficiency gains will be partially offset the projected increases in consumption, but our scenarios show that this will not be sufficient if China's consumption patterns converge to current US levels. Relying on efficiency improvements alone will not stabilize China's future emissions. Our scenarios show that even extremely optimistic assumptions of widespread installation of carbon dioxide capture and storage will only slow the increase in CO 2 emissions.
[52]	Liang Sai, Xu Ming, Liu Zhu, et al.Socioeconomic drivers of mercury emissions in China from 1992 to 2007. Environmental Science & Technology, 2013, 47(7): 3234-3240.https://doi.org/10.1021/es303728d URLPMID:23473539 [本文引用: 4]摘要 Mercury emissions in China have increased by 164% during 1992鈥2007. While major mercury producers were among energy combustion and nonferrous metal sectors, little is known for the socioeconomic factors driving the growth of emissions. In this paper we examine the underlying drivers and their contributions to the change of mercury emissions. Results show that changes in per capita GDP and GDP composition led to increased emissions which offset the reduction of emissions made possible by technology-induced decrease of mercury emissions intensity and changes in final demand mix. In particular, changes in final demand mix caused decreasing mercury emissions from 1992 to 2002 and increasing emissions from 2002 to 2007. Formation of fixed capital was the dominant driver behind the increase of mercury emissions, followed by the increasing urban population and net exports. This systems-based examination of socioeconomic drivers for China's mercury emission increase is critical for emission control by guiding policy-making and targets of technology development.
[53]	Wang Yafei, Zhao Hongyan, Li Liying, et al.Carbon dioxide emission drivers for a typical metropolis using input-output structural decomposition analysis. Energy Policy, 2013, 58: 312-318.https://doi.org/10.1016/j.enpol.2013.03.022 URL [本文引用: 1]摘要 As the capital of China, Beijing is regarded as a major metropolis in the world. Study of the variation in temporal CO 2 emissions generated by the driving forces in Beijing can provide guidance for policy decisions on CO 2 emissions mitigation in global metropolises. Based on input–output structural decomposition analysis (IO-SDA), we analysed the driving forces for the increment in CO 2 emissions in Beijing from both production and final demand perspectives during 1997–2010. According to our results, the CO 2 emission growth in Beijing is driven mainly by production structure change and population growth, partly offset by CO 2 emission intensity reduction as well as the decline in per capita final demand volume during the study period. Final demand structure change has a limited effect on the change in the CO 2 emissions in Beijing. From the final demand perspective, urban trades, urban residential consumption, government consumption and fixed capital formation are mainly responsible for the booming emissions. This study showed how the “top-down” IO-SDA methodology was implemented on a city scale. Policy implications from this study would be helpful for addressing CO 2 emissions mitigation in global capital cities and metropolises.
[54]	Peters G P.From production-based to consumption-based national emission inventories. Ecological Economics, 2008, 65(1): 13-23.https://doi.org/10.1016/j.ecolecon.2007.10.014 URL [本文引用: 1]摘要 Under the United National Framework Convention of Climate Change (UNFCCC) countries are required to submit National Emission Inventories (NEI) to benchmark reductions in greenhouse gas (GHG) emissions. Depending on the definition and system boundary of the NEI, the mitigation options and priorities may vary. The territorial system boundary used by the UNFCCC has been critiqued for not including international transportation and potentially causing carbon leakage. To address these issues, past literature has argued in favour of using consumption-based NEI in climate policy. This article discusses several issues in moving from the standard production-based NEI to consumption-based NEI. First, two distinct accounting approaches for constructing consumption-based NEI are presented. The approaches differ in the allocation of intermediate consumption of imported products. Second, a consistent method of weighting production-based and consumption-based NEI is discussed. This is an extension of the previous literature on shared responsibility to NEI. Third, due to increased uncertainty and a wide system boundary it may be difficult to implement consumption-based NEI directly into climate policy. Several alternative options for incorporating consumption-based inventories into climate policy are discussed.
[55]	Xu Ming, Li Ran, Crittenden John C, et al.CO₂ emissions embodied in China's exports from 2002 to 2008: A structural decomposition analysis. Energy Policy, 2011, 39(11): 7381-7388.https://doi.org/10.1016/j.enpol.2011.08.068 URL Magsci [本文引用: 1]摘要 This study examines the annual CO2 emissions embodied in China's exports from 2002 to 2008 using environmental input鈥搊utput analysis. Four driving forces, including emission intensity, economic production structure, export composition, and total export volume, are compared for their contributions to the increase of embodied CO2 emissions using a structural decomposition analysis (SDA) technique. Although offset by the decrease in emission intensity, the increase of embodied CO2 emissions was driven by changes of the other three factors. In particular, the change of the export composition was the largest driver, primarily due to the increasing fraction of metal products in China's total export. Relevant policy implications and future research directions are discussed at the end of the paper.
[56]	Li Fangyi, Liu Weidong, Tang Zhipeng.Study on inter-regional transfer of embodied pollution in China. Acta Geographica Sinica, 2013, 68(6): 791-801.URL Magsci [本文引用: 1]摘要随着世界经济一体化程度不断加深,国际贸易中的隐含资源和隐含污染受到广泛关注,但目前对中国区域间隐含污染转移的研究较为欠缺。本文首先从理论层面对隐含污染转移现象进行剖析,从地理学角度探讨了污染转移的模式;然后,基于区域间投入产出表,构建区域间隐含污染转移的评估模型;选取SO2、COD、固体废弃物及重金属四种典型工业污染物,评估2007年中国各区域本地最终使用中的隐含污染,及区域间隐含污染转移量,揭示我国隐含污染转移的空间特征。研究发现中国整体上是隐含污染的输出地区,国内隐含污染转移主要是从经济欠发达的中西部地区流向城市化率较高、经济发达的东部沿海地区,实际上是东部地区通过区域间贸易将自身的污染排放负担转移到中西部地区。最后基于隐含污染转移格局,对区域污染物减排政策的制定提出了一系列建议。 [李方一, 刘卫东, 唐志鹏. 中国区域间隐含污染转移研究 . 地理学报, 2013, 68(6): 791-801.]URL Magsci [本文引用: 1]摘要随着世界经济一体化程度不断加深,国际贸易中的隐含资源和隐含污染受到广泛关注,但目前对中国区域间隐含污染转移的研究较为欠缺。本文首先从理论层面对隐含污染转移现象进行剖析,从地理学角度探讨了污染转移的模式;然后,基于区域间投入产出表,构建区域间隐含污染转移的评估模型;选取SO2、COD、固体废弃物及重金属四种典型工业污染物,评估2007年中国各区域本地最终使用中的隐含污染,及区域间隐含污染转移量,揭示我国隐含污染转移的空间特征。研究发现中国整体上是隐含污染的输出地区,国内隐含污染转移主要是从经济欠发达的中西部地区流向城市化率较高、经济发达的东部沿海地区,实际上是东部地区通过区域间贸易将自身的污染排放负担转移到中西部地区。最后基于隐含污染转移格局,对区域污染物减排政策的制定提出了一系列建议。
[57]	Liu Weidong, Zou Jialing.A direction of regional studies: regional interdependence. Areal Research and Development, 2014, 33(1): 1-5, 16.https://doi.org/10.3969/j.issn.1003-2363.2014.01.001 URL Magsci摘要区域发展研究一直都是包括地理学在内的多个学科的重要研究方向.经济地理学对于区域发展研究由来已久,过去30年更是相关研究的黄金时期,产生了大量的研究成果.可以将过去30年的研究概括为两个主要方向：一个是“新区域主义”兴起背景下的地方发展内在动力机制的研究,主要是基于地方综合的视角;另一个是全球化背景下的全球与地方的相互影响,主要是基于尺度关联的视角.相对而言,地理学家忽视了另外一个重要的研究视角,即区域相互依赖性.随着全球化的深入,生产活动专业化分工加深,地区间相互依赖和相互作用逐渐增强并已成为区域发展的重要影响因素.地区间相互依赖对于理解当今的区域发展格局和区域差异具有重要意义,是地理学需要加强的一个重要研究领域和方向. [刘卫东, 邹嘉龄. 区域发展研究方向探讨 . 地域研究与开发, 2014, 33(1): 1-5, 16.]https://doi.org/10.3969/j.issn.1003-2363.2014.01.001 URL Magsci摘要区域发展研究一直都是包括地理学在内的多个学科的重要研究方向.经济地理学对于区域发展研究由来已久,过去30年更是相关研究的黄金时期,产生了大量的研究成果.可以将过去30年的研究概括为两个主要方向：一个是“新区域主义”兴起背景下的地方发展内在动力机制的研究,主要是基于地方综合的视角;另一个是全球化背景下的全球与地方的相互影响,主要是基于尺度关联的视角.相对而言,地理学家忽视了另外一个重要的研究视角,即区域相互依赖性.随着全球化的深入,生产活动专业化分工加深,地区间相互依赖和相互作用逐渐增强并已成为区域发展的重要影响因素.地区间相互依赖对于理解当今的区域发展格局和区域差异具有重要意义,是地理学需要加强的一个重要研究领域和方向.
[58]	Tang Zhipeng, Liu Weidong, Gong Peiping, et al.Measuring of Chinese regional carbon emission spatial effects induced by exports based on Chinese multi-regional input-output table during 1997-2007. Acta Geographica Sinica, 2014, 69(10): 1403-1413.https://doi.org/10.11821/dlxb201410001 URL Magsci [本文引用: 1]摘要基于投入产出分析理论,本文改进了测度出口引致区域碳排放直接、间接、溢出和反馈四种空间效应公式,并将测度经济发展的溢出和反馈理念扩展到出口对区域间碳排放双向影响的研究中.研究结果显示,1997-2007 年全国实际出口引致8 区域碳排放的直接效应均有所下降,除了北部沿海区域和西北区域,其他6 个区域的间接效应也均有所下降.大部分沿海地区出口引致碳排放的溢出效应较高,北部和东部沿海区域由于与内地地区经济联系密切所受到的反馈效应也较高,但南部沿海区域由于加工贸易比重较高,所受到反馈效应相对少一些,京津区域由于城市职能定位经济辐射有限,所受到的反馈效应最低.内陆地区由于长期以来作为沿海地区的能源资源供应地以及对国内市场的依赖,出口引致碳排放的反馈效应普遍较高,其中西北和中部区域较为明显.注重区域间横向联合减排以及适宜性的区域减排政策有助于全国整体减排目标的实现. [唐志鹏, 刘卫东, 公丕萍. 出口对中国区域碳排放影响的空间效应测度: 基于1997-2007年区域间投入产出表的实证分析 . 地理学报, 2014, 69(10): 1403-1413.]https://doi.org/10.11821/dlxb201410001 URL Magsci [本文引用: 1]摘要基于投入产出分析理论,本文改进了测度出口引致区域碳排放直接、间接、溢出和反馈四种空间效应公式,并将测度经济发展的溢出和反馈理念扩展到出口对区域间碳排放双向影响的研究中.研究结果显示,1997-2007 年全国实际出口引致8 区域碳排放的直接效应均有所下降,除了北部沿海区域和西北区域,其他6 个区域的间接效应也均有所下降.大部分沿海地区出口引致碳排放的溢出效应较高,北部和东部沿海区域由于与内地地区经济联系密切所受到的反馈效应也较高,但南部沿海区域由于加工贸易比重较高,所受到反馈效应相对少一些,京津区域由于城市职能定位经济辐射有限,所受到的反馈效应最低.内陆地区由于长期以来作为沿海地区的能源资源供应地以及对国内市场的依赖,出口引致碳排放的反馈效应普遍较高,其中西北和中部区域较为明显.注重区域间横向联合减排以及适宜性的区域减排政策有助于全国整体减排目标的实现.

基于IO-SDA模型的新疆能源消费碳排放影响机理分析

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Influencing mechanism of energy-related carbon emissions in Xinjiang based on IO-SDA model

1 引言