城市化对碳排放影响研究进展

doi:10.11820/dlkxjz.2014.09.009

Abstract

Abstract:

Urbanization is not only a worldwide phenomenon, but also the trend of historical development. As the impact of global climate change on people and the environment grows increasingly profound, the impact of urbanization on greenhouse gas emissions, especially carbon dioxide emissions has received growing academic attention. This article examines a large number of international and Chinese literature on the impact of urbanization on carbon emissions and systematically summarizes and reviews these studies from the following three aspects: the origin and development of research on the impact of urbanization on carbon emissions, research contents, and research methods. The review shows that, current studies focus on the relationship between urbanization and carbon emissions; influencing factors of urbanization on carbon emissions, which include macro- and micro-level factors, and the impact mechanism of urbanization on carbon emissions. The main research methods are quantitative. Varies methods, such as IPAT, STIRPAT, GTWR, IDA, SDA, EBA, have been applied. Although Chinese research on the topic lags behind international studies, rapid progress has been made over recent year. Overall, the impact of urbanization on carbon emissions is a long and complex process, which involves population density, technological progress, industrial structure, level of economic development, and stage and level of urbanization in various countries and regions, as well as many other factors. Although the content of the current research is becoming more detailed and the issues have been addressed more thoroughly, and the research methods have become more scientific and diverse, the study on this problem still needs to be improved. For example, research perspectives should be broadened and research methods should be refined in order to provide a scientific basis for urban development strategies and lay the foundation for the development of low-carbon cities.

Key words: urbanization, carbon emission, influencing factor, research progress

CLC Number:

K901.8

Yaochen QIN, Peijun RONG, Quntao YANG, Xu LI, Xiaoju NING. Research progress of impact of urbanization on carbon emissions[J].PROGRESS IN GEOGRAPHY, 2014, 33(11): 1526-1534.

Figures/Tables 2

Tab. 1

Empirical study of the causal relationship between urbanization and carbon emissions"

典型案例	研究区域/数据年代	方法	因果关系
Jones, 1989	59个发达国家/1980	OLS（普通最小二乘法）	城市化对能源消耗和碳排放呈正向影响
Parikh et al, 1995	78个发达和发展中国家/1965-1987	FE（固定效应模型） OLS（普通最小二乘法）	城市化对人均能源消耗和碳排放呈正向影响
York, 2007a	14个欧盟国家/1960-2000	Prais-Winsten regression(时间序列线性回归模型)	城市化对碳排放成呈正向影响
York, 2007b	14个亚洲国家/1971-2002	Prais-Winsten regression（时间序列线性回归模型）	城市化与碳排放存在显著的正相关关系
Gam et al, 2012	突尼斯（非洲）/1976-2006	协整分析、误差修正、Granger因果检验	城市化与碳排放存在双向正影响关系
Al-mulali et al, 2013	中东和北非地区/1980-2009	OLS（普通最小二乘法）、Granger 因果检验	城市化和碳排放有显著的双向正相关关系
杨子晖, 2010	多个发展中国家/1979-2009	Granger因果检验	工业化和城市化程度对碳排放量存在正向影响
Dong et al, 2011	中国/1979-2009	SVAR model（向量自回归模型）	城市化水平对CO₂排放改变的贡献率大约为18%
周葵等, 2013	中国各省/1978-2009	协整分析;Granger因果检验	城市化率与碳排放量呈正相关关系, 碳排放量受城市化率水平累积的影响
谢守红等, 2013	中国各省/1985-2009	相关分析、协整检验、Granger因果分析、误差修正模型	城市化水平与碳排放存在显著正相关关系
姬世东等, 2013	中国32个城市/1999-2011	面板协整模型	城市化水平对CO₂排放的影响不显著
赵红等, 2013	中国各省/1978-2010	协整模型、Granger因果检验	城市化与碳排放仅存在单向影响机制,二者相互影响均呈一定滞后特征
吴殿廷等, 2011	世界112个独立经济体/2009	多元线性回归分析,主成分分析	单位GDP的碳排放强度随着城市化率的提高而降低

Tab. 1

Tab. 2

Main methods of research for the impact of urbanization on carbon emissions"

方法	简述	优点	缺点	典型案例
IPAT（环境压力等式）	定量反映人类活动对环境的影响,即,I为环境压力;P为人口规模;A为富裕程度;T为技术	建立了人文因素与环境影响之间的账户恒等式,在对碳排放的影响定量研究中应用广泛	只能改变1个变量,而其他因素保持不变;结果只能是对因变量等比例分析	Ehrlish et al, 1971; Dietz et al, 1994; Dietz et al, 1997
STIRPAT模型(IPAT随机模式)	可拓展的随机性的环境影响评估模型（通过对人口、资产、技术3个自变量和因变量之间的关系进行评估）	指数的引入使之可用于分析人文因素对环境的非比例影响,加入了随机性	模型增加的变量需要与指定的乘法形式具有概念上的一致性	Dietz et al, 1994; Cole et al, 2004; Martínez-Zarzoso et al,2011; 杨晓军等, 2013
GTWR(时空地理加权回归模型)	扩展传统地理加权回归模型GWR,假定线性回归系数是地理位置和观测时刻的任意函数,纳入时空特性	可分析回归关系的时空特性,也可解决回归模型的时空非平稳性	权函数的选择要遵循一定规律	Bishop et al, 2011; 肖宏伟等, 2014
IDA（指数分解分析,LMDI较常用）	是把目标变量(如CO₂排放量)的变化分解成若干影响因素的变化,进而评估出各影响因素对目标变量变动的贡献程度	常用的LMDI方法有效的解决了分解中的剩余问题,避免了参数估计的主观性和随意性	对各部门的最终需求等分析较为粗糙;有的影响因素难以纳入模型	Torvanger, 1991; Ang et al, 1998; 汪东等, 2012; 朱勤等, 2013
SDA（结构分解分析）	以消耗系数矩阵为基础,利用投入产出表的比较静态方法,对各影响因素等进行细致分析	使方程式和变量的数目大大减少,解决实际计算的困难。可考察变量变动对其他部门造成的间接影响	要求有严格的投入产出数据	Cortés-Borda et al, 2014; Mc Gregor et al,2008; 王会等, 2011; 汪臻, 2012
协整分析方法	分析非平稳经济变量间数量关系,通过线性误差修正模型(ECM)刻画经济变量之间的线性调整机制	时间序列无需平整,排除单位根带来的随机性趋势,修正误差	对部分协整或协整势低的情况效果不理想	Gam et al, 2012; 孙欣等, 2014
多指标面板数据聚类分析	对各指标进行时间降维,取各指标的年度平均增长率进行聚类分析,说明面板数据的时序和截面特征	分类时,能充分考虑指标在时间和截面两个维度上的特征,且能一次容纳多个指标	数据结构较为复杂	Zhang et al, 2012; Liddle et al, 2014; 涂正革等, 2012
EBA（极值边界分析）	对回归系数进行灵敏性分析,检验目标变量回归系数的“稳健性”,解释抗干扰的“稳健性”的显著关系的多元线性回归模型	结果不是1个统计量,而是1个统计分布,超越传统回归结果的显著性和渐进一致性	需要进行多次回归检验	Sturm et al, 2005; 王立平等, 2014

Tab. 2

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