PROGRESS IN GEOGRAPHY ›› 2014, Vol. 33 ›› Issue (11): 1526-1534.doi: 10.11820/dlkxjz.2014.09.009
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Yaochen QIN(), Peijun RONG, Quntao YANG, Xu LI, Xiaoju NING
Online:
2014-11-25
Published:
2014-09-30
CLC Number:
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.
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. 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 |
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