The Causal Factors of International Inequality In CO2 Emissions Per Capita: A Regression-Based Inequality Decomposition Analysis

Abstract

Within the IEB framework, the Chair of Energy Sustainability promotes research into the production, supply and use of the energy needed to maintain social welfare and development, placing special emphasis on economic, environmental and social aspects. There are three main research areas of interest within the program: energy sustainability, competition and consumers, and energy firms. The energy sustainability research area covers topics as energy efficiency, CO2 capture and storage, R+D in energy, green certificate markets, smart grids and meters, green energy and biofuels. The competition and consumers area is oriented to research on wholesale markets, retail markets, regulation, competition and consumers. The research area on energy firms is devoted to the analysis of business strategies, social and corporative responsibility, and industrial organization. Disseminating research outputs to a broad audience is an important objective of the program, whose results must be relevant both at national and international level.The Chair of Energy Sustainability of the University of Barcelona-IEB is funded by the following enterprises ACS, CEPSA, CLH, Enagas, Endesa, FCC Energia, HC Energia, Gas Natural Fenosa, and Repsol) through FUNSEAM (Foundation for Energy and Environmental Sustainability). The IEB working papers represent ongoing research that is circulated to encourage discussion and has not undergone a peer review process. Any opinions expressed here are those of the author(s) and not those of IEB.

The determinants of the inequality in CO2 emissions per capita between developing countries and their implications for environmental policy

“…Nevertheless, the techniques described above just assign the contribution to inequality to the components of an identity, which provides a restricted view of the driving factors of emissions. Recent research widened the field of emission inequality analysis by employing the regression‐based inequality decomposition method developed by Fields (2003) to study the causes of these inequalities (Duro et al, 2017). In contrast to previous decomposition methods, the regression‐based inequality decomposition method does not restrict the components of inequality to the elements of an identity but allows to test the contribution to inequality of any set of relevant factors (Fields, 2003).…”

“…These studies provide a first picture of inequality and its driving forces but have the limitation of restricting the determinants to the components of the identity used. An alternative appealing method is the application of the regression‐based inequality decomposition approach, developed by Fields (2003) for the analysis of income distribution, which was first applied to the international CO 2 inequality by Duro et al (2017). The main advantage of this method with respect to the previous methods used in this literature is that it enables to test the contribution to inequality of all the factors that are previously identified as relevant determinants of emissions through an econometric regression.…”

Cash transfers in the context of carbon pricing reforms in Latin America and the Caribbean

“…We therefore turn to advances in regression-based inequality decomposition techniques to distill the factors, which account for the largest part of variance in 𝐴𝐶 𝑖 𝑟 . Our approach builds on work by Fields (2003) and by Shorrocks (1982) and finds frequent application in many fields of economics (Cowell and Fiorio 2011;Morduch and Sicular 2002), including environmental economics (Duro et al 2017) and development economics (Lambert et al 2014;Cain et al 2010). Sager (2019) and Farrell (2017)…”

Inequality and driving forces of energy-related CO₂ emissions intensity in China

“…Wang 等 [9] Duro 等 [13] Mussini 等 [10] Remuzgo 等 [19] Grunewald 等 [20] Padilla 等 [24] Duro [23] Ezcurra [15] Duro 等 [22] Heil 等 [17] Duro 等 [4] Camarero 等 [16] Chen 等 [21] Wang 等 [14] Clarke-Sather 等 [18] Wang 等 [12] 颜艳梅等 [7] 杨骞等 [26] 孙耀华等 [25] 赵雲泰等 [27] 1993-2007 年 1991-2011 年 1990-2010 年 1971-2008 年 1990-2009 年 1971-2007 年 1960-1999 年 1971-1999 年 1951-2100 年 1971-2009 年 1996-2008 年 1997-2014 年 1995-2011 年 1997-2007 年 1992-2013 年 1995-2012 年 1995-2009 年 2000-2010 年 1999-2007 [28]…”

“…地 理 科 学 进 展 第 39 卷 均衡性的驱动力对于制定科学合理的节能减排方 案具有重要意义。 目前， 国内外学者采用Gini系数 [10] 、 Theil指数 [11] 、 Atkinson index 指数 [12] 、 方差 [13] 、 变异系数 [14] 、 分布密 度函数 [15] 以及收敛理论 [16] 对人均 CO2和 CO2强度等 变量的非均衡程度进行了测算， 并从群体(以地理、 经济等为划分标准) [17][18][19] 、 能源种类 [4,20] 和经济部门 [9,21] 等角度分析了非均衡的来源， 探析了碳排放非均衡 性的影响因素 [22][23][24] (表 1)。从跨国层面对全球碳强度 非均衡性的相关研究表明， 国际碳强度非均衡性的 主要地理、 部门和能源来源分别为区域间(指亚洲、 非洲、 欧洲等)、 工业和煤炭碳强度的差异， 主要决 定因素为能源强度。 在既有的中国碳强度非均衡性相关研究中， 多 数研究对非均衡性进行了地理区域分解 [25][26][27] ， 较少 研究从经济部门和能源种类视角分析中国碳强度 非均衡性的来源。从研究结论来看， 地理区域分解 结果并不完全一致。例如， 杨骞等 [26] 认为， 2000-2009 年间中国省级碳强度非均衡的主要来源为东 北、 东部、 中部和西部 4 大区域内部。孙耀华等 [25] 则 认为相同时间段内的省际差异主要来源于 4 大区域 间。另外， 在影响因素研究中， 以往研究主要从静态 的角度讨论了每年碳强度非均衡性的影响因素 [7][8]…”