Assessing Global CO2 Emission Inequality From Consumption Perspective: An Index Decomposition Analysis

Wang, H.; Zhou, P.

doi:10.1016/j.ecolecon.2018.08.008

Cited by 87 publications

(39 citation statements)

References 50 publications

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“…Theil index was the first important indicator to measure regional differences in economic development levels. Compared with Gini coefficient, when estimating regional differences, Theil index can easily decompose the regional overall difference (T) into two parts: inter-regional difference (T WB ) and intra-regional difference between different provinces (T WR ), thus analyzing their contribution to the total differences and the main sources of the overall differences [48]. According to this theory, we choose Theil index as an indicator to measure regional differences in carbon emissions of China's eight main economic regions.…”

Section: Theil Indexmentioning

confidence: 99%

Regional Differences in Fossil Energy-Related Carbon Emissions in China’s Eight Economic Regions: Based on the Theil Index and PLS-VIP Method

Liu

Guo

2020

Sustainability

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Determining differences in regional carbon emissions and the factors that affect these differences is important in the realization of differentiated emissions mitigation policies. This paper adopts the Theil index and the partial least square-variable importance of projection (PLS-VIP) method to analyze the change characteristics, regional differences and causes of carbon emissions, as well as the extent to which various factors influenced carbon emissions in China’s eight economic regions in 2005–2017. The results indicate that (1) during the study period, carbon emissions in the eight economic regions displayed a rigid uptrend with a phased characteristic. The growth rates of carbon emissions were different across the studied regions. (2) The overall difference in regional carbon emissions showed an increasing trend, mainly owing to increasing interregional differences. (3) The extent of the influence and explanatory ability of each factor on regional carbon emissions and discrepancies in carbon emissions were different. Population size, economic development, and energy intensity were found to be the three main factors influencing regional carbon emission changes. Industrial structure and urbanization were also contributors to regional differences in emissions. The influence of energy structure on regional carbon emissions and its explanatory power were weak on the whole, but its elastic coefficients and VIP values changed significantly. Finally, regionally targeted proposals for emissions mitigation are offered.

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Section: Theil Indexmentioning

confidence: 99%

Regional Differences in Fossil Energy-Related Carbon Emissions in China’s Eight Economic Regions: Based on the Theil Index and PLS-VIP Method

Liu

Guo

2020

Sustainability

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“…Now, it has also been used to measure the imbalance of regional development. The Theil index can decompose the regional overall differences (T) into two parts: Inter-regional differences (Tbr) and intra-regional differences between different provinces (Twr) to analyze their contribution to the total differences and the main sources of overall differences [ 28 ].…”

Section: Methodologiesmentioning

confidence: 99%

Regional Differences and Dynamic Evolution of Carbon Emission Intensity of Agriculture Production in China

Pang

et al. 2020

IJERPH

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The study of the carbon emission intensity of agricultural production is of great significance for the formulation of a rational agricultural carbon reduction policy. This paper examines the regional differences, spatial–temporal pattern and dynamic evolution of the carbon emission intensity of agriculture production from 1991 to 2018 through the Theil index and spatial data analysis. The results are shown as follows: The overall differences in carbon emission intensity of agriculture production presents a slightly enlarging trend, while the inter-regional differences in carbon emissions intensity is decreasing, but the intra-regional difference of carbon emissions intensity presented an expanding trend. The difference in carbon emission intensity between the eastern and central regions is not obvious, and the difference in carbon emission intensity in the western region shows a fluctuating and increasing trend. The overall differences caused by intra-regional differences; the average annual contribution of intra-regional differences is 67.84%, of which the average annual contribution of western region differences is 64.24%. The carbon emission intensity of agricultural production in China shows a downward trend, with provinces with high carbon emission intensity remaining stable, while provinces with low intensity are expanding. The Global Moran’s I index indicates that China’s carbon emission intensity of agricultural production shows a clear trend of spatial aggregation. The agglomeration trend of high agricultural carbon emission remains stable, and the overall pattern of agricultural carbon emission intensity shows a pattern of increasing differentiation from east to west.

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“…Based on the approach of "jointly created and equally distributed" (Sun, 1996), the perfect decomposition technique splits the residuals among the variables of analysis and is able to provide more accurate results than the Laspeyres and divisia techniques (Ang and Liu, 2001;Shyamal and Bhattacharya 2004;Chen et al, 2018). The index decomposition and the related perfect decomposition technique have been largely used to investigate the main factors influencing energy and emission changes and have proven to be particularly suitable to analyse the main socio-economic drivers of variations (Ang and Su, 2016;Shahiduzzaman and Layton, 2017;Wang and Zhou, 2018;Andreoni, 2019).…”

Section: Perfect Decomposition Techniquementioning

confidence: 99%

From the Celtic Tiger to the Celtic Phoenix: The metabolic profile of Ireland and the main drivers of energy change

Andreoni

2020

Journal of Cleaner Production

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In this paper, the metabolic profile of Ireland and the main drivers of energy variations are analysed for the period 1998-2014. By considering the years that extend from the Celtic Tiger to the Celtic Phoenix, the socioeconomic and energy variables are analysed during periods of economic growth and recession. The main objective is to investigate how demographic and economic trends have influenced the energy used across different levels of analysis. The potential implications of economic growth and population structure are also discussed. The main results show that the standard of living has been the main factor influencing the energy consumption increase. In addition, the reduction of the economic energy intensity, driven by structural changes and implementation of energy efficiency action plans, has contributed to reduce the energy used in relation to gross domestic product and human time. Being characterised by one of the largest financial crashes and one of the quickest economic recovery, together with one of the highest population increase across European Union countries, Ireland represents an interesting case study to investigate the relationships existing between socioeconomic and energy variables. The results can support the design of policies oriented to achieve sustainable energy strategies.

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Assessing Global CO2 Emission Inequality From Consumption Perspective: An Index Decomposition Analysis

Cited by 87 publications

References 50 publications

Regional Differences in Fossil Energy-Related Carbon Emissions in China’s Eight Economic Regions: Based on the Theil Index and PLS-VIP Method

Regional Differences in Fossil Energy-Related Carbon Emissions in China’s Eight Economic Regions: Based on the Theil Index and PLS-VIP Method

Regional Differences and Dynamic Evolution of Carbon Emission Intensity of Agriculture Production in China

From the Celtic Tiger to the Celtic Phoenix: The metabolic profile of Ireland and the main drivers of energy change

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