Factors influencing carbon emissions from China's electricity industry: Analysis using the combination of LMDI and K-means clustering

He, Ying; Xing, Yuantong; Zhang, Xiancheng; Ji, Yijun; Hou, Huimin; Zhang, Yang; Zhu, Zhe

doi:10.1016/j.eiar.2021.106724

Cited by 103 publications

(25 citation statements)

References 47 publications

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“…Calculated Indicator Literature the effect of carbon emissions generated in economic activities carbon dioxide emissions per capita of GDP [9-11] higher economic growth with lower carbon dioxide emissions carbon dioxide emissions per unit of energy [23] energy consumption in economic activities energy consumption per unit of GDP [24] The representative research method to measure carbon emission efficiency differences between countries or regions includes the coefficient of variation [23], the Gini coefficient [16] and the Theil index [24], etc. The Theil index is the most mainstream method which provides accurate estimation results [25].…”

Section: Definitionmentioning

confidence: 99%

“…This single-factor method is good at considering the promotion or suppression effects of economic growth on carbon emissions. The total-factor method regards carbon emissions as an unexpected output [14][15][16][17] and considers carbon emissions in the whole economic system [17,18]. This total-factor method focuses on the contribution of total factors with the results more comprehensive [14].…”

Section: Introductionmentioning

confidence: 99%

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Differences of Carbon Emission Efficiency in the Belt and Road Initiative Countries

Sun

Bai

2022

Energies

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Carbon emission efficiency, which is mainly affected by economic output, energy efficiency and energy structure, is the effect of carbon emissions generated in economic activities. Improving carbon emission efficiency and narrowing regional differences are very important for green development in Belt and Road Initiative regions with developing countries as the main body. The existing literature mostly uses the Theil index to study the temporal differences in carbon emission efficiency, but spatial differences and the reasons for the differences have rarely been examined. This paper measures the differences of carbon emission efficiency using the Theil index and examines the reasons based on the Logarithmic Mean Divisia index (LMDI) method in five groups of 60 Belt and Road Initiative countries. The results show that the Theil index of carbon emission efficiency in these countries is 0.196, with an intra-group difference of 0.165 and an inter-group difference of 0.031. Between most of the groups, energy efficiency is the dominant factor affecting carbon emission efficiency differences, especially between East Asia and Central and Eastern Europe, South Asia and East Asia. Between most of the countries with the highest and lowest carbon emission efficiency in the same group, such as Singapore and Vietnam in East Asia, energy efficiency is still the primary factor affecting the differences. Only some differences between a few groups, countries and sectors have been caused mainly by energy structures. Therefore, improving energy efficiency is the first way for those countries with low carbon emission efficiency to catch up other countries with high carbon emission efficiency, followed by improving the energy structure.

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Section: Definitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Differences of Carbon Emission Efficiency in the Belt and Road Initiative Countries

Sun

Bai

2022

Energies

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“…At the national level, the LMDI method was used to measure the drivers of carbon emissions in Japan [24], India [25], China [26], and Malaysia [27]. With regard to industry, the LMDI approach was used to analyze carbon emissions in the electricity [28], manufacturing [29], and building sectors [30].…”

Section: Literature Reviewmentioning

confidence: 99%

Carbon Mitigation in the Operation of Chinese Residential Buildings: An Empirical Analysis at the Provincial Scale

et al. 2022

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The rapidly growing carbon emissions of residential building operations have become an obstacle to China’s commitment to achieving its carbon-neutral goals by 2060, but they also demonstrate great carbon mitigation potential. To help buildings reach carbon neutrality targets, this study decomposes the drivers of carbon emissions and evaluates the changes in carbon mitigation of residential buildings across 30 Chinese provinces from 2000 to 2018. The results indicate that (1) the operational carbon intensity increased in most provinces and the average annual growth rate across the 30 provinces was 4.2%; (2) from 2001 to 2018, North China and Northeast China had the highest average annual carbon mitigation intensity, at 602.7 and 376.9 kg of carbon dioxide per household, respectively. However, Northwest China had the highest carbon mitigation efficiency, with a carbon mitigation rate of 23.5%; and (3) in most cases, the total carbon mitigations of the operational residential buildings assessed at the provincial scale higher than those assessed nationwide, with a difference of 14.4 million tons of carbon dioxide on average. In addition, this study reviewed the energy efficiency codes for residential buildings and summarized effective energy efficiency measures. Overall, this study fills a gap in our understanding of carbon mitigation tools and provides a reference for the evaluation of historical carbon mitigation effects in the operation of residential buildings.

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“…influencing factor analysis and model analysis. For example, in terms of influencing factors, He et al [6] point out that the carbon emissions of the power industry accounted for more than 40% of China's total emissions. They investigate the influencing factors of carbon emissions in China power industries from the national and provincial levels.…”

Section: Introductionmentioning

confidence: 99%

Research on the community electric carbon emission prediction considering the dynamic emission coefficient of power system

Yang

et al. 2022

Preprint

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Based on the counted power system emission factors of North China Power Grid(NCPG), a community carbon emissions sample database is constructed. The support vector regression (SVR) model is trained to forecast the power carbon emissions, which is optimized by genetic algorithm(GA). A community carbon emission warning system is designed according the results. The dynamic emission coefficient curve of the power system is obtained by fitting the annual carbon emission coefficients of NCPG. The time series SVR carbon emission prediction model is constructed and the GA are improved to optimize its parameters. Taking Beijing Caochang Community as an example, a carbon emission sample database is generated based on the electricity consumption and emission coefficient curve to training and testing the SVR model. The results show that the GA-SVR model fits well with the training set and the testing set, and the prediction accuracy of the testing set reaches 86%. In view of the training model in this paper, the carbon emission trend of community electricity consumption in the next month is predicted. The carbon emission warning system of the community is designed, and the specific strategy of community carbon emission reduction is proposed.

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Factors influencing carbon emissions from China's electricity industry: Analysis using the combination of LMDI and K-means clustering

Cited by 103 publications

References 47 publications

Differences of Carbon Emission Efficiency in the Belt and Road Initiative Countries

Differences of Carbon Emission Efficiency in the Belt and Road Initiative Countries

Carbon Mitigation in the Operation of Chinese Residential Buildings: An Empirical Analysis at the Provincial Scale

Research on the community electric carbon emission prediction considering the dynamic emission coefficient of power system

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