Carbon emission of China’s power industry: driving factors and emission reduction path

Wu, Xiaoyuan; Xu, Changxin; Ma, Teli; Xu, Jingru; Zhang, Chenjun

doi:10.1007/s11356-022-21297-5

Cited by 19 publications

(6 citation statements)

References 32 publications

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“…This conclusion needs to be verified and can be used as a future research direction. The conclusion that energy structure, energy efficiency, and industrial structure have a suppressive effect on carbon emissions has also been verified in the research of Wu [43] and Zhang [44], who studied China and different provinces, respectively, and reached conclusions consistent with this article. By improving energy efficiency, it is possible to reduce carbon intensity and achieve green and sustainable development while maintaining economic development.…”

Section: Discussionsupporting

confidence: 76%

Carbon Emissions Drivers and Reduction Strategies in Jiangsu Province

Deng,

Liu,

Mao

2024

Sustainability

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China embarked on the implementation of a comprehensive national strategy aimed at reducing greenhouse gas (GHG) emissions in 2020, with ambitious targets to achieve peak emissions by 2030 and attain carbon neutrality by 2060. Given the challenges, thoroughly investigating China’s carbon emissions status and outlining reduction pathways for each province is crucial. Based on calculating carbon emissions in Jiangsu Province, this article uses the Logarithmic Mean Divisia Index (LMDI) model to decompose and analyze the factors that affect carbon emissions. This article starts with provincial carbon emissions to find the core factors and then narrows the research scope to the city level to make carbon reduction policies more targeted. When decomposing carbon emissions, this article not only selects energy structure, energy efficiency, economic development, population size, and industrial structure factors commonly used in the LMDI model but also adds the factor of external electricity to research indirect carbon emissions. The final conclusions mainly include the following: firstly, the economic development and energy efficiency factors in Jiangsu Province are the core influencing factors for carbon emissions. The former promotes carbon emissions, while the latter reduces it, and the impact gradually weakens. The energy structure and industrial structure have reduced carbon emissions, while population size and electricity transfer have increased carbon emissions. Furthermore, notable disparities in carbon emissions exist among cities within Jiangsu Province, with varying impacts stemming from diverse driving factors. Upon comprehensive evaluation of the collective carbon reduction impact, Nanjing and Suzhou emerge as cities with a low contribution rate attributable to their industrial structure. Wuxi, Zhenjiang, and Xuzhou, on the other hand, exhibit a low contribution rate associated with their energy structure. Taizhou and Nantong demonstrate a low contribution rate in energy efficiency, while Changzhou, Huai’an, and Yangzhou display a low contribution rate in both industry and energy structure. Lianyungang, Suqian, and Yancheng present low contribution rates across all three factors. Recognizing the distinctive energy and industrial profiles of each city, governmental policies should be formulated with uniformity, fairness, and flexibility, effectively realizing the dual carbon objectives.

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Section: Discussionsupporting

confidence: 76%

Carbon Emissions Drivers and Reduction Strategies in Jiangsu Province

Deng,

Liu,

Mao

2024

Sustainability

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“…Wang et al [12] use the temporal LMDI model to evaluate the driving factors of carbon emissions and the spatial LMDI model to explore regional differences. Wu et al [13] employed LMDI model to analyze the driving factors behind China's power industrial carbon emission changes from 2000 to 2018. Additionally, they simulated the various scenarios evolution trend of carbon emissions by Monte Carlo algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

“…That a expressed a constant, c 1 , c 2 , c 3 ,c 4 ,c 5 are fitting coefficient, β 1 represent scale of economy, β 2 represent the electricity consumption, β 3 represent generating efficiency, β 4 represent energy structure, β 5 represent electric industry structure, e represent the model error. Eq (13) can be obtained as below by transforming the formula (12):…”

Section: Stirpat Modelmentioning

confidence: 99%

Prediction analysis of carbon emission in China’s electricity industry based on the dual carbon background

Ding,

Zhu,

Zhou

et al. 2024

PLoS ONE

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The electric power sector is the primary contributor to carbon emissions in China. Considering the context of dual carbon goals, this paper examines carbon emissions within China’s electricity sector. The research utilizes the LMDI approach for methodological rigor. The results show that the cumulative contribution of economies scale, power consumption factors and energy structure are 114.91%, 85.17% and 0.94%, which contribute to the increase of carbon emissions, the cumulative contribution of power generation efficiency and ratio of power dissipation to generation factor are -19.15% and -0.01%, which promotes the carbon reduction. The decomposition analysis highlights the significant influence of economic scale on carbon emissions in the electricity industry, among the seven factors investigated. Meanwhile, STIRPAT model, Logistic model and GM(1,1) model are used to predict carbon emissions, the average relative error between actual carbon emissions and the predicted values are 0.23%, 8.72% and 7.05%, which indicates that STIRPAT model is more suitable for medium- to long-term predictions. Based on these findings, the paper proposes practical suggestions to reduce carbon emissions and achieve the dual carbon goals of the power industry.

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“…In terms of the influencing factors of carbon emissions, most scholars have utilized methods such as Index Decomposition Analysis (IDA), Structural Decomposition Analysis (SDA), and spatial econometric models. The IDA method primarily involves constructing models such as the LMDI model (He et al, 2022;Wu et al, 2022), the Kaya Identity (Yang et al, 2020;Zeng and He, 2023), and the STIRPAT model (Pan and Zhang, 2020;Fan and Lu, 2022) to thoroughly analyze various influencing factors. The SDA method, based on the input-output model, has also been widely applied in the field of carbon emissions.…”

Section: Introductionmentioning

confidence: 99%

Spatial-temporal characteristics and driving factors of carbon emissions from the construction industry in the Belt and Road region of China

Yin,

Lin

2024

Front. Environ. Sci.

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To promote sustainable economic development in the Belt and Road region of China, reducing carbon emissions is essential. The construction industry is a major contributor to carbon emissions in China. Therefore, studying the dynamic evolution of carbon emissions from the construction industry in this region and its driving factors is of great significance for effectively controlling emissions and achieving China’s carbon peak and carbon neutrality targets. This paper first employs the Slope model, Moran’s I index, and standard deviation ellipse to reveal the spatial-temporal characteristics of carbon emissions from the construction industry, and then applies the geographical detector model to identify the main driving factors of carbon emissions. The results indicate that: (1) From 2006 to 2021, the total carbon emissions showed a fluctuating growth trend, and there were significant differences in emissions among different regions. (2) Carbon emissions in most provinces exhibited a moderate growth trend, and there was significant spatial correlation and aggregation of inter-provincial emissions. Regional carbon emissions from 2006 to 2021 showed a spatial distribution pattern from northeast to southwest, with a weakening trend, and the center of gravity mainly distributed in the east of the region. (3) Labor input, urbanization rate, total output value of the construction industry, degree of opening up, and energy intensity are the main factors influencing the spatial heterogeneity of carbon emissions from the construction industry, and the majority of the interaction types between factors were bivariate enhancement. This study aims to provide theoretical support for policymakers to formulate appropriate policies for building energy conservation and emission reduction.

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Carbon emission of China’s power industry: driving factors and emission reduction path

Cited by 19 publications

References 32 publications

Carbon Emissions Drivers and Reduction Strategies in Jiangsu Province

Carbon Emissions Drivers and Reduction Strategies in Jiangsu Province

Prediction analysis of carbon emission in China’s electricity industry based on the dual carbon background

Spatial-temporal characteristics and driving factors of carbon emissions from the construction industry in the Belt and Road region of China

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