Driving factors of electric carbon productivity change based on regional and sectoral dimensions in China

Chen, Guijing; Hou, Fujun; Chang, Keliang; Zhai, Yubing; Du, Yuqin

doi:10.1016/j.jclepro.2018.09.082

Cited by 29 publications

(9 citation statements)

References 33 publications

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“…It decomposed the LMDI of electricity carbon productivity based on regional and industry demand perspectives. The analysis concluded that the environmental and economic efficiency of the electricity industry, although negative, had a more significant impact on scale effects than technology effects on other industries [47] In addition, a recent study further explored the growth potential of carbon productivity across Chinese provinces based on conventional decomposition analysis combined with cluster analysis, and revealed that policymak-ers should focus on optimizing the industrial structure and reducing energy intensity to promote sustainable development, and fully consider regional heterogeneity [48].…”

Section: Drivers Of Carbon Productivitymentioning

confidence: 99%

Sustainable Growth Drivers: Unveiling the Role Played by Carbon Productivity

Song

Meng

et al. 2022

IJERPH

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In global climate change, improving carbon productivity holds great importance for China’s sustainable growth. Based on panel data of 30 Chinese provinces and cities from 1997–2017, the drivers, spatial effects, and convergence characteristics of carbon productivity in China are explored by combining a factor decomposition framework and a spatial panel model. The findings show that (1) China’s carbon productivity shows continuous positive growth, and the substitution effect of capital for energy dominates this changing pattern; (2) There is a β-convergence trend and club convergence in China’s carbon productivity, and the spatial technology spillover accelerates the convergence rate; (3) With its accelerated industrial transformation and technological upgrading, China’s current carbon productivity converges faster than its earlier stage, and the role of physical capital investment has gradually shifted to suppression. In contrast, the positive push of human capital investment has been strengthened; (4) From the perspective of the realization mechanism, the convergence of carbon productivity in China mainly comes from the convergence of energy restructuring and capital-energy substitution. These findings can help China narrow the inter-provincial carbon productivity gap in terms of improving factor structure, upgrading technology, etc., and provide references for sustainable growth decision making in China and around the world.

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Section: Drivers Of Carbon Productivitymentioning

confidence: 99%

Sustainable Growth Drivers: Unveiling the Role Played by Carbon Productivity

Song

Meng

et al. 2022

IJERPH

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show abstract

“…It is estimated that China needs to increase its carbon productivity by a factor of 10 times to achieve the greenhouse gas emission target of IPCC2025 [ 45 ]. The existing literature has found carbon productivity is affected by economic scale [ 46 ], green capital investment [ 47 ], technological innovation level [ 48 ], trade openness degree [ 12 , 49 ], energy consumption structure [ 47 ], and urbanization level [ 13 ]. However, few have analyzed the effect of ETS on carbon productivity and the associated mediators.…”

Section: Literature Reviewmentioning

confidence: 99%

Does Emission Trading Boost Carbon Productivity? Evidence from China’s Pilot Emission Trading Scheme

Zhou

Liang

Zhou

et al. 2020

IJERPH

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As the country with the largest carbon emissions globally, the effective operation of China’s carbon emissions trading scheme (ETS) is of great importance to the global community in terms of mitigating climate change. This paper considers China’s pilot ETS launched in 2013 as a quasi-natural experiment. Exploring provincial industrial-level data that are more in line with the ETS coverage, the difference-in-difference-in-difference (DDD) model is used to evaluate the impact of the ETS on carbon productivity. Considering different pilot regions and industries, we also analyze the heterogeneous effect of ETS. Moreover, the mediating effects of technical progress and capital investment are explored. We find that China’s pilot ETS boosted carbon productivity. Among pilot regions, the best policy effectiveness appeared in Beijing, while the weakest effectiveness appeared in Chongqing. Among the pilot industries, the pilot ETS had better effectiveness in petrochemical and electric power industries and weaker effectiveness in building materials and transportation industries. Additionally, the pilot ETS promoted carbon productivity through both technological progress and capital investment, and the former contributed more. Our findings can provide empirical references and policy implications for nationwide implementation of ETS to further promote low-carbon economic transformation.

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“…The IDA is an approach that applies index theory to decomposition analysis [26,49]. It has two decomposition forms: additive decomposition and multiplicative decomposition, and requires data at only the national or industrial level [50,51]. The PDA is an approach that applies production theory to decomposition analysis.…”

Section: Structural Decomposition Analysismentioning

confidence: 99%

Dynamic Input–Output Analysis of a Carbon Emission System at the Aggregated and Disaggregated Levels: A Case Study in the Northeast Industrial District

Zang

Zhang

et al. 2020

Sustainability

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Research on carbon emissions of complex interactive activities in urban agglomerations is one of the hotspots of global climate change research. A comprehensive analysis of the urban agglomeration system’s carbon emissions is essential to reveal strategies for reduction and support sustainable development. The objective of this research is to develop an integrated carbon emission network model to explore the impact of different energy types on the Northeast Industrial District (NID), China. Four representative energy groups are considered. Specifically, at the aggregated sector-level, this research quantified the relative contributions of socioeconomic factors to carbon emission changes using structural decomposition analysis and examined the system efficiency and redundancy through robustness analysis. At the disaggregated level, the research investigated carbon emissions of different sectors from production-based, consumption-based, and income-based viewpoints. Moreover, emissions from specific categories of final demand and primary input were quantified. It was found that the increase of final demand level will proceed to push up the carbon emissions of the NID. Changing the production structure contributes to reducing emissions. The carbon emissions system has a high redundancy and low efficiency, illustrating that there are many emission pathways within the system. In addition, the use of crude oil significantly increases system redundancy and inhibits system efficiency. However, the major limitation of the model is that the long-term changes of the system are not considered. Moreover, considering the actual policies, emission reduction simulations could be added in the future.

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Driving factors of electric carbon productivity change based on regional and sectoral dimensions in China

Cited by 29 publications

References 33 publications

Sustainable Growth Drivers: Unveiling the Role Played by Carbon Productivity

Sustainable Growth Drivers: Unveiling the Role Played by Carbon Productivity

Does Emission Trading Boost Carbon Productivity? Evidence from China’s Pilot Emission Trading Scheme

Dynamic Input–Output Analysis of a Carbon Emission System at the Aggregated and Disaggregated Levels: A Case Study in the Northeast Industrial District

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