Spatial-temporal evolution and driving forces of provincial carbon footprints in China: An integrated EE-MRIO and WA-SDA approach

Xu, Wenhao; Xie, Yulei; Ji, Ling; Cai, Yanpeng; Yang, Zhifeng

doi:10.1016/j.ecoleng.2022.106543

Cited by 42 publications

(17 citation statements)

References 38 publications

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“…However, since the SDA method is based on input-output tables, but China's input-output tables are compiled every 5 years, it is difficult to track the evolution of each influencing factor year by year (Su et al 2022;Wang and Han 2021). Moreover, SDA has high data requirements, which limits the selection of influencing factors in relevant studies and reduces the applicability to specific decision-making needs (Xu et al 2022).…”

Section: Literature Review 21 Study Of Carbon Emission Driversmentioning

confidence: 99%

Decomposition of drivers and identification of decoupling states for the evolution of carbon emissions from energy consumption in China

Wang

Zhang

et al. 2023

Preprint

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As the world's largest energy consumer, China's carbon emission reduction efforts play a pivotal role in achieving the world's climate change goals. Identifying the key factors driving the evolution of China's carbon emissions and understanding the contrasting situation of carbon emissions and economic growth are of great practical significance for China to effectively control the growth of carbon emissions and scientifically formulate carbon emission reduction policies. This paper analyzes the drivers of China's carbon emissions at the national and provincial levels using the LMDI model, and analyzes the relationship between China's economic growth and carbon emissions using the Tapio decoupling index, based on the measurement of China's carbon emissions from energy consumption during 2003–2020. Further, the paper combines the Tapio model with the LMDI model to analyze the key factors affecting the decoupling index of carbon emissions in China. The conclusions show that China's carbon emissions grow at an average annual rate of 4.04% from 2003 to 2020, and show a high growth trend until 2013, while the growth rate is more moderate after 2013. The R&D scale effect, urbanization effect, and population scale effect are the factors driving the growth of China's carbon emissions; while the energy structure effect, energy consumption industry structure effect, energy intensity effect, and R&D efficiency effect on carbon emissions inhibit the growth of China's carbon emissions during the study period. Weak decoupling is the most dominant decoupling state in China from 2003 to 2020, and the decoupling state varies significantly among provinces. Based on this, this paper obtains corresponding policy recommendations.

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Section: Literature Review 21 Study Of Carbon Emission Driversmentioning

confidence: 99%

Decomposition of drivers and identification of decoupling states for the evolution of carbon emissions from energy consumption in China

Wang

Zhang

et al. 2023

Preprint

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show abstract

“…The environmental-extended multi-regional input-output (EE-MRIO) analysis, a typical top-down approach, measures EF by incorporating trade networks between regional sectors and their associated environmental impacts (Dejuán et al, 2022;Fry et al, 2022;Hu et al, 2021;Mi et al, 2020;Xing et al, 2022;Xu et al, 2022;Yang et al, 2020). Many existing studies have examined EF in various global sectors.…”

Section: Literature Reviewmentioning

confidence: 99%

Estimating the dynamic environmental footprints of the global finance and business sector towards sustainable development goals

Chen

Shuai

Zhao

2023

Sustainable Development

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The global finance and business is a trillion-dollar sector whose final demand drives a significant amount of economic production and services in other sectors through trade systems. As economic production and services have a significant impact on the environment, they play a fundamental role in supporting the global sustainable development goals (SDGs). Despite this, little attention has been paid to environmental impact driven by the final demand of global finance and business sector (i.e., environmental footprint). In this paper, we estimated the carbon dioxide (CO 2 ), sulfur dioxide (SO 2 ) and particular matter 10 (PM 10 ) footprints of finance and business sector of 187 economies from 2000 to 2016 using a global multi-regional input-output model. Our results show that global environmental footprints peaked 10 years ago, and the latest CO 2 , SO 2 and PM 10 footprints are approximately 1371, 8.2 and 6.1 million tons, respectively. We also found that 99% of environmental footprints originate from 20% economy-sectors, and the critical economy-sectors are identified. In addition, policy interventions on environmental intensity, production efficiency and trade network for mitigating the environmental footprints are proposed as well. Our findings provide a holistic and dynamic picture of the environmental footprints of the global finance and business sector.

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“…Thus, provincial-level analysis is more accurate than national-level analysis. Multi-regional analyses in China have investigated the driving forces behind changes in CO 2 emissions embodied in domestic and foreign trade, explored the driving forces of provincial carbon footprints, and examined the driving forces behind energy-related black carbon (BC) emission changes across provinces . Uncovering common emission sources and driving forces from regional and sectoral perspectives is useful for decision-makers to reduce GHGs and local pollutants accurately and quickly.…”

Section: Introductionmentioning

confidence: 99%

Common Driving Forces of Provincial-Level Greenhouse Gas and Air Pollutant Emissions in China

Liu

Liang

Shuai

2023

Environ. Sci. Technol.

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By developing a filtering framework and a sector-level multi-regional input–output structural decomposition model, this study identifies key common emission sources, motivation sources, and inter-provincial emission flows of both GHGs and air pollutants and reveals the key driving forces of changes in different emissions from 2012 to 2017. Results show that key common emission sources are electricity sector, non-metallic mineral products, and smelting and processing of metals in Shandong and Hebei. However, key common motivation sources are the construction sectors in Guangdong, Henan, Jiangsu, Zhejiang, and Shandong. The key inflow regions include Guangdong and Zhejiang and key outflow regions include Jiangsu and Hebei. The emission reductions are attributed to the emission intensity effect of the construction sector; contrastingly, the emission increase is from the investment scale of the construction sector. Here, Jiangsu could be a key target for future emission reduction because of its high absolute emissions and low past reduction. The scale of investment in construction might be a significant factor in reducing emissions in Shandong and Guangdong. Henan and Zhejiang could concentrate on sound new building planning and resource recycling.

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Spatial-temporal evolution and driving forces of provincial carbon footprints in China: An integrated EE-MRIO and WA-SDA approach

Cited by 42 publications

References 38 publications

Decomposition of drivers and identification of decoupling states for the evolution of carbon emissions from energy consumption in China

Decomposition of drivers and identification of decoupling states for the evolution of carbon emissions from energy consumption in China

Estimating the dynamic environmental footprints of the global finance and business sector towards sustainable development goals

Common Driving Forces of Provincial-Level Greenhouse Gas and Air Pollutant Emissions in China

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