China's carbon emission peak pre-2030: Exploring multi-scenario optimal low-carbon behaviors for China's regions

Sun, Zuoren; Liu, Yandi; Yu, Yantuan

doi:10.1016/j.jclepro.2019.05.159

Cited by 71 publications

(26 citation statements)

References 51 publications

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“…It was found that energy intensity was the leading factor for CEs reduction, and industrial structure optimization would have an important impact on 2020-2030 CEs reduction. Sun et al (2019) found that benefiting from non-fossil energy consumption, China's CI can be reduced by up to 72.7% in 2030 compared to 2005. Some studies have predicted the CEs of a single province in China.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In limited research, Zhang et al (2020) estimated the dynamic trends of CEs in 30 provinces up to 2030, and found that most provinces can achieve the CEs reduction target. Sun et al (2019) estimated that China's CI can be reduced by up to 72.7% in 2030 compared to 2005, and Hebei, Shandong, Shanxi, Liaoning, Xinjiang, and Inner Mongolia were the key provinces. Besides, existing studies ignore the impact of different energy transition policies on CEs at industrial perspective.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It depends on the city positioning of Beijing and Shanghai. As an international metropolis, Beijing and Shanghai have been committed to alleviating energy-intensive industries, but these policies has also led to the CEs growth in neighboring provinces (Sun et al 2019). From 2007 to 2012, local governments introduced a series of environmental and energy policies, and only Ningxia and Xinjiang showed a growth in CI.…”

Section: Evaluation Trend Of Provincial Emissionsmentioning

confidence: 99%

See 2 more Smart Citations

Analyzing driving forces of China’s carbon emissions from 1997 to 2040 and the potential emission reduction path: through decomposition and scenario analysis

Song

Zhao

Wang

2021

Clean Techn Environ Policy

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Energy and environmental policies are important methods for the government to restrain carbon emissions growth. Identifying the potential dynamic trends of China's carbon emissions under different scenarios has important reference significance for the government's policy implementation. This paper firstly predicted China's carbon emissions from 2017 to 2040 based on three energy transition scenarios at the industrial level. Then, Logarithmic Mean Divisia Index decomposition model was applied to evaluate the driving forces of emissions changes during 1997-2040. Finally, the Spatial-Temporal Logarithmic Mean Divisia Index model was used to explore the emissions reduction potential and the potential reduction path at provincial level. The results showed that (1) as the reduction in energy intensity cannot offset the growth of industrial scale, the carbon emissions of all industries have shown an increasing trend from 1997 to 2017; (2) In the current policies scenario, China's carbon emissions cannot reach the peak before 2040. And only in the sustainable development scenario, the carbon emissions of the three industries will all reach the peaks before 2030. And the development of non-fossil energy will reduce carbon emissions by more than 30%; (3) Hebei, Shanxi, Inner Mongolia, Ningxia, and Heilongjiang are key provinces and improving energy efficiency of the secondary industry is a potential way to promote carbon emissions reduction.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Evaluation Trend Of Provincial Emissionsmentioning

confidence: 99%

See 1 more Smart Citation

Analyzing driving forces of China’s carbon emissions from 1997 to 2040 and the potential emission reduction path: through decomposition and scenario analysis

Song

Zhao

Wang

2021

Clean Techn Environ Policy

View full text Add to dashboard Cite

show abstract

“…For carbon emission prediction methods, scholars mainly adopt top-down IPAT and STIRPAT models (Sun et al 2019 ; Zuo et al 2020 ), CGE model (Thepkhun et al 2013 ; Zou et al 2018 ), and the EKC t model Le and Ozturk ( 2020 ). Jiang et al ( 2019 ) and Shimoda et al ( 2021 ) studied the carbon emission forecast of China and Japan, respectively, and the results show that China’s carbon dioxide emission from energy consumption will be reduced by 66% by 2050, while Japan may achieve net zero emission by 2050.…”

Section: Literature Reviewmentioning

confidence: 99%

Carbon emissions index decomposition and carbon emissions prediction in Xinjiang from the perspective of population-related factors, based on the combination of STIRPAT model and neural network

Ziyuan

Yibo

Simayi

et al. 2022

Environ Sci Pollut Res

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In the present study, the STIRPAT model was adopted to examine the impacts of several factors on dioxide emissions using the time series data from 2000 to 2019 in Xinjiang. The said factors included population aging, urbanization, household size, per capita GDP, number of vehicles, per capita mutton consumption, education level, and household direct energy consumption structure. Findings were made that the positive effects of urbanization, per capita GDP, per capita mutton consumption and education on carbon emissions were obvious; the number of vehicles had the biggest positive impact on carbon dioxide emissions; and household size and household direct energy consumption structure had a significantly negative impact on carbon emissions. Based on the aforementioned findings, the GA-BP neural network was introduced to predict the carbon emission trend of Xinjiang in 2020–2050. The results reveal that the peak time of the low-carbon scenario was the earliest, between 2029 and 2033. The peak time of the middle scenario was later than low-carbon scenario, between 2032 and 2037, while the peak time of the high-carbon scenario was the latest and was unlikely to reach the peak before 2050.

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“…It depends on the city positioning of Beijing and Shanghai. As an international metropolis, Beijing and Shanghai have been committed to alleviating energy-intensive industries, but these policies has also led to the CEs growth in neighboring provinces (Sun et al, 2019). From 2007-2012, local governments introduced a series of environmental and energy policies, and only Ningxia and Xinjiang showed a growth in CI.…”

mentioning

confidence: 99%

Analyzing Driving Forces of China’s Carbon Emissions From 1997-2040 and the Potential Emission Reduction Path: Through Decomposition and Scenario Analyses

Song

Zhao

Wang

2021

Preprint

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To assess the evolution trend of China's carbon emissions (CEs) and related driving factors, this paper used scenario analysis to predict China's CEs from 2017 to 2040 at the industrial level. Then, LMDI decomposition model was applied to evaluate the driving forces of CEs changes during 1997-2040. Finally, the ST-LMDI model was used to explore the CEs reduction potential and the potential reduction path at provincial level. The results showed that (1) as the reduction of energy intensity cannot offset the growth of industrial scale, the CEs of all industries have shown an increasing trend from 1997 to 2017; (2) In the current policy scenario, China's CEs cannot reach the peak before 2040. And only in the sustainable development scenario, the CEs of the three industries will all reach the peaks before 2030. And the development of non-fossil energy will reduce CEs by more than 30%; (3) Hebei, Shanxi, Inner Mongolia, Ningxia, and Heilongjiang are key provinces and improving energy efficiency of the secondary industry is a potential way to promote CEs reduction.

show abstract

China's carbon emission peak pre-2030: Exploring multi-scenario optimal low-carbon behaviors for China's regions

Cited by 71 publications

References 51 publications

Analyzing driving forces of China’s carbon emissions from 1997 to 2040 and the potential emission reduction path: through decomposition and scenario analysis

Analyzing driving forces of China’s carbon emissions from 1997 to 2040 and the potential emission reduction path: through decomposition and scenario analysis

Carbon emissions index decomposition and carbon emissions prediction in Xinjiang from the perspective of population-related factors, based on the combination of STIRPAT model and neural network

Analyzing Driving Forces of China’s Carbon Emissions From 1997-2040 and the Potential Emission Reduction Path: Through Decomposition and Scenario Analyses

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