Can China achieve its 2030 carbon emissions commitment? Scenario analysis based on an improved general regression neural network

Niu, Dongxiao; Wang, Keke; Wu, Jian; Sun, Lijie; Liang, Yi; Xu, Xiaomin; Yang, Xiaolong

doi:10.1016/j.jclepro.2019.118558

Cited by 120 publications

(46 citation statements)

References 30 publications

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“…(Zhu et al, 2020) estimated the peak of CEs in China's transportation sector under three scenarios. (Niu et al, 2020) (OECD, 2019;UN, 2018). The GDP and population in year was 39 calculated in Equation ( 3) and ( 4) respectively.…”

Section: Scenario Analysismentioning

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.

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Section: Scenario Analysismentioning

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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“…Although we scientifically select the evaluation index of the marine carrying capacity, but due to limited data, it fails to include more influencing factors, such as the economic loss caused by marine disasters ( Lai et al, 2020 ) as well as the resilience of coastal areas ( Tessler et al, 2015 ; Liu et al, 2017 ). Moreover, other commonly-used prediction method could be applied, such as the scenario-based analysis ( Niu et al, 2020 ), it is a method of making predictions about the likely occurrence or consequences of a predicted object on the premise that a phenomenon or trend will continue into the future.…”

Section: Conclusion and Policy Implicationsmentioning

confidence: 99%

Four-dimensional evaluation and forecasting of marine carrying capacity in China: Empirical analysis based on the entropy method and grey Verhulst model

Tian

Shao

Feng-lan

2020

Marine Pollution Bulletin

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This study separates marine carrying capacity into four key dimensions, i.e., social, economic, resource, and ecological, and uses the entropy method to evaluate the carrying capacity of China's 11 coastal regions during the period 2007–2016. We then predict the values of marine carrying capacity in the subsequent five years (2017–2021) using the grey Verhulst model. Results reveal a significant disparity in marine carrying capacity among the 11 coastal regions of China, and social and ecological carrying capacities illustrate among the four subcategories. Pearl River Delta in the south has the highest marine carrying capacity value and shows an increasing trend, while Yangtze River Delta and Bohai Rim Region in the north are stable. With regard to the predicted values for 2017–2021, forecasting results illustrate that the industrial structure of China's coastal areas is gradually turning towards the mode of diversified and comprehensive utilization of marine resources.

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“…If so, how? This issue has been explored widely in existing literature [1][2][3][4]. It has been accepted that the key to achieving the CEPT is to reduce the use of fossil energy-but fossil energy is still the mainstay of China's energy structure.…”

Section: Introductionmentioning

confidence: 99%

Industrial Structure Optimization and Low-Carbon Transformation of Chinese Industry Based on the Forcing Mechanism of CO2 Emission Peak Target

et al. 2021

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The setting of a CO2 emission peak target (CEPT) will have a profound impact on Chinese industry. An objective assessment of this impact is of great significance, both for understanding/applying the forcing mechanism of CEPT, and for promoting the optimization of China’s industrial structure and the low-carbon transformation of Chinese industry at a lower cost. Based on analysis of the internal logic and operation of the forcing mechanism of CEPT, we employed the STIRPAT model. This enabled us to predict the peak path of China’s CO2 emissions, select the path values that would achieve the CEPT with the year 2030 as the constraint condition, construct a multi-objective and multi-constraint input/output optimization model, employ the genetic algorithm to solve the model, and explore the industrial structure optimization and low-carbon transformation of Chinese industry. The results showed that the setting of CEPT will have a significant suppression effect on high-carbon emission industries and a strong boosting effect on low-carbon emission industries. The intensity of the effect is positively correlated with the target intensity of the CO2 emissions peak. Under the effect of the forcing mechanism of CEPT, Chinese industry can realize a low-carbon transition and the industrial structure can realize optimization. The CEPT is in line with sustainable development goals, but the setting of CEPT may risk causing excessive shrinkage of basic industries—which should be prevented.

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Can China achieve its 2030 carbon emissions commitment? Scenario analysis based on an improved general regression neural network

Cited by 120 publications

References 30 publications

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

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

Four-dimensional evaluation and forecasting of marine carrying capacity in China: Empirical analysis based on the entropy method and grey Verhulst model

Industrial Structure Optimization and Low-Carbon Transformation of Chinese Industry Based on the Forcing Mechanism of CO2 Emission Peak Target

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