Will China peak its energy-related carbon emissions by 2030? Lessons from 30 Chinese provinces

Fang, Kai; Tang, Yidan; Zhang, Qifeng; Song, Junnian; Qi, Wen; Sun, Huaping; Ji, Chenyang; Xu, Anqi

doi:10.1016/j.apenergy.2019.113852

Cited by 203 publications

(68 citation statements)

References 62 publications

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“…Since the CEPT was proposed, academics have conducted extensive discussions on how to achieve this target. Opinions in the existing literature generally agree that if certain emission reduction measures are taken, China could achieve the CEPT around 2030 [13][14][15]. Promoting technological progress [16][17][18][19], reducing CO 2 emission intensity [20], adjusting industrial structure [21,22] or energy structure [23][24][25], and establishing CO 2 emission trading mechanisms [26,27] are considered the main measures required to achieve the CEPT.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In order to verify the rationality of the predicted value, we employed the results provided by Chen [9] and Zhang et al [10] that the CO 2 emissions of the industrial sector accounted for more than 80% of the total CO 2 emissions of China. Thus, 9.57 was converted to 11.97, which fell within the peak range (9-13 billion tons of CO 2 ) widely believed in academia [14,15]. In order to test the robustness of the model, the exogenous parameters in the low-carbon scenario (the details of which can be seen in Appendix A Table A6) were increased and decreased by 5%, respectively, to evaluate the impact of their fluctuations on CO 2 emission forecast results.…”

Section: Prediction For China's Maximum Co 2 Emissionsmentioning

confidence: 99%

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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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Section: Literature Reviewmentioning

confidence: 99%

Section: Prediction For China's Maximum Co 2 Emissionsmentioning

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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“…Based on the statistical analysis from the NBSC the reform related to the reduction of electricity price and the consumption of clean energy have not been met [ 40 ]. At present, the energy policies of China's 13 provinces are related to coal and electricity, 13 regions are involved in the energy-saving renovation, 19 regions are involved in new energy transportation, and 12 regions have launched industrial ecological development plans for clean energy [ 41 ].…”

Section: Several Factors Which Affect China's Energymentioning

confidence: 99%

Electrocatalysts for direct methanol fuel cells to demonstrate China's renewable energy renewable portfolio standards within the framework of the 13th five-year plan

Gao¹,

Liu

Bashir

2021

Catalysis Today

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“…Hence, due to Brazilian regional heterogeneity, we follow previous researchers in studying CO 2 emissions on a regional level (Fang et al 2019 ; Gan et al 2020 ; He et al 2017 ; Wang et al 2016a ; Wang et al 2016b ; Wang et al 2020 ; Wang et al 2017b ; Yang et al 2018 ; Zhou and Liu 2016 ). The economic history of Brazil resulted in regions developing at different paces, generating large economic and demographic discrepancies across the country (Ferreira Filho and Horridge 2006 ; Ribeiro et al 2020 ).…”

Section: Introductionmentioning

confidence: 96%

The drivers of energy-related CO2 emissions in Brazil: a regional application of the STIRPAT model

Polloni‐Silva

Silveira

Ferraz

et al. 2021

Environ Sci Pollut Res

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Since energy is one of the basic inputs for development, emerging economies should make an effort to investigate the environmental impacts of their fast economic growth. However, large emerging economies present significant regional heterogeneity that is usually uncounted for. This study uses the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model and regional data on the 27 Brazilian states to investigate the growth-CO 2 nexus under distinct development stages. To perform this analysis, we divided the states into three groups according to their average annual GDP (i.e., richer, intermediate, and poorer regions). The results suggest that richer and poorer regions, particularly, present economic and demographic developments that are environmentally costly. Also, population and per capita GDP have the largest influences on CO 2 emissions. The roles of the industrial sector and the ascending service sector are also subject to criticism. Moreover, Brazil arguably suffers from technological stagnation as its energy intensity is growing and boosting CO 2 emissions. We discuss the policy implications of these findings and suggest a future research agenda.

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Will China peak its energy-related carbon emissions by 2030? Lessons from 30 Chinese provinces

Cited by 203 publications

References 62 publications

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

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

Electrocatalysts for direct methanol fuel cells to demonstrate China's renewable energy renewable portfolio standards within the framework of the 13th five-year plan

The drivers of energy-related CO2 emissions in Brazil: a regional application of the STIRPAT model

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