How to achieve the dual-control targets of China's CO2 emission reduction in 2030? Future trends and prospective decomposition

Wang, Di; He, Wei; Shi, Ruyi

doi:10.1016/j.jclepro.2018.12.178

Cited by 60 publications

(29 citation statements)

References 43 publications

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“…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. In general, the logic of these researches is a line type positive thinking (as opposed to reverse thinking) that about what measures can be taken to achieve the CEPT.…”

Section: Literature Reviewmentioning

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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Section: Literature Reviewmentioning

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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“…What’s more, existing studies have been paid more attention to spatial-temporal analysis of the differences in regional CO 2 emissions in recent years (Huang and Meng 2013 ; Ang et al 2016 ). However, many literatures on spatial analysis mainly focus on the production sector (Yang et al 2019 ), household sector (Li et al 2017 ), building sector (Chen and Chen 2019 ), and power sector (Wang et al 2019 ), and less literature are involved in the transport sector.…”

Section: Literature Reviewmentioning

confidence: 99%

Driving forces of temporal-spatial differences in CO2 emissions at the city level for China’s transport sector

Liu

Yang

Liu

et al. 2021

Environ Sci Pollut Res

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The paper aims to investigate the influencing factors that drive the temporal and spatial differences of CO2 emissions for the transportation sector in China. For this purpose, this study adopts a Logistic Mean Division Index (LMDI) model to explore the driving forces of the changes for the transport sector’s CO2 emissions from a temporal perspective during 2000–2017 and identifies the key factors of differences in the transport sector’s CO2 emissions of China’s 15 cities in four key years (i.e., 2000, 2005, 2010, and 2017) using a multi-regional spatial decomposition model (M-R). Based on the empirical results, it was found that the main forces for affecting CO2 emissions of the transport sector are not the same as those from temporal and spatial perspectives. Temporal decomposition results show that the income effect is the dominant factor inducing the increase of CO2 emissions in the transport sector, while the transportation intensity effect is the main factor for curbing the CO2 emissions. Spatial decomposition results demonstrate that income effect, energy intensity effect, transportation intensity effect, and transportation structure effect are important factors which result in enlarging the differences in city-level CO2 emissions. In addition, the less-developed cities and lower energy efficiency cities have greater potential to reduce CO2 emissions of the transport sector. Understanding the feature of CO2 emissions and the influencing factors of cities is critical for formulating city-level mitigation strategies of the transport sector in China. Overall, it is expected that the level of economic development is the main factor leading to the differences in CO2 emissions from a spatial-temporal perspective.

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“…Under the baseline scenario, China's total CO 2 emissions in 2030 will reach 14.7-15.4 billion tons, while the policy scenario will be reduced to 13.1-13.7 billion tons. Based on the framework of scenario analysis, Wang et al (2019a) considered that China will achieve the CEI reduction target in 2030 under the current mitigation policy and difficult to achieve the target of CO 2 emission peak, and it is expected that China's total CO 2 emissions will reach 11.289 billion tons in 2030. Therefore, whether China can achieve the CEI mitigation goals and the peak emission in 2030 depends on identifying the influencing factors.…”

Section: Introductionmentioning

confidence: 99%

China’s Energy Transition Policy Expectation and Its CO2 Emission Reduction Effect Assessment

et al. 2021

Self Cite

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Measuring the expected impact of China’s energy transition on carbon dioxide (CO2) mitigation and identifying the key influencing factors in different economic sectors will help to provide better policy recommendations for CO2 emission reduction. Based on the prediction results of China’s CO2 emissions in 2030 under the baseline scenario and the target scenario, this study constructs the control group and the treatment group of the energy transition policy quasinatural experiment and then uses the difference in difference (DID) model to evaluate the CO2 emission reduction effect of China’s energy transition policy. The results reveal that the energy transition policy has a significant impact on CO2 emission reduction and helps to achieve China’s 2030 carbon emission reduction target. The impact of energy structure transition on CO2 emission reduction has significant sectoral heterogeneity, which has a positive reduction effect in the industry sector, wholesale and retail sectors, and accommodation and catering sectors, but its reduction effect is not obvious in transportation, storage, and postal sectors. It is suggested that China should implement the sector-differentiated CO2 mitigation strategy, focus on improving the industrial sector’s energy efficiency, and promote the clean, low-carbon transition of energy consumption structure in construction, transportation, storage, and postal industries.

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How to achieve the dual-control targets of China's CO2 emission reduction in 2030? Future trends and prospective decomposition

Cited by 60 publications

References 43 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

Driving forces of temporal-spatial differences in CO2 emissions at the city level for China’s transport sector

China’s Energy Transition Policy Expectation and Its CO2 Emission Reduction Effect Assessment

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