Socioeconomic impact assessment of China's CO2 emissions peak prior to 2030

Mi, Zhifu; Wei, Yi; Wang, Bing; Meng, Jing; Li, Zhu; Shan, Yuli; Liu, Jingru; Guan, Dabo

doi:10.1016/j.jclepro.2016.11.055

Cited by 368 publications

(132 citation statements)

References 47 publications

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“…Meanwhile, we compare the results of other research to those of this paper. In 2017, Mi et al [30] use IMEC (Integrated Model of Economy and Climate) model to do forecasting. They say China will reach an emission peak at 11.20 Gt (1Gt = 1 × 10 9 tons) in 2026.…”

Section: Alds Mixture Model Vs Qr and Cqrmentioning

confidence: 99%

The STIRPAT Analysis on Carbon Emission in Chinese Cities: An Asymmetric Laplace Distribution Mixture Model

et al. 2017

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Abstract:In cities' policy-making, it is a hot issue to grasp the determinants of carbon dioxide emission in Chinese cities. And the common method is to use the STIRPAT model, where its coefficients represent the influence intensity of each determinants of carbon emission. However, less work discusses estimation accuracy, especially in the framework of non-normal distribution and heterogeneity among cities' emission. To improve the estimation accuracy, this paper employs a new method to estimate the STIRPAT model. The method uses a mixture of Asymmetric Laplace distributions (ALDs) to approximate the true distribution of the error term. Meantime, a designed two-layer EM algorithm is used to obtain estimators. We test the robustness via the comparison results of five different models. We find that the ALDs Mixture Model is more reliable the others. Further, a significant Kuznets curve relationship is identified in China.

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Section: Alds Mixture Model Vs Qr and Cqrmentioning

confidence: 99%

The STIRPAT Analysis on Carbon Emission in Chinese Cities: An Asymmetric Laplace Distribution Mixture Model

et al. 2017

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“…Several authors have questioned Chinese energy and carbon data and revealed that energy consumption in China was underestimated by the Chinese national statistics while its carbon emissions were overestimated by the IPCC and other reports [46][47][48]. The gap between them also affects the results.…”

Section: Uncertainty Analysismentioning

confidence: 99%

Analysis of China’s Carbon Emissions Base on Carbon Flow in Four Main Sectors: 2000–2013

Cui

Wang

2017

Sustainability

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Abstract:Reducing carbon emissions is a major way to achieve green development and sustainability for China's future. This paper elaborates the detailed features of China's carbon flow for 2013 with the carbon flow chart and shows the changing characteristics of China's CO 2 flow from the viewpoint of specific sectors and energies from 2000 and 2013. The results show that (1) from 2000 to 2013, China's CO 2 emissions approximately grew by 9% annually, while the CO 2 intensity of China diminished at different rates. (2) The CO 2 emissions from the secondary industry are prominent from the perspective of four main sectors, accounting for 83.5% of emissions. Manufacturing plays an important part in the secondary industry with 45% of the emissions, in which the "smelting and pressing of metal" takes up a large percentage of about 50% of the emissions from manufacturing. (3) The CO 2 emissions produced by coal consumption are dominant in energy-related emissions with a contribution of 65%, which will decrease in the future. (4) From the aspect of different sectors, the CO 2 emissions mainly come from the "electricity and heating" sector and the "smelting, pressing and manufacturing of metals" sub-sector. It is essential and urgent to propose concrete recommendations for CO 2 emissions mitigation. Firstly, the progression of creative technology is inevitable and undeniable. Secondly, the government should make different CO 2 emissions reduction policies among different sectors. For example, the process emissions play an important role in "non-metallic minerals" while in "smelting and manufacturing of metals" it is energy emissions. Thirdly, the country can change the energy structure and promote renewable energy that is powered by wind or other low-carbon energy sources. Alternatively, coke oven gas can be a feasible substitution. Finally, policy makers should be aware that the emissions from residents have been growing at a fast rate. It is effective to involve the public in energy conservation and carbon emissions reduction, such as reducing the time of personal transportation.

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“…The details of lower calorific value, carbon emissions factors and oxidation rate have been reported in previous literature [49,50].…”

Section: Estimation Of Energy-related Carbon Emissionsmentioning

confidence: 97%

Decomposed Driving Factors of Carbon Emissions and Scenario Analyses of Low-Carbon Transformation in 2020 and 2030 for Zhejiang Province

Xia

et al. 2017

Energies

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Climate change has gained widespread attention, and the rapid growth of the economy in China has generated a considerable amount of carbon emissions. Zhejiang Province was selected as a study area. First, the energy-related carbon emissions from 2000 to 2014 were accounted for, and then the Logarithmic Mean Divisia Index (LMDI) decomposition model was applied to analyse the driving factors underlying the carbon emissions. Finally, three scenarios (inertia, comparative decoupling and absolute decoupling) for 2020 and 2030 were simulated based on the low-carbon city and Human Impact Population Affluence Technology (IPAT) models. The results showed (1) carbon emissions increased by 1.66 times from 2000 to 2014, and trends of carbon emissions were used to divide the study period into three phases (rapid, medium growth and slow decrease phases, with annual growth rates of 12.60%, 4.77% and −1.24%, respectively); (2) the energy intensity effect from 2000-2011 inhibited carbon emissions but was exceeded by the economic output effect, which increased emissions, whereas the energy intensity effect from 2011-2014 outweighed the economic output effect; (3) the scenario analyses revealed that both the comparative and absolute decoupling scenarios would remain consistent with the carbon emissions boundaries in 2020 and 2030, but the comparative decoupling scenario was more reasonable for sustainable development. In addition, appropriate design of emission trading scheme could help to achieve the comparative decoupling by financial incentives.

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Socioeconomic impact assessment of China's CO2 emissions peak prior to 2030

Cited by 368 publications

References 47 publications

The STIRPAT Analysis on Carbon Emission in Chinese Cities: An Asymmetric Laplace Distribution Mixture Model

The STIRPAT Analysis on Carbon Emission in Chinese Cities: An Asymmetric Laplace Distribution Mixture Model

Analysis of China’s Carbon Emissions Base on Carbon Flow in Four Main Sectors: 2000–2013

Decomposed Driving Factors of Carbon Emissions and Scenario Analyses of Low-Carbon Transformation in 2020 and 2030 for Zhejiang Province

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