Constructing energy-consuming right trading system for China's manufacturing industry in 2025

Yang, Mian; Hou, Yaru; Fang, Chao; Duan, Hongbo

doi:10.1016/j.enpol.2020.111602

Cited by 57 publications

(17 citation statements)

References 40 publications

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“…Therefore, clean coal should be used more to replace raw coal, and clean energy such as hydro energy, nuclear energy, wind energy, solar energy, and bioenergy should be developed to reduce the proportion of coal and oil in China's energy consumption, thus reducing China's carbon emissions. (4) Innovating technologies to reduce emissions (Yang et al 2020b). While determined path and method of emission reduction are important, energy-saving and emission reduction technologies are key factors to determine whether emission reduction is effective.…”

Section: Policy Implicationsmentioning

confidence: 99%

Driving factors of carbon emissions in China’s municipalities: a LMDI approach

Liu

Jiang

Liu

et al. 2021

Environ Sci Pollut Res

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Section: Policy Implicationsmentioning

confidence: 99%

Driving factors of carbon emissions in China’s municipalities: a LMDI approach

Liu

Jiang

Liu

et al. 2021

Environ Sci Pollut Res

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“…(4) Innovating technologies to reduce emissions (Yang et al 2020b). While determined path and method of emission reduction are important, energy-saving and emission reduction technologies are key factors to determine whether emission reduction is effective.…”

Section: Policy Implicationsmentioning

confidence: 99%

Driving Factors of Carbon Emissions in China's Municipalities: A LMDI Approach

Liu

Jiang

Liu

et al. 2021

Preprint

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China, as the world’s largest carbon dioxide emitter, is bound to assume the important responsibility of energy conservation and emission reduction. To this end, each city, led by representative municipalities directly under the Central Government, must enhance efforts in carbon emission reduction to jointly realize China’s low-carbon transition. Taking four representative municipalities, namely, Beijing, Tianjin, Shanghai, and Chongqing as the case cities, this paper establishes a decomposition analysis for the driving factors of carbon emissions by applying the LMDI method covering data from 2007 to 2017. Kaya identity is used to decompose the effects into eight driving factors: GDP effect, industrial structure effect, energy intensity effect, overall energy structure effect, population effect, urbanization effect, per capita energy consumption effect, urban and rural energy structure effect. The results show that at the municipality level, the driving factors that promote the growth of carbon emissions are the GDP growth effect and the population effect, with the former still being the most important factor in the municipalities with faster economic growth; and industrial structure effect is the most important factor that inhibits the growth of carbon emissions, followed by energy structure effect. The paper thereby puts forward policy implications for China's economic policies.

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“…In the considered studies, the influence of the business environment on the activities of organizations and on their state in the stock market is mainly studied within the framework of the influence of macro-level factors (Zhu et al, 2009;Yang et al, 2020;Rabi and Spadaro, 2016).…”

Section: Literature Reviewmentioning

confidence: 99%