ORCID Profile
0000-0002-7314-4983
Current Organisations
University of Technology Sydney
,
Beijing Institute of Technology
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Publisher: Elsevier BV
Date: 09-2019
Publisher: AIP Publishing
Date: 05-2015
DOI: 10.1063/1.4921379
Abstract: The aim of this paper is to present new evidence of relationship between economic activities and environmental conditions, and accordingly to obtain emission-cutting focus in China. This study decomposes total CO2 emissions into six determinants derived from three net effects of Environmental Kuznets Curve (EKC): scale, technique, and composition. Meanwhile, in views of huge provincial-disparity in economic situation and environmental conditions, we investigate impact factors of CO2 emissions at sub-national level via generalized method of moment estimation. Test including panel unit root, panel cointegration, Sargan and residual auto-correlation is also performed to maintain strict structure and scientific estimation. Results show that CO2 emissions substantially increase over a decade with an upward annual growth rate and a high provincial disparity sorted in descending order, previous CO2 emissions, coal share, economic growth, and industrial energy consumption are main determinants for accelerated CO2 emissions EKC with an inverted-U shape relationship between environment quality and income is not verified. Accordingly, emission-reducing policies at sub-national level are concluded.
Publisher: Elsevier BV
Date: 04-2018
Publisher: Elsevier BV
Date: 06-2018
Publisher: HARD Publishing Company
Date: 2015
DOI: 10.15244/PJOES/35975
Publisher: Springer Science and Business Media LLC
Date: 07-06-2023
Publisher: Elsevier BV
Date: 03-2019
Publisher: Elsevier BV
Date: 10-2017
Publisher: Elsevier BV
Date: 03-2020
DOI: 10.1016/J.SCITOTENV.2019.135689
Abstract: As the largest sector with decarbonization potential, electricity generation is critical for achieving carbon intensity reduction targets of China by 2020 and 2030. This study combines temporal decomposition and scenario analysis to identify the key drivers and provinces with increasing carbon intensity of electricity generation (CIE) and designs four scenarios by integrating efficiency improvement and structural adjustment in 30 provinces of China, and estimates the possible reduction of CIE by 2020 and 2030. Results show that 1) CIE in China decreases by 7.25% during 2001-2015. The estimated CIE during 12th FYP in this study is 25% lower than the estimation using IPCC emission factors, which is closer to China's reality. 2) Driving forces of CIE changes in 30 provinces vary greatly across provinces. The increasing CIE in four worse-performance regions (i.e. Northeast, South Coast, Southwest, Northwest) is mainly caused by energy mix effect and geographic distribution effect. The CIE growth in South Coast is also related to thermal power share effect. 3) Both 2020/2030 targets can be achieved by regulating the drivers for CIE growth in 30 provinces (i.e., RAK scenario). CIE decline is concentrated in three types of provinces, namely provinces with large economic size, strong policy support and clean energy implementation. The findings and recommendations provide insights into achieving 2020/2030 targets for CIE reduction.
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