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Energies 2017, 10(11), 1747; https://doi.org/10.3390/en10111747

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

1,2
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1,2,* , 1,2
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3
and
4
1
Institute of Land Science and Property, School of Public Affairs, Zhejiang University, Hangzhou 310058, China
2
Laboratory of Rural-Urban Construction Land Economical and Intensive Use, Ministry of Land and Resources, Beijing 100812, China
3
Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
4
School of Public Policy and Administration Department of Public Administration, Xi’an JiaoTong University, Xi’an 710049, China
*
Author to whom correspondence should be addressed.
Academic Editor: Vincenzo Dovì
Received: 9 September 2017 / Revised: 16 October 2017 / Accepted: 26 October 2017 / Published: 31 October 2017
(This article belongs to the Special Issue Lessons from the Evaluation of Existing Emission Trading Schemes)
View Full-Text   |   Download PDF [1000 KB, uploaded 31 October 2017]   |  

Abstract

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. View Full-Text
Keywords: carbon emissions; LMDI; scenario analysis; low-carbon city; IPAT; carbon emission boundaries carbon emissions; LMDI; scenario analysis; low-carbon city; IPAT; carbon emission boundaries
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Xia, C.; Li, Y.; Ye, Y.; Shi, Z.; Liu, J. Decomposed Driving Factors of Carbon Emissions and Scenario Analyses of Low-Carbon Transformation in 2020 and 2030 for Zhejiang Province. Energies 2017, 10, 1747.

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