GSTDTAP  > 气候变化
DOI10.1016/j.enpol.2019.01.058
Determining China's CO2 emissions peak with a dynamic nonlinear artificial neural network approach and scenario analysis
Xu, Guangyue1; Schwarz, Peter2; Yang, Hualiu3
2019-05-01
发表期刊ENERGY POLICY
ISSN0301-4215
EISSN1873-6777
出版年2019
卷号128页码:752-762
文章类型Article
语种英语
国家Peoples R China; USA
英文摘要

The global community and the academic world have paid great attention to whether and when China's carbon dioxide (CO2) emissions will peak. Our study investigates the issue with the Nonlinear Auto Regressive model with exogenous inputs (NARX), a dynamic nonlinear artificial neural network that has not been applied previously to this question. The key advance over previous models is the inclusion of feedback mechanisms such as the influence of past CO2 emissions on current emissions. The results forecast that the peak of China's CO2 emissions will occur in 2029, 2031 or 2035 at the level of 10.08, 10.78 and 11.63 billion tonnes under low-growth, benchmark moderate-growth, and high-growth scenarios. Based on the methodology of the mean impact value (MIV), we differentiate and rank the importance of the influence factors on CO2 emissions whereas previous studies included but did not rank factors. We suggest that China should choose the moderate growth development road and achieve its peak target in 2031, focusing on reducing CO2 emissions as a percent of GDP, less carbon-intensive industrialization, and choosing technologies that reduce CO2 emissions from coal or increasing the use of less carbon-intensive fuels.


英文关键词CO2 emissions peak Dynamic ANN Scenario analysis Mean impact value (MIV) Global climate change
领域气候变化
收录类别SCI-E ; SSCI
WOS记录号WOS:000463688800071
WOS关键词INTEGRATED ASSESSMENT MODELS ; CARBON EMISSIONS ; ENERGY-CONSUMPTION ; NARX
WOS类目Economics ; Energy & Fuels ; Environmental Sciences ; Environmental Studies
WOS研究方向Business & Economics ; Energy & Fuels ; Environmental Sciences & Ecology
引用统计
文献类型期刊论文
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/182780
专题气候变化
作者单位1.Henan Univ, Sch Econ, Kaifeng 475004, Henan, Peoples R China;
2.Univ North Carolina Charlotte, EPIC, Belk Coll Business & Associate, Dept Econ, Charlotte, NC 28223 USA;
3.Tsinghua Univ, Sch Publ Policy & Management, Beijing 100084, Peoples R China
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GB/T 7714
Xu, Guangyue,Schwarz, Peter,Yang, Hualiu. Determining China's CO2 emissions peak with a dynamic nonlinear artificial neural network approach and scenario analysis[J]. ENERGY POLICY,2019,128:752-762.
APA Xu, Guangyue,Schwarz, Peter,&Yang, Hualiu.(2019).Determining China's CO2 emissions peak with a dynamic nonlinear artificial neural network approach and scenario analysis.ENERGY POLICY,128,752-762.
MLA Xu, Guangyue,et al."Determining China's CO2 emissions peak with a dynamic nonlinear artificial neural network approach and scenario analysis".ENERGY POLICY 128(2019):752-762.
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