Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1016/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 |
ISSN | 0301-4215 |
EISSN | 1873-6777 |
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 |
推荐引用方式 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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