气候变化领域集成服务门户(CCPortal): Determining China's CO2 emissions peak with a dynamic nonlinear artificial neural network approach and scenario analysis

CCPortal

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 G.; Schwarz P.; Yang H.
发表日期	2019
ISSN	3014215
起始页码	752
结束页码	762
卷号	128
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. Elsevier Ltd
英文关键词	CO2 emissions peak; Dynamic ANN; Global climate change; Mean impact value (MIV); Scenario analysis
scopus关键词	Climate change; Neural networks; Auto regressive models; Carbon dioxide emissions; CO2 emissions; Dynamic non-linear; Feedback mechanisms; Global climate changes; Impact value; Scenario analysis; Carbon dioxide; artificial neural network; carbon emission; climate change; climate effect; emission control; environmental economics; Gross Domestic Product; scenario analysis; China
来源期刊	Energy Policy
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/176433
作者单位	School of Economics, Henan University, Kaifeng, Henan 475004, China; Economics Department, The Belk College of Business and Associate, Energy Production and Infrastructure Center (EPIC), University of North Carolina at Charlotte, Charlotte, NC 28223, United States; School of Public Policy & Management, Tsinghua University, Beijing, 100084, China
推荐引用方式 GB/T 7714	Xu G.,Schwarz P.,Yang H.. Determining China's CO2 emissions peak with a dynamic nonlinear artificial neural network approach and scenario analysis[J],2019,128.
APA	Xu G.,Schwarz P.,&Yang H..(2019).Determining China's CO2 emissions peak with a dynamic nonlinear artificial neural network approach and scenario analysis.Energy Policy,128.
MLA	Xu G.,et al."Determining China's CO2 emissions peak with a dynamic nonlinear artificial neural network approach and scenario analysis".Energy Policy 128(2019).