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Examining the uncertainty of carbon emission changes: A systematic approach based on peak simulation and resilience assessment
Affiliation:1. School of Management, Guangzhou University, Guangzhou 510006, Guangdong, China;2. School of Economics and Management, Tianjin Chengjian University, Tianjin 300384, Tianjin, China;1. West Center for Economic Research, Southwestern University of Finance and Economics, Chengdu 611130, China;2. Sichuan Normal University, Chengdu 610066, China;3. Department of Economics, Karakoram International University Gilgit, Pakistan;1. School of Humanities and Law, Northeastern University, Shenyang, China;2. School of Economics, Northeastern University at Qinhuangdao, Qinhuangdao, China;3. School of Business Administration, Northeastern University, Shenyang, China;4. F.C. Manning School of Business Administration, Acadia University, Wolfville, Canada;1. School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China;2. School of Mathematics, China University of Mining and Technology, Xuzhou 221116, China;3. Center for Environmental Management and Economics Policy Research, China University of Mining and Technology, Xuzhou 221116, China;1. School of Management Science and Real Estate, Chongqing University, Chongqing, China;2. Department of Construction Engineering and Management, Purdue University, West Lafayette, IN, USA;3. College of Engineering, Architecture, and Technology, Oklahoma State University, Stillwater, OK, USA
Abstract:Efforts to achieve carbon peak is one of the Chinese government's commitments, but the diversity of future development paths leads to the uncertainty of carbon emissions. Based on the carbon peak simulation, this study develops a framework to assess the carbon emission uncertainty, aiming to explore the potential low-carbon paths. The STIRPAT model is firstly introduced to explore the influence of population, economic and technology factors on carbon emissions, which is followed by emission peaks simulation. The resilience theory is then introduced to define the concept of low-carbon resilience (LCR), which refers to the ability to maintain a low level of carbon emissions. The uncertainty of carbon emission changes between different scenarios is identified by considering peaking time, cumulative increase and mitigation process. This study taking 10 Chinese coastal provinces as an example, and results show that all provinces can achieve the target of carbon emission peak in low-emissions scenario, the cumulative growth of carbon emissions is low and can be mitigated over a relatively short term, showing a strong LCR. In high-emissions scenario, Liaoning, Tianjin, Fujian and Guangxi may not have a peak before 2050, the uncertainty of carbon emission changes is relatively high, while Hebei, Jiangsu, Shanghai and Guangdong show relatively low uncertainty for the clear peaking time. The study also designs intermediate scenario to reduce the uncertainty of carbon emission changes to provide reference for each province's emission reduction path. These findings help to understand carbon uncertainty to reduce the risk of increasing cumulative emissions under the scenario of only focusing on peaking times, and provide a basis for future carbon resilience and sustainable emission reduction policies.
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