Predicting China’s carbon intensity through 2050

An integrated analysis method

Bibliographic Data

ID21791832
AuthorsGuangyue Xu (0000-0003-0140-5926, Henan University), Hualiu Yang (0000-0002-6784-1483, China Coal Research Institute (China)), Peter M Schwarz (0000-0003-4066-0102, University of North Carolina at Charlotte, corresponding author)
Year2023
Volume15
Issue4
Pages292-303
Publication date2023-04-21
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueClimate and Development (JOURNAL)
Journal identifiersISSN: 1756-5529 • E-ISSN: 1756-5537
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/17565529.2022.2081117
OpenAlexW4283376703
LanguageEN
Citations received1
References cited46

Carbon intensity is one of the key indexes in the process of decarbonizing development. Therefore, our paper investigates possible pathways of China’s carbon intensity to 2050 depending on China’s development stage and trends. We first investigate the major influence factors of carbon intensity based on the Kaya identity, and then forecast China’s future carbon intensity with a generalized regression neural network model coupled with a scenario analysis with official data from 1978 to 2020. Our results show that: (1) China’s carbon intensity will decrease under the benchmark growth, high growth, and low growth scenarios but vary by amounts and rates of decrease due to the settings of the influences factors; (2) China is able to achieve the reduction targets of 2025 under the benchmark growth and low growth scenario, but cannot achieve the target under the high grow scenario. However, China can meet its reduction target by 2030 in all scenarios. (3) The Chinese government should aim to reduce carbon intensity by 23–25%, 40–44% and 64–70% by 2035, 2040 and 2050 as compared to the level in 2030. Therefore, China should keep promoting the deep low-carbon transformation of economic development model, and realize high-quality economic development in order to achieve these objectives

Carbon fibers · China · Econometrics · Economics · Geography · Natural resource economics · Scenario analysis · Statistics · Computer Science · Energy, Environment, and Transportation Policies · Energy, Environment, Economic Growth · Environmental Impact and Sustainability · Environmental Science · Mathematics

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