Identification of clean energy development routes under carbon emission constraints: A path towards structural adjustment of the power system

Clarifying the clean energy (CE) development path is significant for realizing energy system transformation, coping with climate risks, and ensuring energy and ecological security. This paper uses national data from China from 1978 to 2021 as a sample. It considers the year-by-year dynamic indicator...

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Veröffentlicht in:Journal of cleaner production 2024-01, Vol.434, p.140169, Article 140169
Hauptverfasser: Lian, Wenwei, Sun, Xiaoyan, Wang, Yixin, Duan, Hongmei, Yan, Qiang, Gao, Tianming, Zhang, Yan
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container_start_page 140169
container_title Journal of cleaner production
container_volume 434
creator Lian, Wenwei
Sun, Xiaoyan
Wang, Yixin
Duan, Hongmei
Yan, Qiang
Gao, Tianming
Zhang, Yan
description Clarifying the clean energy (CE) development path is significant for realizing energy system transformation, coping with climate risks, and ensuring energy and ecological security. This paper uses national data from China from 1978 to 2021 as a sample. It considers the year-by-year dynamic indicators of changes in technology, population, industry, and economic factors in the short term. It takes the mandatory carbon emission reduction targets set by the country as the basis for carbon constraints. The Markov chain model was introduced to construct a power demand structure prediction model under scenarios to more accurately quantify the energy-saving and emission-reduction paths under different carbon constraints and economic development scenarios. The results show that China's electricity demand will rapidly grow in the short term. The upper limit constraint of thermal power generation under the high and normal economic growth models still has room to grow in the short term under the target carbon emission constraint scenario. During 2025–2030, the overall lower limit growth rate of the CE power generation structure under the target emission reduction scenario must exceed 8%, and the overall growth rate under the high-efficiency emission reduction scenario must exceed 12%. By 2030, the proportion of hydropower in CE generation is estimated to decrease to 33.7%, while nuclear power, wind power, and photovoltaic power generation will increase to 16.3%, 30.5% and 19.5%, respectively. These results provide a reference for the government to formulate a CE development path against the background of carbon constraints. [Display omitted] •Clarified China's short-term energy development path under carbon constraints.•Thermal power will not exit the market quickly in 2030.•Constructed an energy and power prediction model under carbon constraints.•Wind energy and photovoltaic energy will become the main development energy sources.•Support low-carbon power sector transformation under carbon policies.
doi_str_mv 10.1016/j.jclepro.2023.140169
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This paper uses national data from China from 1978 to 2021 as a sample. It considers the year-by-year dynamic indicators of changes in technology, population, industry, and economic factors in the short term. It takes the mandatory carbon emission reduction targets set by the country as the basis for carbon constraints. The Markov chain model was introduced to construct a power demand structure prediction model under scenarios to more accurately quantify the energy-saving and emission-reduction paths under different carbon constraints and economic development scenarios. The results show that China's electricity demand will rapidly grow in the short term. The upper limit constraint of thermal power generation under the high and normal economic growth models still has room to grow in the short term under the target carbon emission constraint scenario. During 2025–2030, the overall lower limit growth rate of the CE power generation structure under the target emission reduction scenario must exceed 8%, and the overall growth rate under the high-efficiency emission reduction scenario must exceed 12%. By 2030, the proportion of hydropower in CE generation is estimated to decrease to 33.7%, while nuclear power, wind power, and photovoltaic power generation will increase to 16.3%, 30.5% and 19.5%, respectively. These results provide a reference for the government to formulate a CE development path against the background of carbon constraints. [Display omitted] •Clarified China's short-term energy development path under carbon constraints.•Thermal power will not exit the market quickly in 2030.•Constructed an energy and power prediction model under carbon constraints.•Wind energy and photovoltaic energy will become the main development energy sources.•Support low-carbon power sector transformation under carbon policies.</description><identifier>ISSN: 0959-6526</identifier><identifier>EISSN: 1879-1786</identifier><identifier>DOI: 10.1016/j.jclepro.2023.140169</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>carbon ; Carbon constraints ; China ; Clean energy ; climate ; economic development ; electricity ; energy ; energy conservation ; industry ; Markov chain ; nuclear power ; Power forecasting ; power generation ; solar energy ; Structural pathways ; water power ; wind power</subject><ispartof>Journal of cleaner production, 2024-01, Vol.434, p.140169, Article 140169</ispartof><rights>2023 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c290t-9a2abd9aed6b1229acbd862a11d476edf1848a105fa193372f0082b9cf81dbbc3</cites><orcidid>0000-0002-9595-299X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0959652623043275$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids></links><search><creatorcontrib>Lian, Wenwei</creatorcontrib><creatorcontrib>Sun, Xiaoyan</creatorcontrib><creatorcontrib>Wang, Yixin</creatorcontrib><creatorcontrib>Duan, Hongmei</creatorcontrib><creatorcontrib>Yan, Qiang</creatorcontrib><creatorcontrib>Gao, Tianming</creatorcontrib><creatorcontrib>Zhang, Yan</creatorcontrib><title>Identification of clean energy development routes under carbon emission constraints: A path towards structural adjustment of the power system</title><title>Journal of cleaner production</title><description>Clarifying the clean energy (CE) development path is significant for realizing energy system transformation, coping with climate risks, and ensuring energy and ecological security. This paper uses national data from China from 1978 to 2021 as a sample. It considers the year-by-year dynamic indicators of changes in technology, population, industry, and economic factors in the short term. It takes the mandatory carbon emission reduction targets set by the country as the basis for carbon constraints. The Markov chain model was introduced to construct a power demand structure prediction model under scenarios to more accurately quantify the energy-saving and emission-reduction paths under different carbon constraints and economic development scenarios. The results show that China's electricity demand will rapidly grow in the short term. The upper limit constraint of thermal power generation under the high and normal economic growth models still has room to grow in the short term under the target carbon emission constraint scenario. During 2025–2030, the overall lower limit growth rate of the CE power generation structure under the target emission reduction scenario must exceed 8%, and the overall growth rate under the high-efficiency emission reduction scenario must exceed 12%. By 2030, the proportion of hydropower in CE generation is estimated to decrease to 33.7%, while nuclear power, wind power, and photovoltaic power generation will increase to 16.3%, 30.5% and 19.5%, respectively. These results provide a reference for the government to formulate a CE development path against the background of carbon constraints. 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This paper uses national data from China from 1978 to 2021 as a sample. It considers the year-by-year dynamic indicators of changes in technology, population, industry, and economic factors in the short term. It takes the mandatory carbon emission reduction targets set by the country as the basis for carbon constraints. The Markov chain model was introduced to construct a power demand structure prediction model under scenarios to more accurately quantify the energy-saving and emission-reduction paths under different carbon constraints and economic development scenarios. The results show that China's electricity demand will rapidly grow in the short term. The upper limit constraint of thermal power generation under the high and normal economic growth models still has room to grow in the short term under the target carbon emission constraint scenario. During 2025–2030, the overall lower limit growth rate of the CE power generation structure under the target emission reduction scenario must exceed 8%, and the overall growth rate under the high-efficiency emission reduction scenario must exceed 12%. By 2030, the proportion of hydropower in CE generation is estimated to decrease to 33.7%, while nuclear power, wind power, and photovoltaic power generation will increase to 16.3%, 30.5% and 19.5%, respectively. These results provide a reference for the government to formulate a CE development path against the background of carbon constraints. [Display omitted] •Clarified China's short-term energy development path under carbon constraints.•Thermal power will not exit the market quickly in 2030.•Constructed an energy and power prediction model under carbon constraints.•Wind energy and photovoltaic energy will become the main development energy sources.•Support low-carbon power sector transformation under carbon policies.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.jclepro.2023.140169</doi><orcidid>https://orcid.org/0000-0002-9595-299X</orcidid></addata></record>
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subjects carbon
Carbon constraints
China
Clean energy
climate
economic development
electricity
energy
energy conservation
industry
Markov chain
nuclear power
Power forecasting
power generation
solar energy
Structural pathways
water power
wind power
title Identification of clean energy development routes under carbon emission constraints: A path towards structural adjustment of the power system
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