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Pathways for carbon emission prediction and mitigation of sustainable industrial parks: a LEAP model application
Xi An Jiao Tong Univ, China Energy & Elect Power Res Joint Inst, Xian, Peoples R China.;Xi An Jiao Tong Univ, Inst Bldg Environm & Sustainabil Technol, Sch Human Settlements & Civil Engn, Xian, Peoples R China..
Xi An Jiao Tong Univ, Inst Bldg Environm & Sustainabil Technol, Sch Human Settlements & Civil Engn, Xian, Peoples R China..
Xi An Jiao Tong Univ, China Energy & Elect Power Res Joint Inst, Xian, Peoples R China..
Xi An Jiao Tong Univ, China Energy & Elect Power Res Joint Inst, Xian, Peoples R China..
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2024 (English)In: International Journal of Green Energy, ISSN 1543-5075, E-ISSN 1543-5083Article in journal (Refereed) Epub ahead of print
Abstract [en]

Industrial parks play a crucial role as a carrier of industrial clusters and energy consumption. Accurately predicting the energy demand and carbon emissions trend is key to scientifically determining the pathways for low-carbon industrial parks. However, exploration in carbon emission prediction on industrial park scale is still in its infancy stage. This paper investigates fuel demand and carbon emissions from 2021 to 2035 in an industrial park in Jiangsu Province, utilizing the Long-range Energy Alternative Planning (LEAP) model to explore the pathways for low carbon development. Energy-saving and emission-reduction effects of different macro-economic policies and micro-energy planning are analyzed based on the energy balance and emission factor methods. Four scenarios are compared: the baseline scenario (BAS), green development scenario (GDS), low carbon scenario (LCS), and strength low carbon scenario (SLS). Results indicated that energy demand under BAS reached at 31.37 Mtce in 2035, and energy-saving rates of GDS, LCS, and SLS in 2035 were 12.94%, 14.00% and 19.08%, respectively. Carbon emissions reached 53.96 MtCO2e in BAS of 2035. However, in the same year, emissions decreased by 24.88%, 43.09%, and 52.52% in GDS, LCS, and SLS, respectively, with SLS being the most suitable for the park.

Place, publisher, year, edition, pages
Taylor & Francis, 2024.
Keywords [en]
Carbon emission prediction, industrial park, scenario analysis, LEAP, mitigation strategy
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-65791DOI: 10.1080/15435075.2024.2307915ISI: 001147204700001Scopus ID: 2-s2.0-85183038121OAI: oai:DiVA.org:mdh-65791DiVA, id: diva2:1833021
Available from: 2024-01-31 Created: 2024-01-31 Last updated: 2024-01-31Bibliographically approved

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Yang, XiaohuYan, Jinyue

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