Carbon Emission Prediction and the Reduction Pathway in Industrial Parks: A Scenario Analysis Based on the Integration of the LEAP Model with LMDI DecompositionShow others and affiliations
2023 (English)In: Energies, E-ISSN 1996-1073, Vol. 16, no 21, article id 7356Article in journal (Refereed) Published
Abstract [en]
Global climate change imposes significant challenges on the ecological environment and human sustainability. Industrial parks, in line with the national climate change mitigation strategy, are key targets for low-carbon revolution within the industrial sector. To predict the carbon emission of industrial parks and formulate the strategic path of emission reduction, this paper amalgamates the benefits of the “top-down” and “bottom-up” prediction methodologies, incorporating the logarithmic mean divisia index (LMDI) decomposition method and long-range energy alternatives planning (LEAP) model, and integrates the Tapio decoupling theory to predict the carbon emissions of an industrial park cluster of an economic development zone in Yancheng from 2020 to 2035 under baseline (BAS) and low-carbon scenarios (LC1, LC2, and LC3). The findings suggest that, in comparison to the BAS scenario, the carbon emissions in the LC1, LC2, and LC3 scenarios decreased by 30.4%, 38.4%, and 46.2%, respectively, with LC3 being the most suitable pathway for the park’s development. Finally, the paper explores carbon emission sources, and analyzes emission reduction potential and optimization measures of the energy structure, thus providing a reference for the formulation of emission reduction strategies for industrial parks.
Place, publisher, year, edition, pages
Multidisciplinary Digital Publishing Institute (MDPI) , 2023. Vol. 16, no 21, article id 7356
Keywords [en]
carbon emissions, industrial parks, LEAP model, LMDI model, scenario analysis, Tapio decoupling theory
National Category
Environmental Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-64799DOI: 10.3390/en16217356ISI: 001100334500001Scopus ID: 2-s2.0-85176341380OAI: oai:DiVA.org:mdh-64799DiVA, id: diva2:1813810
2023-11-222023-11-222023-12-07Bibliographically approved