Carbon emission prediction model of prefecture-level administrative region: A land-use-based case study of Xi'an city, ChinaShow others and affiliations
2023 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 348, article id 121488Article in journal (Refereed) Published
Abstract [en]
Climate change has become a global concern, and the prediction of carbon emissions is key to achieving carbon-reduction goals. The existing framework cannot accurately reflect the spatial distribution of carbon emissions, making it difficult to guide urban planning and management. Therefore, in this study, a carbon emission spatial simulation and prediction model was established. The model includes the GIS-Kernel Density sub-model for subdividing built-up area, the Land Use-Carbon Emission sub-model for establishing the correlation between land use and carbon emissions, the Multi Objective Programming-Principal Component Analysis-BP neural network sub-model for presetting development scenarios, and the Patch-generating Land-use Simulation sub-model for predicting. Xi'an was chosen as the study site, and two extreme scenarios were determined. A total of 373,318 development paths were segmented from the interval, and the optimal path was selected. All scenarios were simulated, and the carbon emissions and their spatial distribution were calculated. The results showed that the overall accuracy of the simulation exceeded 90%. Under the optimal path, Xi'an's carbon emissions reach 60.6 million tons at peak time, which will be reduced to 47.38 million tons by 2060. In addition, the model analyzed the temporal and spatial changes of carbon sources and sinks and drew up the path of carbon reduction by technology and urban planning. This model can provide a reference for regional carbon-reduction planning and carbon reduction technology implantation. It can propose strategies from the perspective of planning and management.
Place, publisher, year, edition, pages
Elsevier Ltd , 2023. Vol. 348, article id 121488
Keywords [en]
Carbon emission, Land use, Land-use subdivision, Prefecture-level administrative region, Simulation and prediction, China, Shaanxi, Xian, Carbon, Climate change, Multiobjective optimization, Neural networks, Principal component analysis, Spatial distribution, Urban planning, Carbon emissions, Carbon reduction, Case-studies, Emissions prediction, Optimal paths, Prediction modelling, Submodels, environmental impact assessment, environmental policy, GIS, prediction, reduction, Forecasting
National Category
Physical Geography
Identifiers
URN: urn:nbn:se:mdh:diva-64022DOI: 10.1016/j.apenergy.2023.121488ISI: 001046975000001Scopus ID: 2-s2.0-85165670857OAI: oai:DiVA.org:mdh-64022DiVA, id: diva2:1788545
2023-08-162023-08-162023-08-30Bibliographically approved