Manuscript received June 11, 2024; revised August 2, 2024; accepted October 12, 2024; published November 17, 2024
Abstract—The global greenhouse effect has attracted significant attention in recent years. Most of the current models regarding Carbon Dioxide (CO
2) emission apply multiple linear regression analyses on population, Gross Domestic Product (GDP), and energy consumption, disregarding Green Finance Index (GFI) as a driving factor. However, the GFI measures how well a region’s financial activities align with environmental goals, which should have an important impact on carbon emissions. We aim to conduct a comprehensive analysis of carbon emissions in China’s big cities, highlighting the impact of GFI on carbon emissions and modeling the excessive emissions to be represented in monetary values. Specifically, we picked three cities to serve as case studies—Beijing, Chongqing, and Shanghai. This study establishes an Autoregressive Integrated Moving Average (ARIMA) model to predict future values of the four driving forces (resident population, GDP, energy consumption, GFI); a Back Propagation Neural Network (BPNN) model to predict carbon emissions; and a cost model to analyze the cost related to excessive carbon emissions based on a fictitious scenario inspired by the US’s emission goals. The result shows that GFI significantly correlates with lower carbon emissions. Therefore, increasing the GFI is an effective measure to ensure the realization of peak carbon emissions before 2030, which lowers the cost caused by the risk of excessive carbon emission. This study proposes a carbon emission and cost model that can provide a reference for the control of carbon emissions in Beijing, Chongqing, and Shanghai.
Keywords—Autoregressive Integrated Moving Average (ARIMA), Back Propagation Neural Network (BPNN), carbon peak, green finance index
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Cite: Junting Wang, "Modeling Carbon Dioxide Emissions with Green Finance—Using Beijing, Chongqing, and Shanghai as Cases," International Journal of Modeling and Optimization, vol. 14, no. 4, pp. 135-141, 2024.
Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
(CC BY 4.0).
