Accurately identifying the core drivers of carbon emissions and scientifically forecasting the pathway to carbon peaking are pivotal theoretical foundations for advancing the "Dual Carbon" strategic goals. This study focuses on Guangdong Province and innovatively incorporates green GDP (GGDP) as a core driver into the carbon peaking prediction model, addressing the limitations of environmental cost accounting in traditional forecasting methods. Based on the United Nations System of Environmental-Economic Accounting (SEEA) framework, we calculate Guangdong’s GGDP from 2013 to 2023, analyze carbon emission trends using the emission coefficient method, identify key drivers—including population, GGDP contribution rate, industrial structure, and energy structure—through the STIRPAT model, and perform time-series forecasting using a CNN-LSTM hybrid model. Findings indicate that multi-scenario simulations project Guangdong's carbon emissions to peak in 2028 (baseline), 2027 (low-carbon), and 2026 (optimized-growth). Crucially, GGDP, by internalizing environmental costs, provides a scientifically superior basis for carbon peaking targets compared to conventional GDP.

Yichen Zhong, Jiajin Du, Shuyuan You, Guorui Zhao. GGDP-Driven Carbon Peaking Trajectories: Multiscenario Forecasting with STIRPAT-Convolutional LSTM. Academic Journal of Environment & Earth Science (2025), Vol. 7, Issue 4: 33-40. https://doi.org/10.25236/AJEE.2025.070404.

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