The green GDP accounting system based on the BP neural network: an environmental pollution perspective - Zhu, Yinglun; Xu, Yingying; Luo, Yuhui (Frontiers in Environmental Science, 2023)
Introduction: The green GDP accounting system has become the focus of sustainable development, but a comprehensive accounting of environmental pollution cost and resource depletion cost has not yet been formed. Methods: This study measures environmental pollution cost and resource loss cost, and establishes the green GDP accounting system based on the SEEA-2012. To analyze the environmental effects brought by the adoption of green GDP accounting system, a BP neural network model including green GDP, traditional GDP and global climate indicators is constructed to predict the global climate changes. Results: The empirical results show that after the adoption of the green GDP accounting system, the global climate extreme weather can be reduced, the sea level will be lowered, and the climate problem is thus alleviated. Discussion: The findings of this study have important policy implications for establishing and improving the ecological and environmental protection mechanism and improving the level of green GDP accounting.