The green GDP accounting system based on the BP neural network: an environmental pollution perspective

• Published in: Frontiers in environmental science Vol. 11
• Main Authors: Zhu, Yinglun, Xu, Yingying, Luo, Yuhui
• Format: Journal Article
• Language: English
• Published: Lausanne Frontiers Research Foundation 10.11.2023; Frontiers Media S.A
• Subjects: Accounting systems; Adjustment; Agricultural production; Air pollution; Back propagation networks; BP neural network; carbon emission; Climate change; climate changes; Climate prediction; Consumption; Cost accounting; Economic activity; Economic development; Economic growth; Ecosystems; Empirical analysis; Environmental economics; environmental effect; Environmental effects; Environmental protection; Environmental quality; Environmental science; Expenditures; Extreme weather; GDP; GDP accounting system; Global climate; Gross Domestic Product; Methods; Natural resources; Neural networks; Pollution; Resource depletion; Sustainable development; Transition economies; Water pollution; United States > US; China
• ISSN: 2296-665X; 2296-665X
• DOI: 10.3389/fenvs.2023.1277717

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.