Green GDP Accounting Model Based on Material Flow Analysis and BP Neural Network

GDP is an important accounting indicator of national economy, but GDP is difficult to measure the environmental pollution expenditure of a country or a region, which cuts the relationship between economic development and sustainable development and is not in line with the concept of global green and sustainable development. This paper proposes two models for calculating GGDP, one is the CPMF model based on material flow analysis, which achieves the quantification of statistics by monetizing the amount of environmental inputs and environmental outputs; the other is the GGDP accounting model based on BP neural network, which takes environmental pollution expenditure as the input layer and GGDP as the output layer variable to form an accounting model. The two models calculated GGDP as follows: in 2000, the calculated value of GGDP was $9.43×10¹² (CPMF model) and $1.03×10¹³ (BP neural network), while it grew to $1.86×10¹³ (CPMF model) and $1.69×10¹³ (BP neural network) in 2018. In order to provide the government with directions for environmental management, this paper uses gray correlation analysis and coefficient of variation method, and the highly correlated (high weight) variables of environmental pollution expenditure and GGDP are the amount of investment in environmental pollution control and total natural gas consumption.