Exploring the Carbon Footprint of Energy Consumption in Shanxi Province Based on the Tapio Decoupling and Grey BP Neural Network Model

Kejun Guo; Xuemei Wang; Jingrui Song

doi:10.54097/6adh5t60

Authors

Kejun Guo
Xuemei Wang
Jingrui Song

DOI:

https://doi.org/10.54097/6adh5t60

Keywords:

Energy Consumption, Carbon Footprint, LMDI Model, Tapio Decoupling Model, Grey BP Neural Network.

Abstract

This paper focuses on analyzing the carbon footprint of energy consumption in Shanxi Province, China. It utilizes data from sources such as the 'China Energy Statistical Yearbook' (2001-2022), the China Carbon Accounting Database, and the 'Shanxi Statistical Yearbook' for an exploratory analysis of energy consumption and the province's economy. The study calculates the carbon footprint of energy consumption in Shanxi Province and employs the LMDI model to identify influencing factors. Additionally, it uses the Tapio decoupling model to assess the relationship between these factors and economic development. To forecast the carbon footprint from 2022 to 2026, a grey BP neural network prediction model is applied, demonstrating improved accuracy compared to BP and grey models, with MAE and RMSE values of 2.44 and 10.91.

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