Carbon Emission Prediction Model and Analysis in the Yellow River Basin Based on a Machine Learning Method

Abstract

Excessive carbon emissions seriously threaten the sustainable development of society and the environment and have attracted the attention of the international community. The Yellow River Basin is an important ecological barrier and economic development zone in China. Studying the influencing factors of carbon emissions in the Yellow River Basin is of great significance to help China achieve carbon peaking. In this study, quadratic assignment procedure regression analysis was used to analyze the factors influencing carbon emissions in the Yellow River Basin from the perspective of regional differences. Accurate carbon emission prediction models can guide the formulation of emission reduction policies. We propose a machine learning prediction model, namely, the long short-term memory network optimized by the sparrow search algorithm, and apply it to carbon emission prediction in the Yellow River Basin. The results show an increasing trend in carbon emissions in the Yellow River Basin, with significant inter-provincial differences. The carbon emission intensity of the Yellow River Basin decreased from 5.187 t/10,000 RMB in 2000 to 1.672 t/10,000 RMB in 2019, showing a gradually decreasing trend. The carbon emissions of Qinghai are less than one-tenth of those in Shandong, the highest carbon emitter. The main factor contributing to carbon emissions in the Yellow River Basin from 2000 to 2010 was GDP per capita; after 2010, the main factor was population. Compared to the single long short-term memory network, the mean absolute percentage error of the proposed model is reduced by 44.38%.

Regional Differences and Convergence of Carbon Emissions Intensity in Cities along the Yellow River Basin in China

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A model for STIRPAT-based fusion predictive analytics

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Exploring influential factors of CO2 emissions in China’s cities using machine learning techniques

“…In recent years, machine learning models have also been increasingly applied to the study of CO2 emissions. However, while many such studies focus solely on prediction, such as emission size, emission intensity, and emission trend, they also require the selection of influential factors, which are often either derived from existing literature or arbitrarily designated (Li et al 2018, Zhao et al 2022a. Fewer studies have applied explainable methods to explore the relationships between input features and CO2 emissions.…”