我校英国威廉希尔公司帅传敏老师在T2级别期刊——《Environmental science and pollution research international》上发表题为“Influencing factors of carbon emissions and their trends in China and India: a machine learning method”。论文作者帅传敏为英国威廉希尔公司教授，博士生导师。

Abstract / 摘要：

China and India are the largest coal consumers and the most populated countries in the world. With industrial and population growth, the need for energy has increased, which has inevitably led to an increase in carbon dioxide (CO₂) emissions because both countries depend on fossil fuel consumption. This paper investigates the impact of energy consumption, financial development (FD), gross domestic product (GDP), population, and renewable energy on CO₂ emissions. The study applies the long short-term memory (LSTM) method, a novel machine learning (ML) approach, to examine which influencing driver has the greatest and smallest impact on CO₂ emissions; correspondingly, this study builds a model for CO₂ emission reduction. Data collected between 1990 and 2014 were analyzed, and the results indicated that energy consumption had the greatest effect and renewable energy had the smallest impact on CO₂ emissions in both countries. Subsequently, we increased the renewable energy coefficient by one and decreased the energy consumption coefficient by one while keeping all other factors constant, and the results predicted with the LSTM model confirmed the significant reduction in CO₂ emissions. Finally, this study forecasted a CO₂ emission trend, with a slowdown predicted in China by 2022; however, CO₂ emission's reduction is not possible in India until 2023. These results suggest that shifting from nonrenewable to renewable sources and lowering coal consumption can reduce CO₂ emissions without harming economic development.

论文信息;

Title/题目:

Influencing factors of carbon emissions and their trends in China and India: a machine learning method

Authors/作者:

Ahmed Mansoor;Shuai Chuanmin;Ahmed Maqsood

Key Words / 关键词 ：

CO2 emissions;China;India;Influencing factors;LSTM;Machine learning

DOI: 10.1007/S11356-022-18711-3

全文链接：https://pubmed.ncbi.nlm.nih.gov/35190995/