Low-carbon development path optimization of China's steel industry
In the context of global low-carbon development,choosing to pursue the path of energy conservation,emission reduction,and low-carbon green development has become a necessary path for the development of China's steel industry.In order to optimize the high-quality development path of China's steel industry,the CO2 emissions,crude steel production,and electric arc furnace steelmaking proportion of the top five typical countries(China,India,Japan,the United States,and Russia)in the world's steel production from 2000 to 2019 were studied systematical-ly,to find the optimization path for the green and low-carbon development of China's steel industry.Firstly,the i-solated forest method was used for data preprocessing,and a multiple linear regression model was constructed to study the quantitative relationship between the total world CO2 emissions and CO2 emissions from the steel industry in five typical countries.The results show that the R2 is 0.998 of model,indicating that the selected five countries have strong representativeness in terms of CO2 emissions worldwide.Then,multiple regression models are used to establish corresponding calculation models for CO2 emissions from the steel industry in typical countries based on five major influencing factors,i.e.,crude steel production,urban population,steel industry trade openness,per ca-pita GDP,and financial development index.After verifying the sensitivity and robustness of the model,optimizing the configuration of the above five influencing factors can effectively reduce the CO2 emissions of the steel industry.Finally,an optimization model for CO2 emissions in China's steel industry is constructed using goal programming model.Based on the calculation results,an optimization path plan for low-carbon transformation and high-quality development of China's steel industry is proposed.
low-carbon developmentCO2 emissions from steel industryisolated forest methodmultiple linear re-gression modelgoal programming model
NETL
NSTL
万方数据
