城镇化背景下黄河流域能源碳排放影响因素分析及达峰预测OA

Urbanization is accompanied by rapid growth in population,energy consumption and carbon emissions.To forecast the peak of en-ergy-related carbon emissions in the Yellow River Basin under urbanization and provide a reference for advancing ecological conservation and high-quality development in the basin,this study calculated energy-related carbon emissions based on energy consumption data of eight major energy types across provinces(regions)in the Yellow River Basin from 2000 to 2021.The LMDI model was employed to decompose the fac-tors influencing energy-related carbon emissions in the basin.An extended STIRPAT model was used to forecast peak energy-related carbon emissions under multiple scenarios(current,energy-saving,low-carbon and high-energy-consumption).The results indicated that:a)From 2000 to 2021,total energy carbon emissions in the Yellow River Basin continued to grow,but at a declining rate.Economic development,ur-banization and population growth promoted energy carbon emissions,while optimization of energy structure,energy intensity and industrial structure suppressed them.Economic development was the primary driver of emission growth,whereas energy intensity was the main factor suppressing emissions.b)Under the current,energy-saving,and low-carbon development models,the peak range for energy-related carbon emissions in the Yellow River Basin is estimated to be between 1.800 billion and 1.869 billion tons,with the peak occurring between 2030 and 2035.In contrast,under a high energy consumption development model,the peak is unlikely to occur before 2040.c)Regulating carbon emission intensity helps reduce peak energy carbon emissions in the Yellow River Basin,while optimizing the energy structure and improving energy efficiency can facilitate an earlier peak.Recommendations include optimizing urban planning,promoting industrial structure upgrades,strengthening public education on low-carbon awareness,advocating low-carbon lifestyles,expanding renewable energy adoption,and optimizing the energy consumption structure.