College of Resources and Environmental Sciences, Xinjiang University, Urumqi, Xinjiang, 830046, China.
Key Laboratory of Oasis Ecology, Xinjiang University, Ministry of Education Laboratory, Urumqi, Xinjiang, 830046, China.
Environ Sci Pollut Res Int. 2022 May;29(21):31781-31796. doi: 10.1007/s11356-021-17976-4. Epub 2022 Jan 11.
In the present study, the STIRPAT model was adopted to examine the impacts of several factors on dioxide emissions using the time series data from 2000 to 2019 in Xinjiang. The said factors included population aging, urbanization, household size, per capita GDP, number of vehicles, per capita mutton consumption, education level, and household direct energy consumption structure. Findings were made that the positive effects of urbanization, per capita GDP, per capita mutton consumption and education on carbon emissions were obvious; the number of vehicles had the biggest positive impact on carbon dioxide emissions; and household size and household direct energy consumption structure had a significantly negative impact on carbon emissions. Based on the aforementioned findings, the GA-BP neural network was introduced to predict the carbon emission trend of Xinjiang in 2020-2050. The results reveal that the peak time of the low-carbon scenario was the earliest, between 2029 and 2033. The peak time of the middle scenario was later than low-carbon scenario, between 2032 and 2037, while the peak time of the high-carbon scenario was the latest and was unlikely to reach the peak before 2050.
本研究采用 STIRPAT 模型，利用 2000 年至 2019 年新疆的时间序列数据，考察了人口老龄化、城市化、家庭规模、人均 GDP、汽车数量、人均羊肉消费、教育水平和家庭直接能源消费结构等因素对二氧化碳排放的影响。结果表明，城市化、人均 GDP、人均羊肉消费和教育水平对碳排放的积极影响明显；汽车数量对二氧化碳排放的影响最大；家庭规模和家庭直接能源消费结构对碳排放有显著的负向影响。基于上述发现，引入 GA-BP 神经网络对新疆 2020-2050 年的碳排放趋势进行预测。结果表明，低碳情景的峰值时间最早，在 2029 年至 2033 年之间。中碳情景的峰值时间晚于低碳情景，在 2032 年至 2037 年之间，而高碳情景的峰值时间最晚，在 2050 年之前不太可能达到峰值。
