关键词: 碳排放, 夜间灯光, 时空演变, 监测评估, 遥感

Abstract: As the main source region of carbon emissions in China, the analysis of the spatio-temporal evolution of carbon emissions in seven eastern provinces and cities is an important scientific basis for the formulation and implementation of carbon emission reduction strategies. This study aims to analyze the spatio-temporal evolution of carbon emissions in seven eastern provinces and cities from 2012 to 2021, and calculates the per capita carbon emission intensity and carbon emission intensity per unit of GDP. On this basis, a carbon emission and nighttime light fitting model based on “NPP-VIIRS-like” nighttime light data is constructed for carbon emission monitoring and evaluation in seven eastern provinces and cities. The results of the study show that:①Carbon emissions in seven eastern provinces and cities are generally on the rise, and the proportion of total carbon emissions in each province and city remains stable, with the fastest growth rate of carbon emissions in Jiangxi province.②The total carbon emissions show a pattern of “north>south”, with Shandong province and Jiangsu Province accounting for more than 50% of the total carbon emissions. ③The per capita carbon emission intensity shows an increasing trend in the southern region and a decreasing trend in the central region, while the northern region basically remains stable. The carbon emission intensity per unit of GDP shows a steady decreasing trend, especially in Shanghai and Zhejiang province, where the decrease is significant. ④The average correlation coefficient of the fitted model is 0.841 2, and the average relative error is decreasing, which indicate that carbon emission monitoring and evaluation can be effectively achieved based on nighttime lighting data. This study is of great significance to achieve industrial transformation and upgrading and sustainable development in seven eastern provinces and cities.

Key words: carbon emissions, nighttime lights, spatio-temporal evolution, monitoring and evaluation, remote sensing