Spatiotemporal Monitoring And Analysis Of Energy Consumption Carbon Emissions In China From 2010 To 2020 Using Remote Sensing

The spatiotemporal change characteristics and influencing factors of energy consumption carbon emissions are important topics in regional climate change monitoring and sustainable development goals research.As a major carbon emitter,climate and environmental problems caused by the rapid growth of regional carbon emissions in China have gradually attracted much attention.Timely and detailed understanding of the spatial and temporal dynamic distribution of regional carbon emissions in China is a key prerequisite for addressing climate change and achieving the goal of"carbon peaking and carbon neutrality".At present,limited by the scale of statistical data collection,China’s energy consumption carbon emissions are mainly based on the provincial carbon emissions calculated from the provincial energy consumption data,which makes it difficult to analyze the spatiotemporal changes of energy consumption carbon emissions at a finer scale,and it is difficult to assess the trend of carbon emissions and analyze the influencing factors.With the development of new remote sensing platforms and technologies,multisource remote sensing data such as nighttime light remote sensing data and XCO₂ concentration data has become an important information resource for carbon emission monitoring.Therefore,this study monitors and evaluates regional energy consumption carbon emissions in China based on multisource remote sensing data.Firstly,the characteristic factors related to carbon emissions were extracted based on multisource remote sensing data,and combined with the provincial carbon emissions accounting results,an energy consumption carbon emissions estimation model based on partial least squares algorithm was constructed.Then,based on the model,the carbon emission downscaling inversion method is designed,and the China’s energy consumption carbon emission results at1km grid scale are obtained.Secondly,based on the above provincial carbon emissions accounting and 1km grid scale carbon emissions results,the multiscale spatiotemporal variation characteristics of China’s carbon emissions and the influencing factors were analyzed.The main conclusions of this study are as follows:（1）The partial least squares carbon emission estimation model and the downscaling inversion model have high carbon emissions estimation accuracy,and the provincial-level estimated carbon emissions of the two have a high correlation with the statistical carbon emissions,and their R² are 0.86 and 0.87,respectively.（2）The overall spatial pattern of energy consumption carbon emissions in China is high in the east and low in the west,and high in the north and low in the south.It is an overall growth trend at different scales.The high values of regional carbon emissions are mainly distributed in economically developed areas,while the spatial distribution pattern of high values of land-average carbon emissions is inconsistent with the spatial distribution pattern of economic development.The high values of land-average carbon emissions are mainly distributed in western China.（3）From 2010 to 2018,China’s energy consumption carbon emissions changed greatly,and the overall trend was slow and positive.The high-speed growth areas were mainly distributed in Xinjiang and Inner Mongolia in western China,and the negative growth areas were mainly distributed in the southwest and southeast coasts.（4）There is a two-way causality between economic development,urbanization,industrialization and carbon emissions,and the three promote each other with carbon emissions.Based on multisource remote sensing data,this study designed and studied the estimation model of regional energy consumption carbon emissions in China,explored the spatial and temporal variation characteristics of regional multiscale carbon emissions and its influencing factors,and provided scientific reference for China to achieve carbon emission reduction targets.