Spatial-temporal characteristics of carbon emissions corrected by socio-economic driving factors under land use changes in Sichuan Province, southwestern China

The spatial-temporal distribution characteristics of carbon emissions under land use changes can fully reflect the impact of socio-economic development caused by human activities on terrestrial ecosystems. However, previous studies just focused on the traditional carbon emission coefficient method which was applied to calculate carbon emission amounts from different land use types at a large spatial scale over a long-time period. This approach did not consider the effects of spatial heterogeneity of socio-economic factors on carbon emissions, which can lead to overestimating and underestimating carbon emissions in intra-study areas. Therefore, it is urgent to build a corrected method integrating socio-economic factors into carbon emission calculation which can make up for this shortcoming. Firstly, this study calculated the carbon emissions under land use changes through the traditional method based on spatial maps of land uses and fossil energy consumption during 2000-2018 in 21 cities (states) in Sichuan Province. From 2000 to 2018, the overall carbon emissions increased by 43.14%, and the high and low carbon emission values occurred in the east and west of the study site, respectively. Chengdu had the largest carbon emissions, and its maximum value appeared in 2015. Only the Tibetan Autonomous Prefecture of Garz (Garz) had a negative carbon emission value. Furthermore, the total carbon emissions were significantly correlated with Gross Domestic Product (GDP) and population. This study then proposed a method to correct carbon emissions by considering the spatial heterogeneity of GDP and population. There were some obvious differences between uncorrected and corrected carbon emissions. From 2000 to 2018, the corrected carbon emissions also showed an increasing trend, but their values were much higher than uncorrected carbon emissions. The city (state) with the largest corrected carbon emissions was still in Chengdu but the maximum value occurred in 2018. The city (state) with negative corrected carbon emissions was still in Garz, but its corrected values were much lower than uncorrected carbon emissions. Additionally, the center of gravity of positive carbon emissions shifted from Ziyang before the correction to Chengdu after the correction during 2000-2018. In summary, the corrected carbon emissions proposed in this study by considering socio-economic driving factors