A new detection method to assess the influence of human activities and climate change of CO2 emissions in coal field

Abstract

Coal mining is the main factor causing China’s CO2 emissions. However, the composition of CO2 emissions caused by coal mining is still unclear. In this study, we developed a detection method that combines climatologically homogeneous region recognition and semiparametric regression to quantitatively calculate the contribution of human activities and climate to CO2 emissions. On the basis of determining the composition of CO2 emissions in Shanxi coalfield, we calculated the value of four CO2 emissions components (coal mining, climate, other disturbances and random fluctuation), analyzed their temporal and spatial regulation, and discussed the relationship between each component and land use. The results show that the coal mining component accounted for the largest proportion, 81 % of the CO2 emissions, and the climate component accounted for the smallest proportion. From 2000 to 2019, the CO2 emissions and coal mining component in the study area showed a trend of first increasing and then decreasing, reaching maximum monthly values of 65.5 t and 55.6 t in 2009 and declining in 2010 and 2018, respectively. The components of CO2 emissions were strongly correlated with the land cover types. Forestland had the lowest climate component (-0.35 t), as well as other disturbances component (7.37 t). Farmland had the lowest coal mining component (20.14 t). Water and built-up land had the highest value of each component. In the future, the climate component of farms in the study area will show an upward trend, while forestland, grassland and water will show a downward trend. The detection methods of CO2 emission components can be incorporated into the ecosystem model to improve the governance and protection ability of the ecological environment.

Keywords

• CO2 emissions
• Climatologically homogeneous regions
• Semiparametric regression
• LSTM
• Coal mining