Identifying Climatic Drivers of Emissions From Electricity Production : Insights From a Predictive Modeling-Based Approach

It is well established from a variety of studies, that decarbonizion of the electricity sector has to be at the heart of any national and international effort to combat climate change. Central to the debate on electricity generation-related CO2 emissions is the assessment of explicative factors behind such emissions. Whereas the demand for and supply of electricity are strongly connected with climate conditions, far too little attention has been paid to understanding and quantifying the relationship between climate variables and emissions from electricity production. This research sets out a data-driven predictive modeling framework for characterizing the impact of climate conditions on hourly CO2 emissions in the French electric power system from January 2013 to December 2020. To this end, the study puts forward a state-of-the-art interdisciplinary approach to the construction of national climate indices, and evaluates the importance of each index for the prediction of emissions. The results indicate that, while controlling for the effect of time, 86.43\% of the variation in CO2 emissions from electricity production can be explained by climatic predictors. Air temperature and relative humidity are identified as the most and least important factors, respectively, for the prediction of emissions in France. The study also found that the predictive relationship between most climate variables and CO2 emissions from electricity generation was nonlinear in nature over the study period. The findings of the present exploratory research should make an important contribution to the literature on driving factors for CO2 emissions in the electricity sector, and can provide insight into the sensitivity of power-related emissions to climate change