A data-driven approach to estimate the immediate and time-lagged effects of an hourly increase in solar power generation on carbon dioxide (CO2) emissions reduction within the same geographic region and neighboring regions.

The datasets and more details about the data are available in Dataverse.

Download the files and store them in a folder data within the working directory.

1. PreprocessingData.Rmd contains the data preprocessing steps and stores the clean datasets for each region in separate .csv files within a folder called "data".
2. DistributedLagModelGeneration.Rmd contains the script to generate the models aiding the estimations. It creates a folder called tdlm_models_lags12 (considering 12 lags) within the current working directory to store the generated models. Due to the complexity of the computations, executing this file may take several days.
3. Plot.Rmd contains the script to generate the figures and tables. It creates a folder called output in the current working directory and stores all the results.

Combining the regional and interregional analyses, a substantial annual reduction of 8.54 million metric tons of CO2 emissions is found to be associated with a 15% increase in solar power generation across the US electricity sector.