Retail electricity costs and emissions incentives are misaligned for commercial and industrial power consumers

Code and data for analysis and visualization accompanying our manuscript entitled "Electricity costs and emissions incentives are misaligned for commercial and industrial power consumers".

Before you get started, please install the Python dependencies listed in requirements.txt. For example, using pipenv:

pipenv shell
pip install -r requirements.txt

Data sources

Raw data can be found in the data folder, when possible. All data used is from 2023. Some data cannot be republished under MIT license, in which case links to the data sources are available below:

• Average emission factors (AEFs): collected from United States Energy Information Administration (EIA) Hourly Electric Grid Monitor using a method from de Chalendar et al. [1]. Monthly/hourly averaged data available in data/AEFs folder, and raw data by ISO is available in subfolders.
• Marginal emission factors (MEFs): Monthly/hourly averaged data available in data/MEFs/average_mefs.csv, with Monte Carlo simulations computed using the method from Siler-Evans et al. [2] available in subfolders.
• Electricity tariffs: industrial-electricity-tariffs is updated monthly on Zenodo and GitHub [3]. Archived data applicable to 2023 available in data/tariffs/bundled and data/tariffs/delivery_only folders.
• Day-ahead market (DAM) prices: downloaded from GridStatus. Raw historical data not available for re-publication. Monthly/hourly averages are saved to the data/DAMs folder with columns month, hour, and USD_per_MWh
• Incentive-based demand response (IBDR): the Incentive Demand Response Program Parameter (IDroPP) dataset is available from the [Stanford Digital Repository as "US incentive based demand response program parameters" [4]. Data relevant to our analysis copied to data/IBDR folder.
• Geospatial data:
  • Census boundaries for IBDR display from Census.gov
  • Independent System Operator (ISO) shapefile from US Department of Homeland Security's Homeland Infrastructure Foundation-Level Data (HIFLD).
    • NOTE: This dataset is no longer publicly accessible, but similar data is available through EIA's U.S. Energy Atlas under RTO Regions.
• Load profiles:
  • Load profiles were the clusters from the Elmas dataset [5]
    • Flat load profiles are the default (col_idx=0) and the other 18 clusters are described in the Elmas data descriptor.
    • All load profiles are normalized to 1 MW.

Data preprocessing

Code for data preprocessing can be found in code/preprocess. The preprocessing code was run in the following order:

python compile_average_aefs.py
python compile_average_mefs.py
python compile_average_dams.py
python tariff_timeseries.py

Which creates the following preprocessed data (note that this data has been saved to the repository for posterity, but should be recreated by the scripts exactly):

• data/AEFs/: average emission factors (AEFs) averaged by month and hour, with each region having its own CSV file (e.g., CAISOaef.csv or ERCOTaef.csv)
• data/MEFs/average_mefs.csv: marginal emission factor (MEF) samples as monthly/hourly average timeseries.
• data/DAMs/: average day-ahead market (DAM) price as monthly/hourly average timeseries, with each region having its own CSV file (e.g., CAISOcosts.csv or ERCOTcosts.csv).
  • NOTE: compile_average_dams.py will throw an error without data provided by the user since we cannot re-publish the raw DAM data that we collected.
• data/tariffs/bundled/timeseries and data/tariffs/delivery_only/timeseries: tariffs are converted to timeseries format assuming a 1 MW load for future analysis.

Data analysis

Code for data analysis can be found in code/analyze. The analysis code consists of the following stpes:

python min_max_emissions.py
python dam_mef_alignment.py
python tariff_aef_alignment.py
python bay_area_correlation.py
python correlation_versus_tariff_ratio.py

This should create the following results in the data folder:

• data/correlation: CSV files of the Pearson correlation coefficient by region for both DAM/MEF and Tariff/AEF.
  • NOTE: dam_mef_alignment.py will throw an error without data provided by the user since we cannot re-publish the raw DAM data that we collected.
• bay_area_correlation.py does not produce any file output, but prints some statistics included in the manuscript and supplementary information.

Data visualization

The final visualizations can be found in the figures folder after running all the scripts in code/visualize:

python figure2.py
python figure3.py
python figure4.py
python figure5.py
python figure6.py
python supplementary1.py
python supplementary2.py
python supplementary3.py
python supplementary4.py
python supplementary5.py
python supplementary7.py

Note that Figure 1 and Supplementary Figure 6 were made outside of Python. Figures 3, 5, and 6 and Supplementary Figure 1 will throw errors without data provided by the user since we cannot re-publish the raw DAM data that was used to generate the published figures.

File hierarchy

This repository is organized into the following folders:

code
 -> analyze
    -> Scripts to analyze the data
 -> preprocess
    -> Scripts to preprocess the data
 -> visualize
    -> Scripts to visualize the data
data
 -> AEFs
    -> Raw and processed average emission factors
 -> correlation
    -> Computed correlation coefficients
 -> DAMs
    -> Only average price is included since we cannot republish raw data
 -> geospatial
    -> Shapefiles for geospatial plotting
 -> IBDR
    -> Processed incentive-based demand response data
 -> MEFs
    -> Raw and processed marginal emission factors
 -> tariffs
    -> Processed industrial electricity tariff data
figures
 -> PNG and SVG versions of figures

References

[1] de Chalendar, J. A., Taggart, J., & Benson, S. M. (2019). Tracking emissions in the US electricity system. Proceedings of the National Academy of Sciences, 116(51), 25497-25502. https://doi.org/10.1073/pnas.1912950116

[2] Siler-Evans, K., Azevedo, I. L., & Morgan, M. G. (2012). Marginal emissions factors for the US electricity system. Environmental science & technology, 46(9), 4742-4748. https://doi.org/10.1021/es300145v

[3] Chapin, F. T., Rao, A. K., Sakthivelu, A., Chen, C. S., & Mauter, M. S. (2025). Industrial and Commercial Electricity Tariffs in the United States (Version 2023.06.01) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.16739989

[4] David, E., Sakthivelu, A., Rao, A. K., & Mauter, M. S. (2024). US incentive based demand response program parameters (Version 2) [Data set]. https://doi.org/10.25740/ck480bd0124

[5] Bellinguer, K., Girard, R., Bocquet, A., & Chevalier, A. (2023). ELMAS: a one-year dataset of hourly electrical load profiles from 424 French industrial and tertiary sectors. Scientific Data, 10(1), 686. https://doi.org/10.1038/s41597-023-02542-z