DOI: 10.5061/dryad.3r2280gqv
title: Assessing the impact of supply-side policies on oil extraction, health, and employment in California

description: California's ambitious goal to slash GHG emissions by 90% by 2045
 marks a significant shift towards sustainability. Supply-side policies,
 such as Senate Bill 1137, which bans new oil and gas wells within 3,200
 feet of sensitive areas, signal a commitment to environmental and public
 health protection. To gauge SB 1137's impact accurately, the existing
 model must be adapted to incorporate this setback distance. This capstone
 project aims to bridge this gap by updating the model and creating
 accessible educational materials for Californians. Objectives include
 updating the model, predicting well locations and oil extraction using
 machine learning, and developing a public online app with R Shiny. The
 MEDS capstone group will investigate the effects of the 3,200-foot setback
 distance on emissions, employment, and health, contributing to the
 evidence supporting SB 1137.
description: # Assessing the Social and Environmental Impacts of Supply Side Oil
 Policies in California Group members: Haejin Kim
 ([haejin_kim@ucsb.edu](mailto:haejin_kim@ucsb.edu)), Maxwell Patterson
 ([maxwellpatterson@ucsb.edu](mailto:maxwellpatterson@ucsb.edu)), and
 Mariam Garcia ([mkgarcia@ucsb.edu](mailto:mkgarcia@ucsb.edu))
 Corresponding authors: Ranjit Deshmukh (UCSB,
 [rdeshmukh@ucsb.edu](mailto:rdeshmukh@ucsb.edu)); Paige Weber (UNC,
 [paigeweber@unc.edu](mailto:paigeweber@unc.edu)); Kyle Meng (UCSB,
 [kmeng@bren.ucsb.edu](mailto:kmeng@bren.ucsb.edu)) GitHub repository
 author and manager: Tracey Mangin (emLab,
 [tmangin@ucsb.edu](mailto:tmangin@ucsb.edu)) Zenodo repository manager:
 Tracey Mangin (emLab, [tmangin@ucsb.edu](mailto:tmangin@ucsb.edu)) Project
 GitHub Repository:
 [https://github.com/mariamkg00/meds-freshcair-capstone](https://github.com/mariamkg00/meds-freshcair-capstone) ### Geographic Location California, USA ### Data structure This deposit contains three folders: 1)Intermediate, 2) Output, and 3)ShinyDashboards with data files for public consumption, as described below. For more information about the project files and scripts, refer to the GitHub Repository: [https://github.com/mariamkg00/meds-freshcair-capstone](https://github.com/mariamkg00/meds-freshcair-capstone) ## Intermediate Data The intermediate public data is all data necessary for the final extraction model. This data is made public in order for the results and figures to be recreated. There are 2 subfolders in the intermediate subfolder: energy and health. There is also the `scenario_id_list_targets.csv`, an essential data set that contains all of the potential scenarios for oil price, setback scenario, production quota, carbon price scenario, carbon price scenario, carbon capture scenario, innovation scenario, and excise tax scenario. While all of these scenarios were essential for the original study done by the clients, our capstone project is only concerned with the BAU and setback scenarios, namely the added 3,200 foot setback scenario. The following metadata is provided for the intermediate data: `scenario_id_list_targets.csv` * 81432 by 13 * Column names: scen_id, oil_price_scenario, setback_scenario, prod_quota_scenario, carbon_price_scenario, ccs_scenario, innovation_scenario, excise_tax_scenario, target, target_policy, subset_scens, BAU_scen, setback_existing * Contains information on various energy scenario combinations. Used to analyze the impact of these factors on emissions `/energy/location/setback_coverage_R.csv` * 1370 by 8 * Column names: doc_field_code, NAME, area_sq_mi, area_acre, orig_area_m2, setback_scenario, rel_coverage, n_wells * Contains information about oil and gas fields. used to analyze the impact of different setback distances on the coverage and production of oil and gas resources in oil fields. `/energy/location/coverage_map_files/` * Contains spatial files of 1000, 2500, 3200, and 5280ft setback coverages. `/energy/production/crude_prod_x_field_revised.csv` * 11395 by 4 * Column names: doc_field_code, doc_fieldname, year, and total_bbls * Contains information on crude oil production by field and year. This dataset is used to analyze historical trends in crude oil production across different fields over time. `/energy/production/entry_df_final_revised.csv` * 11309 by 24 * Column names: doc_field_code, doc_fieldname, year, doc_prod, capex, capex_bbl_rp, capex_per_bbl_reserves, capex_per_bbl_nom, opex, opex_bbl_rp, opex_per_bbl_nom, m_cumsum_div_my_prod, m_cumsum_div_max_res, capex_imputed, wm_capex_imputed, opex_imputed, wm_opex_imputed, wm_cumsum_div_my_prod, wm_cumsum_div_max_res, wm_cumsum_eer_prod_bbl, brent, new_prod, n_new_wells, top_field * Contains information on oil fields and is be used for in-depth analysis of the economic performance and operational characteristics of oil fields over time. `/energy/production/field_capex_opex_forecast_revised.csv` * 6838 by 6 * Column names: doc_field_code, year, m_opex_imputed, m_capex_imputed, wm_opex_imputed, wm_capex_imputed * Used to project future costs associated with oil production operations. `/energy/production/field-year_peak-production_yearly.csv` * 3161 by 8 * Column names: doc_fieldname, doc_field_code, start_year, peak_prod_year, peak_tot_prod, no_wells, peak_avg_well_prod, peak_well_prod_rate * Contains information about the peak production year for each oil field. Used to analyze the performance and decline characteristics of oil fields based on their peak production levels. `/energy/production/forecasted_decline_parameters_2020_2045.csv` * 6838 by 8 * Column names: doc_field_code, doc_fieldname, year, q_i, D, b, d, int_year * Contains forecasted decline parameters for oil fields from 2020 to 2045. It includes the field identification codes, field names, years of the forecast, initial production rates (q_i), decline rates (D), hyperbolic decline exponents (b), exponential decline rates (d), and the number of years since the start of production (int_year). These parameters are used to project future oil production from oil fields. `/energy/production/ghg_emissions_x_field_2018-2045.csv` * 7420 by 5 * Column names: doc_field_code, doc_fieldname, year, steam_field, upstream_kgCO2e_bbl * Contains information about greenhouse gas (GHG) emissions for oil fields from 2018 to 2045. It includes the field identification codes, field names, years of the data, a binary indicator for whether the field uses steam injection (steam_field), and the upstream GHG emissions intensity in kilograms of CO2 equivalent per barrel of oil produced (upstream_kgCO2e_bbl). This dataset is used to analyze the carbon footprint of oil production across different fields and to project future GHG emissions based on production forecasts. `/energy/production/pred_prod_no_exit_2020-2045_field_start_year_revised.csv` * 410930 by 8 * Column names: doc_field_code, doc_fieldname, setback_scenario, start_year, no_wells, adj_no_wells, year, production_bbl * Contains predicted oil production volumes for fields from 2020 to 2045, considering different setback scenarios and assuming no field exits. It includes the field identification codes, field names, setback scenarios, the starting year of production for each field, the number of wells in the field, the adjusted number of wells based on the setback scenario, the year of the production forecast, and the forecasted production volume in barrels (production_bbl). This dataset is used to analyze the impact of different setback regulations on future oil production at the field level. `/health/emission_reduction_90.csv` * 1 by 1 * Column names: emission_reduction, ghg_emission_MtCO2e * Provides the corresponding GHG emissions in million metric tons of CO2 equivalent (MtCO2e) in the 90% reduction scenario. `/health/excise_tax_non_target_scens.csv` * 156 by 4 Column names: year, tax_rate, excise_tax_scenario, units * Contains information about excise tax rates for non-target scenarios from 2020 to 2058. It includes the year, the tax rate as a fraction of the oil price, the excise tax scenario, and the units of the tax rate (specified as "fraction of oil price"). This dataset is used to analyze the impact of different excise tax scenarios on oil production and revenues. `health/inmap_processed_srm/srm_XX_fieldYY.shp` * Contains spatial information on the distribution of NH3, NOX, PM2.5, SOX, and VOC for 26 oil fields across California. `health/inmap_processed_srm/srm_XX_fieldYY.csv` * 130 x 58 by 4 * Column names: GEOID, total chemical amount (NH3, NOX, PM2.5, SOX, VOC), and average weighted chemical amount * Contains information about the impact of a specific oil field (referred to as "field1") on air quality in different counties of California. The "GEOID" column represents the unique identifier for each county, while "totalXX" and "totalXX_aw" columns represent the chemical concentrations and area-weighted chemical concentrations resulting from emissions related to the oil field's operations. Used to assess the spatial distribution of air quality impacts from the oil field across different counties in California. ## Output Data Output data is all data generated from the final extraction model and all subsequent data. (`00_extraction_steps.R`). There are 4 categories of output data: health, labor, model, and results. The model subfolder contains the data from the final extraction model runs. The results folder is a catch-all that contains all other outputted data. `/health-out/extraction_cluster_affectedpop.csv` * 26 by 4 * Column names: id, share_dac, share_dac_weighted, and numA * contain information about the population affected by oil extraction clusters. `/health-out/social_cost_carbon.csv` * 124 by 5 * Column names: year, discount_rate, social_cost_co2, social_cost_co2_19, scc_ref * Used to assess the economic costs associated with CO2 emissions over time `labor-out/indust_emissions_2000-2019.csv` * 140 by 5 * Column names: segment, unit, year, value, source * Used to analyze trends and patterns in industrial greenhouse gas emissions in California over the past two decades, and to identify the major contributing sectors or subsectors to overall industrial emissions in the state. `/model-out/extraction/state-results/subset_state_results.csv` * 3078 by 32 * Column names: scen_id, year, oil_price_scenario, innovation_scenario, carbon_price_scenario, ccs_scenario, setback_scenario, setback_existing, prod_quota_scenario, excise_tax_scenario, state_pop, total_state_bbl, total_state_revenue, total_state_ghg_kgCO2, c.dire_emp, c.indi_emp, c.indu_emp, c.dire_comp, c.indi_comp, c.indu_comp, total_emp, total_comp, mortality_delta, mortality_level, cost_2019, cost, cost_2019_PV, cost_PV, mean_total_pm25, mean_delta_total_pm25, target, target_policy * Provides information on the potential impacts of various policy scenarios on California's oil industry, economy, public health, and environmental outcomes over the next two decades. `/model-out/extraction/state-results/XX_state_results.csv` * 27 by 30 * Column names: scen_id, year, oil_price_scenario, innovation_scenario, carbon_price_scenario, ccs_scenario, setback_scenario, setback_existing, prod_quota_scenario, excise_tax_scenario, state_pop, total_state_bbl, total_state_revenue, total_state_ghg_kgCO2, c.dire_emp, c.indi_emp, c.indu_emp, c.dire_comp, c.indi_comp, c.indu_comp, total_emp, total_comp, mortality_delta, mortality_level, cost_2019, cost, cost_2019_PV, cost_PV, mean_total_pm25, and mean_delta_total_pm25 * Provides information on the potential impacts of a specific policy scenario on California's oil industry, economy, public health, and environmental outcomes over the next two decades. `model-out/extraction/county-results/subset_county_results.csv` * 52083 by 27 * Column names: scen_id, oil_price_scenario, innovation_scenario, carbon_price_scenario, ccs_scenario, setback_scenario, setback_existing, prod_quota_scenario, excise_tax_scenario, county, dac_share, median_hh_income, year, county_pop, total_county_bbl, total_county_ghg_kgCO2e, revenue, c.dire_emp, c.indi_emp, c.indu_emp, c.dire_comp, c.indi_comp, c.indu_comp, total_emp, total_comp, target, target_policy * Provides information on the potential impacts of various policy scenarios on California's oil industry, economy, and environmental outcomes at the county level over the next two decades. `model-out/extraction/county-results/XX_county_results.csv` * 432 by 25 * Column names: scen_id, oil_price_scenario, innovation_scenario, carbon_price_scenario, ccs_scenario, setback_scenario, setback_existing, prod_quota_scenario, excise_tax_scenario, county, dac_share, median_hh_income, year, county_pop, total_county_bbl, total_county_ghg_kgCO2e, revenue, c.dire_emp, c.indi_emp, c.indu_emp, c.dire_comp, c.indi_comp, c.indu_comp, total_emp, total_comp * Provides information on the potential impacts of this specific policy scenario on California's oil industry, economy, and environmental outcomes at the county level over the next two decades. `model-out/extraction/census-tract-results/subset_census_tract_results.csv` * 24799446 by 19 * Column names: scen_id, census_tract, CES3_score, disadvantaged, median_hh_income, year, weighted_incidence, pop, total_pm25, bau_total_pm25, delta_total_pm25, mortality_delta, mortality_level, cost_2019, cost, cost_2019_PV, cost_PV, target, target_policy * Contains census tract-level results for various policy scenarios related to oil production in California from 2019 to 2045. Allows for a highly granular analysis of the potential impacts of various policy scenarios on California's communities at the census tract level over the next two decades. `model-out/extraction/census-tract-results/XX_ct_results.csv` * 217539 by 17 * Column names: scen_id, census_tract, CES3_score, disadvantaged, median_hh_income, year, weighted_incidence, pop, total_pm25, bau_total_pm25, delta_total_pm25, mortality_delta, mortality_level, cost_2019, cost, cost_2019_PV, cost_PV * Provides information on of the potential impacts of this specific policy scenario on California's communities at the census tract level over the next two decades. `model-out/extraction/state-results/health-sens/subset_state_hs_results.csv` * 3708 by 32 * Column names: scen_id, year, oil_price_scenario, innovation_scenario, carbon_price_scenario, ccs_scenario, setback_scenario, setback_existing, prod_quota_scenario, excise_tax_scenario, state_pop, total_state_bbl, total_state_revenue, total_state_ghg_kgCO2, c.dire_emp, c.indi_emp, c.indu_emp, c.dire_comp, c.indi_comp, c.indu_comp, total_emp, total_comp, mortality_delta, mortality_level, cost_2019, cost, cost_2019_PV, cost_PV, mean_total_pm25, mean_delta_total_pm25, target, target_policy * Contains information related to oil production in California from 2019 to 2045. Includes annual projections for variables such as state population, total oil production (in barrels), state revenue, greenhouse gas emissions (in kg CO2), direct, indirect, and induced employment and compensation in the oil industry, total employment and compensation, changes in mortality rates and costs associated with air pollution (PM2.5), and policy targets. `model-out/extraction/health-county-results/subset_county_hs_results.csv` * 178524 by 18 * Column names: scen_id, GEOID, county, year, dac_share, weighted_incidence, pop, total_pm25, bau_total_pm25, delta_total_pm25, mortality_delta, mortality_level, cost_2019, cost, cost_2019_PV, cost_PV, target, target_policy * Contains county-level results for various policy scenarios related to oil production in California from 2019 to 2045. Includes annual projections for variables at the county level, such as the county GEOID, county name, share of disadvantaged communities (DACs), population-weighted PM2.5 incidence, population, total PM2.5 concentrations, business-as-usual (BAU) PM2.5 concentrations, changes in PM2.5 concentrations, mortality impacts (changes and levels), and the associated costs (in 2019 dollars and present value terms) `results-out/county_level_out_adjusted.csv` * 459 by 19 * Column names: scen_id, oil_price_scenario, innovation_scenario, carbon_price_scenario, ccs_scenario, setback_scenario, setback_existing, prod_quota_scenario, excise_tax_scenario, county, dac_share, median_hh_income, year, county_pop, total_county_bbl, total_county_ghg_kgCO2e, revenue, total_emp, total_comp * Contains county-level results for a specific policy scenario related to oil production in California from 2019 to 2045. Used to analyze the potential impacts of each policy scenario combination on California's oil industry, economy, and environmental outcomes at the county level over the next two decades, with a focus on the distribution of these impacts across different counties and communities within the state. `results-out/extraction_field_cluster_xwalk.csv` * 262 by 4 * Column names: id, input_fid, NAME, doc_field_code * Used to link information from other datasets that use either the extraction cluster ID or the oil field ID as a key, enabling analyses that combine data at both the cluster and field levels. `results-out/extraction_fields.shp` * Spatial data on all of the oil extraction fields across California. `results-out/new_wells_pred_revised.csv` * 11046 by 4 * Column names: doc_field_code, year, n_new_wells, new_wells_pred * Contains information about the number of new wells drilled in each oil field in California from 1978 to 2020, along with predictions for the number of new wells. Used to analyze historical trends in new well drilling activity across different oil fields in California and to compare the actual number of new wells with the predicted values. `well_prod_m_processed.csv` * 38649622 by 27 * Column names: ReportType, APINumber, api_ten_digit, doc_field_code, doc_fieldname, county, county_name, AreaCode, PoolCode, WellTypeCode, well_type_name, ProductionReportDate, year, month, month_year, ProductionStatus, CasingPressure, TubingPressure, BTUofGasProduced, MethodOfOperation, APIGravityofOil, WaterDisposition, OilorCondensateProduced, DaysProducing, GasProduced, WaterProduced, ReportedOrEstimated * Contains detailed monthly production data for individual oil and gas wells in California from 1984 to 2019. Used to measure the production trends over the time period. ## ShinyDashboard `subset_county_hs_results.csv` * 178524 by 18 * Column names: scen_id, GEOID, county, year, dac_share, weighted_incidence, pop, total_pm25, bau_total_pm25, delta_total_pm25, mortality_delta, mortality_level, cost_2019, cost, cost_2019_PV, cost_PV, target, target_policy * Similar to the previous file, and contains county-level results for various policy scenarios related to oil production in California from 2019 to 2045. `datagenerated-buffer_3200ft.xxx` * Contains a shapefile detailing the location of wells across California under various setback scenarios. `county_health_results.csv` * 638 by 11 * Column names: GEOID, county, bau *pm25 2025, bau pm25 2045, total pm25 2025, total pm252045, dac share, pop, bau pm25 change pct, total pm25 change pct, diff*  pm25 * Provides comprehensive PM2.5, data on disadvantaged communities, and overall population statistics by census tract within each county in California, spanning from 2025 to 2045. This information is displayed as a pop-up message when clicking on the map in the dashboard interface.