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Assessing the Social and Environmental Impacts of Supply Side Oil Policies in California

Group members from UCSB MEDS FreshCAir team: Haejin Kim ([email protected]), Maxwell Patterson ([email protected]), and Mariam Garcia ([email protected])

Clients: The 2035 Initiative ([email protected]), and emLab ( [email protected])

Corresponding authors: Ranjit Deshmukh (UCSB, [email protected]); Paige Weber (UNC, [email protected]); Kyle Meng (UCSB, [email protected])

GitHub repository author and manager: Tracey Mangin (emLab, [email protected])

Zenodo repository manager: Tracey Mangin (emLab, [email protected])

Link to original repository: https://github.com/emlab-ucsb/ca-transport-supply-decarb

This project has been completed as a part of the Master of Environmental Data Science program at the Bren School of UC Santa Barbara. Read more

Introduction

California has set an ambitious target to reduce greenhouse gas (GHG) emissions by 90% by 2045 compared to 1990 levels. To achieve this goal, the state is implementing various supply-side policies aimed at curbing emissions from the oil and gas industry. One such policy is Senate Bill 1137 (SB 1137), which prohibits the construction of new oil and gas wells within 3,200 feet of sensitive areas such as schools, hospitals, and residential neighborhoods. This bill marks a significant step towards prioritizing environmental sustainability and public health. In 2023, a study led by Dr. Ranjit Deshmukh and Dr. Paige Weber from the University of California, Santa Barbara, developed a model to assess the impacts of different setback distances on oil production, GHG emissions, employment, and public health in California. The study, titled "Equitable Low-Carbon Transition Pathways for California's Oil Extraction," utilized a comprehensive modeling framework that integrated data on oil well locations, production levels, emissions, and demographic information Deshmukh et al. The model simulated the effects of setback distances of 1,000 feet, 2,500 feet, and 5,280 feet on various environmental and socioeconomic indicators. To accurately gauge the impact of SB 1137, which introduces a 3,200-foot setback distance, it is necessary to adapt the existing model developed by Deshmukh and Weber. This capstone project, undertaken by the Master of Environmental Data Science (MEDS) program at the Bren School of Environmental Science & Management, aims to bridge this gap by updating the model to incorporate the 3,200-foot setback scenario and creating accessible educational materials for the public.

The primary objectives of this project are threefold:

  1. Update the existing model to calculate the effects of the 3,200-foot setback distance on GHG emissions, employment, and public health from 2020 to 2045.
  2. Predict the number and location of new and idle wells through 2045 using machine learning techniques, specifically Random Forest models, to improve upon the original Poisson regression approach.
  3. Develop a public-facing interactive web application using R Shiny to present the findings and implications of SB 1137 in an accessible manner, empowering Californians to make informed decisions in the upcoming referendum vote.

By investigating the impacts of the 3,200-foot setback distance on key environmental and socioeconomic indicators, this project contributes valuable evidence to support the implementation of SB 1137. The updated model and interactive web application will provide policymakers, stakeholders, and the general public with crucial insights into the potential benefits and trade-offs associated with this supply-side policy.

Purpose

The purpose of this Github repository is to maintain a clear and effective history of working progress in the capstone project. This repository contains parts of the data and scripts used to update the well setback distance reflected by the upcoming Senate Bill 1137.

Data Collection

Proprietary data was handed off from emLab 2024-01-10, with missing data shared through Dropbox from 2024-01-10 through 2024-05-09.

Geographic Location

Oil well production and location data is from the California state region.

Funding

No additional funding was required for this project. Proprietary data was handed off from emLab and while the original acqusition of proprietary required compensation to data providers, no additional data was needed for this project.

Data structure

This is the fundamental structure of our data structure. The intermediate data in the public folder, which is publicly accessible, entails the inputs into the final extraction model. Providing these data will allow public users to regenerate the final outputs of the model and to understand the information that is being processed. The private folder contains all other data used in the workflow, ranging from proprietary inputs, sensitive processed versions of this data, and data used to connect the workflow in the preliminary data processing scripts. ​​

├── private/
│   ├── assets/
│   ├── entry-exit/
│   ├── health/                             
│   ├── injection/
│   ├── labor
│   ├── injection
│   ├── inputs
│   ├── labor
│   ├── production
│   ├── rystad-processed
│   ├── scens
│   ├── setback-buffs
│   ├── setback-cov                         
│   └── well-fields                 
├── public
│   ├── inputs
│   │   ├── extraction
│   │   ├── gis
│   │   ├── health
│   │   ├── labor
│   │   └── scenarios
│   ├── intermediate
│   │   ├── energy
│   │   └── health
│   ├── outputs
│   │   ├── health-out
│   │   ├── labor-out
│   │   ├── model-out
│   │   └── results-out

Due to data confidentiality, the user can only run a subset of the scripts. Thus, we provide all of the intermediate outputs needed to run the following scripts:

  • energy/extraction-segment/model/full-run-revised/00_extraction_steps.R
  • energy/extraction-segment/figs-and-results/fig_outputs.R
  • energy/extraction-segment/figs-and-results/

The following section documents all inputs needed to conduct the study. The “Required” column indicates if the file is needed to conduct the analysis, and the “Zenodo” column indicates if the input is included in the Zenodo repository. Note that a asterisk* indicates files that are not publicly available.

3,200ft Setback Scenario

The main objective of the project is to incorporate the 3,200 foot setback scenario, forecast the imact of the new setback scenario on future oil production, and subsequently estimate the health, labor and pollution impacts of the setback. The following scripts have been updated to include the new setback scenario, and are the most important scripts in evaluating the impact of SB 1137:

  • well_setback_sp_prep.R
  • gen_well_setback_status.R
  • county-setback.R
  • extraction_fields.R
  • predict_existing_production.R
  • scenario_list_targets.R
  • load_input_info.R
  • 00_extraction_steps.R

Input Data

File Description Source Required Zenodo
All_wells_20200417.xlsx Geographical and other information for California wells California Department of Conservation (DOC) Yes Yes
AllWells_20210427.csv Geographical and other information for California wells DOC Yes Yes
CaliforniaOilAndGasWellMonthlyProduction.csv Well-level oil and gas monthly production data for 1977-2017 (file names are the same, folders indicate the years included in the data file) DOC Yes Yes
CaliforniaOilAndGasWellMonthlyInjection.csv Well-level oil and gas monthly injection data for 1977-2017 (file names are the same, folders indicate the years included in the data file) DOC Yes Yes
CaliforniaOilAndGasWells.csv Well information (12 files, same name, separate folders) California Department of Conservation Yes Yes
county_codes.csv County names and codes Created using DOC online resources Yes Yes
CA_Counties_TIGER2016_noislands.shp Spatial file: CA counties, no islands https://data.ca.gov/dataset/ca-geographic-boundaries Yes Yes
DOGGR_Admin_Boundaries_Master.shp Spatial file: field boundaries DOC Yes Yes
oil_price_projections_revised.xlsx Oil price projections Energy Information Administration (EIA), International Energy Agency (IEA) Yes Yes
well_type_df.csv Well types and abbreviations Created using DOC online resources Yes Yes
Asset_opex_capex_govtt.csv* Annual CapEx, OpEx, and government take (historic and future) Rystad Yes No
resources_prod_myprod.csv* Resource, production, and production under maximum allowed oil price ($120/bbl) by asset by year Rystad Yes No
capex_per_recoverable_bbl.csv* Annual CapEx value per recoverable barrel by asset Rystad Yes No
asset-wells.csv* Well (API) production by asset Rystad Yes No
capex_per_bbl_nom.csv* Annual CapEx per barrel (nominal) by asset Rystad Yes No
opex_per_bbl_nom.csv* Annual OpEx per barrel (nominal) by asset Rystad Yes No
well_cost_per_eur.csv* Cost of building each well Rystad Yes No
ca_production.csv* Annual California oil and gas production by asset, data type, and data source Rystad Yes No
asset_latlon.csv* Asset latitude and longitude Rystad Yes No
wti_brent.csv* Annual WTI and Brent price per barrel Rystad Yes No
rystad_asset_rename.csv* Rystad asset names and adjusted names to remove periods Created using Rystad data Yes No
CA_Counties_TIGER2016.shp California counties (shape file) https://data.ca.gov/dataset/ca-geographic-boundaries Yes Yes
2000_2019_ghg_inventory_trends_figures.xlsx GHG emissions by sector California Air Resources Board (CARB) Yes Yes
asset_econ_categories.csv* Annual CapEx, OpEx, government take, exploration CapEx, and free cash flow by asset Rystad No No
field_to_asset.csv* Field and asset production days Rystad No No
N9010CA2a.xls California Natural Gas Gross Withdrawals EIA No No

Health Inputs

File Description Source Required Zenodo
ces3results.xlsx CalEnviroScreen 3.0 results Office of Environmental Health Hazard Assessment (OEHHA) Yes Yes
nhgis0001_ts_geog2010_tract.csv 2010 census tract population by age-group National Historical Geographic Information System (NHGIS) Yes Yes
CDOF_p2_Age_1yr_Nosup.csv County-level population projections by age-group California Department of Finance (CDOF) Yes Yes
County_def.shp County shape file Downloaded from BenMAP-CE Yes Yes
age_group_desc.csv Age groups for 2010 Census Created from Codebook from NHGIS data file 'nhgis0001_ts_geog2010_tract' Yes Yes
Mortality Incidence (2015).csv County by age-group mortality rates Downloaded from BenMAP-CE Yes Yes
growth_rates.csv Real GDP growth rates Created from https://www.cbo.gov/publication/56442 Yes Yes
ces3_data.csv Select CalEnviroScreen 3.0 results Created from “ces3results.xlsx” Yes Yes
tl_2019_06_tract.shp Spatial file: census tracts Census Yes Yes
n/a (InMap data can be obtained from InMap open source repository) InMap data and open source model https://github.com/spatialmodel/inmap Yes No
n/a (BenMAP data can be obtained by downloading the software from the US EPA) BenMAP Community Edition (BenMAP-CE) underlying data https://www.epa.gov/benmap Yes No

Labor Inputs

File Description Source Required Zenodo
n/a (proprietary data can be obtained through a license with IMPLAN)* County-specific employment and compensation multipliers for the oil extraction and refining industries IMPLAN, 2018 edition (app.implan.com) Yes No
n/a (proprietary data can be obtained through a license with IMPLAN)* County and industry-specific Multi-Region Input/Output analysis results. Includes direct, indirect, and induced impacts. IMPLAN, 2018 edition (app.implan.com) Yes No
fte-convert.xlsx Industry-specific ratios of the number of job-years per FTE worker IMPLAN (https://implanhelp.zendesk.com/hc/en-us/articles/115002782053-IMPLAN-to-FTE-Conversions) Yes Yes

Scenario Inputs

File Description and source Zenodo
innovation_scenarios.csv Innovation scenario inputs developed for study Yes
carbon_prices_revised.csv Carbon price scenario inputs developed for study using California Air Resources Board: 2020 Annual Auction Reserve Price Notice (2019), United States Government Interagency Working Group on Social Cost of Greenhouse Gases: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866. Technical report (August 2016), and California Air Resources Board: Final Regulation Order: California Cap on Greenhouse Gas Emissions and Market-based Compliance Mechanisms (2018). See Supplementary Information for more details. Yes
ccs_extraction_scenarios.csv CCS scenario inputs developed for study based on Stanford 2020 and Jing et al. 2020. Only scenarios without CCS (‘no CCS’) were included in our study. Yes
CCS_LCFS_45Q.xlsx CCS scenario inputs developed for study (https://sgp.fas.org/crs/misc/IF11455.pdf) Yes
prod_quota_scenarios.csv Production quota scenarios developed for this study. Our study ultimately did not review scenarios with a production quota.) Yes

* Indicates data files that are not public.

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.

Code Scripts

This section contains a summary of the scripts in the project workflow, along with the input and output data associated with each script. The headings below represent folder names and paths.

new-scripts/

eda.R Performs exploratory analysis and visualization on oil production data. Processes and analyzes data on well activity, production volumes, and geographic distribution of wells across counties and census tracts. Examines the coverage of different setback scenarios and creates interactive maps to visualize the distribution of wells and their characteristics.

fr_viz.R Used to create figures for the faculty review presentation.

ml-analysis.R Trains random forest models to predict the number of new and exit wells based on oil price, capital expenditures, operational expenditures, and depletion rate. Generates visualizations to compare the performance of the random forest models with historical data and the Poisson model. Explores the historical and forecasted trends in capex, opex and oil prices

output_review.R Generates plots and summary statistics to compare the effects of different setback scenarios. Wrangles census tract, county, and state-level data.

pred-dev.R Code used in the development of the new and exit well predictive models. Note that the models are implemented in the load_input_info_fc.R script.

rel-coverage.R Calculates the total area covered by each setback scenario, summarizes the relative coverage statistics, and creates plots to show the relationship between setback distance and coverage. Fits a linear model to the setback distance and coverage data, plotting the best-fit line and displaying the equation. These plots are used in the Testing section of this document.

testing.Rmd This code performs data comparisons and checks across numerous datasets related to oil and gas production, emissions, policy scenarios, and environmental justice metrics. It uses the comparedf function from the arsenal package to verify consistency in dimensions, variable names, row counts, and attributes between different versions or sources of data frames to confirm the validity of the data being used for the final model. Data generated by the clients is compared to the new data to ensure consistency in the new outputs. The datasets being checked include crude oil production, greenhouse gas emissions from oil fields, carbon pricing and excise tax scenarios, emission reduction targets, county-level health incidence rates, industrial emissions, disadvantaged community shares, and projected impacts of policy interventions on production, emissions, and health outcomes.

health/

scripts/health_data.R Calculates population increase up until 2045. Analyzes various health and dempographics data sets. Includes environment setup, loading and preprocessing census tract population data, demographics projections, mortality incidence data, and the generation of future population and mortality incidence projections.

  • Inputs:
    • nhgis0001_ts_geog2010_tract.csv
    • age_group_desc.csv
    • CDOF_p2_Age_1yr_Nosup.csv
    • County_def.shp
    • Mortality Incidence (2015).csv
  • Outputs:
    • ct_incidence_ca.csv
    • ct_inc_45.csv
    • ct_pop_45.csv

scripts/source_receptor_matrix.R Reads and transforms shapefiles for census tracts and counties, selects specific areas by excluding islands, determines spatial resolution for source-receptor matrices (SRM), processed pollution data files for different pollutants, and saves the processed data for further analysis.

  • Inputs:
    • tl_2019_06_tract.shp
    • CA_Counties_TIGER2016_noislands.shp (from External data)
  • Outputs:
    • data/health/source_receptor_matrix/inmap_processed_srm/srm_XX_fieldYY.csv (where XX denotes the different pollutants –NH3, PM2.5, SOx, NOx, VOC–) and YY is the different clusters –1-26–).

scripts/obtain_field_cluster_xwalk.do Uses raw data from GIS to obtain the crosswalk between fields and clusters.

  • Inputs:
    • extraction_fields_clusters_10km.csv (dataset obtained from creating 10 km buffers surrounding fields from ArcGIS)
    • extraction_fields_xwalk_id.csv (dataset obtained from crosswalks between the 10km buffers and corresponding fields ids from ArcGIS)
  • Outputs:
    • extraction_fields_xwalk
    • extraction_field_clusters_xwalk

scripts/srm_extraction_population.R Creates inputs for figures.

  • Inputs:
    • ces3results_part.csv (dataset obtained by modifying names of the ces3results.xlsx dataset to be read in Rstudio)
    • srm_XX_fieldYY.csv, (where XX represents nox, pm25, sox, voc; and YY represents numbers 1-26. The numbers represent the 26 oil extraction field clusters) (from InMap, External data)
    • extraction_fields_clusters_10km.csv (created in ArcGIS)
  • Outputs:
    • extraction_cluster_affectedpop.csv
    • extraction_xwalk.csv

labor/processing/

ica_multiplier_process.R Processes and analyzes industry contribution analysis (ICA) data for various segments (extraction, drilling, refining) across California counties, focusing on employment and compensation impacts. Reads in data, merges and reshapes datasets for analysis, calculates averages for missing data, and exports both detailed and aggregated results for further use, including generating a visual representation of direct compensation from the drilling segment across counties.

  • Inputs (all inputs from External data. Files not listed must be obtained from IMPLAN):
    • fte-convert.xlsx
    • ica-emp-ext-kern.csv
    • ica-va-ext-kern.csv
    • ica-emp-drill-kern.csv
    • ica-va-drill-kern.csv
    • ica-emp-ref-kern.csv
    • ica-va-ref-kern.csv
    • ica-emp-ext-la.csv
    • ica-va-ext-la.csv
    • ica-emp-drill-la.csv
    • ica-va-drill-la.csv
    • ica-emp-ref-la.csv
    • ica-va-ref-la.csv
    • ica-emp-ext-sb.csv
    • ica-va-ext-sb.csv
    • ica-emp-drill-sb.csv
    • ica-va-drill-sb.csv
    • ica-emp-ref-sb.csv
    • ica-va-ref-sb.csv
    • ica-emp-ext-monterey.csv
    • ica-va-ext-monterey.csv
    • ica-emp-drill-monterey.csv
    • ica-va-drill-monterey.csv
    • ica-emp-ext-ventura.csv
    • ica-va-ext-ventura.csv
    • ica-emp-drill-ventura.csv
    • ica-va-drill-ventura.csv
    • ica-emp-ext-orange.csv
    • ica-emp-ext-orange.csv
    • ica-emp-ext-orange.csv
    • ica-va-ext-orange.csv
    • ica-emp-drill-orange.csv
    • ica-va-drill-orange.csv
    • ica-emp-ext-fresno.csv
    • ica-va-ext-fresno.csv
    • ica-emp-drill-fresno.csv
    • ica-va-drill-fresno.csv
    • ica-emp-ext-cc.csv
    • ica-va-ext-cc.csv
    • ica-emp-ref-cc.csv
    • ica-va-ref-cc.csv
    • ica-emp-ref-solano.csv
    • ica-va-ref-solano.csv
    • ica-emp-ext-slo.csv
    • ica-va-ext-slo.csv
    • ica-emp-ref-slo.csv
    • ica-va-ref-slo.csv
    • ica-emp-ext-sanbenito.csv
    • ica-va-ext-sanbenito.csv
    • ica-emp-ext-sanbernardino.csv
    • ica-va-ext-sanbernardino.csv
    • ica-emp-ext-tulare.csv
    • ica-va-ext-tulare.csv
    • ica-emp-ext-sanmateo.csv
    • ica-va-ext-sanmateo.csv
    • ica-emp-ext-kings.csv
    • ica-va-ext-kings.csv
    • ica-emp-ext-alameda.csv
    • ica-va-ext-alameda.csv
    • ica-emp-ext-riverside.csv
    • ica-va-ext-riverside.csv
    • ica-emp-ext-santaclara.csv
    • ica-va-ext-santaclara.csv
    • ica-emp-ext-statewide.csv
    • ica-va-ext-statewide.csv
    • ica-emp-drill-statewide.csv
    • ica-va-drill-statewide.csv
    • ica-emp-ref-statewide.csv
    • ica-va-ref-statewide.csv
  • Outputs:
    • ica_multipliers_v2.xlsx
    • ica_multipliers_by_industry_long.csv

energy/

data-processing-prep/

stocks_flows.R This script re-organizes and cleans data relevant to the energy portion of our study.

  • Inputs:
    • Imports_of_Heavy_Sour_to_Los_Angeles_CA.csv (from External data)
    • PET_PRI_SPT_S1_M.xls (from External data)
    • PET_PRI_DFP2_K_M.xls (from External data)
    • MCRFPCA1m.xls (from External data)
    • EmissionsByFacility.csv (from External data)
    • oilgascounty.xls (from External data)
    • MCRFPCA1a.xls (from External data)
    • ghg_inventory_scopingplan_sum_2000-17.pdf (from External data)
    • Ca_oil_refinery_loc_cap.xlsx (from External data)
    • WeeklyFuelsWatch_Summary_2014-2020_North_South.xlsx (from External data)
    • WeeklyFuelsWatch_Summary_2014-2020_North_South.xlsx (from External data)
    • oil_supply_sources_a.csv (from External data)
    • PET_PRI_GND_DCUS_SCA_W.xls (from External data)
    • PET_PRI_GND_DCUS_NUS_W.xls (from External data)
    • ghg_sector_data_og.csv (from External data)
    • ghg_sector_data_refining2.csv (from External data)
    • county_codes.csv (from External data)
    • 2018CaliforniaOilAndGasWellMonthlyProduction.csv (from External data)
    • 2019CaliforniaOilAndGasWellMonthlyProduction.csv (from External data)
    • 2018CaliforniaOilAndGasWellMonthlyInjection.csv (from External data)
    • 2019CaliforniaOilAndGasWellMonthlyInjection.csv (from External data)
    • 2008-ghg-emissions-facility-2012-03-12.xlsx (from External data)
    • 2009-ghg-emissions-facility-2012-03-12.xlsx (from External data)
    • 2010-ghg-emissions-2015-06-15.xlsx (from External data)
    • 2011-ghg-emissions-2018-11-05.xlsx (from External data)
    • 2012-ghg-emissions-2019-11-04.xlsx (from External data)
    • 2013-ghg-emissions-2019-11-04.xlsx (from External data)
    • 2014-ghg-emissions-2019-11-04.xlsx (from External data)
    • 2015-ghg-emissions-2019-11-04.xlsx (from External data)
    • 2016-ghg-emissions-2019-11-04.xlsx (from External data)
    • 2017-ghg-emissions-2019-11-04.xlsx (from External data)
    • 2018-ghg-emissions-2019-11-04.xlsx (from External data)
    • ghg_sector_data_refining2.csv (from External data)
    • epa_emissions.csv (from External data)
    • Historic Permit CalWIMS.xlsx (from External data)
    • Crude Oil Receipts 1981-2020.xlsx (from External data)
    • PET_CRD_CRPDN_ADC_MBBL_A.xls (from External data)
    • Imports_of_Light_Sweet_to_California.csv (from External data)
    • California Transportion Fuel Consumption - Summary 2020-06-01 GDS.xlsx (from EExternal data)
    • 050515staffreport_opgee.pdf (from External data)
    • ci_2012.csv (from External data)
    • ci_2013.csv (from External data)
    • ci_2014.csv (from External data)
    • ci_2015.csv (from External data)
    • ci_2016.csv (from External data)
    • ci_2017.csv (from External data)
    • ci_2018.csv (from External data)
    • wells_19.csv (from clean_doc_prod.R)
    • Total_Energy_Nominal_Prices_Brent.csv (from External data)
    • 2000_2019_ghg_inventory_trends_figures.xlsx
  • Outputs:
    • refinery_capacity.csv (refining segment output)
    • fuel_watch_data.csv (refining segment output)
    • refining_emissions.csv (refining segment output)
    • ghg_mrr_2011-2018.csv (refining segment output)
    • ghg_mrr_2009-2010.csv (refining segment output)
    • ghg_mrr_2008.csv (refining segment output)
    • ghg_inventory_tool.csv (refining segment output)
    • reg_refin_crude_receipts.csv (refining segment output)
    • imports_to_refineries.csv (refining segment output)
    • ca_fuel_consumption_cec.csv (refining segment output)
    • crude_imports_port.csv (not required for analysis)
    • spot_price_wti_m.csv (not required for analysis)
    • domestic_crude_first_p_price_streams.csv (not required for analysis)
    • ca_crude_prod_m.csv (not required for analysis)
    • emissions_by_facility.csv (not required for analysis)
    • oil_gas_county.csv (not required for analysis)
    • ca_crude_prod_a.csv (not required for analysis)
    • summary_emissions.csv (not required for analysis)
    • domestic_import_crude.csv (not required for analysis)
    • ca_gas_d_prices.csv (not required for analysis)
    • usa_retail_p2.csv (not required for analysis)
    • oil_gas_emissions.csv (not required for analysis)
    • all_prod_test.csv (not required for analysis)
    • all_inject_test.csv (not required for analysis)
    • ghg_emissions_epa.csv (not required for analysis)
    • historic_permit_calwims.csv (not required for analysis)
    • eia_ca_crude_prod.csv (not required for analysis)
    • usa_crude_prod.csv (not required for analysis)
    • ci_streams_all_yrs.csv (not required for analysis)
    • brent_oil_price_projections.csv (not required for analysis)
    • field_codes.csv (not required for analysis)
    • indust_emissions_2000-2019.csv (extraction segment output)

create_ccs_scenarios.R This script creates CCS scenarios for extraction and refining segments.

  • Inputs:
    • ccs_extraction_scenarios.csv (from External data)
    • ccs_refining_scenarios.csv (from External data)
  • Outputs:
    • ccs_extraction_scenarios_revised.csv
    • ccs_refining_scenarios_revised.csv

social_cost_carbon.R This script creates a file with social cost of carbon information obtained from Technical Support Document: Social Cost of Carbon and Nitrous Oxide Interim Estimates under Executive Order 13990.

  • Outputs:
    • social_cost_carbon.csv

extraction/clean_doc_prod.R Processes data related to oil production and injection from WellSTAR spanning years 1977 to 2019, includes cleaning and merging these datasets with county and well type information. It also attempts to correct issues with specific well data for the year 2019 by replacing patterns in the dataset, saving the cleaned and processed production, injection, and well data into RDS and CSV files.

  • Inputs:
    • All_wells_20200417.xlsx (from External data)
    • CSV_1977_1985/CaliforniaOilAndGasWellMonthlyProduction.csv (from External data)
    • CSV_1977_1985/CaliforniaOilAndGasWellMonthlyInjection.csv (from External data)
    • CSV_1977_1985/CaliforniaOilAndGasWells.csv (from External data)
    • CSV_1986_1989/CaliforniaOilAndGasWellMonthlyProduction.csv (from External data)
    • CSV_1986_1989/CaliforniaOilAndGasWellMonthlyInjection.csv (from External data)
    • CSV_1986_1989/CaliforniaOilAndGasWells.csv (from External data)
    • CSV_1990_1994/CaliforniaOilAndGasWellMonthlyProduction.csv (from External data)
    • CSV_1990_1994/CaliforniaOilAndGasWellMonthlyInjection.csv (from External data)
    • CSV_1990_1994/CaliforniaOilAndGasWells.csv (from External data)
    • CSV_1995_1999/CaliforniaOilAndGasWellMonthlyProduction.csv (from External data)
    • CSV_1995_1999/CaliforniaOilAndGasWellMonthlyInjection.csv (from External data)
    • CSV_1995_1999/CaliforniaOilAndGasWells.csv (from External data)
    • CSV_2000_2004/CaliforniaOilAndGasWellMonthlyProduction.csv (from External data)
    • CSV_2000_2004/CaliforniaOilAndGasWellMonthlyInjection.csv (from External data)
    • CSV_2000_2004/CaliforniaOilAndGasWells.csv (from External data)
    • CSV_2005_2009/CaliforniaOilAndGasWellMonthlyProduction.csv (from External data)
    • CSV_2005_2009/CaliforniaOilAndGasWellMonthlyInjection.csv (from External data)
    • CSV_2005_2009/CaliforniaOilAndGasWells.csv (from External data)
    • CSV_2010_2014/CaliforniaOilAndGasWellMonthlyProduction.csv (from External data)
    • CSV_2010_2014/CaliforniaOilAndGasWellMonthlyInjection.csv (from External data)
    • CSV_2010_2014/CaliforniaOilAndGasWells.csv (from External data)
    • CSV_2015/CaliforniaOilAndGasWellMonthlyProduction.csv (from External data)
    • CSV_2015/CaliforniaOilAndGasWellMonthlyInjection.csv (from External data)
    • CSV_2015/CaliforniaOilAndGasWells.csv (from External data)
    • CSV_2016/CaliforniaOilAndGasWellMonthlyProduction.csv (from External data)
    • CSV_2016/CaliforniaOilAndGasWellMonthlyInjection.csv (from External data)
    • CSV_2016/CaliforniaOilAndGasWells.csv (from External data)
    • CSV_2017/CaliforniaOilAndGasWellMonthlyProduction.csv (from External data)
    • CSV_2017/CaliforniaOilAndGasWellMonthlyInjection.csv (from External data)
    • CSV_2017/CaliforniaOilAndGasWells.csv (from EExternal data)
    • CSV_2018/CaliforniaOilAndGasWellMonthlyProduction.csv (from External data)
    • CSV_2018/CaliforniaOilAndGasWells.csv (from External data)
    • CSV_2018/CaliforniaOilAndGasWellMonthlyInjection.csv (from External data)
    • CSV_2019/CaliforniaOilAndGasWellMonthlyProduction.csv (from External data)
    • CSV_2019/CaliforniaOilAndGasWellMonthlyInjection.csv (from External data)
    • CSV_2019/CaliforniaOilAndGasWells.csv (from External data)
    • county_codes.csv (from External data)
  • Outputs:
    • well_prod_m.rds
    • well_inject_m.rds
    • wells_19.csv

extraction/process-monthly-prod.R Processes historical and contemporary data on oil well production and injection, correcting and merging datasets from various years to create comprehensive records. It also addresses data quality issues, specifically for the year 2019, by applying pattern replacements to correct errors in the dataset, and then saves the cleaned and processed data for further analysis.

  • Inputs:
    • well_prod_m.rds (from clean_doc_prod.R)
    • wells_19.csv (from clean_doc_prod.R)
  • Outputs:
    • data/stocks-flows/processed/well_prod_m_processed.csv
    • data/stocks-flows/processed/field_info.csv

extraction/process-monthly-inj.R This script processes the monthly injection file to filter out gas fields, add needed columns, and rename columns.

  • Inputs:
    • well_inject_m.rds (from clean_doc_prod.R)
    • wells_19.csv (from clean_doc_prod.R)
  • Outputs:
    • data/stocks-flows/processed/well_inj_m_processed.csv

extraction/opgee-carb-results.R This script takes raw outputs from the OPGEE model and organizes them into a long format.

  • Inputs:
    • OPGEE_v2.0_with-CARB-inputs.xlsm (from External data)
    • opgee_field_names.csv (created externally)
  • Outputs:
    • field-level-emissions-results_processed_revised.csv

extraction/rystad_processing.R Processes greenhouse gas (GHG) emission intensities from the OPGEE (Oil Production Greenhouse Gas Emissions Estimator) model for various oil fields, incorporating specific inputs to adjust for California Air Resources Board (CARB) standards. It reads, transforms, and aggregates the data to calculate upstream emissions and convert emissions from grams per megajoule (gCO2e/MJ) to kilograms per barrel (kgCO2e/bbl) for life cycle analysis, then saves the processed results to a CSV file for further analysis.

  • Inputs:
    • Asset_opex_capex_govtt.csv (from External data)
    • ca_production.csv (from External data)
    • asset_econ_categories.csv (from External data)
    • resources_prod_myprod.csv (from External data)
    • capex_per_recoverable_bbl.csv (from External data)
    • asset-wells.csv (from External data)
    • capex_per_bbl_nom.csv (from External data)
    • opex_per_bbl_nom.csv (from External data)
    • rystad_asset_rename.csv (from External data)
    • well_cost_per_eur.csv (from External data)
    • field_to_asset.csv (from External data)
    • well_prod_m_processed.csv (from process-monthly-prod.R)
  • Outputs:
    • oil_asset_opex_capex_govtt_clean.csv
    • ca_oil_production.csv
    • economically_recoverable_resources_scenarios.csv
    • economically_recoverable_resources_scenarios_wide.csv
    • capex_bbl_reserves.csv
    • rystad_asset_apis.csv
    • field_rystad_match_apis_revised.csv
    • rystad_capex_bbl_nom_clean.csv
    • rystad_opex_bbl_nom_clean.csv
    • well_cost_per_eur_clean.csv (not required for analysis)
    • rystad_field_asset.csv (not required for analysis)
    • asset_economics_cats.csv (not required for analysis)
    • ca_asset_opex_capex_govtt_clean.csv (not required for analysis)

extraction/zero_prod.R Analyzes periods of zero production for oil wells, identifying wells that have ceased production for consecutive months and determining if they were ever reactivated. It calculates the duration of these inactive periods, distinguishes between wells that permanently stopped producing versus those that resumed production, and exports summaries and detailed records of these wells and their statuses for further analysis.

  • Inputs:
    • well_prod_m_processed.csv (from clean_doc_prod.R)
    • AllWells_20210427.csv (from External data)
  • Outputs:
    • no_prod_wells_out.csv

extraction/income_data.R Retrieves and processes U.S. Census data on median household income by census tract and county for California, using the American Community Survey (ACS) 5-year estimates for 2015-2019. It employs the tidycensus R package to access the data, formats the data for clarity and consistency, and saves the processed data sets to CSV files for census tract and county-level median household incomes..

  • Outputs:
    • ca-median-house-income.csv
    • ca-median-house-income-county.csv
    • scenario-prep

ccs_parameterization.R Integrates field-level oil production data with greenhouse gas (GHG) emissions factors and refinery-level emissions data to calculate comprehensive GHG emissions associated with oil extraction and refining processes. It prepares and merges these datasets for a selected year, computes field-level extraction emissions, aligns refinery data, and finally, combines both datasets to analyze and potentially solve for the mean value of a parameter ("b") in relation to carbon capture and storage (CCS) costs and GHG emissions across the extraction and refining sectors.

  • Inputs:
    • crude_prod_x_field_revised.csv (from crude_prod_x_field.R)
    • ghg_emissions_x_field_2018-2045.csv (from forecast_ghg_emission_factors.R)
    • refinery_ghg_emissions.csv (from ghg_emissions.R)
  • Outputs: None

extraction-segment/model-prep

well_setback_sp_prep.R Processes spatial data from the FracTracker Setback dataset to analyze and visualize sensitive receptors (e.g., dwellings, playgrounds, healthcare facilities) around oil and gas extraction sites in California. It involves reading and transforming spatial layers from a Geographic Database (GDB), applying buffers to identify setback areas, simplifying complex geometries for efficiency, and ultimately creating and saving spatial buffers around sensitive sites, which are then visualized using various GIS and mapping libraries.

  • Inputs:
    • FracTrackerSetbackdata.gdb (layers SetbackOutlines_SR_Dwellings_082220, PlaygroundsinCities, DayCareCenters, reselderlyCare, CHHS_adultdayhealthcare_csv_Events, CHHS_altbirthing_csv_Events, CHHS_Dialysis_csv_Events, CHHS_healthcare_facility_locations_csv_Events, CHHS_intermedcarefac_csv_Events, CHHS_PrimaryCareClinic_csv_Events, CHHS_psychclinics_csv_Events, CHHS_rehabclinic_csv_Events, CHHS_skillednursingfacs_csv_Events, CHHS_surgicalclinic_csv_Events, CHHS_acutecarehospital_csv_Events_1, CAAcuteCAreHostpitalslatlon_1, PrivSchoolsCA_1, SchoolPropCA_1, SchoolsCA_Sabins_1, from External data)
  • Outputs:
    • buffer_1000ft.shp
    • buffer_2500ft.shp
    • buffer_3200ft.shp
    • buffer_5280.shp

gen_well_setback_status.R Processes well and field data to determine their proximity to sensitive receptors based on predefined setback distances (1000ft, 2500ft, 3200ft, and 5280ft) around oil and gas extraction sites in California. It involves reading spatial data, creating buffers around sensitive areas, and then calculating which wells and fields fall within these buffers, generating attributes for each well and field regarding their inclusion within the setbacks, and visualizing these relationships through maps.

  • Inputs:
    • buffer_1000ft.shp (from well_setback_sp_prep.R)
    • buffer_2500ft.shp (from well_setback_sp_prep.R)
    • buffer_3200ft.shp (from well_setback_sp_prep.R)
    • buffer_5280.shp (from well_setback_sp_prep.R)
    • allwells_gis/Wells_All.shp (from External data)
    • DOGGR_Admin_Boundaries_Master.shp (from External data)
    • well_prod_m_processed.csv (from process-monthly-prod.R)
  • Outputs:
    • wells_in_setbacks_revised.csv
    • setback_coverage_R.csv
    • coverage_map.html

economically_recoverable_resources.R Processes data on economically recoverable resources (ERR) by reading a dataset of assets, their yearly production, and ERR scenarios, then calculates and summarizes key metrics such as the sum of a custom production scenario (my_production), the maximum resources available per asset, and the cumulative sum of production. It then merges these metrics to analyze the proportion of production relative to the custom scenario and maximum resources, finally saving these summaries as CSV files for further analysis.

  • Inputs:
    • economically_recoverable_resources_scenarios_wide.csv (from rystad_processing.R)
  • Outputs:
    • asset_sum_my-production.csv
    • asset_max_resources.csv
    • asset-year_cumulative_sum_production.csv
    • asset-year_production_my-production_resources.csv

create_entry_econ_variables.R Aggregates and processes various datasets related to oil asset economics, production, and capital and operational expenditures per barrel, collected from Rystad Energy. It adjusts and merges these datasets to create a comprehensive dataframe that includes adjusted location names, economics group filters, production summaries, and calculates economics per barrel. The final dataframe is enriched with additional metrics like cumulative production ratios and resources, then saved for further analysis.

  • Inputs:
    • oil_asset_opex_capex_govtt_clean.csv (from rystad_processing.R)
    • ca_oil_production.csv (from rystad_processing.R)
    • capex_bbl_reserves.csv (from rystad_processing.R)
    • rystad_opex_bbl_nom_clean.csv (from rystad_processing.R)
    • rystad_capex_bbl_nom_clean.csv (from rystad_processing.R)
    • asset_sum_my-production.csv (from economically_recoverable_resources.R)
    • asset_max_resources.csv (from economically_recoverable_resources.R)
    • asset-year_cumulative_sum_production.csv (from economically_recoverable_resources.R)
    • asset-year_production_my-production_resources.csv (from economically_recoverable_resources.R)
  • Outputs:
    • rystad_entry_variables.csv

impute_costs.do This script (a) imputes the asset-level costs (capex and opex) for years in the historic period when the cost data is missing, and (b) extrapolates the asset-level costs into the future period.

  • Inputs:
    • rystad_entry_variables.csv (from create_entry_econ_variables.R)
  • Outputs:
    • Rystad_cost_imputed_all_assets.csv

init_yr_prod.R Identifies the initial year of oil production for each well based on the dataset of well production, processes and aggregates production data to determine top fields and their relative production, and calculates the age of wells from their start date of production. It then creates a balanced dataset of well production over time, merges it with the initial production year data to calculate the age of wells, and saves the processed data for further analysis, ensuring that the annual production data aligns with historical records.

  • Inputs:
    • well_prod_m_processed.csv (from clean_doc_prod.R)
  • Outputs:
    • well_start_prod_api10_revised.csv

match_fields_assets.R Performs several steps to match oil fields to assets based on well API numbers, including aggregating well production data, matching fields to assets through API numbers, and identifying productive fields. It creates a dataset that pairs fields with assets, handles unmatched fields by finding the nearest asset or field spatially, and saves the matched and unmatched datasets for further analysis. The script utilizes spatial data processing and nearest neighbor algorithms to ensure comprehensive field-to-asset mapping, accommodating cases where direct matches are not available by leveraging spatial proximity.

  • Inputs:
    • field_rystad_match_apis_revised.csv (from rystad_processing.R)
    • well_prod_m_processed.csv (from process-monthly-prod.R)
    • DOGGR_Admin_Boundaries_Master.shp (from External data)
    • asset_latlon.csv (from External data)
    • ca_oil_production.csv (from rystad_processing.R)
    • Rystad_cost_imputed_all_assets.csv (from impute_costs.do)
  • Outputs:
    • outputs/stocks-flows/entry-model-input/well_doc_asset_match_revised.csv
    • data/Rystad/data/processed/asset_latlon_adj.csv
    • data/Rystad/data/processed/fieldsAssets_adj_revised.csv
    • outputs/stocks-flows/entry-model-input/field_x_field_match_revised.csv
    • outputs/stocks-flows/entry-model-input/final/field_asset_matches_revised.csv

create_entry_input.R Aggregates various datasets to create an input file for modeling entry decisions in the oil industry. It merges field-to-asset matches, economic data, well production, and price information, performing calculations such as imputed costs and new well production to compile a comprehensive dataset that includes variables like field names, production levels, costs, and oil prices for each field and year. This consolidated dataset is intended for analyzing the economic viability and entry decisions within the oil field assets over time.

  • Inputs:
    • field_asset_matches_revised.csv (from match_fields_assets.R)
    • oil_asset_opex_capex_govtt_clean.csv (from rystad_processing.R)
    • rystad_entry_variables.csv (from create_entry_econ_variables.R)
    • Rystad_cost_imputed_all_assets.csv (from impute_costs.do)
    • well_prod_m_processed.csv (from process-monthly-prod.R)
    • well_start_prod_api10_revised.csv (from init_yr_prod.R)
    • wti_brent.csv (from External data)
  • Outputs:
    • outputs/stocks-flows/entry-input-df/final/field_capex_opex_forecast_revised.csv
    • outputs/stocks-flows/entry-input-df/final/docfield_asset_crosswalk_entrydf_revised.csv
    • outputs/stocks-flows/entry-input-df/final/entry_df_final_revised.csv

entry.do This script prepares the data for the entry model (which predicts the number of new wells in each field in each year in the future period in predict.do).

  • Inputs:
    • entry_df_final_revised.csv (from create_entry_input.R)
  • Outputs:
    • entry_revised.dta
    • entry_revised_real.dta

depl.do This script estimates the total oil resource of each field.

  • Inputs:
    • entry_revised.dta (from entry.do)
  • Outputs:
    • field_resource_revised.csv

predict.do This script predicts the number of new wells in each field in each year in the future period.

  • Inputs:
    • entry_revised.dta (from entry.do)
  • Outputs:
    • new_wells_pred_revised.csv
    • poisson_regression_coefficients_revised.csv

tab_entryexit.do This script produces the regression outputs for Supplementary Table 1.

  • Inputs:
    • entry_revised_real.dta (from entry.do)
    • exit_fields.dta (from exit.do)
  • Outputs: None

crude_prod_x_field.R Processes monthly oil production data at the well level to aggregate and analyze production by oil field and year. It creates a dataset that includes field codes, field names, years, and total barrels produced, excluding fields with zero production, and saves this aggregated production data for further analysis.

  • Inputs:
    • well_prod_m_processed.csv (from clean_doc_prod.R)
  • Outputs:
    • crude_prod_x_field_revised.csv

field_county_production.R Calculates the annual oil production by field and county, determining the proportion of each field's production that comes from each county. It saves two datasets: one with annual production proportions by field and county for all years, and another with proportions for just the last year of non-zero production for each field, organizing the data by year, field code, field name, and county name.

  • Inputs:
    • well_prod_m_processed.csv (from clean_doc_prod.R)
  • Outputs:
    • annual_field_county_production_proportion_revised.csv
    • annual_final_year_field_county_production_proportion_revised.csv

field_emission_factors_2015.R Calculates field-level greenhouse gas emission factors for California oil fields, incorporating data on oil production, injection practices, and emission factors from various sources. It distinguishes between fields using steam injection and those that do not, applying median emission factors accordingly, and calculates total GHG emissions for the year 2015 based on these factors and field-level production data.

  • Inputs:
    • injection-by-well-type-per-field-per-year_1977-2018_revised.csv (from injection-type-by-field.R)
    • field-level-emissions-results_processed_revised.csv (from opgee-carb-results.R)
    • entry_df_final_revised.csv (from create_entry_input.R)
    • crude_prod_x_field_revised.csv (from crude_prod_x_field.R)
  • Outputs:
    • opgee_emission_factors_x_field_2015_revised.csv
    • ghg_emissions_x_field_2015_revised.csv

county-setback.R Calculates and visualizes the percentage of each county in California covered by oil and gas setback zones of different distances (1000ft, 2500ft, 3200ft, and 5280ft) from oil and gas wells. It uses spatial data manipulation to intersect county and field boundaries with setback buffer zones, computes the area covered by each setback within counties, and saves the results for further analysis.

  • Inputs:
    • CA_Counties_TIGER2016.shp (from External data)
    • DOGGR_Admin_Boundaries_Master.shp (from External data)
    • buffer_1000ft.shp (from well_setback_sp_prep.R)
    • buffer_2500ft.shp (from well_setback_sp_prep.R)
    • buffer_3200ft.shp (from well_setback_sp_prep.R)
    • buffer_5280.shp (from well_setback_sp_prep.R)
    • well_prod_m_processed.csv (from process-monthly-prod.R)
    • crude_prod_x_field_revised.csv (from crude_prod_x_field.R)
  • Outputs:
    • county_level_setback_coverage.csv

well_exits.R Explores the exit patterns of oil and gas wells by analyzing their production data, specifically focusing on wells that have been plugged. It loads various datasets related to well production, filters for plugged wells, computes the last year of production for these wells, and generates summaries of final year production across different oil fields. The analysis aims to understand how production levels change leading up to a well being plugged and how these patterns vary across different fields and well vintages.

  • Inputs:
    • well_prod_m_processed.csv (from clean_doc_prod.R)
    • AllWells_20210427.csv (from External data)
  • Outputs:
    • well_exit_volume_x_field_v1_revised.csv

prep_data_field_year.R Processes and analyzes oil production data to assess field-level yearly decline parameters in oil production. It starts by loading various datasets related to oil well production, initializing years, and new well entries. The script then cleans and aggregates the data to calculate oil production metrics, including peak production years and average production rates per well. It identifies decline rates by comparing production rates over time and adjusts these metrics to account for the status of the wells (e.g., active vs. plugged). Finally, the script saves several output files that summarize these analyses, providing insights into oil field productivity and decline trends over time.

  • Inputs:
    • well_prod_m_processed.csv (from process-monthly-prod.R)
    • well_start_prod_api10_revised.csv (from init_yr_prod.R)
    • entry_df_final_revised.csv (from create_entry_input.R)
  • Outputs:
    • field-year_peak-production_yearly.csv
    • production_field-year_yearly_entry.csv
    • production_api10_yearly_start_year.csv
    • adj_val_field-year_pred_prod.csv

field-vintage-exit-rule.R This script creates an output based on an alternative exit rule (based on a production threshold). This rule is not ultimately used in the analysis.

  • Inputs:
    • production_field-year_yearly_entry.csv (from prep_data_field_year)
    • well_exit_volume_x_field_v1_revised.csv (from well_exits.R)
  • Outputs:
    • well_exits_under_rule.csv

field-vintage-exit.R Evaluates and tracks the exit of oil wells from production based on a predefined production threshold rule, comparing annual production for each field-vintage (combination of field code and start year) against a specified exit threshold. It merges datasets containing annual production data and exit thresholds, calculates the number of wells exiting production for each field-vintage annually, aggregates these exits by field and year, and saves the resulting data to a CSV file for analysis of field-level well exits under the applied rule.

  • Inputs:
    • well_prod_m_processed.csv (from clean_doc_prod.R)
    • AllWells_20210427.csv (from External data)
    • no_prod_wells_out.csv (from zero_prod.R)
  • Outputs:
    • well_exits.csv

exit.do This script has the exit model. The outputs are used in the final extraction model.

  • Inputs:
    • well_exits_under_rule.csv (from field-vintage-exit-rule.R)
    • well_exits.csv (from field-vintage-exit.R)
  • Outputs:
    • well_exits_pred.csv
    • exit_regression_coefficients.csv

historic-extraction-emissions.R Analyzes historical oil and gas emissions by processing production data and greenhouse gas inventory data to adjust emissions based on actual production volumes and the energy content of produced oil and natural gas. It calculates adjusted GHG emissions for all oil fields and specifically for fields included in an analysis, incorporates these adjustments into state-level GHG emissions estimates, and saves the adjusted emissions data for further use, providing a nuanced view of the oil and gas sector's impact on emissions.

  • Inputs:
    • well_prod_m.rds (from clean_doc_prod.R)
    • wells_19.csv (from clean_doc_prod.R)
    • N9010CA2a.xls (from External data)
  • Outputs:
    • ghg_sector_data_og_updated.csv
    • historic_ghg_emissions_og.csv
    • historic_ghg_emissions_og_ng_adjusted.csv

prep_data_field_vintage.R Processes and analyzes oil production data to examine decline parameters at the field-vintage level, where "vintage" refers to groups of wells started within specific time periods. It calculates average production rates and decline rates for wells, identifies peak production times, and aggregates this data by field and vintage, aiming to understand how production rates change over time and to support analysis on the longevity and productivity of oil fields.

  • Inputs:
    • well_prod_m_processed.csv (from clean_doc_prod.R) well_start_prod_api10_revised.csv (from init_yr_prod.R) entry_df_final_revised.csv (from create_entry_input.R)
  • Outputs:
    • field-vintage_peak-production_yearly_revised.csv
    • production_field-vintange_yearly_entry_revised.csv
    • production_api10_monthly_revised.csv

decline_parameters_field_start_year.R Performs a detailed analysis to parameterize oil production decline at the field-start year level by fitting hyperbolic and exponential decline models to production data. It systematically processes the production data to identify peak production rates, calculates decline rates for each field and vintage, and applies curve fitting techniques to estimate the decline parameters, ultimately compiling these parameters along with additional field and well information into a comprehensive dataset for further analysis.

  • Inputs:
    • production_field-year_yearly_entry.csv (from prep_data_field_year.R)
    • field-year_peak-production_yearly.csv (from prep_data_field_year.R)
    • entry_df_final_revised.csv (from create_entry_input.R)
  • Outputs:
    • fitted-parameters_field-start-year_yearly_entry.csv

predict_existing_production.R Predicts future oil production from existing wells that have not exited production up to the year 2045. It merges well production data with decline parameters and peak production information, adjusts for wells within setback areas, calculates production per well considering both active and non-setback wells, and aggregates and saves the adjusted production data for analysis, accounting for various scenarios including setbacks and plugged wells.

  • Inputs:
    • production_api10_yearly_start_year.csv (from prep_data_field_year.R)
    • fitted-parameters_field-start-year_yearly_entry.csv (from decline_parameters_field_start_year.R)
    • field-year_peak-production_yearly.csv (from prep_data_field_year.R)
    • adj_val_field-year_pred_prod.csv (from prep_data_field_year.R)
    • wells_in_setbacks_revised.csv (from gen_well_setback_status.R)
  • Outputs:
    • pred_prod_no_exit_2020-2045_field_start_year_revised.csv
    • n_wells_area.csv

analyze-parameters.R Analyzes decline curve parameters for oil production, projecting these parameters from historical data to forecast future production trends from 2020 to 2045. It cleans and processes parameter data, predicts future parameters using linear models, fills in missing field data with median values, and ultimately compiles and saves a comprehensive dataset of forecasted decline parameters for each field and start year. The output is used in v1 of the model, but not the final model.

  • Inputs:
    • crude_prod_x_field_revised.csv (from crude_prod_x_field.R)
    • fitted-parameters_field-start-year_yearly_entry.csv (from decline_parameters_field_start_year.R)
    • entry_df_final_revised.csv (from create_entry_input.R)
  • Outputs:
    • forecasted_decline_parameters_2020_2045.csv

extraction_fields.R Generates a list of oil fields included in an analysis by reading in data from entry and production files, identifying unique field codes, and then matching these field codes against a shapefile of field boundaries to extract the relevant fields. The resulting dataset, which includes field names and codes, is saved as a shapefile for use in further analyses, such as health and labor studies, ensuring that only fields relevant to the study are considered.

  • Inputs:
    • entry_df_final_revised.csv (from create_entry_input.R)
    • pred_prod_no_exit_2020-2045_field_start_year_revised.csv (from predict_existing_production.R)
    • DOGGR_Admin_Boundaries_Master.shp (from External data)
  • Outputs:
    • extraction_fields.shp

extra/injection-type-by-field.R Analyzes and visualizes the types and amounts of water and steam injected into oil wells for enhanced oil recovery, focusing on data from specific fields and years. It processes well injection data to distinguish between wells with single and multiple field associations, calculates the total and type-specific injection volumes, and creates bar plots to illustrate the breakdown of injection types for the top oil-producing fields and those with the highest injection volumes in 2015 and 2018, also noting the carbon intensity values for these fields.

  • Inputs:
    • well_inj_m_processed.csv (from clean_doc_prod.R)
    • well_type_df.csv (from External data) Outputs:
    • injection-by-well-type-per-field_1977-2018_revised.csv
    • injection-by-well-type-per-field-per-year_1977-2018_revised.csv

forecast_ghg_emission_factors.R Forecasts greenhouse gas (GHG) emission factors for oil fields from 2018 to 2045, differentiating between fields using steam injection and those that do not, based on historic and projected production data, injection data, and calculated emissions. It performs linear regressions to predict future emission factors, generates and saves detailed forecasts, and visualizes the trends in emission factors over time for specific categories of fields, such as those using steam injection and the top 10 producing fields.

  • Inputs:
    • Various saved OPGEE results files (copy and pasted from the OPGEE spreadsheet)
    • crude_prod_x_field_revised.csv (from crude_prod_x_field.R)
    • entry_df_final_revised.csv (from create_entry_input.R)
    • injection-by-well-type-per-field-per-year_1977-2018_revised.csv (from injection-type-by-field.R)
  • Outputs:
    • ghg_emissions_x_field_2018-2045.csv
    • ghg_emissions_x_field_historic.csv

extraction-segment/model/full-run-revised

input_prep/emissions-target-90.R Reads a CSV file containing data on greenhouse gas emissions from 2000 to 2019, specifically focusing on the Oil & Gas: Production & Processing segment for the year 2019. It then calculates a target of a 90% reduction from the 2019 GHG emissions level for this segment and saves this target as a new CSV file, indicating the desired emissions reduction goal.

  • Inputs:
    • indust_emissions_2000-2019.csv (from stocks_flows.R)
  • Outputs:
    • emission_reduction_90.csv

input_prep/prep-excise-non-target.R Creates a data frame of hypothetical excise tax scenarios for the years 2020 to 2045 at different tax rates (0, 5%, 10%, 50%, 90%, and 100% of the oil price), assigns a descriptive name to each tax rate scenario, and specifies the units as a fraction of the oil price. It then saves the resulting data frame, which includes the year, tax rate, scenario name, and units, to a CSV file for use in analyzing the impact of various non-target excise tax rates on oil prices.

  • Inputs: None
  • Outputs:
    • excise_tax_non_target_scens.csv

target-functions/func_calc_2045_ghg.R This script contains a function used in the final model to determine a 2045 GHG emissions target.

target-functions/func_calc_carbonpx.R This script contains a function that calculates a stream of carbon prices given a start value.

target-functions/func_calc_inputs_and_ghg.R This script contains a function that prepares input values for the final model.

target-functions/optim_functions.R This script contains functions that find the excise/carbon tax values that result in a target 2045 GHG emission value.

load_input_info.R This script loads input info for the final model. The user will need to update the file paths This script is sourced in 00_extraction_steps.R.

  • Inputs:
    • entry_df_final_revised.csv (from create_entry_input.R)
    • poisson_regression_coefficients_revised.csv (from predict.do)
    • forecasted_decline_parameters_2020_2045.csv (from analyze-parameters.R)
    • field-year_peak-production_yearly.csv (from prep_data_field_year.R)
    • pred_prod_no_exit_2020-2045_field_start_year_revised.csv (from predict_existing_production.R)
    • crude_prod_x_field_revised.csv (from crude_prod_x_field.R)
    • exit_regression_coefficients.csv (from exit.do)
    • field_capex_opex_forecast_revised.csv (from create_entry_input.R)
    • field_resource_revised.csv (from depl.do)
    • oil_price_projections_revised.xlsx (from External data)
    • innovation_scenarios.csv (from Scenario inputs)
    • carbon_prices_revised.csv (from Scenario inputs)
    • ccs_extraction_scenarios_revised.csv (from Scenario inputs)
    • ghg_emissions_x_field_2018-2045.csv (from forecast_ghg_emission_factors.R)
    • setback_coverage_R.csv (from gen_well_setback_status.R)
    • prod_quota_scenarios.csv (from Scenario inputs)
    • excise_tax_non_target_scens.csv (from prep-excise-non-target.R)
    • CCS_LCFS_45Q.xlsx (from Scenario inputs)
    • n_wells_area.csv (from predict_existing_production.R)
    • emission_reduction_90.csv (from emissions-target-90.R)

scenario-list-targets.R This script creates a list of scenarios that is used in 00_extraction_steps.R.

  • Inputs:
    • oil_price_projections_revised.xlsx (from External data)
    • innovation_scenarios.csv (from Scenario inputs)
    • carbon_prices_revised.csv (from Scenario inputs)
    • ccs_extraction_scenarios_revised.csv (from Scenario inputs)
    • setback_coverage_R.csv (from gen_well_setback_status.R)
    • prod_quota_scenarios_with_sb.csv (from setback_quota_scenarios.R)
    • prod_quota_scenarios.csv (from Scenario inputs)
    • excise_tax_non_target_scens.csv (from prep-excise-non-target.R)
    • CCS_LCFS_45Q.xlsx (from Scenario inputs)
  • Output:
    • scenario_id_list_targets.csv

00_extraction_steps.R This script runs the final extraction model.

  • Inputs:
    • scenario_id_list_targets.csv (from scenario-list-targets.R)
  • Outputs:
    • None

fun_extraction_model_targets.R This script runs the final model. This script is sourced in 00_extraction_steps.R.

  • Outputs:
    • run_info.csv
    • XX_vintage.csv (where XX represents a scenario id)
    • XX_field.rds
    • XX_state.rds
    • XX_density.csv
    • XX_exit.csv
    • XX_depletion.csv

extraction-segment/output-review

review_target_out.R This script reviews the outputs of each scenario (particularly 2045 GHG emissions values compared to target values).

  • Inputs:
    • All state-level outputs from fun_extraction_model_targets.R
  • Outputs: None
    • compile-outputs

compile_extraction_outputs_full.R This file compiles the energy, labor, and health outputs for the scenarios.

  • Input files:
    • well_prod_m_processed.csv (from process-monthly-prod.R)
    • oil_price_projections_revised.xlsx (from External data)
    • ghg_emissions_x_field_2018-2045.csv (from forecast_ghg_emission_factors.R)
    • indust_emissions_2000-2019.csv (from stocks_flows.R)
    • ces3results.xlsx (from External data)
    • ca-median-house-income.csv (from income_data.R)
    • ca-median-house-income-county.csv (from income_data.R)
    • ica_multipliers_v2.xlsx (from ica_multiplier_process.R)
    • XX/srm_nh3_fieldYY.csv (where XX represents folders nox, pm25, sox, voc; and YY represents numbers 1-26. The numbers represent the 26 oil extraction field clusters) (from InMap, External data)
    • extraction_fields_clusters_10km.csv (created in ArcGIS)
    • extraction_fields_xwalk_id.dbf (created in ArcGIS)
    • ces3_data.csv (from External data)
    • ct_inc_45.csv (from health_data.R)
    • growth_rates.csv (from External data)
  • Output files:
    • XX_state_results.rds (where XX represents a scenario id)
    • XX_ct_results.rds (where XX represents a scenario id)
    • XX_county_results.rds (where XX represents a scenario id)

compile_subset_csvs.R This file creates summary csvs of the extraction segment outputs.

  • Inputs:
    • scenario_id_list_targets.csv
    • XX_ct_results.rds (where XX represents a scenario id)
    • XX_county_results.rds (where XX represents a scenario id)
    • XX_state_results.rds (where XX represents a scenario id)
  • Outputs:
    • subset_census_tract_results.csv
    • subset_county_results.csv
    • subset_state_results.csv

figs-and-results/

fig_outputs.R This script creates outputs needed to make the figures for the manuscript.

  • Inputs:
    • indust_emissions_2000-2019.csv (from stocks_flows.R)
    • social_cost_carbon.csv (from social_cost_carbon.R)
    • carbon_price_scenarios_revised.xlsx (from Scenario inputs)
    • growth_rates.csv (from External data)
    • ct_inc_45.csv (from health_data.R)
    • subset_state_results.csv (from compile_subset_csvs.R)
    • subset_census_tract_results.csv (from compile_subset_csvs.R)
    • extraction_field_cluster_xwalk.csv (from source_receptor_matrix.do)
  • Outputs:
    • subset_state_results.csv
    • state_levels_all_oil.csv
    • npv_x_metric_all_oil.csv
    • dac_health_labor_all_oil.csv
    • dac_bau_health_labor_all_oil
    • state_dac_ratios.csv

field_characteristics.R This script creates an output used in figures.

  • Inputs:
    • reference case_no_setback_no quota_price floor_no ccs_low innovation_no tax_field_results.rds (from compile_extraction_outputs_full.R)
    • reference case_no_setback_no quota_price floor_no ccs_low innovation_no tax_county_results.rds (from compile_extraction_outputs_full.R)
    • field_capex_opex_forecast_revised.csv (from create_entry_input.R)
    • ghg_emissions_x_field_2018-2045.csv (from forecast_ghg_emission_factors.R)
    • setback_coverage_R.csv (from gen_well_setback_status.R)
    • well_prod_m_processed.csv (from process-monthly-prod.R)
    • ces3results.xlsx (from External data)
    • ct_inc_45.csv (from health_data.R)
  • Outputs:
    • field_characteristics.csv
    • county_characteristics.csv

figure_themes.R This file has figures themes and is sourced in the scripts that create figures for the manuscript.

  • Inputs: None
  • Outputs: None

figure1.R This script creates the components of figure 1.

  • Inputs:
    • well_prod_m_processed.csv (from process-monthly-prod.R)
    • reference case_no_setback_no quota_price floor_no ccs_low innovation_no tax_ct_results.rds (from compile_extraction_outputs_full.R)
    • reference case_no_setback_no quota_price floor_no ccs_low innovation_no tax_county_results.rds (from compile_extraction_outputs_full.R)
    • extraction_fields.shp (from extraction_fields.R)
    • DOGGR_Admin_Boundaries_Master.shp (from External data)
    • CA_Counties_TIGER2016.shp (from External data)
    • CA_Counties_TIGER2016_noislands.shp (from External data)
    • tl_2019_06_tract.shp (from External data)
    • ces3results.xlsx (from External data)
    • new_wells_pred_revised.csv (from predict.do)
    • extraction_fields_clusters_10km.csv (created in ArcGIS)
    • extraction_fields_xwalk_id.dbf (created in ArcGIS)
  • Outputs:
    • Components of figure 1 (arranged together in Adobe Illustrator)

figure2.R This script creates figure 2 in the main text and figures in the Supplementary Information.

  • Inputs:
    • state_levels_all_oil.csv (from fig_outputs.R)
  • Outputs:
    • figure2-ref-case.pdf/csv
    • figure2-low.pdf/csv
    • figure2-high.pdf/csv

figure3.R This script creates figures 2 and 3 in the main text and additional figures in the Supplementary Information.

  • Inputs:
    • npv_x_metric_all_oil.csv (from fig_outputs.R)
    • dac_bau_health_labor_all_oil.csv (from fig_outputs.R)
  • Outputs:
    • figure3-ref-case.pdf/png
    • figure3-low.pdf/png
    • figure3-high.pdf/png
    • figure5-refcase-relBAU.pdf/png

figure6.R This script creates figure 6 in the main text and additional figures in the Supplementary Information.

  • Inputs:
    • state_levels_all_oil.csv (from fig_outputs.R)
    • npv_x_metric_all_oil.csv.csv (from fig_outputs.R)
    • dac_bau_health_labor_all_oil.csv (from fig_outputs.R)
  • Outputs:
    • figure6-ref-case.pdf
    • figure6-ref-case.png

calc_values.R This script calculates values used in the main text and Supplementary Information

  • Inputs:
    • state_levels_all_oil.csv (from fig_outputs.R)
  • Outputs:
    • none

prep_files_for_figs.R This script preps oil price data for figures

  • Inputs:
    • oil_price_projections_revised.xlsx.csv (from External data)
    • wti_brent.csv (from External data) Outputs:
    • historial_brent.csv

si/capex-opex-figs.R This file makes figures of opex and capex (historic and future projections).

  • Inputs:
    • field_capex_opex_forecast_revised.csv (from create_entry_input.R)
    • entry_df_final_revised.csv (from create_entry_input.R)
    • well_prod_m_processed.csv (from process-monthly-prod.R)
  • Outputs:
    • projected-capex-opex-si-fig.png
    • historical-capex-opex-si-fig.png

si/county-level-figs.R This script makes county-level figures.

  • Inputs:
    • ghg_emissions_x_field_2018-2045.csv (from forecast_ghg_emission_factors.R)
    • well_prod_m_processed.csv (from process-monthly-prod.R)
    • field_capex_opex_forecast_revised.csv (from create_entry_input.R)
    • setback_coverage_R.csv (from gen_well_setback_status.R)
    • subset_census_tract_results.csv (from compile_subset_csvs.R)
    • county_level_setback_coverage.csv (from county-setback.R)
    • extraction_field_cluster_xwalk.csv (from source_receptor_matrix.do)
    • oil_price_projections_revised.xlsx (from External data)
    • well_inj_m_processed.csv (from clean_doc_prod.R)
    • ces3results.xlsx (from External data)
    • ica_multipliers_v2.xlsx (from ica_multiplier_process.R)
    • CA_Counties_TIGER2016.shp (from External data)
  • Outputs:

si/entry-exit-figs.R This script makes well entry and exit figures.

  • Inputs:
    • well_prod_m_processed.csv (from process-monthly-prod.R)
    • new_wells_pred_revised.csv (from predict.do)
    • well_exits_pred.csv (from exit.do)
  • Outputs:
    • pred_fullsample_topfield.png
    • pred_fullsample_state.png
    • pred_exit_topfield.png
    • pred_exit_state.png

si/si-results.R This script makes figures for the supplementary document.

  • Inputs:
    • state_levels_all_oil.csv (from fig_outputs.R)
    • oil_price_projections_revised.xlsx (from External data)
    • carbon_prices_revised.csv (from External data)
    • well_prod_m_processed.csv (from process-monthly-prod.R)
  • Outputs:
    • si-oil-prod-oilpx.png
    • si-ghg-oilpx.png
    • si-prod-setback.png
    • si-ghg-setback.png
    • si-excise-tax-fig.png
    • si-carbon-tax-fig.png
    • hist-future-prod.png

si/si-macro-econ-figs.R This script makes figures for the supplementary document.

  • Inputs:
    • oil_price_projections_revised.xlsx (from External data)
    • carbon_prices_revised.csv (from External data)
  • Outputs:
    • Figures

si/ghg_x_cost.R This script makes figures showing GHG intensity by cost of production.

  • Inputs:
    • ghg_emissions_x_field_2018-2045.csv (from forecast_ghg_emission_factors.R)
    • well_prod_m_processed.csv (from process-monthly-prod.R)
    • field_capex_opex_forecast_revised.csv (from create_entry_input.R)
  • Outputs:
    • ghg_x_capex_plus_opex.pdf
    • ghg_x_production_cost.pdf

si/clusters.R This script makes figures showing GHG intensity by cost of production.

  • Inputs:
    • CA_Counties_TIGER2016.shp (from External data)
    • CA_Counties_TIGER2016_noislands.shp (from External data)
    • extraction_fields_clusters_10km.shp (dataset obtained from creating 10 km buffers surrounding fields from ArcGIS)
  • Outputs:
    • cluster_si_fig.png
    • cluster_si_fig.pdf

mechanism

scripts/mechanisms.do This STATA script generates the cross-sectional panels in Fig. 4 of the main text and Fig. S33 in the SI. It requires changing the local file path at the top of the script.

  • Inputs:
    • county_characteristics.csv (from field_characteristics.R)
    • extraction_cluster_affectedpop.csv (from scripts/srm_extraction_population.R)
    • extraction_field_cluster_xwalk.csv (from scripts/obtain_field_cluster_xwalk.do)
    • field_characteristics.csv (from field_characteristics.R)
  • Outputs:
    • Health_emp_mechanism_setback_2500.jpg (Fig. 4 in main text)
    • cluster_ghg_intensity_cost.jpg (Fig. S33 in SI)

Zenodo repository

This section lists the files that are included in the Zenodo repository. It includes all publically available input files, intermediate files needed to run the extraction model, model outputs (including extraction, health, and labor outputs), and files needed to create figures and results in the manuscript. Please note that the user will need to change the file paths and change how some objects are defined before running the codes. A full description of the scripts can be found in the readme for the manuscript’s GitHub repository ca-transport-supply-decarb. Due to data confidentiality, the user can only run a subset of the scripts. Thus, we provide all of the intermediate outputs needed to run the following scripts:

  • ca-transport-supply-decarb/energy/extraction-segment/model/full-run-revised/00_extraction_steps.R - this script runs the energy model that results in oil extraction outputs. To successfully run the energy model, the user should make the following changes in the script:
    • Set zenodo_repo <- TRUE
    • Define zenodo_user_path, which should be the user-specific path that leads up to the ca-transport-supply-decarb-files folder downloaded from Zenodo
    • Define a save_path, which represent the path to where the user would like the outputs to be saved
    • Define a run_name, which is used with the current to generate a folder to save outputs (this folder will be created in the save_path location)
  • ca-transport-supply-decarb/energy/extraction-segment/figs-and-results/fig_outputs.R - this script takes the energy, health, and labor outputs and computes values for the manuscript (i.e., outputs needed to make figures and results presented in the paper). To successfully run the code, the user should make the following changes to the script:
    • Set zenodo_repo <- TRUE
    • Define zenodo_user_path, which should be the user-specific path that leads up to the ca-transport-supply-decarb-files folder downloaded from Zenodo
    • Define save_info_path, which is a path specifying where the user wants the outputs to be saved
    • Zenodo users do not need to specify energy_result_date or comp_result_date.
  • The scripts contained in the ca-transport-supply-decarb/energy/extraction-segment/figs-and-results/ folder (these scripts create figures and values presented in the manuscript). The files figure2.R, figure3.R, and figure6.R are set up so that the user can set zenodo_repo <- TRUE and specify a zenodo_user_path and zenodo_save_path. For all other scripts in this folder, the user will need to update file paths.

List of files contained in Zenodo repository

ca-transport-supply-decarb-files/

  • inputs/
    • gis/
      • CA_counties_noislands/
        • CA_Counties_TIGER2016_noislands.shp (from External data)
      • field-boundaries/
        • DOGGR_Admin_Boundaries_Master.shp (from External data)
      • CA_Counties/
        • CA_Counties_TIGER2016.shp (from External data)
      • census-tract/
        • tl_2019_06_tract.shp (from External data)
    • extraction/
      • monthly-prod-inj-wells/ (from External data)
        • CSV_1977_1985/
          • CaliforniaOilAndGasWellMonthlyProduction.csv
          • CaliforniaOilAndGasWellMonthlyInjection.csv
          • CaliforniaOilAndGasWells.csv
        • CSV_1986_1989/
          • CaliforniaOilAndGasWellMonthlyProduction.csv
          • CaliforniaOilAndGasWellMonthlyInjection.csv
          • CaliforniaOilAndGasWells.csv
        • CSV_1990_1994/
          • CaliforniaOilAndGasWellMonthlyProduction.csv
          • CaliforniaOilAndGasWellMonthlyInjection.csv
          • CaliforniaOilAndGasWells.csv
        • CSV_1995_1999/
          • CaliforniaOilAndGasWellMonthlyProduction.csv
          • CaliforniaOilAndGasWellMonthlyInjection.csv
          • CaliforniaOilAndGasWells.csv
        • CSV_2000_2004/
          • CaliforniaOilAndGasWellMonthlyProduction.csv
          • CaliforniaOilAndGasWellMonthlyInjection.csv
          • CaliforniaOilAndGasWells.csv
        • CSV_2005_2009/
          • CaliforniaOilAndGasWellMonthlyProduction.csv
          • CaliforniaOilAndGasWellMonthlyInjection.csv
          • CaliforniaOilAndGasWells.csv
        • CSV_2010_2014/
          • CaliforniaOilAndGasWellMonthlyProduction.csv
          • CaliforniaOilAndGasWellMonthlyInjection.csv
          • CaliforniaOilAndGasWells.csv
        • CSV_2015/
          • CaliforniaOilAndGasWellMonthlyProduction.csv
          • CaliforniaOilAndGasWellMonthlyInjection.csv
          • CaliforniaOilAndGasWells.csv
        • CSV_2016/
          • CaliforniaOilAndGasWellMonthlyProduction.csv
          • CaliforniaOilAndGasWellMonthlyInjection.csv
          • CaliforniaOilAndGasWells.csv
        • CSV_2017/
          • CaliforniaOilAndGasWellMonthlyProduction.csv
          • CaliforniaOilAndGasWellMonthlyInjection.csv
          • CaliforniaOilAndGasWells.csv
        • CSV_2018/
          • CaliforniaOilAndGasWellMonthlyProduction.csv
          • CaliforniaOilAndGasWellMonthlyInjection.csv
          • CaliforniaOilAndGasWells.csv
        • CSV_2019/
          • CaliforniaOilAndGasWellMonthlyProduction.csv
          • CaliforniaOilAndGasWellMonthlyInjection.csv
          • CaliforniaOilAndGasWells.csv
      • All_wells_20200417.xlsx (from External data)
      • AllWells_20210427.csv (from External data)
      • county_codes.csv (from External data)
      • oil_price_projections_revised.xlsx (from External data)
      • well_type_df.csv (from External data)
      • 2000_2019_ghg_inventory_trends_figures.xlsx (from External data)
    • health/
      • ces3results.xlsx (from External data)
      • nhgis0001_ts_geog2010_tract.csv (from EExternal data)
      • CDOF_p2_Age_1yr_Nosup.csv (from External data)
      • County_def.shp (from External data)
      • age_group_desc.csv (from External data)
      • Mortality Incidence (2015).csv (from External data)
      • growth_rates.csv (from External data)
      • ces3_data.csv (from External data)
    • labor/
      • fte-convert.xlsx (from External data)
    • scenarios/
      • innovation_scenarios.csv (from External dataa)
      • carbon_prices_revised.csv (from External data)
      • ccs_extraction_scenarios.csv (from External data)
      • ccs_extraction_scenarios_revised.csv (from Scenario inputs)
      • CCS_LCFS_45Q.xlsx (from External data)
      • prod_quota_scenarios.csv (from External data)
  • intermediate/
    • extraction-model/
      • refinery_ghg_emissions.csv
      • entry_df_final_revised.csv (from create_entry_input.R)
      • poisson_regression_coefficients_revised.csv (from predict.do)
      • forecasted_decline_parameters_2020_2045.csv (from analyze-parameters.R)
      • field-year_peak-production_yearly.csv (from prep_data_field_year.R)
      • pred_prod_no_exit_2020-2045_field_start_year_revised.csv (from predict_existing_production.R)
      • crude_prod_x_field_revised.csv (from crude_prod_x_field.R)
      • exit_regression_coefficients.csv (from exit.do)
      • field_capex_opex_forecast_revised.csv (from create_entry_input.R)
      • field_resource_revised.csv (from depl.do)
      • ghg_emissions_x_field_2018-2045.csv (from forecast_ghg_emission_factors.R)
      • setback_coverage_R.csv (from gen_well_setback_status.R)
      • excise_tax_non_target_scens.csv (from prep-excise-non-target.R)
      • n_wells_area.csv (from predict_existing_production.R)
      • emission_reduction_90.csv (from emissions-target-90.R)
      • prod_quota_scenarios_with_sb.csv (from setback_quota_scenarios.R)
      • scenario_id_list_targets.csv (from scenario-list-targets.R)
    • inmap-processed-srm-extraction/
      • nh3/srm_nh3_field1.csv (26 files, 1-26) (from InMap, External data)
      • nox/srm_nox_field1.csv (26 files, 1-26) (from InMap, External data)
      • pm25/srm_pm25_field1.csv (26 files, 1-26) (from InMap, External data)
      • sox/srm_sox_field1.csv (26 files, 1-26) (from InMap, External data)
      • voc/srm_voc_field1.csv (26 files, 1-26) (from InMap, External data)
  • outputs/
    • model-out/
      • subset_census_tract_results.csv (from compile_subset_csvs.R)
      • subset_county_results_adj.csv (from compile_subset_csvs.R)
      • subset_state_results.csv (from compile_subset_csvs.R)
    • fig-and-results-out/
      • state_levels_all_oil.csv (from fig_outputs.R)
      • npv_x_metric_all_oil.csv (from fig_outputs.R)
      • dac_bau_health_labor_all_oil.csv (from fig_outputs.R)
      • field_characteristics.csv (from field_characteristics.R)
      • county_characteristics.csv (from field_characteristics.R)
      • extraction_cluster_affectedpop.csv (from field_characteristics.R)
      • extraction_field_cluster_xwalk.csv (from field_characteristics.R)
      • well_prod_m_processed.csv (from process-monthly-prod.R)
      • extraction_fields.shp (from extraction_fields.R)
      • new_wells_pred_revised.csv (from predict.do)
      • reference case-no_setback-no quota-price floor-no ccs-low innovation-no tax-0_ct_results.rds (from fun_extraction_model_targets.R)
      • county_level_out_adjusted.csv (use instead of reference case-no_setback-no quota-price floor-no ccs-low innovation-no tax-0_county_results.rds) (from fun_extraction_model_targets.R)
      • extraction_fields_clusters_10km.csv (dataset obtained from creating 10 km buffers surrounding fields from ArcGIS)
      • extraction_fields_xwalk_id.dbf (created in ArcGIS)
      • social_cost_carbon.csv (from social_cost_carbon.R)
      • ct_inc_45.csv (from health_data.R)
      • growth_rates.csv (from External data)
      • indust_emissions_2000-2019.csv (from stocks_flows.R)

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