wesleyjcole · Aug 28, 2026 · Aug 28, 2026 · Aug 28, 2026 · Aug 28, 2026 · Aug 31, 2026
diff --git a/docs/source/model_documentation.md b/docs/source/model_documentation.md
@@ -1302,9 +1302,9 @@ ReEDS assumes electrolyzer units have a 20-year lifespan and a 10-year electroly
 
 | Technology | Year | Capital Cost (\$/kW) | Variable O&M (\$/kWh) | Fixed O&M (\$/kW-yr) | Electricity Use (kWh/kg) | Natural Gas Use (MMBtu/kg) |
 |:--:|:--:|:--:|:--:|:--:|:--:|:--:|
-| Electrolyzer | 2020 | 1750 | 0 | 101.9 | 56.1 | -- |
-| Electrolyzer | 2035 | 550 | 0 | 32.0 | 53.8 | -- |
-| Electrolyzer | 2050 | 550 | 0 | 25.0 | 51.5 | -- |
+| Electrolyzer | 2020 | 1750 | 0 | 87.5 | 56.1 | -- |
+| Electrolyzer | 2035 | 550 | 0 | 27.5 | 53.8 | -- |
+| Electrolyzer | 2050 | 550 | 0 | 27.5 | 51.5 | -- |
 | Steam methane reforming | 2020 | 649 | 0.087 | 20.9 | 0.88 | 0.192 |
 | Steam methane reforming | 2035 | 634 | 0.087 | 20.4 | 0.88 | 0.192 |
 | Steam methane reforming | 2050 | 622 | 0.087 | 20.0 | 0.88 | 0.192 |
@@ -1379,8 +1379,8 @@ Fixed and variable O&M assumptions are taken from Exhibit 5-19 (Case B31A) of th
 ### Direct Air Capture
 
 The model can also procure negative emissions by removing and storing CO<sub>2</sub> from the atmosphere using direct air capture.
-DAC is represented as a sorbent design that uses only electricity as an input, with an energy consumption of 3.72 MWh per tonne of CO<sub>2</sub> removed.
-Overnight capital costs are assumed to be \$1,932 per tonne-year capture capacity, with annual FOM costs of 4.6% of the capital costs and nonfuel VOM costs of \$21 per tonne.
+DAC is represented as a sorbent design that uses only electricity as an input, with an energy consumption of 4.40 MWh per tonne of CO<sub>2</sub> removed.
+In 2019 dollars, overnight capital costs are assumed to be \$931 per tonne-year of capture capacity, with annual FOM costs of 3.74% of the capital costs and nonfuel VOM costs of \$46.69 per tonne.
 
 
 ### CO<sub>2</sub> Transport and Storage
@@ -2901,9 +2901,8 @@ For imported electricity, the model calculates the regional emissions rate (metr
 In scenarios that also have a national carbon cap that reaches zero emissions, the emission intensity of California imports is also set to zero for years when the national carbon cap is zero.
 
 Because California's greenhouse gas reduction targets are legislated for all economic sectors whereas ReEDS models only the electricity sector, we rely on published economywide modeling results to estimate electric-sector-specific caps that are used in ReEDS.
-In particular, we apply power sector caps based on the annual California electric sector emissions (from in-state and imported electricity) from the California Public Utilities Commission {cite}`cpucDecisionSettingRequirements2018`, which provides guidance for a 42 million tCO<sub>2</sub> cap by 2030.
-We enforce that cap from 2030 to 2050.
-The pre-2030 cap ramps linearly from 60 million tCO<sub>2</sub> in 2020 to the 42 million tCO<sub>2</sub> in 2030.
+In particular, we apply power sector caps based on annual California electric sector emissions (from in-state and imported electricity) and guidance from the California Public Utilities Commission {cite}`cpucDecisionSettingRequirements2018,cpuc_2022`.
+The modeled trajectory declines from approximately 59 million tCO<sub>2</sub> in 2020 to 38 million tCO<sub>2</sub> in 2030 and 15 million tCO<sub>2</sub> in 2045, then remains at 15 million tCO<sub>2</sub> through 2050.
 California's RPS policy is also included in ReEDS.
 
 #### Delaware carbon cap
@@ -3159,7 +3158,8 @@ For these states, the nuclear power plants are not allowed to retire until after
 The policy end dates are taken from EIA {cite:year}`eiaElectricPowerMonthly2019a`.
 
 In addition, there are [several states that do not allow new nuclear power](https://www.ncsl.org/environment-and-natural-resources/states-restrictions-on-new-nuclear-power-facility-construction).
-These states include California, Illinois, Maine, Massachusetts, Minnesota, New York (Long Island only), Oregon, Rhode Island, and Vermont. Nuclear ban is not imposed for Connecticut since Act25-127 allows construction of advanced nuclear facilities in municipalities with referendums.
+These states include California, Maine, Massachusetts, Minnesota, New York (Long Island only), Oregon, Rhode Island, and Vermont.
+A nuclear ban is not imposed for Connecticut since Act25-127 allows construction of advanced nuclear facilities in municipalities with referendums.
 
 
 ### Other Policy Capabilities

diff --git a/docs/source/references.bib b/docs/source/references.bib
diff --git a/inputs/canada_imports/README.md b/inputs/canada_imports/README.md
@@ -1,5 +1,8 @@
 ## Canadian Input Files
 
+All files in this folder are generated by [`exogenous_canadian_trade/`](https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/exogenous_canadian_trade) in the ReEDS_Input_Processing repo,
+from historical trade data and future province-level projections published by the Canada Energy Regulator.
+
 - `can_exports.csv`: Annual exports [MWh] to Canada by county
 
 - `can_exports_szn_frac.csv`: Fraction of annual exports [fraction] to Canada by season.

diff --git a/inputs/capacity_exogenous/README.md b/inputs/capacity_exogenous/README.md
@@ -1,42 +1,16 @@
 ## Capacity Exogenous Input Files
 
-- `cappayments.csv`: 
-
-- `cappayments_ba.csv`: 
+- `cappayments_ba.csv`: Capacity payment to existing (pre-2010) generators [billion $] by region and year
+  - Not used by the model itself (`runfiles.csv` marks it "not used"); it is read only by the BokehPivot system cost report, which inflates it and adds it to `inv_investment_capacity_costs`
+  - Generated by [`existing_capacity_payments/`](https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/existing_capacity_payments) in the ReEDS_Input_Processing repo
 
 - `demonstration_plants.csv`: Nuclear-smr demonstration plants [MW]
   - Active when `GSw_NuclearDemo = 1`
   - For more information, refer to the **notes** section of [https://www.energy.gov/oced/advanced-reactor-demonstration-projects-0](https://www.energy.gov/oced/advanced-reactor-demonstration-projects-0)
 
-- `exog_cap_geohydro_allkm_reference.csv`: 
-
-- `exog_cap_geohydro_reference.csv`: 
-
-- `exog_cap_upv_*.csv`:
-  - `exog_cap_upv_limited.csv`: 
-  - `exog_cap_upv_open.csv`: 
-  - `exog_cap_upv_reference.csv`: 
-
-- `exog_cap_wind-ons_*.csv`:
-  - `exog_cap_wind-ons_limited.csv`: 
-  - `exog_cap_wind-ons_open.csv`: 
-  - `exog_cap_wind-ons_reference.csv`: 
-
-- `interconnection_queues.csv`: 
-
-- `prescribed_builds_wind-ofs_meshed_*.csv`:
-  - `prescribed_builds_wind-ofs_meshed_limited.csv`: 
-  - `prescribed_builds_wind-ofs_meshed_open.csv`: 
-  - `prescribed_builds_wind-ofs_meshed_reference.csv`: 
-
-- `prescribed_builds_wind-ofs_radial_*.csv`:
-  - `prescribed_builds_wind-ofs_radial_limited.csv`: 
-  - `prescribed_builds_wind-ofs_radial_open.csv`: 
-  - `prescribed_builds_wind-ofs_radial_reference.csv`: 
-
-- `prescribed_builds_wind-ons_*.csv`:
-  - `prescribed_builds_wind-ons_limited.csv`: 
-  - `prescribed_builds_wind-ons_open.csv`:  
-  - `prescribed_builds_wind-ons_reference.csv`: 
+- `interconnection_queues.csv`: Near-term capacity [MW] in the interconnection queues by county, technology group, and year
+  - Aggregated to model zones during input processing to form `cap_limit`, which caps total capacity deployed since the start of the interconnection queue data
+  - Generated by [`interconnection_queues/`](https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/interconnection_queues) in the ReEDS_Input_Processing repo
 
-- `ReEDS_generator_database_final_EIA-NEMS.csv`: ReEDS generator database derived from the EIA NEMS plant file and EIA 860M. See https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/nems_database_processing.
+- `ReEDS_generator_database_final_EIA-NEMS.csv`: ReEDS generator database derived from the EIA NEMS plant file and EIA 860M
+  - Generated by [`nems_database_processing/`](https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/nems_database_processing) in the ReEDS_Input_Processing repo
diff --git a/inputs/capacity_exogenous/cappayments.csv b/inputs/capacity_exogenous/cappayments.csv
diff --git a/inputs/climate/README.md b/inputs/climate/README.md
@@ -1,5 +1,9 @@
 ## Climate Input Files
 
-- `climate_heuristics_finalyear.csv`: 
+- `climate_heuristics_finalyear.csv`: Climate heuristic adjustment [fraction] applied in the final year, by climate parameter
+  - Covers hydropower capacity credit, thermal summer capacity, and transmission summer capacity deltas
+  - Scenario is selected by the `GSw_ClimateHeuristics` switch
 
-- `climate_heuristics_yearfrac.csv`: 
+- `climate_heuristics_yearfrac.csv`: Annual scaling factor [fraction] applied to the final-year climate heuristic adjustment
+  - Phases the adjustments in `climate_heuristics_finalyear.csv` in over time, reaching their full value in the final year
+  - Scenario is selected by the `GSw_ClimateHeuristics` switch
diff --git a/inputs/consume/README.md b/inputs/consume/README.md
@@ -20,10 +20,10 @@
 
 - `dollaryear.csv`: Dollar year for DAC costs
 
-- `h2_demand_county_share.csv`: The fraction of national hydrogen demand in that year that corresponds to each county
+- `h2_demand_county_share.csv`: The fraction [fraction] of national hydrogen demand in that year that corresponds to each county
   - Demand estimates come from [https://data.openei.org/submissions/5655](https://data.openei.org/submissions/5655)
   - 2021 demand shares correspond to the "Reference" scenario with light-duty vehicles / biofuels / methanol demand removed and 2050 shares correspond to the "Low Cost Electrolysis" scenario
 
-- `h2_exogenous_demand.csv`: Exogenous hydrogen demand by industries other than the power sector per year
+- `h2_exogenous_demand.csv`: Exogenous hydrogen demand [metric tons/yr] by industries other than the power sector
 
 - `h2_transport_and_storage_costs.csv`: Transport and storage costs of hydrogen per year (in $2004)
diff --git a/inputs/ctus/README.md b/inputs/ctus/README.md
@@ -1,7 +1,10 @@
 ## Ctus Input Files
 
-- `co2_site_char.csv`: 
+- `co2_site_char.csv`: Characteristics of each CO<sub>2</sub> storage site: maximum injection rate [metric tons/hour], maximum storage capacity [Mton], and break-even storage cost [$/metric ton] at a range of capacity factors
+  - Break-even cost columns are named `bec_{CF}`; the column used is selected by the `GSw_CO2_BEC` switch
+  - Costs are in the dollar year given by `dollaryear.csv` and are deflated during input processing
 
-- `cs.csv`: 
+- `cs.csv`: List of CO<sub>2</sub> storage sites (geologic formations) recognized by the model
+  - A superset of the sites in `co2_site_char.csv`; only sites with characteristics are available for storage
 
-- `dollaryear.csv`:
+- `dollaryear.csv`: U.S. dollar year for each cost-related input file in this folder
diff --git a/inputs/degradation/README.md b/inputs/degradation/README.md
@@ -1,3 +1,5 @@
 ## Degradation Input Files
 
-- `degradation_annual_default.csv`: 
+- `degradation_annual_default.csv`: Annual capacity degradation rate [fraction/year] by technology
+  - Applied in the model as `(1 - rate)^years` to derate capacity as plants age
+  - The file is selected by the `degrade_suffix` switch
diff --git a/inputs/demand_response/README.md b/inputs/demand_response/README.md
@@ -1,5 +1,8 @@
 ## Demand Response Input Files
 
-- `dr_shed_avail_scalar.csv`: 
+- `dr_shed_avail_scalar.csv`: Peak availability [fraction of capacity] of shed demand response by year
+  - The hourly shed-DR availability profile is normalized to its own maximum and then scaled by this value
 
-- `dr_shed_capacity_scalar_demo_data_January_2025.csv`: 
+- `dr_shed_capacity_scalar_demo_data_January_2025.csv`: Shed demand response capacity multiplier [unitless] over time by technology and region
+  - The file is selected by the `dr_shedscen` switch and is only used when `GSw_DRShed` is on
+  - Generated by [`demand_response/`](https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/demand_response) in the ReEDS_Input_Processing repo
diff --git a/inputs/dgen_model_inputs/README.md b/inputs/dgen_model_inputs/README.md
@@ -1,20 +1,29 @@
 ## dGen Input Files
 
-- `stscen2023_electrification/distpvcap_stscen2023_electrification.csv`:
+- `stscen2023_electrification/distpvcap_stscen2023_electrification.csv`: Distributed PV summer capacity [MW] by region and year using electricity price outputs from the Standard Scenarios 2023 electrification scenario
+    - Selected using `distpvscen`
 
-- `stscen2023_highng/distpvcap_stscen2023_highng.csv`: Setting for distpv scenario capacity - from standard scenarios 2023 with high NG (including distpv) costs
+- `stscen2023_highng/distpvcap_stscen2023_highng.csv`: Distributed PV summer capacity [MW] by region and year using electricity price outputs from the Standard Scenarios 2023 high natural gas price scenario
+    - Selected using `distpvscen`
 
-- `stscen2023_highre/distpvcap_stscen2023_highre.csv`: Setting for distpv scenario capacity - from standard scenarios 2023 with high RE (including distpv) costs
+- `stscen2023_highre/distpvcap_stscen2023_highre.csv`: Distributed PV summer capacity [MW] by region and year using electricity price outputs from the Standard Scenarios 2023 high renewable energy cost scenario (including high distributed PV capital costs based on the 2023 Annual Technology Baseline)
+    - Selected using `distpvscen`
 
-- `stscen2023_lowng/distpvcap_stscen2023_lowng.csv`: Setting for distpv scenario capacity - from standard scenarios 2023 with low NG (including distpv) costs
+- `stscen2023_lowng/distpvcap_stscen2023_lowng.csv`: Distributed PV summer capacity [MW] by region and year using electricity price outputs from the Standard Scenarios 2023 low natural gas price scenario
+    - Selected using `distpvscen`
 
-- `stscen2023_lowre/distpvcap_stscen2023_lowre.csv`: Setting for distpv scenario capacity - from standard scenarios 2023 with low RE (including distpv) costs
+- `stscen2023_lowre/distpvcap_stscen2023_lowre.csv`: Distributed PV summer capacity [MW] by region and year using electricity price outputs from the Standard Scenarios 2023 high renewable energy cost scenario (including low distributed PV capital costs based on the 2023 Annual Technology Baseline)
+    - Selected using `distpvscen`
 
-- `stscen2023_mid_case/distpvcap_stscen2023_mid_case.csv`:
+- `stscen2023_mid_case/distpvcap_stscen2023_mid_case.csv`: Distributed PV summer capacity [MW] by region and year using electricity price outputs from the Standard Scenarios 2023 mid-case scenario
+    - Selected using `distpvscen`
 
-- `stscen2023_mid_case_95_by_2035/distpvcap_stscen2023_mid_case_95_by_2035.csv`:
+- `stscen2023_mid_case_95_by_2035/distpvcap_stscen2023_mid_case_95_by_2035.csv`: Distributed PV summer capacity [MW] by region and year using electricity price outputs from the Standard Scenarios 2023 95% CO<sub>2</sub> emission reduction by 2035 scenario
+    - Selected using `distpvscen`
 
-- `stscen2023_mid_case_95_by_2050/distpvcap_stscen2023_mid_case_95_by_2050.csv`:
+- `stscen2023_mid_case_95_by_2050/distpvcap_stscen2023_mid_case_95_by_2050.csv`: Distributed PV summer capacity [MW] by region and year using electricity price outputs from the Standard Scenarios 2023 95% CO<sub>2</sub> emission reduction by 2050 scenario
+    - Selected using `distpvscen`
 
-- `stscen2023_taxcredit_extended2050/distpvcap_stscen2023_taxcredit_extended2050.csv`:
+- `stscen2023_taxcredit_extended2050/distpvcap_stscen2023_taxcredit_extended2050.csv`: Distributed PV summer capacity [MW] by region and year using electricity price outputs from the Standard Scenarios 2023 mid-case scenario where tax credits (including for distributed PV) are extended through 2050
+    - Selected using `distpvscen`
 
diff --git a/inputs/disaggregation/README.md b/inputs/disaggregation/README.md
@@ -3,6 +3,8 @@
 - `county_population.csv`: The population of each county, relative values are used as multipliers for downselecting data
   - Data come from the U.S. Census Bureau 2021 county population estimates ([https://www.census.gov/data/tables/time-series/demo/popest/2020s-counties-total.html](https://www.census.gov/data/tables/time-series/demo/popest/2020s-counties-total.html)) 
 
-- `county_state_lpf.csv`: 
+- `county_state_lpf.csv`: Load participation factor [fraction] used to disaggregate state-level quantities to counties
+  - Values sum to 1 within each state
+  - Generated by `write_lpf_naris.py` in [`transmission/`](https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/transmission) of the ReEDS_Input_Processing repo, which aggregates NARIS nodal load to counties and normalizes within each state
 
 - `disagg_hydroexist.csv`: The hydropower capacity fraction of each county within a given ReEDS BA, used as multipliers for downselecting data