VE Inputs by Concept UNDER CONSTRUCTION

==FUTURE: DAN fix section HYPER LINKS? TARA REORG (weave in/move/delete extra on bottom?). Add links to example input .csv files, such as this for azone_hh_pop_by_age.csv. Do we pull from training on monetary dollar year conversions in files, etc.

VisionEval Inputs

This page summarizes VisionEval inputs by Concept, noting the general types of information that needs to be developed and for what dimensions. It also includes links to further documentation of the specific input files for VE-RSPM. The VisonEVal filesummary.xls is a valuable Quick-Start-Guide to all VisionEval inputs.

This page organizes VisionEval Input summaries by the following VE Concepts:

• Household Synthesis & Land Use
• Household Multi-modal Travel
• Vehicles, Fuels & Emissions
• Congestion
• Household Costs & Budgets

FOR MORE INFORMATION: VE Concepts, VE-RSPM full input set, definitions. Best Practices, VETRaining-InputFiles

Inputs: 1. Household Synthesis & Land Use

Q: could repeat the following from Concepts Primer

VisionEval takes user input statewide population by age group, assembles them into households with demographic attributes (lifecycle, per capita income) and allocates them to BZone-level dwelling units inputs. Separately BZones are attributed with employment and land use attributes (location type, built form ‘D’ values, mixed use, employment by type). Household members are identified as workers and/or drivers and number of household vehicles are estimated. Each home and work location is tied to a specific Bzone with its associated attributes. Additionally, some local policies are land use based.

Note: These inputs are specified as category "Demog" or "Land Use" in the VE file summary spreadsheet

Q: parking, TDM/IMP, and Car Service could be defined in multiple places: Concept#1LU, #2Multi-Modal or #5HHcosts

• Pool of Available Households. Modelwide, Census PUMS data represents actual households and representative mix of household composition and demographics for your area it is built into the code (must rebuilt VESimHousehold package for local PUMS data, Oregon data is the default, see [instructions to rebuilt packages])

• Population by Age Control Totals. Azone-level inputs for (1) regular households and (2) group quarters households (can be 0) include population by age group, average per capita income.[azone_hh_pop_by_age.csv][azone_gq_pop_by_age.csv][azone_per_cap_inc]

• Optional Household adjustments. (Optional) constraints on regular households include average household size and proportion of single-person households, adjustments to licensure rate for driving age persons. [azone_hhsize_targets.csv][azone_hh_ave_veh_per_driver][region_hh_ave_driver_per_capita.csv]

• BZone Dwelling Units. Bzone-level numbers of dwelling units by type in each model year and proportions of each in each development type. Income quartiles tied to households in dwelling units help VisionEval assign households to a compatible BZone location. [bzone_dwelling_units.csv][bzone_hh_inc_qrtl_prop.csv][bzone_urban-town_du_proportions.csv]

• BZone Land Use. BZone inputs set the total developable land area, by development type, its location (centroid latitude-longitude) for spatially linking to source data, and input assumptions on the built form Design 'D'. These inputs can change by model run year. [bzone_unprotected_area.csv][bzone_lat_lon.csv][bzone_network_design.csv]

• BZone Employment by type. Inputs for count of employment by type in each model year (BZone). (Optional) constraints on aggregated employment rate` for working age persons (Azone). [bzone_employment.csv][azone_relative_employment.csv]

• Land Use-Household linkages. VisionEval assigns a BZone to each household's home and to each household worker's work location, with the associated BZone attributes. The VisionEval-calculated urban mixed use designation of the BZone can optionally be modified by input targets on the proportion of households assigned that designation in each Bzone in this process.[bzone_urban-mixed-use_prop.csv]

Note: Input files must be consistent. This includes: (1) land area must be specified for each Azone location type that has households or employment assigned to it; (2) dwelling units must be a reasonable match with population (divided by houssehold size); (3) Shares of jobs within each AZone must sum to 1 for all Azones in the Marea.

Inputs: 2. Household Multi-modal Travel

Note: These inputs are specified as category "Actions-*" in the VE file summary spreadsheet

• Transport Supply(metropolitan areas only). Inputs define roadway capacity (lane-miles of arterials and freeways) and transit service levels (annual revenue service miles for each of the 8 transit modes) for the urbanized area portion of each Marea by model run year. A separate Bzone-level input sets neighborhood transit accessibility or Transit D. [marea_lane_miles.csv][marea_transit_service.csv][bzone_transit_service.csv]
• Personal Short Trip/Alt Modes. Use policies for transit, bike and walk modes include: transit service levels and transit accessibility (Transit D) [per Transit Supply above]; Goals for the proportion of SOV diversion (20 miles or less round-trip); while walk is dependent upon....[azone_prop_sov_dvmt_diverted.csv]
• Travel Demand Management. [bzone_travel_demand_mgt.csv]
• Car Service. See Household Costs & Budgets for service levels, geographic coverage, and fees. See Vehicles, Fuels & Emissions and for fleet characteristics.
• Parking. See Household Costs & Budgets for geographic coverage and fees. Q: This could be under Concept#1 land us...

Inputs 3. Vehicles, Fuels and Emissions

Note: These inputs are specified as category "V+F" or "Actions-Transit" in the VE file summary spreadsheet

Powertrain and Fuel Options by Vehicle Group

Vehicle Group	Vehicle Types	Powertrain Options	Veh Inputs	Fuel Options	Fuel input options
Household	automobile, light truck	ICE, HEV, PHEV, EV	(default veh mix), age, %LtTrk	gas/ethanol, diesel/biodiesel, CNG/RNG	default, region composite
Car Service	automobile, light truck	ICE, HEV, EV	veh mix, age (HH veh mix)	gas/ethanol, diesel/biodiesel, CNG/RNG	default, region composite
Commercial Service	automobile, light truck	ICE, HEV, EV	veh mix, age, %LtTrk	gas/ethanol, diesel/biodiesel, CNG/RNG	default, region composite
Heavy Truck	heavy truck	ICE, HEV, EV	veh mix	gas/ethanol, diesel/biodiesel, CNG/LNG	default, region composite
Public Transit	van, bus, rail	ICE, HEV, EV	veh mix	gas/ethanol, diesel/biodiesel, CNG/RNG	default, fuel/biofuel mix, marea or region composite

[Add conceptual discussion of input options(see VE Training Slides, slide 32)

Fuel Input Options.
Three options are available for fuel assumptions. The choices are outlined in the table above and the options described below. User choice of option can vary by vehicle group and where applicable, vehicle type: (1) Default package datasets. These may represent federal or statewide fuel policies that apply to all metropolitan areas and all [vehicle groups] in the model (e.g., state ethanol regulations, low carbon fuel policies). NAs would be placed in all user input files. (2) Detailed fuel and biofuel inputs. Values for the proportions of base fuels types (gasoline, diesel, compressed natural gas), as well as fuel blend proportions (gasoline blended with ethanol, biodiesel blended with diesel, renewable natural gas is blended with natural gas). A third assumption specifies the carbon_intenaity of the fuels (input or default). For example Hvy trucks can be set to 95% diesel, 5% natural gas, with diesel having a 5% biodiesel blend. (3) Composite carbon intensity. This option simplifies the process of modeling emissions policies, particularly low carbon fuels policies by bypasses the need to specify fuel types and biofuel blends. Average carbon intensity by vehicle group and if applicable, vehicle type is specified directly. These inputs, if present and not 'NA', supercede other transit inputs.

Note: Given that transit agencies in different metropolitan areas may have substantially different approaches to using biofuels, transit vehicles have the option of region or metropolitan area specifications for Options (1) and (2).
Note: The proportions in option (2) do not represent volumetric proportions (e.g. gallons), they represent energy proportions (e.g. gasoline gallon equivalents) or DVMT proportions. Note: Individual vehicles are modeled for households. Other groups vehicle and fuel attributes apply to VMT. As a result, PHEVs in all but household vehicles should be split into miles driven in HEVs and miles in EVs.

Specific files for each Vehicle group are noted below: [redundant with table above? split table above into V & F?]

• Household vehicles and fuels. Household vehicle characteristics by Azone and model run year, including vehicle type shares, average vehicle age by vehicle type. (optional) Region-wide composite fuel carbon intensity by [vehicle type]. AZone availability of residential electric vehicle charging stations by `dwelling type', by model run year. [azone_hh_lttrk_prop.csv][azone_hh_veh_mean_age.csv] [region_ave_fuel_carbon_intensity.csv] [azone_charging_availability.csv]

• CarService vehicles and fuels. Car service characteristics by Azone and model run year, including cost per mile by CarService Level, average age of car service vehicles, and limits on households’ substitutability by vehicle type for owned vehicles. Region-level inputs on powertrain mix by model (not sales) year and (optional) region-wide composite fuel carbon intensity. [azone_carsvc_characteristics.csv][region_carsvc_powertrain_prop.csv][region_ave_fuel_carbon_intensity.csv]

• Transit. Transit vehicles characteristics by marea and model run year for transit vehicle types (van, bus, rail), including powertain mix by model (not sales) year and (detailed option) fuel-biofuel shares. Alternatively metropolitan area or region-wide composite fuel carbon intensity may be input by transit vehicle types.[marea_transit_powertrain_prop.csv][marea_transit_biofuel_mix.csv][marea_transit_fuel.csv][marea_transit_ave_fuel_carbon_intensity.csv][region_ave_fuel_carbon_intensity.csv]

• Freight Vehicles & Fuels. Commercial Service vehicle characteristics (Metropolitan areas only) by Azone and model run year, including vehicle type shares, and average vehicle age by vehicle type. (optional) Region-wide composite fuel carbon intensity by vehicle type. Heavy Truck vehicle characteristics are region-level, including powertain mix and composite fuel carbon intensity by model (not sales) year.[region_comsvc_powertrain_prop.csv][region_comsvc_lttrk_prop.csv][region_comsvc_veh_mean_age.csv][region_ave_fuel_carbon_intensity.csv] [region_hvytrk_powertrain_prop.csv]

• Electric Carbon Intensity. Electricity carbon intensity by AZone and model run year. [azone_electricity_carbon_intensity.csv]

Inputs: 4. Congestion

Note: These inputs are specified as category "Actions-ITS" or "set-up" in the VE file summary spreadsheet.

• Set-up Files. Baseyear VMT, both urban and state LDV, HvyTruck (default via state/UzaLookup or input) and growth basis for HvyTrk (population or income) and CommService VMT (population, income, or household VMT), share of LDV, HyTrk, Bus VMT on urban roads by road class. Values for UzaNameLookup must be present in the list provided in the module documentation, otherwise user inputs must specify the data directly. [region_base_year_dvmt.csv][marea_base_year_dvmt.csv][marea_dvmt_split_by_road_class.csv][marea_base_year_VMT.csv]

• ITS-Operational Policies. Proportion of VMT by road class affected by standard ITS-Operation policies on Freeways (xxx) and Arterials (xxx), and other ops provides flexibility for future user-defined freeway and arterial operations program coverage and effectiveness. These programs reduce delay.[marea_operations_deployment.csv][other_ops_effectiveness.csv]

• Speed smoothing programs. Proportion of VMT by road class covered by ITS-speed smoothing programs, and Eco-drive programs(LDV and Heavy Truck VMT). These programs reduce vehicle accelerations and decelerations, but do not affect delay. [marea_speed_smooth_ecodrive.csv]

xxxwhere fits?xxxNote: Literature review of fuel efficiency improvements found that [Speed smoothing] policies could only reasonably achieve a portion of the theoretical maximum; 50% from ITS-Speed Smoothing policies and 33%Fwys/21%Arterials for eco-drive programs. These ratios are applied to arrive at the highest achievable impact given a user input of full deployment (input of 1=100%).

• Congestion Fees. xxxalso in Concept#5 Budgets? [marea_congestion_charges.csv]

Inputs: 5. Household Costs & Budgets

• Household Travel Budget. US BLS CES data is used to estimate the maximum proportion of income the household is willing to pay for vehicle operations . The proportion are default parameters within the model that vary with income, ranging from 30% for low income to less than 10% for high income. [xxxNot an input, should we include here?]

• **Household CarService-Owned Auto substitution ** Per Concept #1, Car service characteristics by Azone and model run year, including cost per mile by CarService Level, average age of car service vehicles, and limits on households’ substitutability by vehicle type for owned vehicles. [azone_carsvc_characteristics.csv]

• Per Mile Vehicle Out-Of-Pocket Costs. Input assumptions on per mile costs used in calculating User annual operating costs that may be limited by the household's income-based maximum annual travel budget. This includes energy costs, car service fees, and fees to recover road and social costs, as noted below.

  • Energy Costs. Unit cost of energy to power household vehicles, both fuel ($/gallon) and electricity ($/kilowatt-hour). [azone_fuel_power_cost.csv]

  • CarService Fees. If Car Service are used, the per mile fees paid for that service, outside of energy costs. Per Concept #1, Car service characteristics cost per mile by CarService Level by Azone and model run year.[]

  • Road Cost Recovery VMT fee. Inputs include per mile gas tax or levying a fuel-equivalent tax on travel by some/all electric vehicles (PevSurchgTaxProp), for their use of roads in lieu of gas purchases. User can also directly specify a VMT (mileage) fee, to further recover road costs, or (optional) flag VisionEval to iteratively estimate the VMT fee to fully recover road costs incurred by household VMT. This user-estimated VMT fee, utilizes user inputs on unit costs for freeway and arterial operation, preservation, modernization, and other($/light-duty VMT or $/lane-mile). [xxxIs PCE used here or only in congestion model?][xxxflag??.csv][region_road_cost.csv][azone_veh_use_taxes.csv]

  • Social Cost Recovery/Carbon Fees. (Optional) Inputs allow per mile fee to cover social costs or externalities, not recovered in this way today, but instead incur costs elsewhere in the economy (e.g., safety, health). This is the cost imposed on society and future generations, not the cost to the vehicle user. This requires assumptions on the cost incurred from these externalities (per mile, per gallon) and the proportion to be paid by drivers as a per mile fee (varies by vehicle powertrain). The proportion of carbon costs (e.g., impact on fuel price from cap & trade policy) imposed on drivers is specified separately from other social costs, so it can be assessed on its own if desired; including (optionally) specifying the cost of carbon to over-ride the default value of carbon. [region_prop_externalities_paid.csv][region_co2e_costs.csv]

• Per Mile Time-Equivalent Costs. Only included in XXX calculations...includes travel time (model-calculated), plus time to access vehicle on both ends of trip (between vehicle parking location and origin or end destination), multiplied by value of time (default model parameter).[azone_vehicle_access_times.csv][model_parameters.jsn]

• Annual Vehicle Ownership costs. Azone inputs by year includes annual vehicle fees (flat fee and/or tax on vehicle value), PAYD insurance participation rates, residential parking limitations and fees, that are combined with model-estimated ownership costs (financing, depreciation, insurance) [azone_hh_veh_own_taxes.csv] [azone_payd_insurance_prop.csv]+ xxxConcept#1[Payd][res pkg]

• Congestion Fees. xxxIs this only in Concept#4 re: congestion?[marea_congestion_charges.csv]

===========KEEP THIS?

Inputs -- Local policy actions

ITS/Operations programs

ITS impact is modeled within RSPM, through speed reductions from basic and enhanced traffic operations, and active management of speed smoothing operational programs. Average speed on roadways in RSPM, is calculated as a function of congestion level and the type and amount of deployment of traffic operations programs. An average speed is associated with each roadway functional class (freeway or arterial) and congestion level. Those speeds are modified depending on the cumulative effect of user-specified deployment of the following traffic operations programs:

• Freeway ramp metering - Metering freeways can reduce delay by keeping mainline vehicle density below unstable levels. It creates delay for vehicles entering the freeway, but this is typically more than offset by the higher speeds and postponed congestion on the freeway facility. The Urban Mobility Report cites a delay reduction of 0 to 12%, with an average of 3%, for 25 U.S. urban areas with ramp metering. Only urban areas with Heavy, Severe, and Extreme freeway congestion can benefit from ramp metering in RSPM
• Freeway incident management - Incident Response programs are designed to quickly detect and remove incidents which impede traffic flow. The UMR study reports incident-related freeway delay reductions of 0 to 40%, with an average of 8%, for the 79 U.S. urban areas with incident response programs. This reflects the combined effects of both service patrols to address the incidents and surveillance cameras to detect the incidents. Effects were seen in all sizes of urban area, though the impacts were greater in larger cities.
• Arterial access management– Access management on arterials can increase speeds by reducing the number of enter/exit points on the arterial and reduce crashes by reducing conflict points. Although improvements such as raised medians can reduce throughput by causing turning queue spillback during heavy congestion, other types of access management, such as reduced business ingress/egress points, show consistent benefits system-wide.
• Arterial signal coordination – Traffic signal coordination, particularly for adaptive traffic signals, can reduce arterial delay by increasing throughput in peak flow directions. UMR and other analysis estimates delay reductions of up to 6-9% due to signal coordination, with more potential savings from more sophisticated control systems. An average arterial delay savings was found to be about 1%.
• Enhanced ITS/Speed Smoothing programs– Insufficient aggregate performance data is available for a number of other current and future ITS/operations strategies. These include: speed limit reductions, speed enforcement, and variable speed limits that reduce the amount of high-speed freeway travel; advanced signal optimization techniques that reduce stops and starts on arterials; and truck/bus-only lanes that can move high-emitting vehicles through congested areas at improved efficiency.
• Other Ops programs – Ability within VE allows flexibility within the model to accommodate future enhancements (other_ops.csv, other_ops_effectiveness.csv). Further research and significant program investment would be needed to justify benefits in these enhanced ITS programs.

Inputs specifying the level of deployment of several roadway Intelligent Transportation System (ITS) programs, determine the area roadway speeds which influence fuel efficiency.

Eco-Driving Practices (autos and trucks)

Eco-driving involves educating motorists on how to drive in order to reduce fuel consumption and cut emissions. Examples of eco-driving practices include avoiding rapid starts and stops, matching driving speeds to synchronized traffic signals, and avoiding idling. Practicing eco-driving also involves keeping vehicles maintained in a way that reduces fuel consumption such as keeping tires properly inflated and reducing aerodynamic drag. In RSPM, fuel economy benefits of improved vehicle maintenance are included in the eco-driving benefit. A default 19% improvement in vehicle fuel economy is assumed Vehicle operations and maintenance programs (e.g. eco-driving) based on policy assumptions about the degree of deployment of those programs and the household characteristics. Vehicle operating programs (eco-driving) reduces emissions per VMT

The fuel economy of all household vehicles of participating households is increased by a factor representing the average fuel economy gains of persons who are trained in eco-driving techniques. An RSPM input (speed_smooth_ecodrive.csv) specifies the proportion of light duty vehicle drivers who exhibit eco-driving habits. The same file makes similar assumptions on the proportion of other (commercial,heavy truck) drivers who are eco-drivers.

Transportation Options Programs

In RSPM, each household is assigned as a participant or not in a number of travel demand management programs (e.g. employee commute options program, individualized marketing) based on policy assumptions about the degree of deployment of those programs and the household characteristics. Individual households are also identified as candidate participants for car sharing programs based on their household characteristics and input assumptions on the market penetration of car sharing vehicles.

Workplace TDM Programs

Level of deployment assumptions for TDM (at work and home locations) lead to reduced VMT, diverting travel to other modes. Car Sharing reduces VMT through changes in auto ownership and per mile costs. Employee commute options (ECO) programs are work-based travel demand management programs. They may include transportation coordinators, employer-subsidized transit passes, bicycle parking, showers for bicycle commuters, education and promotion, carpool and vanpool programs, etc. The default assumption is that that ECO programs reduce the average commute DVMT of participating households by 5.4%. It is assumed that all work travel of the household will be reduced by this percentage if any working age persons are identified as ECO participants The proportion of employees participating in ECO programs is a policy input at the district level (prop_wrk_eco.csv). The input assumes workers participate in a strong employee commute options programs (e.g., free transit pass, emergency ride home, bike rider facilities, etc.).

Individualized Marketing Program

Individualized marketing (IM) programs are travel demand management programs focused on individual households in select neighborhoods. IM programs involve individualized outreach to households that identify residents’ travel needs and ways to meet those needs with less vehicle travel. Customized to the neighborhood, IM programs work best in locations where a number of travel options are available. RSM assumes that households participating in an IM program reduce their DVMT by 9% based on studies done in the Portland area. IM programs target work as well as non-work travel and produce larger reductions than ECO work-based programs. Only the IM reduction is used for households that are identified as participating in both ECO and IM programs.

RSPM district-level inputs for IM programs (imp_prop_goal.csv) include an overall assumption for the percentage of households participating in an IM program. A minimum population density of 4,000 persons per square mile necessary to implement a successful IM program and the requirement that the household reside an urban mixed-use district. The number of households identified as participating is the minimum of the number needed to meet the program goal or the number of qualifying households.

Vehicle/Fuels Technology Inputs

Vehicle and Fuel Technology are expected to change significantly during the next 20-50 years as vehicles turn-over and the newer fleets are purchased. The characteristics of the fleet of new cars and trucks are influenced by federal CAFÉ standards as well as state energy policies and promotions. Local areas can contribute through decisions about the light-duty fleet used by local transit agencies and by assisting in deployment of electric vehicle charging stations and their costs in work and home locations, but otherwise have less influence on the characteristics of the future vehicle fleet, including auto, light truck, and heavy truck vehicles. As a consequence, the RSPM inputs on vehicle and fuel technology are largely specified at the state level. These include inputs that reflect the default assumptions included in the Metropolitan GHG target rules and a more aggressive future as specified in the Oregon Statewide Transportation Strategy. These will both be available to provide sensitivity test to assess their impact on energy use and GHG emissions in the metropolitan area.

The key local contribution to these inputs is the bus electric/fuels inputs; although defaults can be used if no additional local data is available. These variables are briefly summarized below.

Vehicle age, fuel economy, and congestion

Several input files specify vehicle attributes and fuel economy for autos, light trucks, heavy truck, and transit vehicles. Four vehicle powertrain types are modeled : • ICE - Internal Combustion Engines having no electrical assist; • HEV - Hybrid-Electric Vehicles where all motive power is generated on-board; • PHEV - Plug-in Hybrid Electric Vehicles where some motive power comes from charging an on-board battery from external power supplies; • EV - Electric Vehicles where all motive power comes from charging an on-board battery from external power supplies.

Household owned vehicles -- sales mix; %LtTrks & veh age from household and the regional trends for its area. These combine with sales mix (powertain mix). Each Powertrain in each year has an associated fuel efficiency and power efficiency assumptions for PHEVs (MPG for PHEVs in charge-sustaining mode). For EVs and PHEVs, battery range is specified.
All other vehicles -- skip sales and jump directly to the mix of vehicles on the road in the modeled year, adjusted by inputs.

User inputs on vehicle age adjustment factors by vehicle type and year. The purpose of this input is to allow scenarios to be developed which test faster or slower turn-over of the vehicle fleet Households and commercial fleets operate a mix of passenger autos and light trucks or SUVs. This mix has an impact on fuel economy. In RSPM a file contains base year and target values for the proportion of the passenger vehicle fleet that is light trucks for each Metropolitan division (lttruck_prop.csv),

NOTE: the actual EV-HEV split depends on whether enough households have their 95Th percentile daily travel within the EV battery range

Vehicle Fuel Technology

A second set of inputs specifies the attributes of the fuels and their contributions to GHG emissions (fuel_co2.csv). This file contains information on lifecycle CO2 equivalent emissions by fuel type in grams per mega joule of fuel energy content. Fuel types are ultra-low sulfur diesel (ULSD), Biodiesel, reformulated gasoline (RFG), CARBOB (California Reformulated Gasoline Blendstock for Oxygenate Blending), Ethanol, compressed natural gas (CNG), LtVehComposite. The latter category is a blend of the carbon values of all of the fuel types relative to the proportions in which they were used in 1990. This allows the model to be more easily run to simulate lower carbon content of fuels without having to specify the relative proportions of each specific fuel type. The additives in fuel sold that contribute to GHG emissions. These include the average ethanol proportion in gasoline and biodiesel proportion in diesel (auto_lighttruck_fuel.csv, comm_service_fuel.csv, heavy_truck_fuel.csv).

Fuel Mix Shares (the remaining share is assumed to be diesel fuel):

• PropGas – The proportion of bus miles using gasoline
• PropCng – The proportion of bus miles using compressed natural gas

Biofuel Additives:

• DieselPropBio – The biodiesel proportion of diesel fuel used
• GasPropEth – The ethanol proportion of gasoline used

Electric Emissions Rate (Co2e lbs/ kwhr) of electricity consumed

Since electricity generation varies across the state, a local input to the model is the average cost and GHG emission rates of the local area. The average cost of electricity per kilowatt hour (kWh) in dollars across the metropolitan study area is included in the file costs.csv, while the emissions rate (in average pounds of CO2 equivalents generated per kilowatt hour of electricity consumed by the end user) by district and forecast year is found in a separate input file (power_co2.csv). Statewide default values for these inputs are available, if no local source is obtained.