Dimensions of Analysis

SISEPUEDE facilitates large-scale computational experiments by organizing data around Dimensions of Analysis. These dimensions of analysis–including design_id, future_id, strategy_id, and time_series_id (under development)– are based on the XLRM logical framework (see Lempert, Popper, and Bankes (2003)). The framework organizes robust, scenario-based modeling excercises into the following components:

• X Exogenous uncertertainties

• L Levers (controls and strategies)

• R Relationships (including models)

• M Metrics

In SISEPUEDE, Strategies (Ls) are used to denote different collections of levers that can be applied to a baseline, and strategies are indexed by strategy_id. A strategy can represent any combination of levers, including no levers (baseline, which is required to specify); a single lever; or a collection of levers. Futures represent different plausible states of the future–including both exogenous uncertainties (Xs) and the ability of levers to acheieve expected effects (LEs)– and are indexed by dimension future_id. Futures are explored over using Latin Hybercube (LHC) samples, which ensure that the uncertainty space is maximally explored given some number of futures (defined at runtime or in configuration files).

SISPEUEDE furthermore allows users to combine Xs and different characterizations of LEs through different Experimental Designs, which are identified using the design_id. All inputs for variables, strategies and futures (sampling ranges) are managed using templates and attribute tables, each of which are discussed below.

Futures

Futures are the

Strategies

Strategies, indexed by strategy_id, combine transformations to generate whole-sector and economy-wide transformations.

A number of strategies. Note the numbering scheme; AFOLU-specific transformations occupy 1001-1999; Circular Economy, 2001-2999; Energy, 3001-3999; IPPU, 4001-4999; and cross-sector, 5001-5999.

strategy_id	strategy	strategy_code	baseline_strategy_id	Description
0	Baseline NDP	BASE	1	Base assumptions to which all transformations are applied
1001	AGRC: All transformations	AGRC:ALL	0	All agricultural transformations
1002	AGRC: All transformations with partial land use reallocation	AGRC:ALL_PLUR	0	All agricultural transformations
1003	AGRC: Decrease Exports	AGRC:DEC_EXPORTS	0	Decrease agricultural exports by 50% (relative to final time period)
1004	AGRC: Expand conservation agriculture	AGRC:INC_CONSERVATION_AGRICULTURE	0	Decrease soil organic carbon loss in croplands and leave crop residues (reduce fraction removed and burned) Croplands: Increases \(F_{MG}\) for croplands to 1.067 (see V4, Chapter 5, Table 5.5 [2019R] for croplands management factors \(F_{MG}\) under different management regimes–mean of No-Till)
1005	AGRC: Improve crop productivity	AGRC:INC_PRODUCTIVITY	0	Increase crop yield factors by 20%
1006	AGRC: Improve crop productivity with partial land use reallocation	AGRC:INC_PRODUCTIVITY_PLUR	0
1007	AGRC: Improve rice management	AGRC:DEC_CH4_RICE	0	Reduce \(\text{CH}_4\) emissions from rice production by 45% due to improved management and reduced flooding.
1008	AGRC: Reduce supply chain losses	AGRC:DEC_LOSSES_SUPPLY_CHAIN	0	Reduce waste food waste in the agricultural (crop) supply chain (pre-consumer)
1009	AGRC: Reduce supply chain losses with partial land use reallocation	AGRC:DEC_LOSSES_SUPPLY_CHAIN_PLUR	0
1010	LNDU: Increase Reforestation	LNDU:INC_REFORESTATION	0	Increase reforestation (currently doubles target for secondary forest by final time period)
1011	LNDU: Stop deforestation	LNDU:DEC_DEFORESTATION	0	Halt deforestation of primary and secondary forests
1012	LNDU: Expand silvopasture	LNDU:INC_SILVOPASTURE	0	Increase the use of silvopasture; modeled by shifting 30% of pasture (in final time period) to secondary forests over time; livestock carrying capacities increase to meet the change, while pasture fractions of grassland decrease.
1013	LNDU: Expand silvopasture with partial land use reallocation	LNDU:INC_SILVOPASTURE_PLUR	0	Increase the use of silvopasture; modeled by shifting 30% of pasture (in final time period) to secondary forests over time; livestock carrying capacities increase to meet the change, while pasture fractions of grassland decrease.
1014	LNDU: Expand sustainable grazing practices	LNDU:INC_SUSTAINABLE_GRAZING	0	Decrease soil organic carbon loss in grasslands through sustainable grazing practices Grasslands: Increase \(F_{MG}\) for grasslands to 1.157 (see V4, Chapter 6, Table 6.2 [2019R] for grasslands management factors \(F_{MG}\) under different management regimes–mean of Improved Grassland)
1015	LNDU: Partial land use reallocation	LNDU:PLUR	0	Set land use reallocation factor to 0.5
1016	LNDU: Rehabilitate degraded land	LNDU:INC_LAND_REHABILITIATION	0
1017	LSMM: Improve manure management for cattle and pigs	LSMM:INC_MANAGEMENT_CATTLE_PIGS	0	95% of manure from cattle (dairy and non-dairy) and pigs is treated. The manure treated is sent to the following pathways: 37.5% to Anaerobic Digestion 12.5% to Composting 25.0% to Daily Spread 12.5% to Deep Bedding 12.5% to Solid Storage
1018	LSMM: Improve manure management for other animals	LSMM:INC_MANAGEMENT_OTHER	0	95% of manure from all other animals is treated. The manure treated is sent to the following pathways: 37.5.% to Composting 12.5% to Dry Lot 25.0% to Daily Spread 25.0% to Solid Storage
1019	LSMM: Improve manure management for poultry	LSMM:INC_MANAGEMENT_POULTRY	0	95% of manure from poultry (chickens) and pigs is treated. The manure treated is sent to the following pathways: 100% to Poultry Management
1020	LSMM: Increase biogas capture at anaerobic decomposition facilities	LSMM:INC_CAPTURE_BIOGAS	0	85% of Biogas captured at manure treated at Anaerobic Treatment facilities
1021	LSMM: Manure management bundle	LSMM:BUNDLE_INC_MANAGEMENT	0	All livestock manure management transformations in addition to increased biogas collection
1022	LVST: All transformations	LVST:ALL	0
1023	LVST: All transformations with partial land use reallocation	LVST:ALL_PLUR	0
1024	LVST: Decrease exports	LVST:DEC_EXPORTS	0	Decrease exports of livestock and livestock products by 50% (relative to final time period)
1025	LVST: Increase livestock productivity	LVST:INC_PRODUCTIVITY	0	Increase inferred livestock carrying capacity by 20%
1026	LVST: Increase livestock productivity with partial land use reallocation	LVST:INC_PRODUCTIVITY_PLUR	0	Increase inferred livestock carrying capacity by 20%
1027	LVST: Reduce enteric fermentation	LVST:DEC_ENTERIC_FERMENTATION	0	Reduce enteric fermentation (20% in buffalo and cattle and 28% in goats and sheep)
1028	SOIL: Improve fertilizer application	SOIL:DEC_N_APPLIED	0	Decrease total nitrogen applied to soils by 5% through more precise fertilizer use
1029	SOIL: Improve lime application	SOIL:DEC_LIME_APPLIED	0	Decrease lime applied to soils by 5%
1030	AF: All AFOLU transformations with partial land use reallocation and without stopping deforestation	AF:ALL_NO_STOPPING_DEFORESTATION_PLUR	0
1031	AF: All AFOLU transformations	AF:ALL	0
1032	AF: All AFOLU transformations with partial land use reallocation	AF:ALL_PLUR	0
2001	TRWW: All wastewater treatment transformations	TRWW:ALL	0
2002	TRWW: Increase biogas capture	TRWW:INC_CAPTURE_BIOGAS	0
2003	TRWW: Increase septic compliance	TRWW:INC_COMPLIANCE_SEPTIC	0
2004	WALI: All transformations	WALI:ALL	0
2005	WALI: Improved industrial wastewater treatment	WALI:INC_TREATMENT_INDUSTRIAL	0
2006	WALI: Improved rural wastewater treatment	WALI:INC_TREATMENT_RURAL	0
2007	WALI: Improved urban wastewater treatment	WALI:INC_TREATMENT_URBAN	0
2008	WASO: All solid waste	WASO:ALL	0
2009	WASO: Biogas for energy production	WASO:INC_ENERGY_FROM_BIOGAS	0
2010	WASO: Consumer food waste reduction	WASO:DEC_CONSUMER_FOOD_WASTE	0
2011	WASO: Incineration for energy production	WASO:INC_ENERGY_FROM_INCINERATION	0
2012	WASO: Increase biogas capture	WASO:INC_CAPTURE_BIOGAS	0
2013	WASO: Increase composting and biogas	WASO:INC_ANAEROBIC_AND_COMPOST	0
2014	WASO: Increase landfilling	WASO:INC_LANDFILLING	0
2015	WASO: Increase recycling	WASO:INC_RECYCLING	0
2016	CE: All Circular Economy transformations	CE:ALL	0
3001	CCSQ: Increase direct air capture	CCSQ:INCREASE_CAPTURE	0	Increase Direct Air Capture deployment to 50 MT CO2e by 2050
3002	CCSQ: Increase direct air capture with renewable energy production	CCSQ:INCREASE_CAPTURE_REP	0	Increase Direct Air Capture deployment to 50 MT CO2e by 2050 + Renewable Energy Production
3003	ENTC: 95% of electricity is generated by renewables in 2050	ENTC:TARGET_RENEWABLE_ELEC	0
3004	ENTC: Least cost solution	ENTC:LEAST_COST	0
3005	ENTC: Reduce transmission losses	ENTC:DEC_LOSSES	0
3006	ENTC: Reduce transmission losses with renewable energy production	ENTC:DEC_LOSSES_REP	0
3007	FGTV: All Fugitive Emissions transformations	FGTV:ALL	0
3008	FGTV: All Fugitive Emissions transformations with renewable energy production	FGTV:ALL_REP	0
3009	FGTV: Maximize flaring	FGTV:INC_FLARE	0
3010	FGTV: Maximize flaring with renewable energy production	FGTV:INC_FLARE_REP	0
3011	FGTV: Minimize leaks	FGTV:DEC_LEAKS	0
3012	FGTV: Minimize leaks with renewable energy production	FGTV:DEC_LEAKS_REP	0
3013	INEN: All Industrial Energy transformations	INEN:ALL	0
3014	INEN: All Industrial Energy transformations with renewable energy production	INEN:ALL_REP	0
3015	INEN: Fuel switch low-temp thermal processes to industrial heat pumps	INEN:FUEL_SWITCH_LO_HEAT	0
3016	INEN: Fuel switch low-temp thermal processes to industrial heat pumps with renewable energy production	INEN:FUEL_SWITCH_LO_HEAT_REP	0
3017	INEN: Fuel switch medium and high-temp thermal processes to hydrogen and electricity	INEN:FUEL_SWITCH_HI_HEAT	0
3018	INEN: Fuel switch medium and high-temp thermal processes to hydrogen and electricity with renewable energy production	INEN:FUEL_SWITCH_HI_HEAT_REP	0
3019	INEN: Maximize industrial energy efficiency	INEN:INC_EFFICIENCY_ENERGY	0
3020	INEN: Maximize industrial energy efficiency with renewable energy production	INEN:INC_EFFICIENCY_ENERGY_REP	0
3021	INEN: Maximize industrial production efficiency	INEN:INC_EFFICIENCY_PRODUCTION	0
3022	INEN: Maximize industrial production efficiency with renewable energy production	INEN:INC_EFFICIENCY_PRODUCTION_REP	0
3023	SCOE: All Stationary Combustion and Other Energy transformations	SCOE:ALL	0
3024	SCOE: All Stationary Combustion and Other Energy transformations with renewable energy production	SCOE:ALL_REP	0
3025	SCOE: Increase appliance efficiency	SCOE:INC_EFFICIENCY_APPLIANCE	0
3026	SCOE: Increase appliance efficiency with renewable energy production	SCOE:INC_EFFICIENCY_APPLIANCE_REP	0
3027	SCOE: Reduce end-use demand for heat energy by improving building shell	SCOE:DEC_DEMAND_HEAT	0
3028	SCOE: Reduce end-use demand for heat energy by improving building shell with renewable energy production	SCOE:DEC_DEMAND_HEAT_REP	0
3029	SCOE: Switch to electricity for heat using heat pumps, electric stoves, etc.	SCOE:FUEL_SWITCH_HEAT	0
3030	SCOE: Switch to electricity for heat using heat pumps, electric stoves, etc. with renewable energy production	SCOE:FUEL_SWITCH_HEAT_REP	0
3031	TRNS: All Transportation transformations	TRNS:ALL	0
3032	TRNS: All Transportation transformations with renewable energy production	TRNS:ALL_REP	0
3033	TRNS: Demand management bundle	TRNS:BUNDLE_DEMAND_MANAGEMENT	0
3034	TRNS: Demand management bundle with renewable energy production	TRNS:BUNDLE_DEMAND_MANAGEMENT_REP	0
3035	TRNS: Electrify light duty road transport	TRNS:FUEL_SWITCH_LIGHT_DUTY	0
3036	TRNS: Electrify light duty road transport with renewable energy production	TRNS:FUEL_SWITCH_LIGHT_DUTY_REP	0
3037	TRNS: Electrify rail	TRNS:FUEL_SWITCH_RAIL	0
3038	TRNS: Electrify rail with renewable energy production	TRNS:FUEL_SWITCH_RAIL_REP	0
3039	TRNS: Fuel switch bundle	TRNS:BUNDLE_FUEL_SWITCH	0
3040	TRNS: Fuel switch bundle with renewable energy production	TRNS:BUNDLE_FUEL_SWITCH_REP	0
3041	TRNS: Fuel switch maritime	TRNS:FUEL_SWITCH_MARITIME	0
3042	TRNS: Fuel switch maritime with renewable energy production	TRNS:FUEL_SWITCH_MARITIME_REP	0
3043	TRNS: Fuel switch medium duty road transport	TRNS:FUEL_SWITCH_MEDIUM_DUTY	0
3044	TRNS: Fuel switch medium duty road transport with renewable energy production	TRNS:FUEL_SWITCH_MEDIUM_DUTY_REP	0
3045	TRNS: Increase occupancy for private vehicles	TRNS:INC_OCCUPANCY	0
3046	TRNS: Increase occupancy for private vehicles with renewable energy production	TRNS:INC_OCCUPANCY_REP	0
3047	TRNS: Increase transportation energy efficiency	TRNS:INC_EFFICIENCY	0
3048	TRNS: Increase transportation energy efficiency with renewable energy production	TRNS:INC_EFFICIENCY_REP	0
3049	TRNS: Mode shift bundle	TRNS:BUNDLE_MODE_SHIFT	0
3050	TRNS: Mode shift bundle with renewable energy production	TRNS:BUNDLE_MODE_SHIFT_REP	0
3051	TRNS: Mode shift freight	TRNS:MODE_SHIFT_FREIGHT	0
3052	TRNS: Mode shift freight with renewable energy production	TRNS:MODE_SHIFT_FREIGHT_REP	0
3053	TRNS: Mode shift passenger vehicles to others	TRNS:MODE_SHIFT_PASSENGER	0
3054	TRNS: Mode shift passenger vehicles to others with renewable energy production	TRNS:MODE_SHIFT_PASSENGER_REP	0
3055	TRNS: Mode shift regional passenger travel	TRNS:MODE_SHIFT_REGIONAL	0
3056	TRNS: Mode shift regional passenger travel with renewable energy production	TRNS:MODE_SHIFT_REGIONAL_REP	0
3057	TRDE: Reduce demand for transport	TRDE:DEC_DEMAND	0
3058	TRDE: Reduce demand for transport with renewable energy production	TRDE:DEC_DEMAND_REP	0
3059	EN: Efficiency bundle	EN:BUNDLE_EFFICIENCY	0
3060	EN: Efficiency bundle with renewable energy production	EN:BUNDLE_EFFICIENCY_REP	0
3061	EN: Fuel switch bundle	EN:BUNDLE_FUEL_SWITCH	0
3062	EN: Fuel switch bundle with renewable energy production	EN:BUNDLE_FUEL_SWITCH_REP	0
3063	EN: All Energy transformations	EN:ALL	0
4001	IPPU: Demand management	IPPU:DEC_DEMAND	0
4002	IPPU: Reduce cement clinker	IPPU:DEC_CLINKER	0	Reduces use of clinker in cement production
4003	IPPU: Reduce fluorinated gasses bundle	IPPU:BUNDLE_DEC_FGAS	0
4004	IPPU: Reduce Nitrous Oxide emissions	IPPU:DEC_N2O	0
4005	IPPU: Reduce other fluorinated compounds	IPPU:DEC_OTHER_FCS	0	Includes SF6, NF3, HCFCs, and others
4006	IPPU: Reduce use of HFCs	IPPU:DEC_HFCS	0
4007	IPPU: Reduce use of PFCs	IPPU:DEC_PFCS	0
4008	IP: All IP transformations	IP:ALL	0
5001	PFLO: Better baseline	PFLO:BETTER_BASE	0	Business as usual, but done better. Does not include any paradigm shifts or major scioeconomic transformations.
5002	PFLO: Healthier diets	PFLO:BETTER_DIETS	0	Reduce the fraction of the population eating red meat by 30% (equivalent to reducing total consumption by 30%) and reduce demand for sugar.
5003	PFLO: Healthier diets with partial land use reallocation	PFLO:BETTER_DIETS_PLUR	0	Reduce the fraction of the population eating red meat by 30% (equivalent to reducing total consumption by 30%) and reduce demand for sugar.
5004	PFLO: Change consumer behavior	PFLO:CHANGE_CONSUMPTION	0	Sweeping changes to consumer behavrior and social norms
5005	PFLO: Industrial point of capture CCSQ	PFLO:IND_INC_CCS	0	Specifies point-of-capture carbon capture and sequestration in IPPU and INEN
5006	PFLO: Supply side technologies and transformations	PFLO:SUPPLY_SIDE_TECH	0	Macro-scale transformations without consumer behavioral shifts
5007	PFLO: All transformations without stopping deforestation and partial land use reallocation	PFLO:ALL_NO_STOPPING_DEFORESTATION_PLUR	0	All transformations and partial land use reallocation except for stopping deforestation.
5008	PFLO: All transformations	PFLO:ALL	0	All transformations
5009	PFLO: All transformations with partial land use reallocation	PFLO:ALL_PLUR	0	All transformations
5010	PFLO: All transformations with partial land use reallocation, no silvopasture	PFLO:ALL_PLUR_NO_SILVOPASTURE	0	All transformations except for silvopasture
5011	PFLO: Acceptable net zero acheivement from lever uncertainty exploration 1	PFLO:NET_ZERO_FROM_LE_EXPLORATION_1	0	Strategy 5011 is converted from ID sisepuede_run_2023-10-06T20:10:09.932282 primary_id = 514914 (design_id: 3; strategy_id: 5009; future_id: 400;)
5012	PFLO: Acceptable net zero acheivement from lever uncertainty exploration 2	PFLO:NET_ZERO_FROM_LE_EXPLORATION_2	0	Strategy 5012 is converted from ID sisepuede_run_2023-10-06T20:10:09.932282 primary_id = 514926 (design_id: 3; strategy_id: 5009; future_id: 412;)
5013	PFLO: Acceptable net zero acheivement from lever uncertainty exploration 3	PFLO:NET_ZERO_FROM_LE_EXPLORATION_3	0	Strategy 5013 is converted from ID sisepuede_run_2023-10-06T20:10:09.932282 primary_id = 514978 (design_id: 3; strategy_id: 5009; future_id: 464;)
5014	PFLO: Acceptable net zero acheivement from lever uncertainty exploration 4	PFLO:NET_ZERO_FROM_LE_EXPLORATION_4	0	Strategy 5014 is converted from ID sisepuede_run_2023-10-06T20:10:09.932282 primary_id = 515112 (design_id: 3; strategy_id: 5009; future_id: 598;)
5015	PFLO: Acceptable net zero acheivement from lever uncertainty exploration 5	PFLO:NET_ZERO_FROM_LE_EXPLORATION_5	0	Strategy 5015 is converted from ID sisepuede_run_2023-10-06T20:10:09.932282 primary_id = 515483 (design_id: 3; strategy_id: 5009; future_id: 969;)
5016	PFLO:Placeholder 1	PFLO:PLACEHOLDER_1	0	placeholder
5017	PFLO:Placeholder 2	PFLO:PLACEHOLDER_2	0	placeholder
5018	PFLO:Placeholder 3	PFLO:PLACEHOLDER_3	0	placeholder
5019	PFLO:Placeholder 4	PFLO:PLACEHOLDER_4	0	placeholder
5020	PFLO:Placeholder 5	PFLO:PLACEHOLDER_5	0	placeholder
5021	PFLO:Placeholder 6	PFLO:PLACEHOLDER_6	0	placeholder
5022	PFLO:Placeholder 7	PFLO:PLACEHOLDER_7	0	placeholder
5023	PFLO:Placeholder 8	PFLO:PLACEHOLDER_8	0	placeholder
5024	PFLO:Placeholder 9	PFLO:PLACEHOLDER_9	0	placeholder
5025	PFLO:Placeholder 10	PFLO:PLACEHOLDER_10	0	placeholder
6001	LNDU: Partial land use reallocation with India Climate Change	LNDU:PLUR_INDIA_CC	0	Set land use reallocation factor to 0.5 + India’s expected climate change impacts on yields
6002	LNDU: India CCDR	PFLO:INDIA_CCDR_PLUR	0	India CCDR with partial land use reallocation
6003	PFLO: India CCDR and India Climate Change Impacts	PFLO:INDIA_CCDR_PLUR_INDIA_CC	0	India CCDR with partial land use reallocation + India’s expected climate change impacts on yields
6004	PFLO: All transformations with partial land use reallocation and India Climate Change Impacts	PFLO:ALL_PLUR_INDIA_CC	0	All transformations with partial land use reallocation + India’s expected climate change impacts on yields

Using the input data system, the SamplingUnits is instantiated for each variable or collection of variables (specified as a variable trajectory group) and infers whether or not a variable is an X or an L–any variable that changes with strategies is determined to be an L–then determines the implicit lever effect for each strategy since effects might vary by strategy.

Designs and Lever Effects

The Design dimension of analyais, which is indexed by design_id, is used to manage computational experiments and control two uncertainties: exogenous uncertainties, characterized by X in the XLRM matrix, and lever effect uncertainties, which represent undertainties in the ability to acheieve lever or strategy specifications. As described above in the Strategy section, the implicit lever effect, or lever delta, is inferred by the SamplingUnit class (sampling_units.py). The design_id allows the user to sample arund this effect and consider scenarios where strategic goals or expectations are not met or are exceeded. The specification of uncertainty designs are controled in the design_id attribute table, located at ../../sisepuede/attributes/attribute_dim_design_id.csv.

Current specifications of designs in attribute_dim_design_id.csv

design_id	vary_l	vary_x	linear_transform_l_m	linear_transform_l_b	linear_transform_l_inf	linear_transform_l_sup	design_name	include
0	0	1	1.0	0.0	1.0	1.0	Vary Xs (design 0)	1
1	1	1	0.75	0.25	0.25	1.0	Vary Xs and Les; Cap LE at 1 (design 1)	1
2	1	1	1.25	0.0	0.25	1.0	Vary Xs and LEs; Cap LE at 1.1 (design 2, 20% chance that a given variable will face maximum implementation or 20% that it will face 25% implementation)	1
3	1	0	0.9	0.1	0.1	1.0	Vary LEs; (design 3)	1
4	1	1	1.0	0.0	0.0	1.0	Vary Xs and LEs with full range of LE	1

A brief description of input fields is included below. Note that fields that begin with linear_transform_ld_ give parameter values for the linear transformation of LHC samples, which are then applied as scalars to LEs. Mathematically, suppose an LHC sample \(x\) is such that \(x \sim U(0, 1)\). Then the transformation applied to generate scalars for lever effects (LEs) is \(d(x) = \max\{\min\{mx + b, a_1\}, a_0\}\).

Description of fields in attribute_dim_design_id.csv

field	description
design_id	Integer index for experimental designs. 0 is baseline by convention.
vary_lever_deltas	Binary (1 or 0). A 1 indicates that this design will quantify uncertainty around the lever deltas, and a 0 will leave the lever delta fixed (for each strategy).
vary_uncertainties	Binary (1 or 0). A 1 indicates that this design will quantify uncertainty around incertidumbres (parameters of type incertidumbre), and a 0 will only assess fixed trajectories (no variation under futures).
linear_transform_ld_m	Real number, gives the value of \(m\) in \(d(x)\).
linear_transform_ld_b	Real number, gives the value of \(b\) in \(d(x)\).
min_lever_deltas	Real number, gives the value of \(a_0\) in \(d(x)\).
max_lever_deltas	Real number, gives the value of \(a_1\) in \(d(x)\).
experimental_design	The name of the design.
include	Binary (1 or 0). A 1 indicates that this design will be included in the analysis, while a 0 indicates it will not be included.

Primary Key