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rffscghg · Oct 17, 2022 · 56db778 · 56db778
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diff --git a/examples/main.jl b/examples/main.jl
@@ -5,10 +5,10 @@ N = 10000
 seed = 350
 
 Random.seed!(seed)
-MimiIWG.run_scc_mcs(DICE, gas=:CO2, trials = N)
+MimiIWG.run_scc_mcs(DICE, gas=:CO2, trials=N)
 
 Random.seed!(seed)
-MimiIWG.run_scc_mcs(FUND, gas=:CO2, trials = N)
+MimiIWG.run_scc_mcs(FUND, gas=:CO2, trials=N)
 
 Random.seed!(seed)
-MimiIWG.run_scc_mcs(PAGE, gas=:CO2, trials = N)
+MimiIWG.run_scc_mcs(PAGE, gas=:CO2, trials=N)
diff --git a/examples/other_ghg.jl b/examples/other_ghg.jl
@@ -22,13 +22,13 @@ scn2o = MimiIWG.compute_scc(PAGE, USG1, gas=:N2O, year=2020, discount=0.025)
 
 # DICE
 # (will run for all 3 IWG discount rates if none are specified) 
-MimiIWG.run_scc_mcs(DICE, gas=:CH4, trials=10_000, perturbation_years=[2020], output_dir = "output")
-MimiIWG.run_scc_mcs(DICE, gas=:N2O, trials=10_000, perturbation_years=[2020], output_dir = "output")
+MimiIWG.run_scc_mcs(DICE, gas=:CH4, trials=10_000, perturbation_years=[2020], output_dir="output")
+MimiIWG.run_scc_mcs(DICE, gas=:N2O, trials=10_000, perturbation_years=[2020], output_dir="output")
 
 # FUND
-MimiIWG.run_scc_mcs(FUND, gas=:CH4, trials=10_000, perturbation_years=[2020], output_dir = "output")
-MimiIWG.run_scc_mcs(FUND, gas=:N2O, trials=10_000, perturbation_years=[2020], output_dir = "output")
+MimiIWG.run_scc_mcs(FUND, gas=:CH4, trials=10_000, perturbation_years=[2020], output_dir="output")
+MimiIWG.run_scc_mcs(FUND, gas=:N2O, trials=10_000, perturbation_years=[2020], output_dir="output")
 
 # PAGE
-MimiIWG.run_scc_mcs(PAGE, gas=:CH4, trials=10_000, perturbation_years=[2020],  output_dir = "output")
-MimiIWG.run_scc_mcs(PAGE, gas=:N2O, trials=10_000, perturbation_years=[2020],  output_dir = "output")
+MimiIWG.run_scc_mcs(PAGE, gas=:CH4, trials=10_000, perturbation_years=[2020], output_dir="output")
+MimiIWG.run_scc_mcs(PAGE, gas=:N2O, trials=10_000, perturbation_years=[2020], output_dir="output")
diff --git a/src/MimiIWG.jl b/src/MimiIWG.jl
@@ -8,7 +8,7 @@ using Interpolations
 using Mimi
 using MimiDICE2010
 using MimiFUND          # pinned to version 3.8 in package registration Compat.toml
-using MimiPAGE2009      
+using MimiPAGE2009
 using StatsBase
 using XLSX: readxlsx, openxlsx, readdata
 using CSVFiles
@@ -17,7 +17,7 @@ using Query
 using Statistics
 
 export DICE, FUND, PAGE, # export the enumerated model_choice options
-        USG1, USG2, USG3, USG4, USG5 # export the enumerated scenario_choice options 
+    USG1, USG2, USG3, USG4, USG5 # export the enumerated scenario_choice options 
 
 # General constants and functions
 include("core/constants.jl")
@@ -54,4 +54,4 @@ include("montecarlo/PAGE_mcs.jl")
 include("montecarlo/run_scc_mcs.jl")
 
 
-end
+end
diff --git a/src/components/IWG_DICE_ScenarioChoice.jl b/src/components/IWG_DICE_ScenarioChoice.jl
@@ -6,35 +6,35 @@
     scenarios = Index()
 
     # Variables (each one has its value set for the chosen scenario in the first timestep)
-    l       = Variable(index = [time])  # Population
-    E       = Variable(index = [time])  # Total CO2 emissions
-    forcoth = Variable(index = [time])  # other forcing
-    al      = Variable(index = [time])  # total factor productivity
-    k0      = Variable()                # initial capital stock
+    l = Variable(index=[time])  # Population
+    E = Variable(index=[time])  # Total CO2 emissions
+    forcoth = Variable(index=[time])  # other forcing
+    al = Variable(index=[time])  # total factor productivity
+    k0 = Variable()                # initial capital stock
 
     # The number for which scenario to use 
     scenario_num = Parameter{Integer}()
 
     # Parameters (each one holds all five scenarios)
-    l_all       = Parameter(index = [time, scenarios])
-    E_all       = Parameter(index = [time, scenarios])
-    forcoth_all = Parameter(index = [time, scenarios])
-    al_all      = Parameter(index = [time, scenarios])
-    k0_all      = Parameter(index = [scenarios])
+    l_all = Parameter(index=[time, scenarios])
+    E_all = Parameter(index=[time, scenarios])
+    forcoth_all = Parameter(index=[time, scenarios])
+    al_all = Parameter(index=[time, scenarios])
+    k0_all = Parameter(index=[scenarios])
 
     function run_timestep(p, v, d, t)
         if is_first(t)
             # Get the specified scenario
             scenario_num = p.scenario_num
-            if ! (scenario_num in d.scenarios)
+            if !(scenario_num in d.scenarios)
                 error("Invalid :scenario_num in :IWGScenarioChoice component: $scenario_num. :scenario_num must be in $(d.scenarios).")
             else
                 # Copy over all of the values for that scenario
-                v.l[:]          = p.l_all[:, scenario_num]
-                v.E[:]          = p.E_all[:, scenario_num]
-                v.forcoth[:]    = p.forcoth_all[:, scenario_num]
-                v.al[:]         = p.al_all[:, scenario_num]
-                v.k0            = p.k0_all[scenario_num]
+                v.l[:] = p.l_all[:, scenario_num]
+                v.E[:] = p.E_all[:, scenario_num]
+                v.forcoth[:] = p.forcoth_all[:, scenario_num]
+                v.al[:] = p.al_all[:, scenario_num]
+                v.k0 = p.k0_all[scenario_num]
             end
         end
     end

diff --git a/src/components/IWG_DICE_climatedynamics.jl b/src/components/IWG_DICE_climatedynamics.jl
@@ -1,14 +1,14 @@
 # Since the IWG sampled from the Roe & Baker distribution for equilibrium climate sensitivity (t2xco2), they added in the catch case for values less than 0.5 in the code below.
 @defcomp IWG_DICE_climatedynamics begin
 
-    TATM    = Variable(index=[time])    # Increase in temperature of atmosphere (degrees C from 1900)
-    TOCEAN  = Variable(index=[time])    # Increase in temperature of lower oceans (degrees C from 1900)
-    TA_eq   = Variable(index=[time])    
-
-    FORC    = Parameter(index=[time])   # Increase in radiative forcing (watts per m2 from 1900)
-    fco22x  = Parameter()               # Forcings of equilibrium CO2 doubling (Wm-2)
-    t2xco2  = Parameter(default=3)      # Equilibrium temp impact (oC per doubling CO2)
-    tatm0   = Parameter()               # Initial atmospheric temp change (C from 1900) in 2005
+    TATM = Variable(index=[time])    # Increase in temperature of atmosphere (degrees C from 1900)
+    TOCEAN = Variable(index=[time])    # Increase in temperature of lower oceans (degrees C from 1900)
+    TA_eq = Variable(index=[time])
+
+    FORC = Parameter(index=[time])   # Increase in radiative forcing (watts per m2 from 1900)
+    fco22x = Parameter()               # Forcings of equilibrium CO2 doubling (Wm-2)
+    t2xco2 = Parameter(default=3)      # Equilibrium temp impact (oC per doubling CO2)
+    tatm0 = Parameter()               # Initial atmospheric temp change (C from 1900) in 2005
     tocean0 = Parameter()               # Initial lower stratum temp change (C from 1900)
 
     # Transient TSC Correction ("Speed of Adjustment Parameter")
@@ -32,16 +32,16 @@
         elseif p.t2xco2 < 0.5
             v.TATM[t] = v.TA_eq[t]
         else
-            v.TATM[t] = v.TATM[t - 1] + p.c1 * ((p.FORC[t] - (p.fco22x/p.t2xco2) * v.TATM[t - 1]) - (p.c3 * (v.TATM[t - 1] - v.TOCEAN[t - 1])))
+            v.TATM[t] = v.TATM[t-1] + p.c1 * ((p.FORC[t] - (p.fco22x / p.t2xco2) * v.TATM[t-1]) - (p.c3 * (v.TATM[t-1] - v.TOCEAN[t-1])))
         end
 
         # Define function for TOCEAN
         if is_first(t)
             v.TOCEAN[t] = p.tocean0
         else
-            v.TOCEAN[t] = v.TOCEAN[t - 1] + p.c4 * (v.TATM[t - 1] - v.TOCEAN[t - 1])
+            v.TOCEAN[t] = v.TOCEAN[t-1] + p.c4 * (v.TATM[t-1] - v.TOCEAN[t-1])
         end
 
     end
 
-end
+end
diff --git a/src/components/IWG_DICE_co2cycle.jl b/src/components/IWG_DICE_co2cycle.jl
@@ -3,23 +3,23 @@
 #       - E is in units of GtCO2 per decade instead of per year (so it doesn't get mulitiplied by 10)
 @defcomp IWG_DICE_co2cycle begin
 
-    MAT     = Variable(index=[time])    # Carbon concentration increase in atmosphere (GtC from 1750)
-    ML      = Variable(index=[time])    # Carbon concentration increase in lower oceans (GtC from 1750)
-    MU      = Variable(index=[time])    # Carbon concentration increase in shallow oceans (GtC from 1750)
+    MAT = Variable(index=[time])    # Carbon concentration increase in atmosphere (GtC from 1750)
+    ML = Variable(index=[time])    # Carbon concentration increase in lower oceans (GtC from 1750)
+    MU = Variable(index=[time])    # Carbon concentration increase in shallow oceans (GtC from 1750)
 
-    E       = Parameter(index=[time])   # Total CO2 emissions (GtCO2 per decade)
-    mat0    = Parameter()               # Initial Concentration in atmosphere 2010 (GtC)
-    ml0     = Parameter()               # Initial Concentration in lower strata (GtC)
-    mu0     = Parameter()               # Initial Concentration in upper strata (GtC)
+    E = Parameter(index=[time])   # Total CO2 emissions (GtCO2 per decade)
+    mat0 = Parameter()               # Initial Concentration in atmosphere 2010 (GtC)
+    ml0 = Parameter()               # Initial Concentration in lower strata (GtC)
+    mu0 = Parameter()               # Initial Concentration in upper strata (GtC)
 
     # Parameters for long-run consistency of carbon cycle
-    b11     = Parameter()               # Carbon cycle transition matrix atmosphere to atmosphere
-    b12     = Parameter()               # Carbon cycle transition matrix atmosphere to shallow ocean
-    b21     = Parameter()               # Carbon cycle transition matrix biosphere/shallow oceans to atmosphere
-    b22     = Parameter()               # Carbon cycle transition matrix shallow ocean to shallow oceans
-    b23     = Parameter()               # Carbon cycle transition matrix shallow to deep ocean
-    b32     = Parameter()               # Carbon cycle transition matrix deep ocean to shallow ocean
-    b33     = Parameter()               # Carbon cycle transition matrix deep ocean to deep oceans
+    b11 = Parameter()               # Carbon cycle transition matrix atmosphere to atmosphere
+    b12 = Parameter()               # Carbon cycle transition matrix atmosphere to shallow ocean
+    b21 = Parameter()               # Carbon cycle transition matrix biosphere/shallow oceans to atmosphere
+    b22 = Parameter()               # Carbon cycle transition matrix shallow ocean to shallow oceans
+    b23 = Parameter()               # Carbon cycle transition matrix shallow to deep ocean
+    b32 = Parameter()               # Carbon cycle transition matrix deep ocean to shallow ocean
+    b33 = Parameter()               # Carbon cycle transition matrix deep ocean to deep oceans
 
 
     function run_timestep(p, v, d, t)
@@ -28,14 +28,14 @@
         if is_first(t)
             v.MU[t] = p.mu0
         else
-            v.MU[t] = v.MAT[t - 1] * p.b12 + v.MU[t - 1] * p.b22 + v.ML[t - 1] * p.b32
+            v.MU[t] = v.MAT[t-1] * p.b12 + v.MU[t-1] * p.b22 + v.ML[t-1] * p.b32
         end
 
         # Define function for ML
         if is_first(t)
             v.ML[t] = p.ml0
         else
-            v.ML[t] = v.ML[t - 1] * p.b33 + v.MU[t - 1] * p.b23
+            v.ML[t] = v.ML[t-1] * p.b33 + v.MU[t-1] * p.b23
         end
 
         # Define function for MAT
@@ -44,9 +44,9 @@
             # and also calculate MAT[2] below
         end
         if !is_last(t)
-            v.MAT[t + 1] = v.MAT[t] * p.b11 + v.MU[t] * p.b21 + p.E[t]
+            v.MAT[t+1] = v.MAT[t] * p.b11 + v.MU[t] * p.b21 + p.E[t]
         end
 
     end
 
-end
+end
diff --git a/src/components/IWG_DICE_neteconomy.jl b/src/components/IWG_DICE_neteconomy.jl
@@ -2,23 +2,23 @@
 #       In the original component, there are different values for the first and last timestep calculations of C.
 @defcomp IWG_DICE_neteconomy begin
 
-    ABATECOST   = Variable(index=[time])    # Cost of emissions reductions  (trillions 2005 USD per year)
-    C           = Variable(index=[time])    # Consumption (trillions 2005 US dollars per year)
-    CPC         = Variable(index=[time])    # Per capita consumption (thousands 2005 USD per year)
-    CPRICE      = Variable(index=[time])    # Carbon price (2005$ per ton of CO2)
-    I           = Variable(index=[time])    # Investment (trillions 2005 USD per year)
-    Y           = Variable(index=[time])    # Gross world product net of abatement and damages (trillions 2005 USD per year)
-    YNET        = Variable(index=[time])    # Output net of damages equation (trillions 2005 USD per year)
-
-    cost1       = Parameter(index=[time])   # Abatement cost function coefficient
-    DAMFRAC     = Parameter(index=[time])   # Damages (fraction of gross output)
-    l           = Parameter(index=[time])   # Level of population and labor
-    MIU         = Parameter(index=[time])   # Emission control rate GHGs
-    partfract   = Parameter(index=[time])   # Fraction of emissions in control regime
-    pbacktime   = Parameter(index=[time])   # Backstop price
-    S           = Parameter(index=[time])   # Gross savings rate as fraction of gross world product
-    YGROSS      = Parameter(index=[time])   # Gross world product GROSS of abatement and damages (trillions 2005 USD per year)
-    expcost2    = Parameter()               # Exponent of control cost function
+    ABATECOST = Variable(index=[time])    # Cost of emissions reductions  (trillions 2005 USD per year)
+    C = Variable(index=[time])    # Consumption (trillions 2005 US dollars per year)
+    CPC = Variable(index=[time])    # Per capita consumption (thousands 2005 USD per year)
+    CPRICE = Variable(index=[time])    # Carbon price (2005$ per ton of CO2)
+    I = Variable(index=[time])    # Investment (trillions 2005 USD per year)
+    Y = Variable(index=[time])    # Gross world product net of abatement and damages (trillions 2005 USD per year)
+    YNET = Variable(index=[time])    # Output net of damages equation (trillions 2005 USD per year)
+
+    cost1 = Parameter(index=[time])   # Abatement cost function coefficient
+    DAMFRAC = Parameter(index=[time])   # Damages (fraction of gross output)
+    l = Parameter(index=[time])   # Level of population and labor
+    MIU = Parameter(index=[time])   # Emission control rate GHGs
+    partfract = Parameter(index=[time])   # Fraction of emissions in control regime
+    pbacktime = Parameter(index=[time])   # Backstop price
+    S = Parameter(index=[time])   # Gross savings rate as fraction of gross world product
+    YGROSS = Parameter(index=[time])   # Gross world product GROSS of abatement and damages (trillions 2005 USD per year)
+    expcost2 = Parameter()               # Exponent of control cost function
 
 
     function run_timestep(p, v, d, t)
@@ -42,10 +42,10 @@
         v.CPC[t] = 1000 * v.C[t] / p.l[t]
 
         # Define function for CPRICE
-        if t.t==26
-            v.CPRICE[t] = v.CPRICE[t - 1]
+        if t.t == 26
+            v.CPRICE[t] = v.CPRICE[t-1]
         else
-            v.CPRICE[t] = p.pbacktime[t] * 1000 * p.MIU[t] ^ (p.expcost2 - 1)
+            v.CPRICE[t] = p.pbacktime[t] * 1000 * p.MIU[t]^(p.expcost2 - 1)
         end
 
     end

diff --git a/src/components/IWG_DICE_radiativeforcing.jl b/src/components/IWG_DICE_radiativeforcing.jl
@@ -1,23 +1,23 @@
 # Difference from the original DICE radiativeforcing component:
 #   - In the final timestep, the IWG calculates MAT_avg differently
 @defcomp IWG_DICE_radiativeforcing begin
-    FORC      = Variable(index=[time])   # Increase in radiative forcing (watts per m2 from 1900)
+    FORC = Variable(index=[time])   # Increase in radiative forcing (watts per m2 from 1900)
 
-    forcoth   = Parameter(index=[time])  # Exogenous forcing for other greenhouse gases
-    MAT       = Parameter(index=[time])  # Carbon concentration increase in atmosphere (GtC from 1750)
-    fco22x    = Parameter()              # Forcings of equilibrium CO2 doubling (Wm-2)
+    forcoth = Parameter(index=[time])  # Exogenous forcing for other greenhouse gases
+    MAT = Parameter(index=[time])  # Carbon concentration increase in atmosphere (GtC from 1750)
+    fco22x = Parameter()              # Forcings of equilibrium CO2 doubling (Wm-2)
 
 
     function run_timestep(p, v, d, t)
 
         if !is_last(t)
-            MAT_avg = (p.MAT[t] + p.MAT[t + 1]) / 2
-        else 
-            MAT_avg = 0.9796 * (p.MAT[t - 1] + p.MAT[t]) / 2
+            MAT_avg = (p.MAT[t] + p.MAT[t+1]) / 2
+        else
+            MAT_avg = 0.9796 * (p.MAT[t-1] + p.MAT[t]) / 2
         end
 
-        v.FORC[t] = p.fco22x * (log((MAT_avg + 0.000001) / 596.4 ) / log(2)) + p.forcoth[t]
+        v.FORC[t] = p.fco22x * (log((MAT_avg + 0.000001) / 596.4) / log(2)) + p.forcoth[t]
 
     end
 
-end
+end