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Burkina_MC.R
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Burkina_MC.R
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setwd("/Users/macbook/Publications/17_Burkina_Vignette")
library(decisionSupport)
#library(DAutilities) #compound_figure AND empirical_EVPI ARE ONLY IN DAutilities
sediment_calc<-function(x, varnames)
{
### 4 ex-post risks, impacts on the benefits ###
HazardEvent<-chance_event(NaturHazard,1,0,n=n_years)
BadMaintEvent<-chance_event(BadMaintenance,1,0,n=n_years)
BadDesignEvent<-chance_event(BadDesign,1,0,n=n_years, one_draw = TRUE)
Hazard_scaling_irrig_area<-1-HazardEvent*vv(Hazard_reduction_irrigated_area,var_CV,n=n_years)/100
BadMaint_scaling_irrig_area<-1-BadMaintEvent*vv(BadMaint_reduction_irrigated_area,var_CV,n=n_years)/100
BadDesign_scaling_irrig_area<-1-BadDesignEvent*vv(BadDesign_reduction_irrigated_area,var_CV,n=n_years)/100
### 3 ex-ante risks, impacts on the implementation of interventions ###
dredge_NonPopInvolvEvent<-chance_event(dredge_NonPopInvolv,1,0,n=1)
dredge_NonDonorsInvolvEvent<-chance_event(dredge_NonDonorsInvolv,1,0,n=1)
check_NonPopInvolvEvent<-chance_event(check_NonPopInvolv,1,0,n=1)
check_NonInstInvolvEvent<-chance_event(check_NonInstInvolv,1,0,n=1)
check_NonDonorsInvolvEvent<-chance_event(check_NonDonorsInvolv,1,0,n=1)
buffer_NonPopInvolvEvent<-chance_event(buffer_NonPopInvolv,1,0,n=1)
buffer_NonInstInvolvEvent<-chance_event(buffer_NonInstInvolv,1,0,n=1)
buffer_NonDonorsInvolvEvent<-chance_event(buffer_NonDonorsInvolv,1,0,n=1)
##calculation of common random draws for all intervention model runs
TLU<-vv(TLU_no_buffer,var_CV,n_years)
TLU_profit<-vv(profit_per_TLU,var_CV,n_years)
precalc_buffer_fruit_benefits<-vv(buffer_fruit_area_ha,var_CV,n_years)*
vv(buffer_fruit_yield_t_ha,var_CV,n_years)*
vv(buffer_fruit_profit_USD_t,var_CV,n_years)
precalc_buffer_vegetable_benefits<-vv(buffer_vegetable_area_ha,var_CV,n_years)*
vv(buffer_vegetable_yield_t_ha,var_CV,n_years)*
vv(buffer_vegetable_profit_USD_t,var_CV,n_years)
precalc_buffer_rainfed_crop_benefits<-vv(buffer_rainfed_crop_area_ha,var_CV,n_years)*
vv(buffer_rainfed_crop_yield_t_ha,var_CV,n_years)*
vv(buffer_rainfed_crop_profit_USD_t,var_CV,n_years)
precalc_scheme2_vegetable_yield_t_ha<-vv(scheme2_vegetable_yield_t_ha,var_CV,n_years)
precalc_scheme2_vegetable_profit_USD_t<-vv(scheme2_vegetable_profit_USD_t,var_CV,n_years)
precalc_scheme2_rice_yield_t_ha<-vv(scheme2_rice_yield_t_ha,var_CV,n_years)
precalc_scheme2_rice_profit_USD_t<-vv(scheme2_rice_profit_USD_t,var_CV,n_years)
precalc_irrigation_scheme_vegetable_yield_t_ha<-vv(irrigation_scheme_vegetable_yield_t_ha,var_CV,n_years)
precalc_irrigation_scheme_vegetable_profit_USD_t<-vv(irrigation_scheme_vegetable_profit_USD_t,var_CV,n_years)
precalc_irrigation_scheme_rice_yield_t_ha<-vv(irrigation_scheme_rice_yield_t_ha,var_CV,n_years)
precalc_irrigation_scheme_rice_profit_USD_t<-vv(irrigation_scheme_rice_profit_USD_t,var_CV,n_years)
precalc_proportion_irrigation_scheme_rice<-vv(proportion_irrigation_scheme_rice,var_CV,n_years)
precalc_fish_hazards<-HazardEvent*vv(Hazard_reduction_fish_perc/100,var_CV,n=n_years)
precalc_current_fish_value<-vv(current_annual_fish_value_USD,var_CV,n_years)
for (decision_dredging in c(FALSE,TRUE))
for (decision_check_dams in c(FALSE,TRUE))
for (decision_buffer_strips in c(FALSE,TRUE))
{
### Intervention 1: dredging ###
if(decision_dredging)
{dredging<-TRUE
dredging_PlanningCost<-TRUE
dredging_Cost<-TRUE} else
{dredging<-FALSE
dredging_PlanningCost<-FALSE
dredging_Cost<-FALSE}
if (dredge_NonPopInvolvEvent){ dredging<-FALSE ; dredging_Cost<-FALSE}
# Non institutional involvement is assumed to have no effect #
if (dredge_NonDonorsInvolvEvent){ dredging<-FALSE ; dredging_Cost<-FALSE ; dredging_PlanningCost<-FALSE}
### Intervention 2: check_dams ###
if(decision_check_dams)
{check_dams<-TRUE
check_dams_PlanningCost<-TRUE
check_dams_Cost<-TRUE} else
{check_dams<-FALSE
check_dams_PlanningCost<-FALSE
check_dams_Cost<-FALSE}
if (check_NonPopInvolvEvent){check_dams<-FALSE ; check_dams_Cost<-FALSE}
if (check_NonInstInvolvEvent){check_dams<-FALSE ; check_dams_Cost<-FALSE}
if (check_NonDonorsInvolvEvent){check_dams<-FALSE ; dredging_Cost<-FALSE ; check_dams_PlanningCost<-FALSE}
### Intervention 3: buffer_strips ###
if(decision_buffer_strips)
{buffer_strips<-TRUE
buffer_strips_PlanningCost<-TRUE
buffer_strips_Cost<-TRUE} else
{buffer_strips<-FALSE
buffer_strips_PlanningCost<-FALSE
buffer_strips_Cost<-FALSE}
if (buffer_NonPopInvolvEvent){buffer_strips<-FALSE ; buffer_strips_Cost<-FALSE}
if (buffer_NonInstInvolvEvent){buffer_strips<-FALSE ; buffer_strips_Cost<-FALSE}
if (buffer_NonDonorsInvolvEvent){buffer_strips<-FALSE ; buffer_strips_Cost<-FALSE ; buffer_strips_PlanningCost<-FALSE}
###Costs
if(dredging_Cost) {cost_dredging<-dredging_supervision_cost+dredging_admin_cost+dredging_transport_cost+
dredging_culvert_supervision_cost
} else cost_dredging<-0
if(check_dams_Cost) {cost_check_dams<-check_supervision_cost+check_training_cost+check_tech_devices_cost+check_material_cost+
check_rocks_cost+check_transport_cost
} else cost_check_dams<-0
if(buffer_strips_Cost) {cost_buffer_strips<-buffer_adaptation_cost+buffer_tech_devices_cost+buffer_nursery_cost+buffer_wells_cost+
buffer_training_cost+buffer_mngmt_oprt_cost+buffer_mngmt_follow_cost+buffer_mngmt_audit_cost
} else cost_buffer_strips<-0
if(dredging_PlanningCost) {plan_cost_dredging<-dredging_study_cost+dredging_communication_cost+dredging_culvert_feasibility_cost
} else plan_cost_dredging<-0
if(check_dams_PlanningCost) {plan_cost_check_dams<-check_location_cost+check_feasibility_cost+check_topobatymetry_cost+check_communication_cost
} else plan_cost_check_dams<-0
if(buffer_strips_PlanningCost) {plan_cost_buffer_strips<-buffer_communication_cost+buffer_zoning_cost
} else plan_cost_buffer_strips<-0
maintenance_cost<-rep(0,n_years)
if(check_dams) maintenance_cost<-maintenance_cost+vv(maintenance_check_dams,var_CV,n_years)
if(buffer_strips) maintenance_cost<-maintenance_cost+vv(maintenance_buffer_strips,var_CV,n_years)
intervention_cost<-maintenance_cost
intervention_cost[1]<-intervention_cost[1]+cost_dredging+cost_check_dams+cost_buffer_strips+
plan_cost_dredging+plan_cost_check_dams+plan_cost_buffer_strips
###irrigation scheme 1 - area decline and delay by interventions
gompertz_time1_time_until_irrigated_area_declines<-sum(c(baseline_time_until_irrig_area_declines,
dredging*dredging_delay_of_irrig_area_decline,
check_dams*check_dam_delay_of_irrig_area_decline,
buffer_strips*buffer_strip_delay_of_irrig_area_decline))
gompertz_time2_time_until_irrigated_area_halved<-sum(c(gompertz_time1_time_until_irrigated_area_declines,
baseline_start_losses_to_half_irrig_area_lost,
dredging*dredging_delay_of_irrig_area_halved,
check_dams*check_dam_delay_of_irrig_area_halved,
buffer_strips*buffer_strip_delay_of_irrig_area_halved))
irrig_scheme1_area_share<-1-gompertz_yield(max_harvest=1,
time_to_first_yield_estimate=gompertz_time1_time_until_irrigated_area_declines,
time_to_second_yield_estimate=gompertz_time2_time_until_irrigated_area_halved,
first_yield_estimate_percent=10,
second_yield_estimate_percent=50, n_years=n_years, var_CV = 0,
no_yield_before_first_estimate = TRUE)
irrig_scheme1_area_share<-irrig_scheme1_area_share[1:30]
irrig_scheme1_area<-current_irrig_area*irrig_scheme1_area_share
###irrigation scheme 1 - risk of blockage and pipe clearing
gompertz_time2_time_until_pipe_blockage_occurs_every_second_year<-sum(c(baseline_time_until_pipes_blocked_every_second_year,
dredging*dredging_delay_of_pipes_blocked_every_second_year,
check_dams*check_dam_delay_of_pipes_blocked_every_second_year,
buffer_strips*buffer_strip_delay_of_pipes_blocked_every_second_year))
risk_blockage<-gompertz_yield(max_harvest=1,
time_to_first_yield_estimate=0,
time_to_second_yield_estimate=gompertz_time2_time_until_pipe_blockage_occurs_every_second_year,
first_yield_estimate_percent=100*current_risk_of_pipe_blockage,
second_yield_estimate_percent=50,
n_years=n_years, var_CV = 0,
no_yield_before_first_estimate = TRUE)
risk_blockage[which(risk_blockage>1)]<-1
risk_blockage[which(risk_blockage<0)]<-0
gompertz_time2_time_until_chance_cleared_50percent<-sum(c(baseline_time_until_chance_cleared_50percent,
dredging*dredging_delay_of_time_until_chance_cleared_50percent,
check_dams*check_dam_delay_of_time_until_chance_cleared_50percent,
buffer_strips*buffer_strip_delay_of_time_until_chance_cleared_50percent))
chance_cleared<-gompertz_yield(max_harvest=1,
time_to_first_yield_estimate=0,
time_to_second_yield_estimate=gompertz_time2_time_until_chance_cleared_50percent,
first_yield_estimate_percent=100*current_chance_of_blocked_pipe_cleared,
second_yield_estimate_percent=50,
n_years=n_years, var_CV = 10,
no_yield_before_first_estimate = TRUE)
chance_cleared[which(chance_cleared>1)]<-1
chance_cleared[which(chance_cleared<0)]<-0
### Creation of intermediate variables: irrigation area potentially irrigated given the risk of pipe blockage (in agricultural development) ###
### Risk that pipes are blocked/cleared given the time period ###
### Irrigated area given the risk of pipe blockage (in agricultural development) ###
pipe_clogging<-sapply(1:n_years,function(x) rbinom(1,1,risk_blockage[x]))
pipe_cleared<-sapply(1:n_years,function(x) rbinom(1,1,chance_cleared[x]))
pipe_blocked <- pipe_clogging
for (i in 2:length(pipe_blocked))
if (pipe_clogging[i] == 0)
if (pipe_blocked[i - 1] == 1)
if (!pipe_cleared[i] == 1) pipe_blocked[i] <- 1
irrig_scheme1_irrigated_area_ex_ante<-irrig_scheme1_area*(1-pipe_blocked*vv(pipe_blocked_area_lost_perc/100,var_CV,n_years))
### Impact of ex-post risks on irrigated area###
irrigated_area_scheme1<-irrig_scheme1_irrigated_area_ex_ante*
Hazard_scaling_irrig_area*BadMaint_scaling_irrig_area*
BadDesign_scaling_irrig_area #*NonCompli_scaling_irrig_area
### Benefits from rice cultivation in the shore of the reservoir (==0 if buffer strips implemented) ###
if (buffer_strips)
buffer_strip_cultivation<-TRUE else buffer_strip_cultivation<-FALSE
scheme2_time_until_benefits_gone<-scheme2_time_until_dredging_benefits_gone_baseline+
check_dams*check_dams_added_scheme2_area_benefit_time
scheme2_area_scaler<-gompertz_yield(max_harvest=1,
time_to_first_yield_estimate=1,
time_to_second_yield_estimate=scheme2_time_until_benefits_gone,
first_yield_estimate_percent=100,
second_yield_estimate_percent=0,
n_years=n_years, var_CV = 0,
no_yield_before_first_estimate = TRUE)
scheme2_area_ha<-scheme2_area_no_dredging_ha*(1+
dredging*scheme2_area_scaler*dredging_bump_scheme2_area_perc/100)
#rice area remains unchanged (except dredging bump), because the
#water comes from very close to where it's needed in the rainy season.
scheme2_rice_benefits<-as.numeric(!buffer_strips)*
vv(scheme2_area_ha,var_CV,n_years)*
precalc_scheme2_rice_yield_t_ha*
precalc_scheme2_rice_profit_USD_t
gompertz_time1_time_until_irrigated_area2_declines<-sum(c(baseline_time_until_irrig_area2_declines,
dredging*dredging_delay_of_irrig_area2_decline,
check_dams*check_dam_delay_of_irrig_area2_decline))
gompertz_time2_time_until_irrigated_area2_halved<-sum(c(gompertz_time1_time_until_irrigated_area2_declines,
baseline_start_losses_to_half_irrig_area2_lost,
dredging*dredging_delay_of_irrig_area2_halved,
check_dams*check_dam_delay_of_irrig_area2_halved))
irrig_scheme2_area_share<-1-gompertz_yield(max_harvest=1,
time_to_first_yield_estimate=gompertz_time1_time_until_irrigated_area2_declines,
time_to_second_yield_estimate=gompertz_time2_time_until_irrigated_area2_halved,
first_yield_estimate_percent=0,
second_yield_estimate_percent=50, n_years=n_years, var_CV = 0,
no_yield_before_first_estimate = TRUE)
scheme2_vegetable_area_ha<-scheme2_area_ha*irrig_scheme2_area_share
scheme2_vegetable_benefits<-as.numeric(!buffer_strips)*
vv(scheme2_vegetable_area_ha,var_CV,n_years)*
precalc_scheme2_vegetable_yield_t_ha*
precalc_scheme2_vegetable_profit_USD_t
buffer_fruit_benefits<-as.numeric(buffer_strips)*precalc_buffer_fruit_benefits
buffer_vegetable_benefits<-as.numeric(buffer_strips)*precalc_buffer_vegetable_benefits
buffer_rainfed_crop_benefits<-as.numeric(buffer_strips)*precalc_buffer_rainfed_crop_benefits
rainy_season_rice_area_scheme1<-irrigated_area_scheme1*precalc_proportion_irrigation_scheme_rice
rainy_season_vegetable_area_scheme1<-irrigated_area_scheme1-rainy_season_rice_area_scheme1
irrigation_season_rainy_season_benefits_scheme1<-rainy_season_rice_area_scheme1*
precalc_irrigation_scheme_rice_yield_t_ha*
precalc_irrigation_scheme_rice_profit_USD_t+
rainy_season_vegetable_area_scheme1*
precalc_irrigation_scheme_vegetable_yield_t_ha*
precalc_irrigation_scheme_vegetable_profit_USD_t
irrigation_season_dry_season_benefits_scheme1<-irrigated_area_scheme1*
precalc_irrigation_scheme_vegetable_yield_t_ha*
precalc_irrigation_scheme_vegetable_profit_USD_t
### Total benefits from crop production (agricultural development and riparian zone) ###
crop_production<-scheme2_rice_benefits+
scheme2_vegetable_benefits+
buffer_fruit_benefits+
buffer_vegetable_benefits+
buffer_rainfed_crop_benefits+
irrigation_season_rainy_season_benefits_scheme1+
irrigation_season_dry_season_benefits_scheme1
### Benefits from fishing ###
### Impact of interventions on fish population ###
time_to_start_fish_decline<-sum(c(time_to_start_fish_decline_baseline,
dredging_delay_start_fish_decline,
check_dams_delay_start_fish_decline,
buffer_strips_delay_start_fish_decline))
time_to_fish_population_halved<-sum(c(time_to_start_fish_decline,
time_to_halve_fish_population_baseline,
dredging_delay_in_time_to_halve_fish_population,
check_dams_delay_in_time_to_halve_fish_population,
buffer_strips_delay_in_time_to_halve_fish_population))
fish_benefit_scaler<-1-gompertz_yield(max_harvest=1,
time_to_first_yield_estimate=time_to_start_fish_decline,
time_to_second_yield_estimate=time_to_fish_population_halved,
first_yield_estimate_percent=0,
second_yield_estimate_percent=50, n_years=n_years, var_CV = 10,
no_yield_before_first_estimate = TRUE)
risk_adjusted_fish_benefits<-fish_benefit_scaler*(1-precalc_fish_hazards)
### Fish benefits ###
Fish_benefits<-precalc_current_fish_value*risk_adjusted_fish_benefits
### Benefits from livestock ###
# The following allows considering that buffer strips may
# restrict access to the reservoir for livestock.
if(buffer_strips) TLU_intervention<-TLU*(1+change_TLU_buffer_perc/100) else TLU_intervention<-TLU
livestock_benefits<-TLU_intervention*TLU_profit
total_benefits<-crop_production+Fish_benefits+livestock_benefits
net_benefits<-total_benefits-intervention_cost
if(decision_dredging & decision_check_dams & decision_buffer_strips) result_dredge_check_buff<-net_benefits
if(decision_dredging & decision_check_dams & !decision_buffer_strips) result_dredge_check_nbuff<-net_benefits
if(decision_dredging & !decision_check_dams & decision_buffer_strips) result_dredge_ncheck_buff<-net_benefits
if(decision_dredging & !decision_check_dams & !decision_buffer_strips) result_dredge_ncheck_nbuff<-net_benefits
if(!decision_dredging & decision_check_dams & decision_buffer_strips) result_ndredge_check_buff<-net_benefits
if(!decision_dredging & decision_check_dams & !decision_buffer_strips) result_ndredge_check_nbuff<-net_benefits
if(!decision_dredging & !decision_check_dams & decision_buffer_strips) result_ndredge_ncheck_buff<-net_benefits
if(!decision_dredging & !decision_check_dams & !decision_buffer_strips) result_ndredge_ncheck_nbuff<-net_benefits
} #close intervention loop bracket
NPV_dredge_check_buff<-discount(result_dredge_check_buff,discount_rate,calculate_NPV = TRUE)
NPV_dredge_check_nbuff<-discount(result_dredge_check_nbuff,discount_rate,calculate_NPV = TRUE)
NPV_dredge_ncheck_buff<-discount(result_dredge_ncheck_buff,discount_rate,calculate_NPV = TRUE)
NPV_dredge_ncheck_nbuff<-discount(result_dredge_ncheck_nbuff,discount_rate,calculate_NPV = TRUE)
NPV_ndredge_check_buff<-discount(result_ndredge_check_buff,discount_rate,calculate_NPV = TRUE)
NPV_ndredge_check_nbuff<-discount(result_ndredge_check_nbuff,discount_rate,calculate_NPV = TRUE)
NPV_ndredge_ncheck_buff<-discount(result_ndredge_ncheck_buff,discount_rate,calculate_NPV = TRUE)
NPV_ndredge_ncheck_nbuff<-discount(result_ndredge_ncheck_nbuff,discount_rate,calculate_NPV = TRUE)
return(list(NPV_dredge_check_buff=NPV_dredge_check_buff-NPV_ndredge_ncheck_nbuff,
NPV_dredge_check_nbuff=NPV_dredge_check_nbuff-NPV_ndredge_ncheck_nbuff,
NPV_dredge_ncheck_buff=NPV_dredge_ncheck_buff-NPV_ndredge_ncheck_nbuff,
NPV_dredge_ncheck_nbuff=NPV_dredge_ncheck_nbuff-NPV_ndredge_ncheck_nbuff,
NPV_ndredge_check_buff=NPV_ndredge_check_buff-NPV_ndredge_ncheck_nbuff,
NPV_ndredge_check_nbuff=NPV_ndredge_check_nbuff-NPV_ndredge_ncheck_nbuff,
NPV_ndredge_ncheck_buff=NPV_ndredge_ncheck_buff-NPV_ndredge_ncheck_nbuff,
cashflow_NPV_dredge_check_buff=result_dredge_check_buff-result_ndredge_ncheck_nbuff,
cashflow_NPV_dredge_check_nbuff=result_dredge_check_nbuff-result_ndredge_ncheck_nbuff,
cashflow_NPV_dredge_ncheck_buff=result_dredge_ncheck_buff-result_ndredge_ncheck_nbuff,
cashflow_NPV_dredge_ncheck_nbuff=result_dredge_ncheck_nbuff-result_ndredge_ncheck_nbuff,
cashflow_NPV_ndredge_check_buff=result_ndredge_check_buff-result_ndredge_ncheck_nbuff,
cashflow_NPV_ndredge_check_nbuff=result_ndredge_check_nbuff-result_ndredge_ncheck_nbuff,
cashflow_NPV_ndredge_ncheck_buff=result_ndredge_ncheck_buff-result_ndredge_ncheck_nbuff))
}
###############################################################################################################
### Running of the model ###
decisionSupport("Sediment-2.csv",
outputPath='results',
welfareFunction=sediment_calc,
numberOfModelRuns=10000,
functionSyntax="plainNames")
mc<-read.csv("results/mcSimulationResults.csv")
legend_table<-read.csv("Sediment_input_table.csv")
mc_EVPI<-mc[,-grep("cashflow",colnames(mc))]
dir.create("Figures")
empirical_EVPI(mc_EVPI,"NPV_dredge_check_buff",write_table=TRUE,fileformat="png",outfolder="Figures",
p_spearman=0.05, legend_table=read.csv("Sediment_legend.csv"),#legend_table,
output_legend_table=read.csv("Sediment_legend.csv"))#legend_table)
#produce compound figures
#variable_name="implementer_NPV"
for (variable_name in c("NPV_dredge_check_buff","NPV_dredge_check_nbuff",
"NPV_dredge_ncheck_buff","NPV_dredge_ncheck_nbuff",
"NPV_ndredge_check_buff","NPV_ndredge_check_nbuff",
"NPV_ndredge_ncheck_buff"))
compound_figure(variable_name=variable_name,
MC_table=mc,
PLS_table=read.csv(paste("results/",variable_name,"_pls_results.csv",sep="")),
EVPI_table=read.csv(paste("Figures/","EVPI_table_",variable_name,".csv",sep="")),
cash_flow_vars=paste("cashflow_",variable_name,sep=""),
nbreaks=100,scaler="auto",percentile_remove=c(.01,.99),
npls=15,plsthreshold=0.8,colorscheme="ICRAF_colors",MCcolor="mango",fonttype='sans',
borderlines=FALSE,lwd=2,
fileformat="png",filename=paste("Figures/","Combined_",variable_name,sep=""),
legend_table=read.csv("Sediment_legend.csv"))
make_variables<-function(est,n=1)
{ x<-random(rho=est, n=n)
for(i in colnames(x)) assign(i, as.numeric(x[1,i]),envir=.GlobalEnv)}
make_variables(estimate_read_csv("Sediment_input_table.csv"))