USEPA · bl-young · Dec 5, 2024 · Sep 4, 2024 · Sep 4, 2024
diff --git a/R/CalculationFunctions.R b/R/CalculationFunctions.R
@@ -147,50 +147,51 @@ calculateResultsWithExternalFactors <- function(model, perspective = "FINAL", de
   }
 
   ## Description of result components apply to both FINAL and DIRECT perspectives
-  # r1 - Domestic emissions from domestic production
-  # r2 - Emissions from imported goods consumed as intermediate products
-  # r3 - Emissions from imported goods consumed as final products
+  # see equations 4-7 and 9-12 in EPA 600/R-24/116
+  # G_d - Domestic emissions from domestic production
+  # G_mi - Emissions from imported goods consumed as intermediate products by domestic industries
+  # G_mf - Emissions from imported goods consumed as final products
 
   if(perspective == "FINAL") {
     # Calculate Final Perspective LCI (a matrix with total impacts in form of sector x flows)
     logging::loginfo("Calculating Final Perspective LCI and LCIA with external import factors...")
-    subscript <- "f"
-    r1 <- model$B %*% model$L_d %*% diag(as.vector(y_d))
-    r2 <- model$M_m %*% model$A_m %*% model$L_d %*% diag(as.vector(y_d))
+    subscript <- "l"
+    G_d <- model$B %*% model$L_d %*% diag(as.vector(y_d))
+    G_mi <- model$M_m %*% model$A_m %*% model$L_d %*% diag(as.vector(y_d))
 
   } else { # Calculate direct perspective results.
     # Calculate Direct Perspective LCI (a matrix with total impacts in form of sector x flows)
     logging::loginfo("Calculating Direct + Imported Perspective LCI and LCIA with external import factors...")
-    subscript <- "d"
+    subscript <- "r"
     s <- getScalingVector(model$L_d, y_d)
-    r1 <- t(calculateDirectPerspectiveLCI(model$B, s))
-    r2 <- t(calculateDirectPerspectiveLCI(model$M_m, (model$A_m %*% model$L_d %*% y_d)))
+    G_d <- t(calculateDirectPerspectiveLCI(model$B, s))
+    G_mi <- t(calculateDirectPerspectiveLCI(model$M_m, (model$A_m %*% model$L_d %*% y_d)))
   }
-  r3 <- model$M_m %*% diag(as.vector(y_m))
+  G_mf <- model$M_m %*% diag(as.vector(y_m))
 
   if (use_domestic_requirements) {
     # zero out the import results
-    r2[] <- 0
-    r3[] <- 0
+    G_mi[] <- 0
+    G_mf[] <- 0
   }
 
   if(show_RoW) {
     if(model$specs$IODataSource=="stateior") {
       # collapse third term for SoI and RoUS
-      r3 <- r3[, 1:sector_count] + r3[, (sector_count+1):(sector_count*2)]
+      G_mf <- G_mf[, 1:sector_count] + G_mf[, (sector_count+1):(sector_count*2)]
 
       if(perspective == "DIRECT") {
         # collapse second and third term for SoI and RoUS
-        r2 <- r2[, 1:sector_count] + r2[, (sector_count+1):(sector_count*2)]
+        G_mi <- G_mi[, 1:sector_count] + G_mi[, (sector_count+1):(sector_count*2)]
       }
     }
     if(perspective == "DIRECT") {
-      LCI <- cbind(r1, r2 + r3) # Term 2 and Term 3 are assigned to RoW
+      LCI <- cbind(G_d, G_mi + G_mf) # Term 2 and Term 3 are assigned to RoW
     } else {
-      LCI <- cbind(r1 + r2, r3) # Term 3 is assigned to RoW
+      LCI <- cbind(G_d + G_mi, G_mf) # Term 3 is assigned to RoW
     }
   } else {
-    LCI <- r1 + r2 + r3 # All three terms combined and regions do not change 
+    LCI <- G_d + G_mi + G_mf # All three terms combined and regions do not change 
   }
 
   # Calculate LCIA (matrix with direct impacts in form of sector x impacts)
@@ -210,8 +211,8 @@ calculateResultsWithExternalFactors <- function(model, perspective = "FINAL", de
     LCI <- rbind(LCI, hh)
     LCIA <- rbind(LCIA, hh_lcia)
   }
-  result[[paste0("LCI_", subscript)]] <- LCI
-  result[[paste0("LCIA_", subscript)]] <- LCIA
+  result[[paste0("G_", subscript)]] <- LCI
+  result[[paste0("H_", subscript)]] <- LCIA
   return(result)
 
 }
@@ -255,24 +256,24 @@ calculateStandardResults <- function(model, perspective, demand, use_domestic_re
     # Calculate Direct Perspective LCI (a matrix with direct impacts in form of sector x flows)
     logging::loginfo("Calculating Direct Perspective LCI...")
     s <- getScalingVector(L, f)
-    result$LCI_d <- calculateDirectPerspectiveLCI(model$B, s)
+    result$G_r <- calculateDirectPerspectiveLCI(model$B, s)
     # Calculate Direct Perspective LCIA (matrix with direct impacts in form of sector x impacts)
     logging::loginfo("Calculating Direct Perspective LCIA...")
-    result$LCIA_d <- calculateDirectPerspectiveLCIA(model$D, s)
+    result$H_r <- calculateDirectPerspectiveLCIA(model$D, s)
     if(household_emissions) {
-      result$LCI_d <- rbind(result$LCI_d, hh)
-      result$LCIA_d <- rbind(result$LCIA_d, hh_lcia)
+      result$G_r <- rbind(result$G_r, hh)
+      result$H_r <- rbind(result$H_r, hh_lcia)
     }
   } else if (perspective=="FINAL") {
     # Calculate Final Perspective LCI (a matrix with total impacts in form of sector x flows)
     logging::loginfo("Calculating Final Perspective LCI...")
-    result$LCI_f <- calculateFinalPerspectiveLCI(M, f)
+    result$G_l <- calculateFinalPerspectiveLCI(M, f)
     # Calculate Final Perspective LCIA (matrix with total impacts in form of sector x impacts)
     logging::loginfo("Calculating Final Perspective LCIA...")
-    result$LCIA_f <- calculateFinalPerspectiveLCIA(N, f)
+    result$H_l <- calculateFinalPerspectiveLCIA(N, f)
     if(household_emissions) {
-      result$LCI_f <- rbind(result$LCI_f, hh)
-      result$LCIA_f <- rbind(result$LCIA_f, hh_lcia)
+      result$G_l <- rbind(result$G_l, hh)
+      result$H_l <- rbind(result$H_l, hh_lcia)
     }
   }
 
@@ -304,9 +305,9 @@ getScalingVector <- function(L, demand) {
 #' Journal of Cleaner Production 158 (August): 308–18. https://doi.org/10.1016/j.jclepro.2017.04.150.
 #' SI1, Equation 9.
 calculateDirectPerspectiveLCI <- function(B, s) {
-  lci_d <- t(B %*% diag(as.vector(s), nrow(s)))
-  rownames(lci_d) <- rownames(s)
-  return(lci_d)
+  G_r <- t(B %*% diag(as.vector(s), nrow(s)))
+  rownames(G_r) <- rownames(s)
+  return(G_r)
 }
 
 #' The final perspective LCI aligns flows with sectors consumed by final users.
@@ -319,10 +320,10 @@ calculateDirectPerspectiveLCI <- function(B, s) {
 #' Journal of Cleaner Production 158 (August): 308–18. https://doi.org/10.1016/j.jclepro.2017.04.150.
 #' SI1, Equation 10.
 calculateFinalPerspectiveLCI <- function(M, y) {
-  lci_f <- t(M %*% diag(as.vector(y)))
-  colnames(lci_f) <- rownames(M)
-  rownames(lci_f) <- colnames(M)
-  return(lci_f)
+  G_l <- t(M %*% diag(as.vector(y)))
+  colnames(G_l) <- rownames(M)
+  rownames(G_l) <- colnames(M)
+  return(G_l)
 }
 
 #' The direct perspective LCIA aligns impacts with sectors consumed by direct use.
@@ -335,9 +336,9 @@ calculateFinalPerspectiveLCI <- function(M, y) {
 #' Journal of Cleaner Production 158 (August): 308–18. https://doi.org/10.1016/j.jclepro.2017.04.150.
 #' SI1, Equation 9.
 calculateDirectPerspectiveLCIA <- function(D, s) {
-  lcia_d <- t(D %*% diag(as.vector(s), nrow(s)))
-  rownames(lcia_d) <- rownames(s)
-  return(lcia_d)
+  H_r <- t(D %*% diag(as.vector(s), nrow(s)))
+  rownames(H_r) <- rownames(s)
+  return(H_r)
 }
 
 #' The final perspective LCIA aligns impacts with sectors consumed by final users.
@@ -350,10 +351,10 @@ calculateDirectPerspectiveLCIA <- function(D, s) {
 #' Journal of Cleaner Production 158 (August): 308–18. https://doi.org/10.1016/j.jclepro.2017.04.150.
 #' SI1, Equation 10.
 calculateFinalPerspectiveLCIA <- function(N, y) {
-  lcia_f <- t(N %*% diag(as.vector(y)))
-  colnames(lcia_f) <- rownames(N)
-  rownames(lcia_f) <- colnames(N)
-  return(lcia_f)
+  H_l <- t(N %*% diag(as.vector(y)))
+  colnames(H_l) <- rownames(N)
+  rownames(H_l) <- colnames(N)
+  return(H_l)
 }
 
 #' Divide/Normalize a sector x flows matrix by the total of respective flow (column sum)

diff --git a/R/ValidateModel.R b/R/ValidateModel.R
@@ -456,7 +456,7 @@ validateImportFactorsApproach <- function(model, demand = "Consumption"){
   print(all.equal(LCI, LCI_dm))
 
   # Calculate LCIA using standard approach
-  LCIA <- t(model$C %*% M %*% diag(as.vector(y))) #same as result_std_consumption$LCIA_f, above
+  LCIA <- t(model$C %*% M %*% diag(as.vector(y)))
   colnames(LCIA) <- rownames(model$N_m)
   rownames(LCIA) <- colnames(model$N_m)
 
@@ -491,6 +491,6 @@ validateHouseholdEmissions <- function(model) {
   flows <- setNames(flows$FlowAmount, flows$Flow)
 
   cat("\nTesting that LCI emissions from households are equivalent to calculated result from Total Consumption.\n")
-  result <- r$LCI_f[codes, names(flows)]
+  result <- r$G_l[codes, names(flows)]
   all.equal(flows, result)
 }
diff --git a/README.md b/README.md
@@ -146,34 +146,34 @@ Note: `S00402/US - Used and secondhand goods` and `S00300/US - Noncomparable imp
 
 #### Examples
 
-Rank sectors based a composite score of selected total impacts (LCIA_d or LCIA_f) associated with total US demand (US production or consumption vector).
+Rank sectors based a composite score of selected total impacts (H_r or H_l) associated with total US demand (US production or consumption vector).
 Comparing rankings may also be used as another form of model validation that incorporates the demand vectors and the indicators as well as the model result matrices.
 
 ```r
-# Calculate model LCIA_d and LCIA_f
+# Calculate model LCIA_d (H_r) and LCIA_f (H_l)
 result <- c(calculateEEIOModel(model, perspective = 'DIRECT', demand = "Production"),
             calculateEEIOModel(model, perspective = 'FINAL', demand = "Consumption"))
-colnames(result$LCIA_d) <- model$Indicators$meta[match(colnames(result$LCIA_d),
-                                                       model$Indicators$meta$Name),
+colnames(result$H_r) <- model$Indicators$meta[match(colnames(result$H_r),
+                                                    model$Indicators$meta$Name),
                                                  "Code"]
-colnames(result$LCIA_f) <- colnames(result$LCIA_d)
+colnames(result$H_l) <- colnames(result$H_r)
 # Define indicators
 indicators <- c("ACID", "CCDD", "CMSW", "CRHW", "ENRG", "ETOX", "EUTR", "GHG",
                 "HRSP", "HTOX", "LAND", "MNRL", "OZON", "SMOG", "WATR")
 # Create figure on the left
 heatmapSectorRanking(model,
-                     matrix = result$LCIA_d,
+                     matrix = result$H_r,
                      indicators,
                      sector_to_remove = "",
                      N_sector = 20,
-                     x_title = "LCIA_d (DIRECT perspective) & US production demand")
+                     x_title = "H_r (DIRECT perspective) & US production demand")
 # Create figure on the right
 heatmapSectorRanking(model,
-                     matrix = result$LCIA_f,
+                     matrix = result$H_l,
                      indicators,
                      sector_to_remove = "",
                      N_sector = 20,
-                     x_title = "LCIA_f (FINAL perspective) & US consumption demand")
+                     x_title = "H_l (FINAL perspective) & US consumption demand")
 ```
 
 ![](inst/img/ranking_direct_prod_final_cons_v2.0.1.png)

diff --git a/format_specs/Calculation.md b/format_specs/Calculation.md
@@ -1,13 +1,13 @@
 ## Calculation Result
-A result object from a Model calculation (`calculateEEIOModel()`) contains an LCI and an LCIA matrix. The matrices have a suffix appended to them in the form `_x`, where `x` indicates the calculation perspective that was used to prepare them. 
+A result object from a Model calculation (`calculateEEIOModel()`) contains an LCI (G) and an LCIA (H) matrix. The matrices have a suffix appended to them in the form `_x`, where `x` indicates the calculation perspective that was used to prepare them. 
 
 | Matrix | Shape | Description | 
 | --- | ---- | ---|
-| LCI |  sector x flow | Direct + indirect resource use and emissions by sector |  
-| LCIA |  sector x indicator | Direct + indirect impacts by sector |
+| G |  sector x flow | Direct + indirect resource use and emissions by sector (LCI) |  
+| H |  sector x indicator | Direct + indirect impacts by sector (LCIA) |
 
 ## Calculation Perspectives
 |Suffix |Perspective|Definition|
 |---|---|---|
-|_d|DIRECT|Associates results with sector where emissions occur.|
-|_f|FINAL|Associates results with sector is a final consumer. Final consumption is use by a final demand component.|
+|_r|DIRECT|Associates results with sector where emissions occur.|
+|_l|FINAL|Associates results with sector is a final consumer. Final consumption is use by a final demand component.|
diff --git a/inst/doc/Example.Rmd b/inst/doc/Example.Rmd
@@ -48,18 +48,18 @@ print(paste("Sectors failing:", paste(econval$Failure$rownames, collapse = ", ")
 ```{r include=FALSE}
 result <- c(useeior::calculateEEIOModel(model, perspective = 'DIRECT', demand = "Production"),
             useeior::calculateEEIOModel(model, perspective = 'FINAL', demand = "Consumption"))
-colnames(result$LCIA_d) <- model$Indicators$meta[match(colnames(result$LCIA_d), model$Indicators$meta$Name), "Code"]
-colnames(result$LCIA_f) <- colnames(result$LCIA_d)
+colnames(result$H_r) <- model$Indicators$meta[match(colnames(result$H_r), model$Indicators$meta$Name), "Code"]
+colnames(result$H_l) <- colnames(result$H_r)
 indicators <- c("ACID", "CCDD", "CMSW", "CRHW", "ENRG", "ETOX", "EUTR", "GHG",
                 "HRSP", "HTOX", "LAND", "MNRL", "OZON", "SMOG", "WATR")
 model_list <- list("USEEIOv2.0.1-411" = model)
 ```
 
 ```{r "ranking_direct_prod_final_cons_v2.0.1", fig.width = 20, fig.height = 12}
-p1 <- heatmapSectorRanking(model, matrix = result$LCIA_d, indicators,
-                           sector_to_remove = "", N_sector = 20, x_title = "LCIA_d (DIRECT perspective) & US production demand")
-p2 <- heatmapSectorRanking(model, matrix = result$LCIA_f, indicators,
-                           sector_to_remove = "", N_sector = 20, x_title = "LCIA_f (FINAL perspective) & US consumption demand")
+p1 <- heatmapSectorRanking(model, matrix = result$H_r, indicators,
+                           sector_to_remove = "", N_sector = 20, x_title = "H_r (DIRECT perspective) & US production demand")
+p2 <- heatmapSectorRanking(model, matrix = result$H_l, indicators,
+                           sector_to_remove = "", N_sector = 20, x_title = "H_l (FINAL perspective) & US consumption demand")
 gridExtra::grid.arrange(p1, p2, nrow = 1)
 ```
 
@@ -71,7 +71,7 @@ plotMatrixCoefficient(model_list, matrix_name = "N", coefficient_name = coeffs,
 ```{r "domestic_proportion_impact_USconsumption_v2.0.1", include=FALSE, fig.height = 12, fig.width = 12}
 fullcons <- calculateEEIOModel(model, perspective = "DIRECT", demand = "Consumption",  use_domestic_requirements = FALSE)
 domcons <- calculateEEIOModel(model, perspective = "DIRECT", demand = "Consumption", use_domestic_requirements = TRUE)
-barplotFloworImpactFractionbyRegion(R1_calc_result = domcons$LCIA_d, Total_calc_result = fullcons$LCIA_d, x_title = "")
+barplotFloworImpactFractionbyRegion(R1_calc_result = domcons$H_r, Total_calc_result = fullcons$H_r, x_title = "")
 ```
 
 ```{r "indicator_score_v2.0.1", fig.height = 12, fig.width = 12}