Introduction
Recca (R Energy Conversion Chain Analysis)
contains many functions useful for aggregating industries and products
in energy conversion chains.
The types of aggregation are shown in the following table.
| Function | Meaning |
|---|---|
primary_aggregates() |
Aggregates primary energy |
finaldemand_aggregates() |
Aggregates final demand energy, the last stage in an energy conversion chain (ECC) |
region_aggregates() |
Aggregates regions |
despecified_aggregates() |
Despecifies industries and products, then aggregates |
grouped_aggregates() |
Groups industries and products, then aggregates |
pfu_aggregates() |
Aggregates to primary, final, and useful stages of an ECC |
This vignette describes those functions and demonstrates their use.
primary_aggregates()
primary_aggregates() creates a column of primary energy
sums coming from the industries specified in the
p_industries (primary industries) argument. These primary
industries are often found in the R,
V, and Y matrices of the PSUT
framework. Typical p_industries are Resources, Imports, and
Exports. In the examples below, primary aggregates are calculated for
the four rows of the UKEnergy2000mats data frame.
In the first example, only one aggregate column is created (“EX.p”), because there is no difference between net and gross aggregation for primary energy. Net and gross primary aggregates are identical.
library(matsbyname)
p_industries <- c("Resources [of Crude]", "Resources [of NG]")
# Calculate primary total aggregates
ECCs <- UKEnergy2000mats %>%
tidyr::pivot_wider(names_from = "matrix.name", values_from = "matrix")
ECCs
#> # A tibble: 4 × 12
#> Country Year EnergyType LastStage R U U_EIOU U_feed
#> <chr> <dbl> <chr> <chr> <list> <list> <list> <list>
#> 1 GBR 2000 E Final <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 2 GBR 2000 E Services <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 3 GBR 2000 E Useful <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 4 GBR 2000 X Services <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> # ℹ 4 more variables: V <list>, Y <list>, r_EIOU <list>, S_units <list>
res <- ECCs %>%
Recca::primary_aggregates(p_industries = p_industries, by = "Total")
tibble::as_tibble(res)
#> # A tibble: 4 × 13
#> Country Year EnergyType LastStage R U U_EIOU U_feed
#> <chr> <dbl> <chr> <chr> <list> <list> <list> <list>
#> 1 GBR 2000 E Final <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 2 GBR 2000 E Services <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 3 GBR 2000 E Useful <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 4 GBR 2000 X Services <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> # ℹ 5 more variables: V <list>, Y <list>, r_EIOU <list>, S_units <list>,
#> # EXp <dbl>
res[[Recca::aggregate_cols$aggregate_primary]]
#> [1] 93000 93000 93000 98220In the next example, both net and gross aggregation columns are
created (“EX.p_net” and “EX.p_gross”), for compatibility with the
finaldemand_aggregates() function discussed below.
res2 <- ECCs %>%
Recca::primary_aggregates(p_industries = p_industries,
add_net_gross_cols = TRUE,
by = "Total")
tibble::as_tibble(res2)
#> # A tibble: 4 × 14
#> Country Year EnergyType LastStage R U U_EIOU U_feed
#> <chr> <dbl> <chr> <chr> <list> <list> <list> <list>
#> 1 GBR 2000 E Final <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 2 GBR 2000 E Services <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 3 GBR 2000 E Useful <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 4 GBR 2000 X Services <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> # ℹ 6 more variables: V <list>, Y <list>, r_EIOU <list>, S_units <list>,
#> # EXpnet <dbl>, EXpgross <dbl>
res2[[Recca::aggregate_cols$net_aggregate_primary]]
#> [1] 93000 93000 93000 98220
res2[[Recca::aggregate_cols$gross_aggregate_primary]]
#> [1] 93000 93000 93000 98220
finaldemand_aggregates()
finaldemand_aggregates() is similar to
primary_aggregates(), except that it aggregates energy at
the final demand stage in the Y matrix. Both net and
gross aggregate final demand are calculated. Gross final demand includes
energy industry own use (EIOU). Net final demand does not.
fd_sectors <- c("Residential", "Transport")
res <- ECCs %>%
Recca::finaldemand_aggregates(fd_sectors = fd_sectors, by = "Sector")
tibble::as_tibble(res)
#> # A tibble: 4 × 14
#> Country Year EnergyType LastStage R U U_EIOU U_feed
#> <chr> <dbl> <chr> <chr> <list> <list> <list> <list>
#> 1 GBR 2000 E Final <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 2 GBR 2000 E Services <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 3 GBR 2000 E Useful <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 4 GBR 2000 X Services <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> # ℹ 6 more variables: V <list>, Y <list>, r_EIOU <list>, S_units <list>,
#> # EXfdnet <list>, EXfdgross <list>
res[[Recca::aggregate_cols$net_aggregate_demand]]
#> [[1]]
#> Product
#> Residential 31000
#> Transport 40750
#> attr(,"rowtype")
#> [1] "Industry"
#> attr(,"coltype")
#> [1] "Product"
#>
#> [[2]]
#> Product
#> Residential 5.000750e+14
#> Transport 6.429166e+11
#> attr(,"rowtype")
#> [1] "Industry"
#> attr(,"coltype")
#> [1] "Product"
#>
#> [[3]]
#> Product
#> Residential 4200.40
#> Transport 21714.98
#> attr(,"rowtype")
#> [1] "Industry"
#> attr(,"coltype")
#> [1] "Product"
#>
#> [[4]]
#> Product
#> Residential 5.000750e+14
#> Transport 6.429166e+11
#> attr(,"rowtype")
#> [1] "Industry"
#> attr(,"coltype")
#> [1] "Product"
res[[Recca::aggregate_cols$gross_aggregate_demand]]
#> [[1]]
#> Product
#> Residential 31000
#> Transport 40750
#> attr(,"rowtype")
#> [1] "Industry"
#> attr(,"coltype")
#> [1] "Product"
#>
#> [[2]]
#> Product
#> Residential 5.000750e+14
#> Transport 6.429166e+11
#> attr(,"rowtype")
#> [1] "Industry"
#> attr(,"coltype")
#> [1] "Product"
#>
#> [[3]]
#> Product
#> Residential 4200.40
#> Transport 21714.98
#> attr(,"rowtype")
#> [1] "Industry"
#> attr(,"coltype")
#> [1] "Product"
#>
#> [[4]]
#> Product
#> Residential 5.000750e+14
#> Transport 6.429166e+11
#> attr(,"rowtype")
#> [1] "Industry"
#> attr(,"coltype")
#> [1] "Product"
region_aggregates()
region_aggregates() sums regions according to
many_colname and few_colname. To demonstrate
this function, we need to modify the The example data frame slightly. It
contains only one country (“GBR”), but it should contain different
countries and a “few” column. Furthermore, the last stage and energy
type columns should be the same where aggregation is required. The
following code produces this modification to pretend that rows of the
data frame apply to different countries.
ECCs_for_region_agg <- ECCs |>
dplyr::mutate(
Country = c("USA", "GBR", "CAN", "FRA"),
Continent = c("NoAmr", "Europe", "NoAmr", "Europe"),
EnergyType = "E",
LastStage = c("Final", "Services", "Final", "Services")
)
ECCs_for_region_agg |>
dplyr::select(Country, Continent, EnergyType, LastStage)
#> # A tibble: 4 × 4
#> Country Continent EnergyType LastStage
#> <chr> <chr> <chr> <chr>
#> 1 USA NoAmr E Final
#> 2 GBR Europe E Services
#> 3 CAN NoAmr E Final
#> 4 FRA Europe E ServicesGiven this modification, we aggregate to continents with the following code.
continent_aggregations <- ECCs_for_region_agg |>
Recca::region_aggregates(many_colname = "Country", few_colname = "Continent")
continent_aggregations
#> # A tibble: 2 × 12
#> Year EnergyType LastStage Country R S_units U_EIOU U_feed
#> <dbl> <chr> <chr> <chr> <list> <list> <list> <list>
#> 1 2000 E Final NoAmr <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 2 2000 E Services Europe <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> # ℹ 4 more variables: V <list>, Y <list>, U <list>, r_EIOU <list>By default, many_colname is replaced by the common
values in few_colname. A simple rename can override this
default behaviour.
continent_aggregations |>
dplyr::rename(
Continent = Country
)
#> # A tibble: 2 × 12
#> Year EnergyType LastStage Continent R S_units U_EIOU U_feed
#> <dbl> <chr> <chr> <chr> <list> <list> <list> <list>
#> 1 2000 E Final NoAmr <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 2 2000 E Services Europe <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> # ℹ 4 more variables: V <list>, Y <list>, U <list>, r_EIOU <list>
despecified_aggregates()
When the row and column names of the PSUT matrices are “specified,”
they will look like “Resources [of Crude]” or “NG [from Wells]”. Many
labels can have a “noun [preposition object]” structure, which we call
“specified.” despecified_aggregates() eliminates the
specification in the row and/or column names, keeping the desired
piece (by default the “noun”), and aggregates (sums) rows
or columns with resulting identical names. See the comments in the
examples below.
despecified_aggs <- ECCs |>
# Accept the default "noun" aggregation
# and the default "_aggregated" suffix for
# aggregated columns.
despecified_aggregates()
# This is the original R matrix.
# Its row names are specified.
ECCs$R[[1]]
#> Crude NG
#> Resources [of Crude] 50000 0
#> Resources [of NG] 0 43000
#> attr(,"rowtype")
#> [1] "Industry"
#> attr(,"coltype")
#> [1] "Product"
# This is the despecified R matrix.
# Note the single Resources row.
despecified_aggs$R_aggregated[[1]]
#> Warning: Unknown or uninitialised column: `R_aggregated`.
#> NULL
# Here is an original use matrix.
# Many rows are specified.
ECCs$U[[2]]
#> Car engines Cars Crude dist. Diesel dist. Elect. grid
#> Crude 0 0.0 0 0.00e+00 0
#> Crude [from Dist.] 0 0.0 0 0.00e+00 0
#> Crude [from Fields] 0 0.0 47500 0.00e+00 0
#> Diesel 0 0.0 0 1.55e+04 0
#> Diesel [from Dist.] 0 0.0 0 0.00e+00 0
#> Elect 0 0.0 0 0.00e+00 6400
#> Elect [from Grid] 0 0.0 25 0.00e+00 0
#> Freight [tonne-km/year] 0 0.0 1666685185 1.50e+09 0
#> Light 0 0.0 0 0.00e+00 0
#> LTH 0 0.0 0 0.00e+00 0
#> MD [from Car engines] 0 3000.4 0 0.00e+00 0
#> MD [from Truck engines] 0 0.0 0 0.00e+00 0
#> NG 0 0.0 0 0.00e+00 0
#> NG [from Dist.] 0 0.0 0 0.00e+00 0
#> NG [from Wells] 0 0.0 0 0.00e+00 0
#> Petrol 0 0.0 0 0.00e+00 0
#> Petrol [from Dist.] 26000 0.0 0 0.00e+00 0
#> Furnaces Gas wells & proc. Homes Light fixtures
#> Crude 0 0 0 0
#> Crude [from Dist.] 0 0 0 0
#> Crude [from Fields] 0 0 0 0
#> Diesel 0 0 0 0
#> Diesel [from Dist.] 0 50 0 0
#> Elect 0 0 0 0
#> Elect [from Grid] 0 25 0 6000
#> Freight [tonne-km/year] 0 0 0 0
#> Light 0 0 0 0
#> LTH 0 0 20000 0
#> MD [from Car engines] 0 0 0 0
#> MD [from Truck engines] 0 0 0 0
#> NG 0 43000 0 0
#> NG [from Dist.] 25000 0 0 0
#> NG [from Wells] 0 0 0 0
#> Petrol 0 0 0 0
#> Petrol [from Dist.] 0 0 0 0
#> NG dist. Oil fields Oil refineries Petrol dist.
#> Crude 0 50000 0 0.00e+00
#> Crude [from Dist.] 0 0 47000 0.00e+00
#> Crude [from Fields] 0 0 0 0.00e+00
#> Diesel 0 0 0 0.00e+00
#> Diesel [from Dist.] 0 50 0 0.00e+00
#> Elect 0 0 0 0.00e+00
#> Elect [from Grid] 25 25 75 0.00e+00
#> Freight [tonne-km/year] 1666685185 0 0 2.25e+09
#> Light 0 0 0 0.00e+00
#> LTH 0 0 0 0.00e+00
#> MD [from Car engines] 0 0 0 0.00e+00
#> MD [from Truck engines] 0 0 0 0.00e+00
#> NG 0 0 0 0.00e+00
#> NG [from Dist.] 0 0 0 0.00e+00
#> NG [from Wells] 41000 0 0 0.00e+00
#> Petrol 0 0 0 2.65e+04
#> Petrol [from Dist.] 0 0 0 0.00e+00
#> Power plants Rooms Truck engines Trucks
#> Crude 0 0 0 0.00
#> Crude [from Dist.] 0 0 0 0.00
#> Crude [from Fields] 0 0 0 0.00
#> Diesel 0 0 0 0.00
#> Diesel [from Dist.] 0 0 15050 0.00
#> Elect 0 0 0 0.00
#> Elect [from Grid] 100 0 0 0.00
#> Freight [tonne-km/year] 0 0 0 0.00
#> Light 0 1200 0 0.00
#> LTH 0 0 0 0.00
#> MD [from Car engines] 0 0 0 0.00
#> MD [from Truck engines] 0 0 0 1799.98
#> NG 0 0 0 0.00
#> NG [from Dist.] 16000 0 0 0.00
#> NG [from Wells] 0 0 0 0.00
#> Petrol 0 0 0 0.00
#> Petrol [from Dist.] 0 0 0 0.00
#> attr(,"rowtype")
#> [1] "Product"
#> attr(,"coltype")
#> [1] "Industry"
# Here is the corresponding despecified use matrix.
# None of the rows are specified.
despecified_aggs$U_aggregated[[2]]
#> Warning: Unknown or uninitialised column: `U_aggregated`.
#> NULL
# Here is an original make matrix
# with many specified columns.
ECCs$V[[3]]
#> MD [from Car engines] Crude [from Dist.] Diesel [from Dist.]
#> Car engines 3000.4 0 0
#> Crude dist. 0.0 47000 0
#> Diesel dist. 0.0 0 15150
#> Elect. grid 0.0 0 0
#> Furnaces 0.0 0 0
#> Gas wells & proc. 0.0 0 0
#> Light fixtures 0.0 0 0
#> NG dist. 0.0 0 0
#> Oil fields 0.0 0 0
#> Oil refineries 0.0 0 0
#> Petrol dist. 0.0 0 0
#> Power plants 0.0 0 0
#> Truck engines 0.0 0 0
#> Elect [from Grid] LTH NG [from Wells] Light NG [from Dist.]
#> Car engines 0 0 0 0 0
#> Crude dist. 0 0 0 0 0
#> Diesel dist. 0 0 0 0 0
#> Elect. grid 6275 0 0 0 0
#> Furnaces 0 20000 0 0 0
#> Gas wells & proc. 0 0 41000 0 0
#> Light fixtures 0 0 0 1200 0
#> NG dist. 0 0 0 0 41000
#> Oil fields 0 0 0 0 0
#> Oil refineries 0 0 0 0 0
#> Petrol dist. 0 0 0 0 0
#> Power plants 0 0 0 0 0
#> Truck engines 0 0 0 0 0
#> Crude [from Fields] Diesel Petrol Petrol [from Dist.] Elect
#> Car engines 0 0 0 0 0
#> Crude dist. 0 0 0 0 0
#> Diesel dist. 0 0 0 0 0
#> Elect. grid 0 0 0 0 0
#> Furnaces 0 0 0 0 0
#> Gas wells & proc. 0 0 0 0 0
#> Light fixtures 0 0 0 0 0
#> NG dist. 0 0 0 0 0
#> Oil fields 47500 0 0 0 0
#> Oil refineries 0 15500 26500 0 0
#> Petrol dist. 0 0 0 26000 0
#> Power plants 0 0 0 0 6400
#> Truck engines 0 0 0 0 0
#> MD [from Truck engines]
#> Car engines 0.00
#> Crude dist. 0.00
#> Diesel dist. 0.00
#> Elect. grid 0.00
#> Furnaces 0.00
#> Gas wells & proc. 0.00
#> Light fixtures 0.00
#> NG dist. 0.00
#> Oil fields 0.00
#> Oil refineries 0.00
#> Petrol dist. 0.00
#> Power plants 0.00
#> Truck engines 1799.98
#> attr(,"rowtype")
#> [1] "Industry"
#> attr(,"coltype")
#> [1] "Product"
# None of the columns are specified
# in the despecified version.
despecified_aggs$V_aggregated[[3]]
#> Warning: Unknown or uninitialised column: `V_aggregated`.
#> NULL
# This original final demand matrix
# has specified rownames
# that provide units.
ECCs$Y[[4]]
#> Transport Residential
#> Freight [tonne-km/year] 142916629629 0.0e+00
#> Illumination [lumen-hrs/yr] 0 5.0e+14
#> Passenger [passenger-km/yr] 500000000000 0.0e+00
#> Space heating [m3-K] 0 7.5e+10
#> attr(,"rowtype")
#> [1] "Product"
#> attr(,"coltype")
#> [1] "Industry"
# Despecifying this matrix eliminates the units,
# resulting in information loss.
despecified_aggs$Y_aggregated[[4]]
#> Warning: Unknown or uninitialised column: `Y_aggregated`.
#> NULLAs the last example (with the final demand matrix,
Y) shows, aggregating despecified row and column names
can lead to information loss. Thus,
despecified_aggregates() will normally be called only at
the last step in a calculation chain.
grouped_aggregates()
Grouping provides the capability to aggregate specific energy products to classes of energy and groupings of processing stages or final demand categories. For example, Anthracite and Brown coal can be grouped to Coal and coal products. An aggregation map is required.
To demonstrate grouped aggregation, we first establish an aggregation map. An aggregation map is a named list where list members are aggregated to member names.
agg_map <- list(`Crude oil` = c("Crude", "Crude [from Dist.]", "Crude [from Fields]"),
`Oil and oil products` = c("Diesel", "Diesel [from Dist.]",
"Petrol", "Petrol [from Dist.]"),
NG = c("NG", "NG [from Dist.]", "NG [from Wells]"),
Electricity = c("Elect", "Elect [from Grid]"))The aggregation map can be used with
grouped_aggregates() to do the desired aggregation.
Aggregation columns are added to the right of the .sut_data
data frame.
# Here is an original use matrix.
ECCs$U[[1]]
#> Crude dist. Diesel dist. Elect. grid Gas wells & proc.
#> Crude 0 0 0 0
#> Crude [from Dist.] 500 0 0 0
#> Crude [from Fields] 47500 0 0 0
#> Diesel 0 15500 0 0
#> Diesel [from Dist.] 25 350 0 50
#> Elect 0 0 6400 0
#> Elect [from Grid] 25 0 0 25
#> NG 0 0 0 43000
#> NG [from Dist.] 0 0 0 0
#> NG [from Wells] 0 0 0 2000
#> Petrol 0 0 0 0
#> Petrol [from Dist.] 0 0 0 0
#> NG dist. Oil fields Oil refineries Petrol dist.
#> Crude 0 50000 0 0
#> Crude [from Dist.] 0 0 47000 0
#> Crude [from Fields] 0 2500 0 0
#> Diesel 0 0 5000 0
#> Diesel [from Dist.] 25 50 0 250
#> Elect 0 0 0 0
#> Elect [from Grid] 25 25 75 0
#> NG 0 0 0 0
#> NG [from Dist.] 0 0 0 0
#> NG [from Wells] 41000 0 0 0
#> Petrol 0 0 0 26500
#> Petrol [from Dist.] 0 0 0 500
#> Power plants
#> Crude 0
#> Crude [from Dist.] 0
#> Crude [from Fields] 0
#> Diesel 0
#> Diesel [from Dist.] 0
#> Elect 0
#> Elect [from Grid] 100
#> NG 0
#> NG [from Dist.] 16000
#> NG [from Wells] 0
#> Petrol 0
#> Petrol [from Dist.] 0
#> attr(,"rowtype")
#> [1] "Product"
#> attr(,"coltype")
#> [1] "Industry"
# Aggregate to the desired groups.
ECCs_grouped_aggs <- ECCs |>
grouped_aggregates(aggregation_map = agg_map)
# Here is the aggregated use matrix.
# Note that the rows are summed
# and named according to the agg_map.
ECCs_grouped_aggs$U_aggregated[[1]]
#> Warning: Unknown or uninitialised column: `U_aggregated`.
#> NULL
pfu_aggregates()
Aggregating to primary, final, and useful stages of the energy
conversion chain (ECC) can be accomplished with
pfu_aggregates(). Aggregates are added in columns to the
right of the .sutdata data frame. Primary industries and
final demand sectors are specified as character vectors in the
p_industries and fd_sectors arguments.
# Get the defaul separator for column names
sep <- Recca::all_stages$last_stage_sep
# Set primary industry names and final demand sector names
p_industries <- c("Resources [of Crude]", "Resources [of NG]")
fd_sectors <- c("Residential", "Transport", "Oil fields")
# Calculate TOTAL aggregates
pfu_aggs_total <- UKEnergy2000mats |>
tidyr::pivot_wider(names_from = matrix.name, values_from = matrix) |>
pfu_aggregates(p_industries = p_industries, fd_sectors = fd_sectors,
by = "Total")
# Look at the column names.
# The naming scheme is
# EX.stage.gn___lsStage
# EX = energy or exergy aggregate (depending on the value in the EnergyType column)
# stage = the stage at which the aggregation is computed
# gn = whether the aggregate is "gross" or "net"
# ____ls = the separator (Recca::all_stages$last_stage_sep)
# Stage = The last stage in the ECC for this column.
names(pfu_aggs_total)
#> [1] "Country" "Year" "EnergyType"
#> [4] "R___lsFinal" "U___lsFinal" "U_feed___lsFinal"
#> [7] "U_EIOU___lsFinal" "r_EIOU___lsFinal" "V___lsFinal"
#> [10] "Y___lsFinal" "S_units___lsFinal" "R___lsUseful"
#> [13] "U___lsUseful" "U_feed___lsUseful" "U_EIOU___lsUseful"
#> [16] "r_EIOU___lsUseful" "V___lsUseful" "Y___lsUseful"
#> [19] "S_units___lsUseful" "R___lsServices" "U___lsServices"
#> [22] "U_feed___lsServices" "U_EIOU___lsServices" "r_EIOU___lsServices"
#> [25] "V___lsServices" "Y___lsServices" "S_units___lsServices"
#> [28] "EXpnet___lsFinal" "EXpgross___lsFinal" "EXpnet___lsUseful"
#> [31] "EXpgross___lsUseful" "EXpnet___lsServices" "EXpgross___lsServices"
#> [34] "EXfnet___lsFinal" "EXfgross___lsFinal" "EXfnet___lsUseful"
#> [37] "EXfgross___lsUseful" "EXfnet___lsServices" "EXfgross___lsServices"
#> [40] "EXunet___lsFinal" "EXugross___lsFinal" "EXunet___lsUseful"
#> [43] "EXugross___lsUseful" "EXunet___lsServices" "EXugross___lsServices"
#> [46] "EXsnet___lsFinal" "EXsgross___lsFinal" "EXsnet___lsUseful"
#> [49] "EXsgross___lsUseful" "EXsnet___lsServices" "EXsgross___lsServices"
# Check some aggregation values.
# Note that not all aggregations are available,
# based on the structure of the ECC.
# Net primary energy aggregation when last stage is Final
pfu_aggs_total |>
dplyr::filter(EnergyType == "E") |>
magrittr::extract2("EX.p_net___lsFinal") |>
magrittr::extract2(1)
#> NULL
# Net primary exergy aggregation when last stage is Services
pfu_aggs_total |>
dplyr::filter(EnergyType == "X") |>
magrittr::extract2("EX.p_net___lsServices") |>
magrittr::extract2(1)
#> NULL
# Gross final energy aggregation when last stage is Useful
pfu_aggs_total |>
dplyr::filter(EnergyType == "E") |>
magrittr::extract2("EX.f_gross___lsFinal") |>
magrittr::extract2(1)
#> NULL
# Gross useful energy aggregation when last stage is Useful
pfu_aggs_total |>
dplyr::filter(EnergyType == "E") |>
magrittr::extract2("EX.u_gross___lsUseful") |>
magrittr::extract2(1)
#> NULL
# Net useful energy aggregation when last stage is Useful
pfu_aggs_total |>
dplyr::filter(EnergyType == "E") |>
magrittr::extract2("EX.u_net___lsUseful") |>
magrittr::extract2(1)
#> NULL
# Calculate PRODUCT aggregates
pfu_aggs_product <- UKEnergy2000mats |>
tidyr::pivot_wider(names_from = matrix.name, values_from = matrix) |>
pfu_aggregates(p_industries = p_industries, fd_sectors = fd_sectors,
by = "Product")
# Net primary energy aggregation when last stage is Final
# Note that all results are now vectors which
# show aggregation by product
pfu_aggs_product |>
dplyr::filter(EnergyType == "E") |>
magrittr::extract2("EX.p_net___lsFinal") |>
magrittr::extract2(1)
#> NULL
pfu_aggs_product |>
dplyr::filter(EnergyType == "E") |>
magrittr::extract2("EX.u_net___lsUseful") |>
magrittr::extract2(1)
#> NULL
# Calculate INDUSTRY aggregates
# Note that all results are now vectors which
# show aggregation by industry
pfu_aggs_industry <- UKEnergy2000mats |>
tidyr::pivot_wider(names_from = matrix.name, values_from = matrix) |>
pfu_aggregates(p_industries = p_industries, fd_sectors = fd_sectors,
by = "Industry")
pfu_aggs_industry |>
dplyr::filter(EnergyType == "E") |>
magrittr::extract2("EX.p_net___lsFinal") |>
magrittr::extract2(1)
#> NULL
pfu_aggs_industry |>
dplyr::filter(EnergyType == "E") |>
magrittr::extract2("EX.u_net___lsUseful") |>
magrittr::extract2(1)
#> NULLConclusion
Several functions in the Recca package assist with
aggregations in energy conversion chains.
primary_aggregates(),
finaldemand_aggregates(), region_aggregates(),
despecified_aggregates(),
grouped_aggregates(), and pfu_aggregates() all
assist with different types of aggregations. Please see help for these
functions for more details.