There is available data on the webiste of Department of Energy, Philippines that shows the power capacity and generation. The data is publicly available here: https://doe.gov.ph/key-energy-statistics-dashboards/power-capacity-and-generation.
The Objective of this analysis is to see how at a high level how close the Philippines is to reducing emissions via renewable energy sources: - Solar Energy - Wind Energy - Hydropower - Geothermal Energy - Biomass Energy - Ocean Energy
This dataset shows data over the last 20 years in GWh:
pwr <-read_csv("GenerationTablePH.csv", show_col_types = FALSE)
pwr## # A tibble: 31 × 10
## Years Biomass Coal Geothermal Hydro `Natural Gas` `Oil-based` Solar Wind
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1990 0 1934 5466 6062 0 12434 0 0
## 2 1991 0 1942 5758 5145 0 12804 0 0
## 3 1992 0 1791 5700 4440 0 13939 0 0
## 4 1993 0 2015 5667 5030 0 13867 0 0
## 5 1994 0 1348 6320 5862 0 16929 0 0
## 6 1995 0 2109 6135 6232 0 19078 0 0
## 7 1996 0 4855 6534 7030 0 18288 0 0
## 8 1997 0 7363 7237 6069 12 19116 0 0
## 9 1998 0 9388 8914 5084 20 18174 0 0
## 10 1999 0 11183 10594 7840 16 11799 0 0
## # ℹ 21 more rows
## # ℹ 1 more variable: `Grand Total` <dbl>What we can see from the data is that the Philippines does not use Ocean Energy but has all the other energy types.
Next is to understand using summary statistics to create an initial analysis framework and comparing this with data from the latest available year (2020)
summary(pwr)## Years Biomass Coal Geothermal
## Min. :1990 Min. : 0.0 Min. : 1348 Min. : 5466
## 1st Qu.:1998 1st Qu.: 0.0 1st Qu.: 8376 1st Qu.: 8076
## Median :2005 Median : 0.0 Median :16194 Median :10242
## Mean :2005 Mean : 201.9 Mean :20746 Mean : 9247
## 3rd Qu.:2012 3rd Qu.: 189.5 3rd Qu.:30173 3rd Qu.:10454
## Max. :2020 Max. :1261.0 Max. :58176 Max. :11626
## Hydro Natural Gas Oil-based Solar
## Min. : 4440 Min. : 0 Min. : 2474 Min. : 0.0
## 1st Qu.: 6631 1st Qu.: 14 1st Qu.: 4766 1st Qu.: 0.0
## Median : 7870 Median :16366 Median : 6293 Median : 1.0
## Mean : 7794 Mean :11174 Mean : 8817 Mean : 204.3
## 3rd Qu.: 9260 3rd Qu.:19547 3rd Qu.:12619 3rd Qu.: 1.5
## Max. :10252 Max. :22354 Max. :19116 Max. :1373.0
## Wind Grand Total
## Min. : 0.0 Min. : 25649
## 1st Qu.: 0.0 1st Qu.: 40614
## Median : 17.0 Median : 56568
## Mean : 217.2 Mean : 58402
## 3rd Qu.: 81.5 3rd Qu.: 74094
## Max. :1153.0 Max. :106041subset(pwr, Years==2020)## # A tibble: 1 × 10
## Years Biomass Coal Geothermal Hydro `Natural Gas` `Oil-based` Solar Wind
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2020 1261 58176 10757 7192 19497 2474 1373 1026
## # ℹ 1 more variable: `Grand Total` <dbl>Based on what we know coming from the overall table, we can create an initial segmentation based on the data provided split up into the following metrics:
Given this analysis, we can start by using R to analyze the information and properly segmenting as necessary, defining the trend and the precentage of the total:
# creating multiple vectors
energy_source <- names(pwr)[2:9] # exclude years and grand total
latest_year <- as.numeric(pwr[which(pwr[,"Years"]=="2020"),2:9]) # get the 2020 row and get the data
first_year <- as.numeric(pwr[which(pwr[,"Years"]=="1990"),2:9]) # get the 1990 row and get the data
mean <- c(mean(pwr$Biomass),mean(pwr$Coal),mean(pwr$Geothermal),mean(pwr$Hydro),mean(pwr$`Natural Gas`),mean(pwr$`Oil-based`),mean(pwr$Solar),mean(pwr$Wind)) # get the means on per column
combined_data <- data.frame(energy_source,first_year,latest_year,mean) # create a dataframe
# build categories
ren <- energy_source[c(1,3,4,7,8)] #list of renewable energy sources
combined_data$nr_inc <- (!combined_data$energy_source %in% ren) & combined_data$latest_year >= combined_data$mean # non-renewable energy sources that are increasing
combined_data$nr_dec <- (!combined_data$energy_source %in% ren) & combined_data$latest_year < combined_data$mean # non-renewable energy sources that are decreasig
combined_data$new_ren <- combined_data$first_year==0 & combined_data$energy_source %in% ren # newly introduced renewable sources of ernergy
combined_data$existing_ren <- combined_data$first_year!=0 & combined_data$energy_source %in% ren # available source
# defining the trend if increasing. Increasing is good for renewable but bad for non-renewable
combined_data$inc_trend <- combined_data$latest_year >= combined_data$mean
# lastly, get the total as a percentage of the year 2020
combined_data$total_2020 <- as.numeric(pwr[which(pwr[,"Years"]=="2020"),"Grand Total"])
combined_data$share <- combined_data$latest_year/combined_data$total_2020We have now completed the analysis table in for the categorization, trends and share.
combined_data## energy_source first_year latest_year mean nr_inc nr_dec new_ren
## 1 Biomass 0 1261 201.9032 FALSE FALSE TRUE
## 2 Coal 1934 58176 20746.1613 TRUE FALSE FALSE
## 3 Geothermal 5466 10757 9247.3871 FALSE FALSE FALSE
## 4 Hydro 6062 7192 7793.8710 FALSE FALSE FALSE
## 5 Natural Gas 0 19497 11173.6129 TRUE FALSE FALSE
## 6 Oil-based 12434 2474 8816.9677 FALSE TRUE FALSE
## 7 Solar 0 1373 204.2581 FALSE FALSE TRUE
## 8 Wind 0 1026 217.2258 FALSE FALSE TRUE
## existing_ren inc_trend total_2020 share
## 1 FALSE TRUE 101756 0.01239239
## 2 FALSE TRUE 101756 0.57172059
## 3 TRUE TRUE 101756 0.10571367
## 4 TRUE FALSE 101756 0.07067888
## 5 FALSE TRUE 101756 0.19160541
## 6 FALSE FALSE 101756 0.02431306
## 7 FALSE TRUE 101756 0.01349306
## 8 FALSE TRUE 101756 0.01008294We can now analyze them one by one:
subset(combined_data,nr_dec==TRUE,c(energy_source,mean,latest_year,share))## energy_source mean latest_year share
## 6 Oil-based 8816.968 2474 0.02431306The Philippines used to rely more heavily on oil but has declined substantially since the start. To see how this compares, we compare it to the data in 1990:
print(paste("Oil Based in 1990: ",round(as.numeric(pwr[which(pwr[,"Years"]=="1990"),"Oil-based"]) / as.numeric(pwr[which(pwr[,"Years"]=="1990"),"Grand Total"]),2)))## [1] "Oil Based in 1990: 0.48"In 1990, Oil was 48% of the energy production but it is now 2% which is good sign.
subset(combined_data,nr_inc==TRUE,c(energy_source,mean,latest_year,share))## energy_source mean latest_year share
## 2 Coal 20746.16 58176 0.5717206
## 5 Natural Gas 11173.61 19497 0.1916054The Philippines continues to rely on Coal and Natural Gas and it accounts for 76% of the energy source. We compare the same data to how it was in 1990:
print(paste("Coal in 1990: ",round(as.numeric(pwr[which(pwr[,"Years"]=="1990"),"Coal"]) / as.numeric(pwr[which(pwr[,"Years"]=="1990"),"Grand Total"]),2))) # coal## [1] "Coal in 1990: 0.07"print(paste("Natural Gas in 1990: ",round(as.numeric(pwr[which(pwr[,"Years"]=="1990"),"Natural Gas"]) / as.numeric(pwr[which(pwr[,"Years"]=="1990"),"Grand Total"]),2))) # natural gas## [1] "Natural Gas in 1990: 0"In 1990, these two sources are not primary sources back in 1990 only accounting for 7%. Unfortunately, these two energy sources were heavily invested in and now account for majority of the energy production in the country.
subset(combined_data,existing_ren==TRUE,c(energy_source,mean,latest_year,share))## energy_source mean latest_year share
## 3 Geothermal 9247.387 10757 0.10571367
## 4 Hydro 7793.871 7192 0.0706788817% of the energy output in the Philippines comes from Geothermal and Hyro sources. We can compare this to the 1991 data:
print(paste("Geothermal: ",round(as.numeric(pwr[which(pwr[,"Years"]=="1990"),"Geothermal"]) / as.numeric(pwr[which(pwr[,"Years"]=="1990"),"Grand Total"]),2))) # coal## [1] "Geothermal: 0.21"print(paste("Hydro: ",round(as.numeric(pwr[which(pwr[,"Years"]=="1990"),"Hydro"]) / as.numeric(pwr[which(pwr[,"Years"]=="1990"),"Grand Total"]),2))) # natural gas## [1] "Hydro: 0.23"The interesting thing about these two sources of energy is that: - Geothermals trend is increasing versus the mean, but the min and max values are not too far from one another:
paste("Min of Geothermal: ",min(pwr$Geothermal))## [1] "Min of Geothermal: 5466"paste("Max of Geothermal: ",max(pwr$Geothermal))## [1] "Max of Geothermal: 11626"paste("Min of Hydro: ",min(pwr$Hydro))## [1] "Min of Hydro: 4440"paste("Max of Hydro: ",max(pwr$Hydro))## [1] "Max of Hydro: 10252"We can then hypothesize that apart from these two sources coming from the environment,
subset(combined_data,new_ren==TRUE,c(energy_source,mean,latest_year,share))## energy_source mean latest_year share
## 1 Biomass 201.9032 1261 0.01239239
## 7 Solar 204.2581 1373 0.01349306
## 8 Wind 217.2258 1026 0.01008294Biomass, Solar and Wind are increasing over time but the share versus the total generation is quite small at 3.5%.
There’s a long way to go for the Philippines to rely on renewable sources of energy but will require additional investment along the way to increase generation capacity. However, it’s good news that oil-based has gone down substantially and with that use case, the same could happen to coal and natural gas once significant investment is made in renewable energy.
| Category | Sub Category | Energy Sources | Strategy |
|---|---|---|---|
| Non Renewable | Declining | Oil-based | Decline / Stop using |
| Increasing | Coal, Natural Gas | Keep Steady or decline | |
| Renewable | Existing | Geothermal, Hydro | Increase/Expand Capacity |
| New | Biomass, Solar, Wind | Increase/Expand Capacity |