Problem: Global population continues to grow as we move towards year=“2050”. This significant growth would traditionally result in significant energy demand increases and carbonfootprint / Green House Gas (GHG) increase.

Explore what does the energy demand forecast look like for the future and the expected carbon footprint/ GHG change.

What is the forecast change in energy demand, GHG/CO2 emissions with respect to population demand increasing? What are the predicted changes in energy sourcing and mix? How are the sector primary energy supply/ mix supporting the forecast to reduce GreenHouseGas(GHG)/ CO2 emissions? Explore how biomass use sector is mitigating GHG?

knitr::opts_chunk$set(echo = TRUE)

require(readr)
## Loading required package: readr
require(dplyr)
## Loading required package: dplyr
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
require(RCurl)
## Loading required package: RCurl
require(ggplot2)
## Loading required package: ggplot2
# Initial project setup to import the data set via CSV reader of file into the Import Data Set
#dataimport<-data.frame(Energy_carbonfootprint01)
#dataimport2<-data.frame(Energy_carbonfootprint02)
#dataimport<-data.frame(dataimport)
#dataimport2<-data.frame(dataimport2)
#summary(dataimport)

# Raw data URL for dataimport for initial file of energy and carbon footprint data from github
# https://raw.githubusercontent.com/schmalmr/HW3_Energy_Carbonfootprint01/main/Energy_carbonfootprint01.csv

urlfile="https://raw.githubusercontent.com/schmalmr/HW3_Energy_Carbonfootprint01/main/Energy_carbonfootprint01.csv"

dataimport<-read_csv(url(urlfile))
## Rows: 61 Columns: 92
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Carbontax_United_States, Carbontax_Canada, CarbonTax_China_mainlan...
## dbl (43): Date, Global_Biomass_EmmissionReduction, Global_CO2_Emissions_per_...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
dataimport
## # A tibble: 61 × 92
##     Date Global_Total_GHG… `Global_Non-energ… `Global_Energy-r… GlobalTotal_CO2…
##    <dbl>             <dbl>              <dbl>             <dbl>            <dbl>
##  1  1990             34674              10377             24297            24678
##  2  1991             33886              10337             23549            23913
##  3  1992             33916              10409             23506            23853
##  4  1993             34048              10458             23590            23960
##  5  1994             34219              10456             23763            23996
##  6  1995             34861              10537             24324            24487
##  7  1996             35037              10134             24903            24575
##  8  1997             36115              11109             25006            25738
##  9  1998             35184              10269             24916            24902
## 10  1999             35812              10022             25789            25552
## # … with 51 more rows, and 87 more variables:
## #   Global_Nonenergy_related_CO2_emissions <dbl>,
## #   Global_Energy_related_CO2_emissions <dbl>, Global_Oil_CO2Emissions <dbl>,
## #   GlobaNatural_gas_Emissions <dbl>, Global_Coal_Emissions <dbl>,
## #   Global_Biomass_EmmissionReduction <dbl>,
## #   Global_CO2_Emissions_per_GDP <dbl>, Global_CO2_Emissionsper_Capita <dbl>,
## #   GlobalCO2_Emissions_per_Primary_Energy_Consumption <dbl>, …
# Raw data URL for dataimport2 for energy and carbon footprint analysis *(second file) from github
# https://raw.githubusercontent.com/schmalmr/HW3_Energy_Carbonfootprint01/main/Energy_carbonfootprint02.csv
# https://raw.githubusercontent.com/schmalmr/HW3_EnergyandCarbonfootprint02/main/Energy_carbonfootprint02.csv

urlfile2="https://raw.githubusercontent.com/schmalmr/HW3_EnergyandCarbonfootprint02/main/Energy_carbonfootprint02.csv"

dataimport2<-read_csv(url(urlfile2))
## Rows: 61 Columns: 57
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (8): Carbontax_Canada, CarbonTax_China_mainland, Carbontax_France, Carb...
## dbl (23): Date, Global_Biomass_EmmissionReduction, Global_CO2_Emissions_per_...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
dataimport2
## # A tibble: 61 × 57
##     Date Global_Total_GHG… `Global_Non-energ… `Global_Energy-r… GlobalTotal_CO2…
##    <dbl>             <dbl>              <dbl>             <dbl>            <dbl>
##  1  1990             34674              10377             24297            24678
##  2  1991             33886              10337             23549            23913
##  3  1992             33916              10409             23506            23853
##  4  1993             34048              10458             23590            23960
##  5  1994             34219              10456             23763            23996
##  6  1995             34861              10537             24324            24487
##  7  1996             35037              10134             24903            24575
##  8  1997             36115              11109             25006            25738
##  9  1998             35184              10269             24916            24902
## 10  1999             35812              10022             25789            25552
## # … with 51 more rows, and 52 more variables:
## #   Global_Nonenergy_related_CO2_emissions <dbl>,
## #   Global_Energy_related_CO2_emissions <dbl>, Global_Oil_CO2Emissions <dbl>,
## #   GlobaNatural_gas_Emissions <dbl>, Global_Coal_Emissions <dbl>,
## #   Global_Biomass_EmmissionReduction <dbl>,
## #   Global_CO2_Emissions_per_GDP <dbl>, Global_CO2_Emissionsper_Capita <dbl>,
## #   GlobalCO2_Emissions_per_Primary_Energy_Consumption <dbl>, …
#Scatter Plot
library(ggplot2)
ggplot (dataimport, aes(x=Date,y=Global_Total_GHG_emissions))+geom_point(size=2)

ggplot(dataimport,aes(x=Date,y=Global_Energy_related_CO2_emissions))+geom_point(size=2)

ggplot(dataimport,aes(x=Date,y=Global_Total_Energy_Demand_MTOE))+geom_point(size=2)

ggplot(dataimport,aes(x=Population_million_people,y=Global_Total_Energy_Demand_MTOE))+geom_point(size=2)

ggplot(dataimport,aes(x=Population_million_people,y=Global_Energy_related_CO2_emissions))+geom_point(size=2)

ggplot(dataimport,aes(x=Global_Biomass_EmmissionReduction,y=Global_Oil_CO2Emissions))+geom_point(size=2)

ggplot(dataimport,aes(x=Date,y=Global_CO2_Emissionsper_Capita))+geom_point(size=2)

ggplot(dataimport,aes(x=Date,y=Carbontax_United_States))+geom_point(size=2)

ggplot(dataimport,aes(x=Population_million_people, y=Global_Oil_Demand_MTOE))+geom_point(size=2)

#BOX Plot
ggplot(dataimport,aes(x=Population_million_people,y=Global_Energy_related_CO2_emissions))+geom_boxplot()
## Warning: Continuous x aesthetic -- did you forget aes(group=...)?

ggplot(dataimport,aes(x=Global_Biomass_EmmissionReduction,y=Global_Oil_CO2Emissions))+geom_boxplot()
## Warning: Continuous x aesthetic -- did you forget aes(group=...)?

#Histogram
ggplot(dataimport,aes(x=Global_CO2_Emissionsper_Capita))+geom_histogram(binwidth=0.25)

#Assign new variable names to make calculations easier
#new column names and renaming of columns
#rename(dataimport, new name = old name)


dataimport<-rename(dataimport,globalenerydemand=Global_Total_Energy_Demand_MTOE)
dataimport<-rename(dataimport,globalelectricdemand=Global_Total_Electricity_Demand_MTOE)
dataimport<-rename(dataimport,globaloildemand=Global_Oil_Demand_MTOE )
dataimport<-rename(dataimport,globalngasdemand=Global_Natural_gas_demandMTOE)
dataimport<-rename(dataimport,globalcoaldemand=Global_CoalMTOE)
dataimport<-rename(dataimport,globalhydrogendemand=Global_Hydrogen_Demand_MTOE)
dataimport<-rename(dataimport,globalmodernbiomassdemand=GlobalModern_biomassMTOE)
dataimport<-rename(dataimport,globaltraditionbiomass=GlobalTraditional_biomassMTOE)


dataimport <- dataimport %>% mutate(sumrow= globalmodernbiomassdemand+globaltraditionbiomass)

## Including Plots
# Bubbleplot:  Area related to Green House Gas (GHG) emissions from energy with y axis the change in global energy demand over time and with the expected population changes to year=2050
bubbleplot<-ggplot(dataimport,aes(x=Population_million_people, y=globalenerydemand, size=Global_Energy_related_CO2_emissions))+geom_point(shape=21,color="black",fill="blue")
bubbleplot+scale_size_area(max_size=10)

bubbleplot<-ggplot(dataimport,aes(x=Date, y=globalenerydemand, size=Global_Energy_related_CO2_emissions))+geom_point(shape=21,color="black",fill="red")
bubbleplot+scale_size_area(max_size=10)

#Global emissions by industry sector to see what sector is driving the changes / expected to drive the improvement in GHG 
bubbleplot<-ggplot(dataimport2,aes(x=Date, y=Total_Demand_by_sector_MTOE, size=Global_Residential_Emissions))+geom_point(shape=21,color="black",fill="yellow")
bubbleplot+scale_size_area(max_size=10)

bubbleplot<-ggplot(dataimport2,aes(x=Date, y=Total_Demand_by_sector_MTOE, size=Global_Commercial_emissions))+geom_point(shape=21,color="black",fill="orange")
bubbleplot+scale_size_area(max_size=10)

bubbleplot<-ggplot(dataimport2,aes(x=Date, y=Total_Demand_by_sector_MTOE, size=Global_Transportation_Emissions))+geom_point(shape=21,color="black",fill="purple")
bubbleplot+scale_size_area(max_size=10)

bubbleplot<-ggplot(dataimport2,aes(x=Date, y=Total_Demand_by_sector_MTOE, size=Global_Agricultural_emmissions))+geom_point(shape=21,color="black",fill="green")
bubbleplot+scale_size_area(max_size=10)

#Shift in primary energy sourcing plans 
bubbleplot<-ggplot(dataimport,aes(x=Date, y=globalenerydemand, size=Primary_Energy_Oil_percent))+geom_point(shape=21,color="black",fill="red")
bubbleplot+scale_size_area(max_size=10)

bubbleplot<-ggplot(dataimport,aes(x=Date, y=globalenerydemand, size=Primary_Energy_Gas_percent))+geom_point(shape=21,color="black",fill="white")
bubbleplot+scale_size_area(max_size=10)

bubbleplot<-ggplot(dataimport,aes(x=Date, y=globalenerydemand, size=Primary_Coal_percent))+geom_point(shape=21,color="black",fill="brown")
bubbleplot+scale_size_area(max_size=10)

bubbleplot<-ggplot(dataimport,aes(x=Date, y=globalenerydemand, size=Primary_Nuclear_percent))+geom_point(shape=21,color="black",fill="pink")
bubbleplot+scale_size_area(max_size=10)

bubbleplot<-ggplot(dataimport,aes(x=Date, y=globalenerydemand, size=Primary_Hydro_percent))+geom_point(shape=21,color="black",fill="light blue")
bubbleplot+scale_size_area(max_size=10)

bubbleplot<-ggplot(dataimport,aes(x=Date, y=globalenerydemand, size=Primary_Renewables_percent))+geom_point(shape=21,color="black",fill="light green")
bubbleplot+scale_size_area(max_size=10)

bubbleplot<-ggplot(dataimport,aes(x=Date, y=globalenerydemand, size=Primary_Modern_biomass_percent))+geom_point(shape=21,color="black",fill="dark green")
bubbleplot+scale_size_area(max_size=10)

#Emission CO2 impact with the shift in energy demand by source
bubbleplot<-ggplot(dataimport,aes(x=Date, y=Global_Oil_CO2Emissions, size=Primary_Energy_Oil_percent))+geom_point(shape=21,color="black",fill="dark blue")
bubbleplot+scale_size_area(max_size=10)

bubbleplot<-ggplot(dataimport,aes(x=Date, y=GlobaNatural_gas_Emissions, size=Primary_Energy_Gas_percent))+geom_point(shape=21,color="black",fill="grey")
bubbleplot+scale_size_area(max_size=10)

bubbleplot<-ggplot(dataimport,aes(x=Date, y=Global_Coal_Emissions, size=Primary_Coal_percent))+geom_point(shape=21,color="black",fill="brown")
bubbleplot+scale_size_area(max_size=10)

bubbleplot<-ggplot(dataimport,aes(x=Date, y=Global_Biomass_EmmissionReduction, size=Primary_Modern_biomass_percent))+geom_point(shape=21,color="black",fill="dark green")
bubbleplot+scale_size_area(max_size=10)

Executive Summary

Primary energy demand will continue to increase for new few years (to 2028) while CO2/GHG emissions will immediately begin to decline to reach net zero by 2050 as the energy transition shifts to supply the energy needs of a significantly growing population. The population will growth 45-50% from 2020 to 2050 while efficiency improvements in energy use and new sources will reduce the energy demand per capita.

Energy supply from 2020 will change dramatically with major reductions in the use of carbon based fuels (oil, coal, natural gas) while by 2050 the major sources of energy will be renewables(solar,wind), biomass,hydro and nuclear while will ensure the dramatic drop in CO2 emissions can be achieved.

The forecast data provide by market agency and is subject to significant uncertainty of achievement which are not apparent in the data. Conclusions provide clarity on what needs to happen the significant changes required to accomplish net zero by 2050.

Detailed conclusions

GHG Emissions Change: The global total green house gas (GHG) and the Global Energy related CO2 emissions follow a similar pattern of decline. - Both appear to undergo a transition period from 2020 to 2030 as the technology adaption increases and then the change accelerates after 2030. - Both are forecast to drop with a 2030 inflection point where the rate of drop accelerates to reach a net zero point in 2050. - This change is independent of population growth forecast through 2050

Global Energy Demand: The global energy demand is forecast to increase through approx 2028 to ~ 11,000 Million MTOE and declines through an inflection point in 2040 with it aspotoically approaching 8000 Million Metric Tons of Oil Equivalent (MTOE).
Population increases to 8.5 Billion with energy also increasing and then energy demand begins to delcine with the expectation that efficiency improvements are driving the energy improvements.

Global energy demand increases to ~2028-2030 while CO2 emissions is continously decreasing from 2023 on. The sector demand shifts significantly and the effieciency required much be enhances to maintain standards of living or improve it for many in the world.

Shifts in sector energy demand as approximate figures from graphs in Millions of tons of oil equivalent (MTOE):

  1. Year 2020 Residential energy of 2100 mil MTOE to a year 2050 residential energy of 900 mil MTOE which is a net drop of-1200 million MTOE

  2. Year 2020 Commercial energy of 1000 mil MTOE to a year 2050 commercial energy demand of 400 Mil MTOe which is a -600 million MTOE drop.

  3. Year 2020 Transport eneryg demand of 8000 mio MTOE to ayear 2050 transport demand of 2000 mio MTOE which is a net drop of -6000 million MTOE.

  4. 2020 year has agriculture energy demand of 450 mio MTOE which drops to a year 2050 demand of 250 mio MTOE which is a dro of -200 mio MTOE.

Transportation sector requires the biggest change while the Residential sector is the second largest improvement required to improve efficiency all for a ~45-50% larger global population.

The effectiveness of the CO2/GHG gas reduction is going to be driven by the new forecast mix in energy sourcing as shown below as percent of the total supply for major primary sources:

  1. The 2020 Oil supply was 35% of the total energy mix which drops to 15% by 2050.

  2. The 2020 Natural gas supply was 20% of the total energy mix which drops to 10% by 2050.

  3. The 2020 coal energy supply was 15% of the toal energy mix which drops to 10% by 2050.

  4. The 2020 nuclear energy supply was ~3% of the total energy mix which increases to 4.5% in 2050

  5. The 2020 Hydroelectric supply was 6-7% of the total energy mix which increases to 17.5% in 2050.

  6. The 2020 renewables (solar, wind) was 3-4% of the total energy mix which increases to 30% by 2050. This is the most significant change in the overall forecast to achieve the GHG reduction.

  7. The 2020 biomass was ~4% of the total energy mix which icreases to 10% by 2050.

Global Emissions Global CO2 emissions per capita are forecast to flat through 2023 and then decline significantly.

Improvement in primary energy CO2 emissions based on approx figures from graphs.

1)In 2020 the CO2 emissions from Oil were ~11,000 and are forecast to drop 9000 mio ton by 2050.

  1. In 2020 the CO2 emissions from Natural Gas were 9,000 and are forecast to drop 6,500 mio ton by 2050.

  2. In 2020 the Co2 emissions from Coal were 15,000 and are forecast to drop 14,000 mio ton by 2050.

  3. In 2020 the impact of biomass on reducing CO2 was zero by 2050 the biomass use will reduce CO2 emissions by 900 mio tons.

The shift out of carbon based resources are the biggest drivers in CO2 emissions reductions. Coal and then Oil are the biggest impacts with Natural gas following. Increased use of more CO2 neutral biomass also has a positive impact.

R Markdown

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.

When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this: