class: center, middle, inverse, title-slide # World Energy Consumption ## For 1990-2020 ### Loren, Owen, and Jackie ### Bates College ### 2022-04-12 --- ### We see the impact of climate change in the news and all around us every day, but what are countries doing about it? .pull-left[ - A recent report from the Intergovernmental Panel on Climate Change highlighted that the world needs to invest three to six times what it’s currently spending on mitigating climate change if it wants to limit global warming to 1.5 or 2 degrees Celsius (1) - Although wealthier countries contribute the most to climate change, poor countries feel the effects disproportionately ] .pull-right[ <div class="figure" style="text-align: right"> <img src="img/wildfire.jpeg" alt="The Caldor fire in Eldorado National Forest near Pollock Pines, Calif., last year. [2]" width="70%" /> <p class="caption">The Caldor fire in Eldorado National Forest near Pollock Pines, Calif., last year. [2]</p> </div> ] .footnote[ [1] Source: Zhong, Raymond. “5 Takeaways From the U.N. Report on Limiting Global Warming.” The New York Times, 4 Apr. 2022, https://www.nytimes.com/2022/04/04/climate/ipcc-report-explained.html. Accessed 10 Apr. 2022. [2] Source: Lukpat, Alyssa. "Biden Administration Announces Plan to Spend Billions to Prevent Wildfires." The New York Times, 19 Jan. 2022, https://www.nytimes.com/2022/01/19/climate/biden-administration-wildfire-plan.html ] --- class: left, middle ### Our Data .pull-left[- Our data, "World Energy Consumption", consists of key metrics of energy usage from Kaggle. - This data set is part of Our World in Data, which seeks to collect data and research the world's largest problems. - The data set contained 122 columns of variables, and over 17,000 observations for each country from 1900 to the present. There were many observations which had “NA" as their entry. - Our analysis is for 1990-2020 for selected countries of interest and their energy consumption ] .pull-right[ <div class="figure" style="text-align: right"> <img src="img/windmills.jpeg" alt="Wind turbines and solar panels in Palm Springs, California." width="90%" /> <p class="caption">Wind turbines and solar panels in Palm Springs, California.</p> </div> ] .footnote[Image credit: Vanja Terzic/iStock] --- class: left ### A glimpse of energy consumption .center[ <img src="presentation_files/figure-html/barchart-transformed-fig-1.png" title="A quick glimpse of a visual representation of energy consumption for a few developed countries." alt="A quick glimpse of a visual representation of energy consumption for a few developed countries." width="80%" /> ] --- class: left ### Distribution of energy in China and US .center[ <img src="presentation_files/figure-html/china-usa-bar-1.png" title="A simple visualization comparing energy consumption between the US and China." alt="A simple visualization comparing energy consumption between the US and China." width="80%" height="200%" /> ] --- class: inverse, bottom background-image: url(https://miro.medium.com/max/1400/1*iA0-8OxW4UWFsp151oR2rg.jpeg) # Which countries have improved their share of renewable energy the most between 1990-2018? --- class: left, top ### Visualizing changes in renewable energy shares ```r testleaflet <- testleaflet %>% left_join(world_spdf2, by = c("country" = "NAME")) %>% st_as_sf() ``` ```r #2018 leaflet pal2 <- colorNumeric(palette = "RdYlGn", domain = testleaflet$renewables_share_energy) labels <- sprintf("<strong>%s</strong><br/>%s ", testleaflet$country, testleaflet$renewables_share_energy) %>% lapply(htmltools::HTML) m <- leaflet(data = testleaflet) %>% addTiles() %>% setView( lat=10, lng=0 , zoom=1) %>% addPolygons(fillColor = ~pal2(testleaflet$renewables_share_energy), fillOpacity = 1, color = "white", opacity = 0.7, weight = 1, label = labels) %>% addLegend("bottomleft", pal = pal2, values = ~testleaflet$renewables_share_energy, title = "2018 Renewable<br> Energy Share", opacity = 1) ``` --- class: center, middle
--- class: left ### Are China and the US committed to reducing fossil fuels? .pull-left[ <p align="center"> <iframe src="https://dcs-210.github.io/w2022-project-lorenowenjackie/proposal/map_anim.gif" width="5016", height="516", frameBorder="0"></iframe> </p> ] .pull-right[ - Note the increase in slope for China after 2003, and the gradual decrease in consumption for the US after 2010 - In 1990, the US used nearly double the amount of fossil fuels as China - 3 decades later, China uses nearly twice as much fossil fuels as the US. ] --- class: left ### What do these trends look like in the future? .center[ ```r m_USA <- linear_reg() %>% set_engine("lm") %>% fit(fossil_fuel_consumption ~ year, data = year_1990_USA) new_fossil_USA <- data.frame(year = seq(1990, 2030, by = 2), country = rep("United States", 21 )) new_fossil_USA <- new_fossil_USA %>% mutate(predicted = predict(m_USA, year_1990_USA)$.pred, actual = year_1990_USA$fossil_fuel_consumption) ``` ] - Linear regression projecting fossil fuel data from 1900 to 2030 - Selected code from after data cleaning and transforming --- class: left, top ### Results of our linear model .pull-left[ ``` ## year country predicted actual ## 1 1990 United States 21314.21 19813.402 ## 2 1992 United States 21407.93 20083.125 ## 3 1994 United States 21501.64 20893.884 ## 4 1996 United States 21595.35 21883.039 ## 5 1998 United States 21689.07 22301.748 ## 6 2000 United States 21782.78 23224.264 ## 7 2002 United States 21876.49 22941.458 ## 8 2004 United States 21970.20 23499.054 ## 9 2006 United States 22063.92 23185.686 ## 10 2008 United States 22157.63 22789.636 ## 11 2010 United States 22251.34 22174.395 ## 12 2012 United States 22345.05 21197.905 ## 13 2014 United States 22438.77 21940.02 ## 14 2016 United States 22532.48 21428.95 ## 15 2018 United States 22626.19 22196.473 ## 16 2020 United States 22719.90 <NA> ## 17 2022 United States 22813.62 <NA> ## 18 2024 United States 22907.33 <NA> ## 19 2026 United States 23001.04 <NA> ## 20 2028 United States 23094.75 <NA> ## 21 2030 United States 23188.47 <NA> ``` ] .pull-right[ ``` ## year country predicted actual ## 1 1990 China 4542.133 7620.275 ## 2 1992 China 6595.772 8456.527 ## 3 1994 China 8649.411 9572.766 ## 4 1996 China 10703.050 10365.629 ## 5 1998 China 12756.689 10411.263 ## 6 2000 China 14810.328 11119.51 ## 7 2002 China 16863.967 12699.612 ## 8 2004 China 18917.606 17375.205 ## 9 2006 China 20971.244 21671.211 ## 10 2008 China 23024.883 24004.399 ## 11 2010 China 25078.522 26704.582 ## 12 2012 China 27132.161 29660.252 ## 13 2014 China 29185.800 30841.824 ## 14 2016 China 31239.439 30872.326 ## 15 2018 China 33293.078 32388.702 ## 16 2020 China 35346.717 <NA> ## 17 2022 China 37400.356 <NA> ## 18 2024 China 39453.995 <NA> ## 19 2026 China 41507.634 <NA> ## 20 2028 China 43561.273 <NA> ## 21 2030 China 45614.912 <NA> ``` ] --- class: left ###Plot of actual vs. predicted data .center[ <img src="presentation_files/figure-html/USA-vs-China-fossil-predictions-1.png" title=" The visualization predicts the projected values of fossil fuel consumptin in the future. Notice that the prediction model for China is more accurate than the US." alt=" The visualization predicts the projected values of fossil fuel consumptin in the future. Notice that the prediction model for China is more accurate than the US." width="80%" /> ] --- class: left ###Plot of actual vs. predicted data, zoomed in .center[ <img src="presentation_files/figure-html/USA-vs-China-fossil-predictions-zoomed-1.png" width="80%" /> ] --- class: left, top ### Carbon emissions from electricity - g CO2/KWh <p align="center"> <iframe src="https://dcs-210.github.io/w2022-project-lorenowenjackie/presentation/carbon.html" width="5016", height="516", frameBorder="0"></iframe> </p> --- class: inverse, left, top background-image: url(https://www.science.org/do/10.1126/science.aax7477/abs/solar_16x9_2.jpg) ## Who are the leaders in renewables: breaking down the difference between total consumption and per capita .footnote[Photo credit: CPG Grey] --- class: left, top ### Leaders in Wind: total consumption .center[
] --- ###Leaders in Wind: Per Capita
--- class: left, top ## Who leads the world in solar consumption?
--- class: left, top ## And for per capita solar?
--- class: left, top ## Conclusions .pull-left[ - Renewable energy is on the rise, but there has not been substantial increase in renewable energy production for the countries which use the most energy - Smaller countries like Finland and Germany lead the charge in renewable energy - Despite efforts to increase renewables, there needs to be greater effort to ween off of fossil fuels - More effort needs to be given to reporting energy usage and sources ] .pull-right[ <div class="figure" style="text-align: right"> <img src="img/emissions.jpeg" alt="A smokestack emitting gases" width="90%" /> <p class="caption"> Jaenschwalde Power Station near Peitz, eastern German</p> </div> ] .footnote[ Image credit: John MacDougall/AFP via Getty Images ] --- class: left, top ## Areas of Improvement for our Analysis and Data Future research directions: - Find more comprehensive datasets for multiple countries/regions and combine for a better understanding of global trends - Further research carbon emissions for countries, not just electricity - Understand the relationship between GDP and emissions or renewable generation - Research current policies to understand how legislation impacts renewable efforts Statistical analysis: - Understand why the r-squared for our fossil fuel projections for the USA were so low, possible that a more complex model is necessary - Large amount of NA's prevented us from being able to predict some trends --- class: inverse, center, top background-image:url(http://i.imgur.com/ue1ksjY.jpg) ## Questions? .footnote[Photo credit: Bernard Spit Polar Bear (Via Imgur)]