Clean Energy

How the focus on clean energy overshadows our massive usage of unclean energy sources, in data.

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General Overview

In today’s world, we're concentrating more on boosting our use of renewable energy sources like wind and solar power to combat climate change. While this effort is commendable, it's based on a flawed assumption that just by adding more renewable energy to our mix, we're making progress. This overlooks a critical point: our overall energy consumption is rising, and so is our use of non-renewable energies like coal, oil, and natural gas. The problem lies not just in increasing the share of renewables but in reducing the absolute consumption of non-renewables. Merely increasing the percentage of energy coming from renewables does not fully address climate change. For example, planting more trees does not contribute to negating the consequences of deforestation if we keep cutting down more and more trees. Therefore, the real challenge is to both increase our dependence on clean energy and reduce the use of non-renewables. This does not mean that we should just use more solar panels and wind turbines. It signifies the importance of reducing our total energy consumption and to avoid trends such as overconsumption in today’s capitalist society. Only then can we make a remarkable impact on reducing climate change. This issue requires collective effort from everyone towards the sustainability of our planet.

Behind the story: Our Data Analysis

For the first graph, we went to see the Internation Energy Association's World Energy Balance Highlights, a dataset that collects in petajoules the amount of energy that is produced, imported, exported and consumed in the world, having a lot of data for over 50 countries that range from the 70s to today. The IEA's dataset has this energy balance for every country separated by energy source which is very useful to our goal, to see if the total energy supply of a country is more composed of clean energy sources or non-clean energy sources. To accomplish this, we made a graph specifically for Canada that showed over the years the total energy supply of the country, along with the energy supply in clean and un-clean sources, and the energy supply that came from each individual source. The reason why we looked at total energy supply is because it includes all the energy that the country is receiving for itself, and excludes exports because it would be counted twice if only looked at the energy the country imports and produces.

For the second graph, we sourced the data from worldbank.org which shows that their data set has been sourced from multiple reputable organizations such as the IEA, the IRENA and the WHO. This graph takes into consideration the Renewable energy consumption % of the total energy for the top 5 highest GDP and top 5 lowest GDP countries. The richest countries are grouped by having different shades of blue while the poorest ones are colored with different shades of red. We expect developed countries to be more dependent on renewables since they have more resources to adopt these modern types of energy, but we were surprised when the data showed the complete opposite. There is a huge gap where the developing countries use way more clean energy than the developed ones. We asked ourselves why is this the case, and we concluded that developing countries focus more on building renewable energy infrastructure because not only does it pose an advantage for the long term, but it also fulfills their energy needs. Richer countries get their wealth from trade and therefore their economic activity requires a much higher amount of energy, which renewables cannot fulfill with today's technology.

For the third graph, it presents the percentage of renewable energy use as part of total primary energy consumption from 1965 to 2022 among selected countries. The countries displayed here—Brazil, Canada, China, Germany, India, and the United States—are among the top 10 energy consumers globally. We observe that Brazil has consistently led with a high percentage of renewables, peaking at nearly 50%. The size of the dots indicates the amount of energy consumed for the given year, showing Brazil's increase in the use of renewables even if it's total energy consumption is also on the rise. Canada and the US show a moderate percentage of renewables, with the United States displaying a slight increase over time. China, while having a lower percentage, shows a steady increase in the use of renewable energy. The large dot sizes indicate high overall energy consumption, which emphasizes the importance of their shift toward renewable sources. Germany’s renewable percentage has shown a notable increase since the 2000s, reflecting its commitment to energy transition. A deeper dive on Germany reveals that they are commited towards prioritizing clean energy by implementing laws such as the Renewable Energy Sources Act which benefit producers of clean energy by offering them financial support.

More insight on the data manipulation of the fourth and fifth graph will be provided during the presentation.

What are our data sources ?

First, Fourth and Fifth graph : https://docs.google.com/spreadsheets/d/18P0YCMEIZxlgH70mmgpzX2p53R053nwL/edit?usp=sharing&ouid=110309533355432698760&rtpof=true&sd=true https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser?country=DENMARK&fuel=Key%20indicators&indicator=NetImports

Second graph : https://data.worldbank.org/indicator/EG.FEC.RNEW.ZS

Third graph : https://ourworldindata.org/explorers/energy?tab=table&facet=none&hideControls=false&Total+or+Breakdown=Total&Energy+or+Electricity=Primary+energy&Metric=Annual+consumption&country=USA~GBR~CHN~OWID_WRL~IND~BRA~ZAF https://ourworldindata.org/grapher/primary-energy-cons?tab=table

How did we use ChatGPT

Firstly, we used ChatGPT to create the thumbnail for the Devpost : https://cdn.discordapp.com/attachments/710463923257737291/1221457337479598251/image.png?ex=6612a5ca&is=660030ca&hm=3892d1e07eca6b992ed0a7a8ad2cdde4636cd7767b436224aeec13092676fee0& https://cdn.discordapp.com/attachments/710463923257737291/1221457381863588001/image.png?ex=6612a5d5&is=660030d5&hm=6bed6b36cbf3247130cae06d725042e87799cec5c7ef88e2564f315dcef79c1e& We then used ChatGPT to create all the visualizations for the project from the data sources. Here are all our individual reflections :

First Graph :

Chat gpt log : https://chat.openai.com/share/e/206039df-3b1e-430e-a2f1-4eafdc6f2eb7 Dataset fed : https://docs.google.com/spreadsheets/d/18P0YCMEIZxlgH70mmgpzX2p53R053nwL/edit?usp=sharing&ouid=110309533355432698760&rtpof=true&sd=true

My conversation with chatgpt had the goal of coming up with code that can show a clear visualization of the data provided by the International Energy Agency, particularly to show the total energy supply of Canada in clean energy and un-clean energy, but also by specific energy source. The visualization clearly shows that the line of the total energy supply of unclean sources is extremely similar to the line of the total energy supply for all sources, and that the total line varies exactly like the unclean energy line. We can also see that Canada's clean energy total supply is a lot lower than the unclean one. We can clearly see that Canada, while making effort to increase the supply in clean energy, is still extremely dependant on unclean energy. I had to repair the graph because ChatGPT miswrote one of the categories as Nuclear energy when it was written Nuclear on the google sheet.

Second graph :

My conversation with ChatGPT began with a request to identify the top five least fortunate countries based on GDP and living conditions. I want to identify these countries in order to visualize the data related to them in the graph for the % consumption of renewable energy. After, I asked ChatGPT to identify the five most developed and richest countries by GDP so that I could compare the % renewable energy used between both extremes (richest and poorest countries). I would expect the wealthier countries to use more renewables, but from my surprise, the data visualization coded by ChatGPT showed a huge gap where the poorer countries are way more dependent on renewables than rich countries. I wanted to modify the colors and the scale of the graph, so I used ChatGPT to aid me in the coding part and to sucessfully modify / fix errors in the code. ChatGPT chat link: https://chat.openai.com/share/e/e869aef1-2b37-4a68-bfc5-4ced59b3d4ce

Third graph:

Link to ChatGPT Log: https://docs.google.com/document/d/11F0MRSlhR1vN-GXKjh3f70OUhyMgb5D0A39CmjWt_Ns/edit?usp=sharing In sum, ChatGPT was used to get started with making the graphs using python. In other words, it gave us the foundation of the python code that would be used to create a graph explaining the data from the CSV file that was pasted. It was also used to add features to our graph and ask questions to it such as a trend line, different sized points, colors and for the formatting. I asked chatGPT to combine two data tables, one containing the percentage of the total consumed energy that is renewable and one containing the total consumed energy.

Other graphs: ChatGPT wasn't utilized for the creation, but it was used to learn about data manipulation.

https://colab.research.google.com/drive/1J1oUEWXviNCsVou_Z_4iqvH7nLHnJmov?usp=sharing

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