Pollution Perspectives: A World in Emission
This project aims to focus on analyzing global CO2 emissions, diving into data analytics to uncover trends, patterns, and insights across various countries and income classes. I challenged myself to use SQL and Excel for this project instead of my usual go-to tool, Python. Having taken some courses in SQL, I figured it was the perfect time to practice my skills and see just how proficient I’ve become. Plus, I hadn’t used Excel in a while, so I thought it would be a great chance to brush up on it and make some cool visuals.
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Dataset used: https://ourworldindata.org/co2-and-greenhouse-gas-emissions
We begin with a line graph comparing the top four emitters— the U.S., China, India, and Russia— alongside the rest of the world, highlighting stark differences in their emissions trajectories. While emissions in the U.S. and Russia have declined, China and India show significant increases.
China’s emissions began to rise sharply in the early 2000s, reflecting its rapid industrialization. By 2021, China leads the world in emissions, with a 200% increase since 2000, followed by India, which has seen a 180% rise in the same period.
This trend can be largely attributed to the global shift of manufacturing from countries like the U.S. and Russia to nations with cheaper labour and lower production costs. Simultaneously, the U.S. and Russia have implemented stricter emissions control laws and shifted towards renewable energy. Notably, Russia experienced a sharp drop in emissions in 1991, coinciding with the collapse of the Soviet Union, which triggered an economic crisis that severely impacted industries such as manufacturing and agriculture.
Breakdown by continents
In this stacked area graph representing CO2 emissions across continents from 1945 to 2021, the data is visualized as a percentage of global emissions for each continent, showing changes over time. Here’s a numerical analysis of key trends
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​Asia's growing contribution since 1945 has accounted for about 10-15% of global emissions. By 2021, Asia’s share has risen significantly, now representing roughly 45-50% of total emissions. This sharp increase aligns with rapid industrialization, urbanization, and population growth in countries like China and India.
Africa and South America have relatively smaller shares, consistently under 10% throughout the period. However, Africa has shown a slight increase since the 1990s due to growing urbanization and industrialization, while South America’s emissions remain fairly stable.
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North America contributed over 50% of global emissions in the 1940's, making it the largest emitter at the time. By 2021 Its share has decreased to about 20%, reflecting shifts towards cleaner energy, stricter environmental regulations, and the outsourcing of manufacturing.
The same trend is seen in Europe as well. As a major emitter, accounting for about 35-40% of global emissions. By the end of 2021, its share has dropped to around 15-20%, indicating a steady transition towards greener energy sources and emissions reduction initiatives.
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This visualization illustrates how emissions have evolved for each continent. The rising emissions from Africa and Asia are particularly concerning, suggesting potential challenges as developing nations continue to industrialize.In contrast, the European Union's strict shifts towards renewable energy and the implementation of environmental laws have positively impacted their emission rates.
This graph shows the comparison of CO2 emissions in Asia between India and China versus the rest of Asia from 1945 to 2021. The graph highlights the contribution of India and China to the total CO2 emissions in Asia, using stacked areas for emissions, with China and India in blue, and the rest of Asia in red.
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When focusing on India and China during the post-2000 the graph shows a sharp increase in emissions. By 2021, their emissions surpass 20 Trillion tones, making up the majority of Asia's total emissions. India and China combined now account for more than 75% of Asia's total CO2 emissions. Between 1945 and 2000, emissions were relatively modest, with a steady but slow increase. Emissions only started rising dramatically around the year 2000, indicating the rapid industrialization in both countries during this period.
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The rest of Asia’s emissions have also increased, but not at the same rate as India and China. Before 2000, the rest of Asia had a significant contribution to emissions (almost on par with India and China combined), but after 2000, their share flattened out and remained steady at around 5-10 Trillion tones per year.
The overall CO2 emissions in Asia have grown sharply, from less than 2 Trillion in 1950 to over 25 Trillion in 2021. The majority of this growth is driven by India and China, while the rest of Asia shows only a moderate increase in comparison. The 1970s and 1980s show some gradual increase in emissions across Asia, but the dramatic spike started in the early 2000s for India and China. Emissions for the rest of Asia seem to plateau from 2010 onwards, indicating more stable or slower industrial growth.
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This graph emphasizes how India and China have become the dominant contributors to CO2 emissions in Asia over the last two decades, especially since 2000. While emissions from the rest of Asia have grown, they have not kept pace with the rapid industrialization and economic growth in these two countries. By 2021, India and China alone will be responsible for the majority of emissions in the region.
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This visual is my favorite from the project as it puts everything in perspective and highlights the countries inflating overall CO2 emissions in Asia. While Asia may seem like the largest emitter, it’s important to note that India and China were responsible for 64% of the region's emissions in 2021.
Breakdown by income
This chart illustrates CO2 emissions by income class from 1945 to 2021. Initially, high-income countries led global CO2 emissions, peaking around the late 2000s. However, emissions have gradually declined since 2010, reflecting the adoption of renewable energy, stricter environmental regulations, and a move towards sustainability. The Upper-Middle-Income group, which includes nations like China and other rapidly industrializing economies, shows a significant surge in emissions starting around 2000. By 2021, upper-middle-income countries surpass high-income countries, with emissions steadily increasing, likely due to industrialization and economic growth.
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Emissions from the Lower-Middle-Income group have also risen significantly, although at a slower rate than upper-middle-income countries. The rise is more gradual, starting in the 1990s and continuing to increase up to 2021. Emissions from low-income countries have remained relatively flat, contributing minimally to global CO2 emissions throughout the period.
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There's a clear shift from high-income countries, which historically contributed the most emissions, to upper-middle-income countries as major emitters, largely driven by industrial growth in emerging economies.
The trend suggests that as countries industrialize and increase income levels, CO2 emissions tend to rise, with high-income countries leading initially and then declining as they move towards cleaner energy sources.
Summary and Evaluation
This project examines global CO2 emissions trends from 1945 to 2021, with a focus on key contributors like China, India, the U.S., and Russia. The visualizations reveal a sharp rise in emissions from China (200% increase) and India (180% increase) since the early 2000s, driven by industrialization. In contrast, emissions from the U.S. and Russia have stabilized or declined. The analysis also highlights Asia's dominance, with China and India accounting for most of the region’s emissions. Overall, the project provides a clear overview of how emissions have shifted globally due to economic and industrial changes.
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I thoroughly enjoyed working on this project as it was my first experience utilizing both SQL and Excel together for data analysis. It provided me with a great opportunity to explore the diverse functionalities of these powerful tools. With SQL, I could efficiently handle large datasets, execute complex queries, and manage data retrieval, which made the analysis process much more streamlined. Meanwhile, Excel allowed me to leverage its robust features for data visualization, pivot tables, and statistical analysis. By combining both, I gained a deeper understanding of how these tools complement each other, enhancing my ability to derive meaningful insights from the data. This project helped me expand my technical skill set, and I look forward to applying these skills in future analyses.