{"id":38748,"date":"2025-04-17T04:21:34","date_gmt":"2025-04-17T08:21:34","guid":{"rendered":"https:\/\/studentwork.prattsi.org\/infovis\/?p=38748"},"modified":"2025-04-17T04:21:37","modified_gmt":"2025-04-17T08:21:37","slug":"how-the-world-emits-mapping-the-sources-efficiency-and-structure-of-global-co%e2%82%82-emissions-2022","status":"publish","type":"post","link":"https:\/\/studentwork.prattsi.org\/infovis\/labs\/maps\/how-the-world-emits-mapping-the-sources-efficiency-and-structure-of-global-co%e2%82%82-emissions-2022\/","title":{"rendered":"How the World Emits: Mapping the Sources, Efficiency, and Structure of Global CO\u2082 Emissions (2022)<\/strong>"},"content":{"rendered":"\n

Introduction<\/h2>\n\n\n\n

In an age defined by climate urgency, understanding the geography of carbon emissions is more critical than ever. While conversations often focus on \u201cwho emits the most,\u201d it\u2019s equally important to ask: how<\/em> do countries emit? How efficient<\/em> are their economies per unit of carbon? And what sectors<\/em> are driving this burden? In this report, I use three Tableau-based visualizations to map global CO\u2082 emissions in 2022 from three perspectives: dominant emission sectors<\/strong>, CO\u2082 per GDP efficiency<\/strong>, and sectoral composition of national footprints<\/strong>.<\/p>\n\n\n\n

Context & Methodology<\/h2>\n\n\n\n

These visualizations are built on data from Our World in Data\u2019s CO\u2082 and Greenhouse Gas Emissions dataset<\/strong>, a comprehensive, annually updated CSV containing national-level data on emissions by fuel type, sector, per capita metrics, and economic context (GDP). The original dataset includes over 60 columns and over 200 country\/regional records per year.<\/p>\n\n\n\n

To create the three maps, I followed a structured data cleaning and transformation workflow in Excel:<\/p>\n\n\n\n

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  1. Filtered to the year 2022<\/strong> to ensure consistency across all visualizations.<\/li>\n\n\n\n
  2. Removed aggregate regions<\/strong> (e.g: “World”, “Asia”, “High-income countries”) to avoid duplication and bias.<\/li>\n\n\n\n
  3. Dropped countries with null or zero values<\/strong> in critical fields like co2<\/code>, gdp<\/code>, or per-sector emissions.<\/li>\n\n\n\n
  4. Created new fields for analysis<\/strong>, such as:\n