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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>G7 + China: Per-Capita GHG Emissions 2022</title>
<link rel="stylesheet" href="styles.css">
</head>
<body>
<main id="whitehat-card" class="card whitehat">
<div class="eyebrow neutral">White Hat Visualization · OECD Greenhouse Gas Emissions Dataset</div>
<h1>Per-Capita Greenhouse Gas Emissions in Selected Major Economies (2023)</h1>
<p class="subtitle">
This chart compares total greenhouse gas emissions excluding LULUCF, measured in kilograms of
CO₂-equivalent per person, for the G7 countries plus China. The goal is to present the data
clearly and neutrally using a zero baseline, consistent scale, and accurate labels.
</p>
<div id="whitehat-chart-wrap" role="img"
aria-label="Horizontal bar chart showing per-capita greenhouse gas emissions in 2023 for the G7 countries and China.">
</div>
<div id="whitehat-tooltip" class="tooltip"></div>
<p class="writeup">
This visualization is designed using white-hat principles to present greenhouse gas emissions data in an accurate
and unbiased manner. First, the chart uses a zero based x-axis, ensuring that bar lengths are
proportional to their actual values. Starting the axis at zero prevents exaggeration and allows viewers to make
fair
comparisons between countries.
</p>
<p class="writeup">
Second, the dataset selection is transparent and clearly communicated. The chart clearly states that it includes
the G7 countries plus China and presents them as a selected group. Unlike the black-hat version, it does not frame
this subset as a complete comparison, preventing misleading conclusions from omitted countries. By clearly disclosing the scope,
the visualization avoids misleading the viewer through
omission. Additionally, all values are labeled with consistent units and the correct reporting year.
</p>
<p class="writeup">
Third, the visualization uses neutral titles, labels, and annotations to avoid influencing interpretation. The
title describes the content without assigning blame or suggesting conclusions, and no annotations are
added to imply trends. Supporting elements such as the OECD average reference line provide useful context without
distorting the data. Overall, design decisions like accurate scaling, transparent data selection, and neutral
presentation ensure that the visualization communicates information honestly and allows viewers to draw their own
conclusions.
</p>
<p class="footnote">
Source: OECD Greenhouse Gas Emissions Inventories dataset. Measure: Total emissions excluding
LULUCF. Unit: kilograms of CO₂-equivalent per person. Countries shown are the G7 plus China.
Values reflect the 2023 reporting period where available.
</p>
</main>
<main id="card">
<div class="eyebrow">Climate Policy Analysis · OECD Greenhouse Gas Emissions Dataset</div>
<h1>G7 Nations + China: America’s Emissions Crisis in Context</h1>
<p class="subtitle">
Per-capita data reveals a troubling pattern: while European allies cut emissions year over year, North American
output remains stubbornly high, with the United States showing no sign of meaningful decline (2022)
to the global climate, and which have the most ground to make up (2022)
</p>
<div id="chart-wrap" role="img"
aria-label="Horizontal bar chart showing per-capita GHG emissions for G7 nations and China in 2022. The United States and Canada have the highest emissions.">
</div>
<div id="tooltip" class="tooltip"></div>
<p class="writeup">
This visualization uses three black-hat techniques to manipulate interpretation and exaggerate
perceived differences in greenhouse gas emissions. First, the most impactful distortion is the truncated
x-axis, which begins at 4 instead of 0. By redefining the x-origin, the bar lengths are inflated, which makes the
differences between countries appear far larger than they actually are. For example, the United States appears to
emit
drastically more greenhouse gases than the United Kingdom, suggesting an extreme disparity. In reality, the ratio
is much
smaller. The US emits about three times as much as the UK when the chart makes it appear close to ten.
This encoding choice exploits how viewers interpret bar length relative to a x-axis origin of zero, thereby
misleading
perception.
</p>
<p class="writeup">
Second, the dataset itself is selected to reinforce a specific narrative. By limiting the scope to “G7
+ China,” the visualization excludes several high emitting countries such as Australia, Korea, and Saudi Arabia,
which
would challenge the implied ranking of the chart. This cherry picking makes the data seem complete, leading
viewers to
think they're seeing the most important global contributors when key countries are actually left out.
Additionally,
China's data is labeled as 2022 even though it's from 2021, creating a small but important error that reduces
accuracy
while still appearing precise.
</p>
<p class="writeup">
Third, the use of misleading annotations and labeling reinforces a biased interpretation. The annotation “↑
Still rising” on the United States bar suggests an increasing trend, despite the data showing a decline
since 2014. This fabricated narrative leverages viewers' trust in text annotations, causing them to accept
the message without questioning the actual data. Combined with a morally suggestive title, the visualization
guides the audience toward a predetermined conclusion. Overall, these techniques demonstrate how small design
decisions like axis scaling, data selection, and labeling can distort reality and shape perception in
a compelling but deceptive way.
</p>
<p class="footnote">
Source: OECD Greenhouse Gas Emissions Inventories Dataset. Total GHG, CO₂-equivalent
(kilograms per person), 2022 reporting period. China emissions reflect most recent available
OECD reporting period. OECD member-economy average: 10.70 kg CO₂eq/cap.
</p>
</main>
<h2 class = "development-heading"><br>Development Process and Data Source</h2>
<p class = "development-writeup">
Gavin created the outline and completed the black-hat visualization. Colin created the white-hat visualization.
James wrote the writeup. In total, the project took approximately 8 to 10 person hours to complete. The most time-consuming aspect was
designing both visualizations in a way that showcased the difference between deceptive and non-deceptive visualization techniques.
<br><br>Data Source: <a href = "https://data-explorer.oecd.org/vis?df[ds]=DisseminateFinalDMZ&df[id]=DSD_AIR_GHG%40DF_AIR_GHG&df[ag]=OECD.ENV.EPI&dq=.A.GHG._T.KG_CO2E_PS&pd=2014%2C&to[TIME_PERIOD]=false" target="_blank">OECD</a>
Greenhouse Gas Emissions since 1990.
</p>
<script src="https://cdnjs.cloudflare.com/ajax/libs/d3/7.8.5/d3.min.js"></script>
<script src="script.js"></script>
</body>
</html>