![]() Countries begin in the bottom-left of the chart (at low CO 2 and low GDP), then move upwards and to the right (higher CO 2 and higher GDP).Įmissions tend to rise as we get richer because we gain access to, and increase our consumption of, electricity, heating, transport and other goods that require energy inputs. If you use the blue time slider on the chart you can see this trend over time for a specific country. ![]() This is true both geographically (across countries in a single year, as shown), but also over time. Overall we see a very strong correlation between CO 2 emissions and income. This chart shows the relationship between per capita CO 2 emissions and GDP per capita. One of the four factors behind the Kaya Identity equation is affluence: GDP, or the average income per person. If improvements in energy or carbon intensity were slow (or in some cases non-existent), then CO 2 emissions grew rapidly. What differentiates whether CO 2 emissions have greatly increased, stabilized, or fallen was whether countries could reduce their energy and carbon intensity fast enough to offset this large increase in GDP (and increases in population). This has been a major driver of emissions – a stronger driver than the increase in population. What’s common between countries is that most have seen a large increase in GDP over this period. ![]() In the box below we explain how to interpret this chart. Also shown is total annual CO 2 emissions – the final result. This interactive chart shows the relative change in the four factors: population, GDP per capita, energy intensity (energy per unit of GDP), and carbon intensity (CO 2 per unit of energy) over time. With data we can look more closely at how the four factors in the Kaya Identity are driving emissions across different countries, and over time.
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