We're sorry but this page doesn't work properly without JavaScript enabled. Please enable it to continue.
Feedback

Transitions in pathways of human development and carbon emissions

Formal Metadata

Title
Transitions in pathways of human development and carbon emissions
Title of Series
Number of Parts
16
Author
License
CC Attribution 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
Identifiers
Publisher
Release Date
Language

Content Metadata

Subject Area
Genre
Abstract
Countries are known to follow diverse pathways of life expectancy and carbon emissions, but little is known about factors driving these dynamics. In this letter we estimate the cross-sectional economic, demographic and geographic drivers of consumption-based carbon emissions. Using clustering techniques, countries are grouped according to their drivers, and analysed with respect to a criteria of one tonne of carbon emissions per capita and a life expectancy over 70 years (Goldemberg's Corner). Five clusters of countries are identified with distinct drivers and highly differentiated outcomes of life expectancy and carbon emissions. Representatives from four clusters intersect within Goldemberg's Corner, suggesting diverse combinations of drivers may still lead to sustainable outcomes, presenting many countries with an opportunity to follow a pathway towards low-carbon human development. By contrast, within Goldemberg's Corner, there are no countries from the core, wealthy consuming nations. These results reaffirm the need to address economic inequalities within international agreements for climate mitigation, but acknowledge plausible and accessible examples of low-carbon human development for countries that share similar underlying drivers of carbon emissions. In addition, we note differences in drivers between models of territorial and consumption-based carbon emissions, and discuss interesting exceptions to the drivers-based cluster analysis.