Resource heterogeneity leads to unjust effort distribution in climate change mitigation

Climate change mitigation is a shared global challenge that involves collective action of a set of individuals with different tendencies to cooperation. However, we lack an understanding of the effect of resource inequality when diverse actors interact together towards a common goal. Here, we report...

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Bibliographic Details
Published in:PloS one 2018-10, Vol.13 (10), p.e0204369
Main Authors: Vicens, Julian, Bueno-Guerra, Nereida, Gutiérrez-Roig, Mario, Gracia-Lázaro, Carlos, Gómez-Gardeñes, Jesús, Perelló, Josep, Sánchez, Angel, Moreno, Yamir, Duch, Jordi
Format: Article
Language:English
Subjects:
Adolescent
Adult
Aged
Awareness
Biology and Life Sciences
Canvis climàtics
Child
Citizens
Climate Change
Climate change mitigation
Climate justice
Climatic changes
Collective action
Conservation of Natural Resources - methods
Cooperation
Cooperative Behavior
Decision making
Earth Sciences
Ecology and Environmental Sciences
Environmental justice
Environmental policy
Female
Game theory
Games, Experimental
Gene expression
Global climate
Heterogeneity
Humans
Inequality
International cooperation
Machine learning
Male
Middle Aged
Mitigation
Physical Sciences
Physics
Presa de decisions
Public goods
Risk
Social Justice
Social Sciences
Socioeconomic factors
Teoria de jocs
Unsupervised Machine Learning
Young Adult
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Summary:Climate change mitigation is a shared global challenge that involves collective action of a set of individuals with different tendencies to cooperation. However, we lack an understanding of the effect of resource inequality when diverse actors interact together towards a common goal. Here, we report the results of a collective-risk dilemma experiment in which groups of individuals were initially given either equal or unequal endowments. We found that the effort distribution was highly inequitable, with participants with fewer resources contributing significantly more to the public goods than the richer -sometimes twice as much. An unsupervised learning algorithm classified the subjects according to their individual behavior, finding the poorest participants within two "generous clusters" and the richest into a "greedy cluster". Our results suggest that policies would benefit from educating about fairness and reinforcing climate justice actions addressed to vulnerable people instead of focusing on understanding generic or global climate consequences.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0204369