  Fighting against climate change is a global challenge shared by nations with
heterogeneous economical resources and individuals with diverse propensity for
cooperation. However, we lack a clear understanding of the role of key factors
such as inequality of means when diverse agents interact together towards a
common goal. Here, we report the results of a collective-risk dilemma
experiment in which groups of subjects were initially given either equal or
unequal endowments. We found that although the collective goal was always
achieved regardless of the initial capital distribution, the effort
distribution was highly inequitable. Specifically, participants with fewer
resources contributed significantly more to the public goods than the richer
-sometimes twice as much. An unsupervised learning algorithm clustered the
subjects according to their individual behavior. We found that the poorest
participants congregated within the two "generous clusters" whereas the richest
were mostly classified into a "greedy cluster". Our findings suggest that
future policies would benefit both from reinforcing climate justice actions
addressed to most vulnerable people and educating fairness instead of focusing
on understanding of generic or global climate consequences, as the latter has
not proven to drive equitable contributions.
