Abstract

Mitigating climate change effects involves strategic decisions by individuals that may choose to limit their emissions at a cost. Everyone shares the ensuing benefits and thereby individuals can free ride on the effort of others, which may lead to the tragedy of the commons. For this reason, climate action can be conveniently formulated in terms of Public Goods Dilemmas often assuming that a minimum collective effort is required to ensure any benefit, and that decision-making may be contingent on the risk associated with future losses. Here we investigate the impact of reward and punishment in this type of collective endeavors — coined as collective-risk dilemmas — by means of a dynamic, evolutionary approach. We show that rewards (positive incentives) are essential to initiate cooperation, mostly when the perception of risk is low. On the other hand, we find that sanctions (negative incentives) are instrumental to maintain cooperation. Altogether, our results are gratifying, given the a-priori limitations of effectively implementing sanctions in international agreements. Finally, we show that whenever collective action is most challenging to succeed, the best results are obtained when both rewards and sanctions are synergistically combined into a single policy.

Details

Title
Reward and punishment in climate change dilemmas
Author
Góis, António R 1 ; Santos, Fernando P 2 ; Pacheco, Jorge M 3 ; Santos, Francisco C 4   VIAFID ORCID Logo 

 INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, IST-Taguspark, Porto, Salvo, Portugal; ATP-group, Porto, Salvo, Portugal; Unbabel, R. Visc. de Santarém 67B, Lisboa, Portugal 
 Department of Ecology and Evolutionary Biology, Princeton University, Princeton, USA; INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, IST-Taguspark, Porto, Salvo, Portugal; ATP-group, Porto, Salvo, Portugal 
 Centro de Biologia Molecular e Ambiental, Universidade do Minho, Braga, Portugal; Departamento de Matemática e Aplicações, Universidade do Minho, Braga, Portugal; ATP-group, Porto, Salvo, Portugal 
 INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, IST-Taguspark, Porto, Salvo, Portugal; ATP-group, Porto, Salvo, Portugal; Machine Learning Group, Université Libre de Bruxelles, Boulevard du Triomphe CP212, Bruxelles, Belgium 
Pages
1-9
Publication year
2019
Publication date
Nov 2019
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2312798457
Copyright
© 2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.