Resource heterogeneity leads to unjust effort distribution in climate change mitigation

被引:22
|
作者
Vicens, Julian [1 ,2 ,3 ]
Bueno-Guerra, Nereida [4 ]
Gutierrez-Roig, Mario [2 ,5 ]
Gracia-Lazaro, Carlos [6 ,7 ]
Gomez-Gardenes, Jesus [6 ,8 ]
Perello, Josep [2 ,3 ]
Sanchez, Angel [6 ,7 ,9 ,10 ]
Moreno, Yamir [6 ,7 ,11 ,12 ]
Duch, Jordi [1 ,13 ]
机构
[1] Univ Rovira & Virgili, Dept Engn Informat & Matemat, Tarragona, Spain
[2] Univ Barcelona, Dept Fis Mat Condensada, Barcelona, Spain
[3] Univ Barcelona, Inst Complex Syst UBICS, Barcelona, Spain
[4] Comillas Pontifical Univ, Dept Psychol, Madrid, Spain
[5] Univ Warwick, Warwick Business Sch, Behav Sci Grp, Coventry, W Midlands, England
[6] Univ Zaragoza, Inst Biocomputat & Phys Complex Syst BIFI, Zaragoza, Spain
[7] UMICCS, UV UZ UC3M, Leganes, Spain
[8] Univ Zaragoza, Dept Condensed Matter Phys, Zaragoza, Spain
[9] Univ Carlos III Madrid, Unidad Matemat Modelizac & Ciencia Computac, GISC, Leganes, Spain
[10] Univ Carlos III Madrid, Inst BS Financial Big Data UC3M, Getafe, Spain
[11] Univ Zaragoza, Dept Theoret Phys, Zaragoza, Spain
[12] ISI Fdn, Turin, Italy
[13] Northwestern Univ, Northwestern Inst Complex Syst, Evanston, IL 60208 USA
来源
PLOS ONE | 2018年 / 13卷 / 10期
基金
欧盟地平线“2020”;
关键词
RISK; JUSTICE; IMPACT; POOR; RICH;
D O I
10.1371/journal.pone.0204369
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:17
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