Simulation of policies to promote pro‐environmental behavior in a dynamic learning model of social networks: Cases of Brazil, the United States, Japan, Germany, China, and India

Abstract Pro‐environmental behavior does not seem to diffuse sufficiently in Brazil, China, Germany, Japan, India, or the United States. Based on the current distribution of carbon footprints and learning patterns (DeGroot learning, best practice, and free riding) in social networks in the six countries, what will happen to the diffusion of pro‐environmental behaviors in each country? What policy measures will be most effective? This study aims to simulate the diffusion of pro‐environmental behavior and to test the effectiveness of various policy measures in a dynamic social network model in the six countries. The distribution of pro‐environmental behaviors is generated by global gridded carbon footprint data. The distribution of learning patterns in social networks is estimated in each country, using data from the World Values Survey. This study considers three policy measures: increasing the number of people pursuing best pro‐environmental behavior practices (best practice promotion, [BPP]), decreasing the number of free riders (free‐riding reduction, [FRR]), and converting the learning patterns of people with high degrees of centrality into best practice (centrality‐based best practice promotion, [CBPP]). Interesting findings were threefold. First, there are large differences in the estimated degrees of individual pro‐environmental behavior and the proportions of learning patterns in the six countries. Second, FRR and BPP are effective in promoting pro‐environmental behavior, but the choice of FRR or BPP depends on the initial proportion of free riders. Third, CBPP is the most effective policy of the three in all countries. Centrality in social networks must be considered when implementing intervention.