Development of policy measures for diffusing human pro-environmental behavior in social networks—Computer simulation of a dynamic model of mutual learning

Abstract

Pro-environmental behavior does not diffuse sufficiently in society. Is there a way to enhance the degree of people's pro-environmental behavior? This study aims to develop a dynamic model of mutual learning in social networks to simulate the diffusion of pro-environmental behavior and to search for promising policies for promoting it. This study considers two policy measures: enhancing pro-environmental behavior of target people and changing the learning patterns of target people. The people targeted for intervention are determined by random selection, selection in descending order of degree centrality, and selection in descending order of eigenvector centrality. Centralities measure an influence of a node on other nodes through a network, based on the number of direct or indirect links. An interesting finding is that changing individual learning patterns is much more effective for enhancing the degree of pro-environmental behavior in social networks than trying to directly enhance its degree. In addition, selection of target people based on the centralities is more influential in encouraging environmentally friendly behavior than random selection, particularly in the policy of changing learning patterns. Multiplier effects are also measured: the ratio of the net increase in the number of people who enhance their degree of pro-environmental behavior at the end of a certain number of time steps beyond business as usual to the number of people intervened. Multiplier effects are always positive when learning patterns are changed. Six potential approaches to changing learning patterns are discussed: persuasion, reputation, competition, awareness of economic returns, information provisioning, and education.

Keywords

• Social network
• Mutual learning
• Multiplier effect
• Degree centrality
• Eigenvector centrality
• Barabási–Albert model