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

Kyoi, Shinsuke; Mori, Koichiro

doi:10.1016/j.wds.2023.100118

Cited by 2 publications

(1 citation statement)

References 62 publications

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“…Meanwhile, the content of online environmental community posts on social media is a valuable source of information about the characteristics and activities of online communities [13,14]. Social networks have a significant impact on changing social practices [15,16]. Internet communities perform the functions of recruitment, attribution, aggregation of interests, and mobilization of resources [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

GreenRu: A Russian Dataset for Detecting Mentions of Green Practices in Social Media Posts

Zakharova,

Glazkova

2024

Applied Sciences

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Green practices are social practices that aim to harmonize the relations between people and the natural environment. They may involve minimizing the use of resources and the generation of waste and emissions. Detecting green practices in social media posts helps to understand which green practices are currently common and to develop recommendations on the scaling of green practices to reduce environmental problems. This paper describes GreenRu, a novel Russian social media dataset for detecting the mentions of green practices related to waste management. It has a sentence-level markup and consists of 1326 posts collected in Russian online communities. The total number of mentions of green waste practices is 3765. The paper assessed the effectiveness of the multi-label and one-versus-rest BERT-based models for detecting the mentions of green practices in social media posts and compared several data augmentation methods in terms of both classification metrics and human evaluation. To augment the dataset, a backtranslation method and generative language models, such as RuGPT, RuT5, and ChatGPT, were used in this study. The results enable researchers to monitor the green waste practices on social networks and develop environmental policies. Additionally, GreenRu can support machine learning models to analyze social media content, assess the prevalence and effectiveness of green waste practices, and identify ways to expand them.

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Section: Introductionmentioning

confidence: 99%

GreenRu: A Russian Dataset for Detecting Mentions of Green Practices in Social Media Posts

Zakharova,

Glazkova

2024

Applied Sciences

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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

Kyoi,

Uchiyama,

Mori

2024

Sustainable Development

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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.

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

Cited by 2 publications

References 62 publications

GreenRu: A Russian Dataset for Detecting Mentions of Green Practices in Social Media Posts

GreenRu: A Russian Dataset for Detecting Mentions of Green Practices in Social Media Posts

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

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