Can Knowledge, Responsibility and Environmentalism Explain Preference Heterogeneity? A Latent-Class Probit Model Analysis for Plastic Pollution Abatement

This study explores the impact of prior experiences, environmental concern and awareness levels on pro-environmental behavior in the context of mitigation of plastic pollution. Using survey and geo-location data after a tsunami and a monsoon season, this survey employs a latent class analysis through a Generalized Structural Equation Model (GSEM) to identify similarities between groups' willingness to pay (WTP) to mitigate macroplastic pollution in riverbeds and beaches in Indonesia. Results show that more environmentally-conscious respondents were also more sensitive to the issue of pollution while having observed more plastic pollution also increases support for pollution mitigation. Proximity to polluted waterways also increased WTP, especially to urban participants. Overall, accounting for prior experiences, environmental concern and awareness levels does lead to statistical differences between classes, with those scoring higher in these categories being also more willing to monetarily contribute to mitigate that issue. The use of such an integrated latent class model (LCM) can help with disentangling drivers of preferences, especially in the context of determining levels of support for pollution abatement in a developing country.