Investigating the determinants of Singaporean citizens' attitudes toward marine litter pollution control: A policy acceptance model

Abstract

The problem of marine litter has caused significant threat to marine environment and human health, and has attracted wide attention. It is estimated that the weight of plastic waste in the oceans will exceed that of fish by 2050. Since a large part of marine debris originate from land-based domestic waste, developing relevant policies to manage the disposal of domestic garbage can effectively prevent and control marine litter pollution. Public attitudes toward relevant environmental policies will affect their implementation and final outcomes. However, there is little research on public attitudes toward environmental policies. Therefore, this study draws on the framework of Technology Acceptance Model (TAM) to explore the factors that affect public attitudes toward policy, the affect theory, trust theory and habit are integrated into the model. An online survey for Singaporean residents was conducted, 450 questionnaires were collected and 417 of which were used for data analysis. The results suggest that 13 of the 14 hypotheses presented in the model are accepted. Perceived ease of implementation (β = 0.365), perceived policy effectiveness (β = 0.341) and trust in government policy (β = 0.319) are the main factors that directly affect citizens' attitude toward environmental policy. Perceived policy effectiveness is positively affected by the perceived ease of implementation (β = 0.457), while trust in government policies is positively influenced by both perceived ease of implementation (β = 0.142) and perceived policy effectiveness (β = 0.373). The model showed good explanatory power, explaining 74.6 % of the variance in public attitude toward policy. In this study, a relatively complete model for predicting public acceptance of marine litter prevention policies is proposed for the first time. The model presented in this paper also has the potential to be applied to evaluate policies of various scenarios.