Exploring the impact of decision rule heterogeneity on public preferences for air quality improvement policies

Exploring public preferences and willingness to pay (WTP) for air quality policies via choice experiments (CE) has conventionally relied on the random utility maximization (RUM) framework, which overlooks the significant influence of decision rules on individual choice behaviour. While recent random regret minimization (RRM) models have challenged this assumption, existing environmental applications remain scarce and, critically, have not yet accommodated the possibility that decision rules may vary across different attributes within the same choice task—a significant gap, as individuals may weigh health outcomes differently from cost attributes. To address this, we introduce a generalized RRM (GRRM) model incorporating attribute-specific regret-weights, enabling heterogeneous decision rules at the attribute level. The results indicated that the GRRM model outperformed both the RUM model and the conventional RRM model in term of fitting. Meanwhile, based on the regret-weight, respondents showed a greater propensity to weigh attributes ‘clean air days’, ‘haze days’, and ‘mortality’ according to the regret rule, while tending to weigh the attributes ‘policy delay’ and ‘payment’ according to the utility rule, demonstrating heterogeneity in the decision-rule for these attributes. The study further revealed correlations between preference heterogeneity and socio-economic characteristics such as age and gender, demonstrating that decision-rules based solely on a single attribute not only compromise model fit capability but also yield inaccurate estimates of public WTP. By introducing attribute-level regret-weighted GRRM, this study fills a critical gap in the air quality valuation literature and provides more precise, behaviourally realistic insights for policy design.