Farmland eco-compensation, as a typical payment for ecosystem services scheme, aims to address trade-offs between environmental and developmental objectives. As indispensable eco-compensation supporters, the public’s willingness to pay (WTP) for farmland eco-compensation and the allocation to farmers directly affect ecological safety and sustainable development for farmland. Therefore, this study links the public’s WTP for the farmland eco-compensation to the financial subsidies received by farmers and presents a theoretical framework and research approach that connects stakeholders, applying improved choice experiments for empirical study in the black soil region of northeastern China. The results showed that the public has a positive WTP for the farmland eco-compensation program that improves the area, soil thickness, and organic content expeditiously. The public’s WTP allocation for eco-compensation varies considerably, with the share allocated to farmers in their WTP averaging 46.96%, showing a benchmark for compensation standards. The results revealed the influential relationship between the socioeconomic characteristics of the public with WTP allocation and the preferences for farmland eco-compensation, such as the positive correlation between age with WTP allocation and females’ greater preference for eco-compensation. These findings can provide new perspectives and approaches to exploring sustainable pathways for farmland eco-compensation.

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.