Action‐ versus results‐based policy designs for agricultural climate change mitigation

Abstract

Reducing agricultural greenhouse gas (GHG) emissions is key to achieve overall climate policy goals. Effective and efficient policy instruments are needed to incentivize farmers' adoption of on‐farm climate change mitigation practices. We compare action‐ and results‐based policy designs for GHG reduction in agriculture and account for farmers' heterogeneous behavioral characteristics such as individual farming preferences, reluctance to change and social interactions. An agent‐based bio‐economic modeling approach is used to simulate total GHG reduction, overall governmental spending and farm‐level marginal abatement cost of Swiss dairy and beef cattle farms under both action‐ and results‐based policy designs. We find that total governmental spending associated with the compared policy designs depends on the cost and benefits of the considered measures as well as behavioral characteristics of farmers. More precisely, if farmers are reluctant to change, additional incentives are needed to increase adoption of a win‐win measure. In such a case, targeting the payment on the cost of that particular measure (action‐based design) instead of paying a uniform amount for abated emissions (results‐based design) can lower governmental spending for agricultural climate change mitigation. Farm‐level marginal cost of reducing GHG emissions are lower with results‐based payments independent of the cost of measures. Moreover, we find that farmers' individual preferences and reluctance to change substantially lower the adoption of mitigation measures and hence overall GHG reduction potential of farms.

A note on European farmers' preferences under cumulative prospect theory

“…Moreover, Kreft et al. (2023) show that accounting for farmers' preferences under cumulative prospect theory can imply differences in farmers' responses to climate change mitigation policies. The quantification of risk aversion, loss aversion and probability distortion provided in our paper allows numerical consideration in future policy analysis, such as in modelling (Huber et al., 2022).…”

Quantifying the impact of farmers' social networks on the effectiveness of climate change mitigation policies in agriculture

“…Third, whether a farmer will implement certain measures on the farm also depends on the underlying farm structures and processes, that is, farm size and type, that result in individual farm abatement costs. Finally, the adoption decision is also influenced by the policy measures, that is, the level of payment and how it changes the relation of costs and profits (Kreft, Finger et al, 2023) Although bio-economic modelling approaches can well represent farm specific abatement costs and the impact of a policy on the uptake of mitigation measures, the added value of our modelling framework is to combine the strength of farm-level modelling with behavioural factors and social network effects (see next section).…”

“…In another contribution, we assess the effect of different payment designs on the adoption of climate change mitigation measures (Kreft, Finger et al, 2023).…”

“…Third, whether a farmer will implement certain measures on the farm also depends on the underlying farm structures and processes, that is, farm size and type, that result in individual farm abatement costs. Finally, the adoption decision is also influenced by the policy measures, that is, the level of payment and how it changes the relation of costs and profits (Kreft, Finger et al, 2023)…”

“…Understanding farmers' decision‐making with respect to climate change mitigation is crucial for the design and implementation of such policy incentives. However, the role of behavioural factors in general, and social learning in particular, remains poorly understood in this context (Kreft, Angst et al, 2023; Kreft, Finger et al, 2023; Kreft, Huber, et al, 2021; Niles et al, 2016). Although bio‐economic modelling approaches are key tools used for the (ex‐ante) assessment of agricultural policies and their impact on actual GHG reduction potential as well as production and farm incomes (e.g., De Cara et al, 2005; Lengers et al, 2014), they usually lack integration of individual behavioural factors and social interactions.…”