What Drives Marginal Abatement Costs of Greenhouse Gases on Dairy Farms? A Meta‐modelling Approach
通过元建模方法，研究奶牛场温室气体边际减排成本与牛群规模、产奶量、劳动力等因素的关系，发现简单指标导致更高成本，且减排超过1-5%后成本急剧上升。
Abstract This paper examines the relationships between the marginal abatement costs ( MAC ) of greenhouse gas ( GHG ) emissions on dairy farms and factors such as herd size, milk yield and available farm labour, on the one hand, and prices, GHG indicators and GHG reduction levels, on the other. A two‐stage Heckman procedure is used to estimate these relationships from a systematically designed set of simulations with a highly detailed mixed integer bio‐economic farm‐level model. The resulting meta‐models are then used to analyse how MAC vary across farm‐level conditions and GHG measures. We find that simpler GHG indicators lead to significantly higher MAC , and that MAC strongly increase beyond a 1–5% emission reduction, depending on farm attributes and the chosen indicator. MAC decrease rapidly with increasing farm size, but the effect levels off beyond a herd size of 40 cows. As expected, the main factors driving gross margins per dairy cow also significantly influence mitigation costs. Our results indicate high variability of MAC on real life farms. In contrast to time consuming simulations with the complex mixed integer bio‐economic programming model, the meta‐models allow the distribution of MAC in a farm population to be efficiently derived and thus could be used to upscale to regional or sector level.