To reduce agricultural greenhouse gas (GHG) emissions, farmers need to change current farming practices. However, farmers' climate change mitigation behaviour and particularly the role of social and individual characteristics remains poorly understood. Using an agent-based modelling approach, we investigate how knowledge exchange within farmers' social networks affects the adoption of mitigation measures and the effectiveness of a payment per ton of GHG emissions abated. Our simulations are based on census, survey and interview data for 49 Swiss dairy and cattle farms to simulate the effect of social networks on overall GHG reduction and marginal abatement costs. We find that considering social networks increases overall reduction of GHG emissions by 45% at a given payment of 120 Swiss Francs (CHF) per ton of reduced GHG emissions. The per ton payment would have to increase by 380 CHF (i.e., 500 CHF/tCO₂eq) to reach the same overall GHG reduction level without any social network effects. Moreover, marginal abatement costs for emissions are lower when farmers exchange relevant knowledge through social networks. The effectiveness of policy incentives aiming at agricultural climate change mitigation can hence be improved by simultaneously supporting knowledge exchange and opportunities of social learning in farming communities.

中文翻译：

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量化农民社交网络对农业气候变化减缓政策有效性的影响

为了减少农业温室气体 (GHG) 排放，农民需要改变当前的耕作方式。然而，农民的气候变化减缓行为，特别是社会和个人特征的作用仍然知之甚少。使用基于代理的建模方法，我们研究了农民社交网络内的知识交换如何影响缓解措施的采用以及每吨温室气体减排量支付的有效性。我们的模拟基于 49 个瑞士奶牛场和养牛场的人口普查、调查和访谈数据，以模拟社交网络对整体温室气体减排和边际减排成本的影响。我们发现，考虑到社交网络，如果每减少一吨温室气体排放量支付 120 瑞士法郎 (CHF)，则可以使温室气体排放总量减少 45%。每吨付款必须增加 380 瑞士法郎（即 500 瑞士法郎/tCO ₂当量）才能在没有任何社会网络效应的情况下达到相同的总体温室气体减排水平。此外，当农民通过社交网络交流相关知识时，边际减排成本会更低。因此，通过同时支持农业社区的知识交流和社会学习机会，可以提高旨在减缓农业气候变化的政策激励措施的有效性。