Agent-based modeling of policy induced agri-environmental technology adoption

Ran Sun (), James Nolan and Suren(dra) Kulshreshtha
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Ran Sun: University of Saskatchewan

SN Business & Economics, 2022, vol. 2, issue 8, 1-26

Abstract: Abstract This paper seeks to calibrate the dynamic policy-induced adoption/diffusion of an agri-environmental beneficial technology. The paper develops an agent-based model to integrate the adoption problem into a complex farmer adoption decision-making system involving different components (e.g., GIS environment, agents, network, production and adoption, policy). Based on the model, a case on cost-effectiveness evaluation of a hypothetical agricultural extension (AE) program is exemplified in this study to explain how this model can support the agri-environmental policy design. As a result, farmers’ adoption decision-making under the influence of the AE program can be brought forward to an average ten-year ahead with a higher upper boundary of ultimate adoption rate than no policy scenario. Furthermore, this study presents simulated policy evaluation from different participation rates of the AE program to compare policy effects and thus assess their cost-effectiveness. The comparison results imply that a higher participation rate does not positively increase the performance of the AE program. Our ex-ante agent-based modeling (ABM) simulation method can be applied in agri-environmental policy design, evaluation, and long-term policy monitor. In addition, the model provides a flexible quantitative tool to predict farmers’ policy-induced adoption decision-making and outcomes in a future period. We also introduce potential improvements to extend the inherent farmers’ adoption behavior algorithm, computing capability, and model validation for future research.

Keywords: ABM; Beneficial water management practices; Technology adoption; Agri-environmental policy (search for similar items in EconPapers)
JEL-codes: C63 Q52 Q58 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s43546-022-00275-6

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