Estimating regional agricultural supply of greenhouse gas abatements by land-based biological carbon sequestration: a Bayesian sampling-based simulation approach/ created by Yong Jiang and Won W. Koo

In this study, we develop a sampling-based simulation approach for estimating regional agricultural supply of greenhouse gas (GHG) emission abatements by land-based biological carbon sequestration. We explicitly consider producer behaviour in a market setting that would pay for carbon sequestration depending on current land use and management, target practice to be adopted and spatial location. We construct a behaviour model in the benefit-cost framework to characterise producer decision in relation to preferences and production attributes. We combine the Markov Chain Monte Carlo technique and choice modelling in a Bayesian setting to develop an empirical procedure that may be calibrated by observed producer behaviour and agricultural census data and that can simulate regional agricultural carbon sequestration by sampling individual preferences and production attributes. An empirical application of our approach depicts potential agricultural supply of GHG abatements by carbon sequestration in a production region in the USA. This approach is flexible to be applied to different regions with minimum information requirement while accounting for spatial heterogeneity of both preferences and production.