An agent‐based modelling approach to evaluate factors influencing bioenergy crop adoption in north‐east Scotland

Abstract

An agent-based modelling (ABM) framework was adapted to assess bioenergy crop uptake and integrate social and economic processes with biophysical elements. Survey results indicated that economic rationalisation was intrinsic to farmers' decision-making, but was not the only consideration. This study presents an approach, set within an established resource management framework, to incorporate a number of key socio-economic factors, which we call Mitigation Willingness Factors (MWFs), using survey data collected from farmers and land managers, into the ABM. The MWFs represent farmers' willingness to compromise revenue in order to reduce GHG emissions, derived from their attitudes to climate change and the ability of different economic mechanisms to stimulate energy crop uptake. Adoption of bioenergy crops of different farmer types and farming enterprises was also assessed. Adoption rates and scenarios that take into account noneconomic factors are presented, and particular farming enterprises that may respond more positively to policy initiatives are identified.

Spatial agent‐based modeling for dedicated energy crop adoption and cellulosic biofuel commercialization

“…found that the estimated total dedicated energy crop area was much higher when farmers had a high initial willingness (25% initial innovators) to adopt energy crops than the basic case (2.5% initial innovators). Brown et al . incorporated mitigation willingness factors that represent the willingness of farmers to compromise revenue to reduce environmental impacts on an ABM to model bioenergy crop adoption.…”

“…Agent‐based modeling can identify the heterogeneity of agents in different spatial regions. Brown et al . integrated socio‐economic rationalization to indicate that the willingness of Scottish farmers to adopt dedicated energy crops is associated with their willingness to compromise their profits.…”

Behavioral models of climate change adaptation and mitigation in land‐based sectors

“…Active mitigation in a changing climate (either in terms of lessening climate change or lessening the impacts of climate change) is rarely treated, even where models focus on relevant processes such as reforestation or the adoption of bioenergy crops (e.g., Refs ). Instead, mitigation is represented as an economic process driven by taxes or subsidies designed to reduce greenhouse gas emissions 53,59,63,66,71,74,79,80 . In some cases, economically‐driven mitigation is balanced against climatically driven adaptation 68,74,79 .…”

“…Though less common, a number of models consider competition between agriculture and other land uses such as forestry 53,68,74,75 or urban development 85 . Most agricultural models allow decision‐making on the basis of direct climatic impacts, but several (especially cross‐sectoral models) simulate responses to taxes or subsidies designed to encourage adaptation or mitigation 49,59,66,67,71,74,79,80 . These decisions tend not to be subject to social pressures, except in studies of communal or marginal agriculture (e.g., Ref ) or of water resource management 36,54,60,84 …”

“…This is regularly acknowledged in modeling papers, but is also used to excuse a subsequent neglect of institutional actors and effects. In some cases, it is explicitly argued that scenarios of institutional interventions are more transparent and appropriate than attempts to model institutional behavior (e.g., Ref ), and this certainly seems a logical minimum requirement for models intended to evaluate policy options 49,53,59,63,66–68,79,99 …”