The CREM Economic Models

The purpose of integrating economic models into mechanistic biophysical models is to base human action and their influences on biogeochemical cycles (e.g., from land use, nutrient loading, and irrigation) on microeconomic foundations rather than on arbitrary rules (Harou, et al., 2009). A two-pronged approach is taken to model economic behavior by utilizing 1) a computable general equilibrium (CGE) model of the regional economy and 2) an agent based model (ABM). While both work from a foundation of economically-optimizing decision-makers, they differ significantly in terms of temporal/spatial disaggregation and model structure. The CGE and ABM models are run separately but will be integrated on annual time-steps. Multiple groups have applied CGE modeling of regional economies to represent agricultural production (e.g., Röhm and Dabbert, 2003; Howitt, 2005; Heckelei and Britz, 2005; Medellin-Azuara, et al., 2009; Tanaka, et al., 2006). For a review see Harou et al. (2009). CGE modeling is suited for capturing the complex array of possible economic responses to changing conditions, including input substitution, output substitution, and investment. Climate and surface hydrology models inform water availability, and crop growth models are used to parameterize crop production functions; e.g., for BioEarth VIC-CropSyst is used to generate "crop response curves" to varying levels of deficit irrigation under a range of climate and atmospheric CO2 conditions. Input use determined by the CGE model defines the timing of water use and other inputs, including fertilizers that can be fed back into other models. It is also straight forward to define sub-regions that are differentiated by their parameterization and constraints, which is usually important for representing production in irrigated agriculture where water is a limiting resource.

In many instances the lack of spatial resolution in CGE models becomes problematic. While the development of sub-regions within the regional CGE model can account for spatial heterogeneity in growing conditions and resource constraints, they usually involve a significant level of spatial aggregation and are ill-suited towards modeling spatially-dependent decision-making. This is an important limitation because of the extensive spatial interactions that result from moving water around in time and space as a result of farm-level adaptation, water transfers, or any other changes in water management. This weakness provides the motivation for the development of an ABM to represent economic decision-making.

ABM allows for improved modeling of nutrient use and water quality impacts by more precisely locating each agent in space relative to water systems. Another advantage of the ABM approach is that it is possible to separate the region of study into grid-cells that match those of the biophysical models. This allows for full coupling between human and environmental system models. Adoption of approaches like ABM among economists remains small relative to more accepted economic modeling frameworks because they typically require a more simplistic representation of economic decision-making. However, they are becoming more common as recognition of the importance of spatial interactions has grown (Irwin 2010). Increased funding for interdisciplinary research and computing power are also encouraging increased use of ABM. Literature in computational finance (LeBaron 2000) and in analyzing collective action problems (Berger et al. 2007) also demonstrates increased value of the ABM approach.

References:

• Berger, T., Birner, R., Díaz, J., McCarthy, N., Wittmer, H., 2007. Capturing the complexity of water uses and water users within a multi-agent framework, in: Craswell, E., Bonnell, M., Bossio, D., Demuth, S., Giesen, N.V.D. (Eds.), Integrated Assessment of Water Resources and Global Change. Springer Netherlands, pp. 129–148.
• Harou, J.J., Pulido-Velazquez, M., Rosenberg, D.E., Medellín-Azuara, J., Lund, J.R., Howitt, R.E., 2009. Hydro-economic models: Concepts, design, applications, and future prospects. J. Hydrol. 375, 627–643.
• Heckelei, T. Britz, W, 2005. Model based on Positive Mathematical Programming: state of the art and further extensions. Proceedings of the 89th European Seminar of the European Association of Agricultural Economists, Parma.
• Howitt, R.E., 2005. PMP Based Production Models- Developement and Integration. PMP Extensions Altern. Methods Eur. Assoc. Agric. Econ. Cph. Den.
• LeBaron, B., n.d. Agent-Based Computational Finance: Suggested Readings and Early Research (SSRN Scholarly Paper No. ID 224048). Social Science Research Network, Rochester, NY.
• Medellín-Azuara, J., Mendoza-Espinosa, L.G., Lund, J.R., Harou, J.J., Howitt, R.E., 2009. Virtues of simple hydro-economic optimization: Baja California, Mexico. J. Environ. Manage. 90, 3470–3478.
• Röhm, O., Dabbert, S., 2003. Integrating Agri-Environmental Programs into Regional Production Models: An Extension of Positive Mathematical Programming. Am. J. Agric. Econ. 85, 254–265.
• Tanaka, S.K., Zhu, T., Lund, J.R., Howitt, R.E., Jenkins, M.W., Pulido, M.A., Tauber, M., Ritzema, R.S., Ferreira, I.C., 2006. Climate Warming and Water Management Adaptation for California. Clim. Change 76, 361–387.