2.1 IIASA modeling framework

1 2007 Open image in new window RCP8.5 was developed using the IIASA Integrated Assessment Modeling Framework that encompasses detailed representations of the principal GHG-emitting sectors—energy, industry, agriculture, and forestry. The framework combines a careful blend of rich disciplinary models that operate at different spatial resolutions that are interlinked and integrated into an overall assessment framework (Fig.). Integration is achieved through a series of hard and soft linkages between the individual components, to ensure internal scenario consistency and plausibility (Riahi et al.).

The three principal models of the IA framework (Fig. 1) are MESSAGE–MACRO (Messner and Strubegger 1995; Rao and Riahi 2006), DIMA (Rokityanskiy et al. 2007) and AEZ–WFS (Fischer et al. 2007) (see below for further details). The three models are driven by a set of harmonized inputs at the regional, national, and grid (0.5 × 0.5°) level. For this purpose, the regional population and GDP scenarios of the A2r scenario (see Section 3.1) are disaggregated to the level of countries through a combination of decomposition and optimization methods. In a subsequent second step, national results are further disaggregated to the grid-cell level, which provides spatially explicit patterns of population and economic activities (Grubler et al. 2007). The latter indicators are particularly important for the spatially explicit modeling of emissions and land-cover changes in the forestry and agriculture sectors. They provide the basis for the estimation of comparable indicators (such as relative land prices or population exposures to pollutant emissions) that define e.g. the relative comparative advantages of agriculture- and forestry-based activities or the stringency of spatial pollutant emissions reductions.

The MESSAGE model (Model for Energy Supply Strategy Alternatives and their General Environmental Impact) stands at the heart of the integrated assessment framework. It is a systems-engineering optimization model used for medium-to long-term energy system planning, energy policy analysis and scenario development. The model maps the entire energy system with all its interdependencies from resource extraction, imports and exports, conversion, transport and distribution to end-use services. The model’s current version provides global and sub-regional information on the utilization of domestic resources, energy imports and exports and trade-related monetary flows, investment requirements, the types of production or conversion technologies selected (technology substitution), pollutant emissions, inter-fuel substitution processes, as well as temporal trajectories for primary, secondary, final, and useful energy. In addition to the energy system, the model includes a stylized representation of the forest and agricultural sector and related GHG emissions mitigation potentials. It is a long-term global model operating at the level of 11 world-regions and a time horizon of a century (1990–2100). For each scenario the model calculates the least cost solution for the energy system given a set of assumptions about main drivers such as energy demand, resources, technology performance and environmental constraints.2

The AEZ–WFS (Agro-Ecological Zoning—World Food System) model framework projects alternative development paths of the agriculture sector using three components: (i) a spatially detailed agronomic module assessing crop suitability and land productivity (AEZ); (ii) an applied general equilibrium model of the world food system (WFS); and (iii) a spatial downscaling model allocating the aggregate WFS production levels and agricultural land use to spatial biophysical resources. AEZ simulates land-resource availability, crop suitability, farm-level management options, and crop production potentials as a function of climate, technology, economic productivity, and other factors (for further details see Fischer et al. 2002, 2009; Fischer 2009). Land is broadly classified as built-up land, cultivated land, forests, grass/wood land areas, including managed and natural grassland areas, and sparsely vegetated and other land. WFS is an agro-economic model (Fischer et al. 2005, 2009) that estimates regional agricultural consumption, production, trade and land use. Applying the AEZ-WFS framework, use and conversion of land is determined for food and feed production to meet the global demand in accordance with agronomic requirements, availability of land resources, and consistent with national incomes and lifestyles of consumers. Land for residential use and transport infrastructure is assigned according to spatial population distribution and density. The remaining land, i.e. part of grass/wood land, forest areas and sparsely vegetated areas, is further evaluated in the DIMA model (see below) for possible use in dedicated bioenergy systems and for forestry purposes (for additional details see Tubiello and Fischer 2007). Agricultural residue supplies based on the agricultural land use are also available for energy use and picked up where cost-effective. The delineation of pasture and unmanaged grasslands is based on the projections of livestock numbers computed in the WFS model.

The DIMA model (Dynamic Integrated Model of Forestry and Alternative Land Use; Rokityanskiy et al. 2007) is used to quantify the economic potential of global forests, explicitly modeling the interactions and feedbacks between ecosystems and land use related activities. Regional demand trajectories for timber and prices for carbon and bioenergy are major drivers for the relevant estimates. Food security is maintained by introducing an exogenous scenario-specific minimum amount of agricultural and urban land per grid cell as projected by AEZ-WFS (and used as input by DIMA). The DIMA model is a spatial model operating on a 0.5 × 0.5° grid raster. It determines for each grid and time interval, which of the forestry processes (afforestation, reforestation, deforestation, or conservation and management options) would be applied in order to meet a specific regional timber demand and how much woody bioenergy and forest sink potential would be available for a given combination of carbon and bioenergy prices. Main determinants of the land-use choices in each grid are assumptions about the costs of forest production and harvesting, land-prices and productivity, age structure of standing forest, and age-specific plant growth. Forest dynamics are thus a result of interactions between demand-pull (price of bioenergy and carbon as well as timber demand), and inertia on the supply-side (imputed through growth limitation of the forest). A schematic illustration of the main linkages between the three principal models is shown in Fig. 1.

In the sequel of Section 2 we discuss the main methodological improvements for the RCP8.5. We particularly focus on those aspects most relevant for the development of spatial land-cover and emissions projections, which serve as inputs to the climate modeling community (see also Hurtt et al. 2011 and Lamarque et al. 2011 in this SI).