Global Level of Analysis

The 1990s saw the very rapid development of approaches variously called "integrated assessment modeling," GCMs (global circulation models), and other approaches at a biosphere level of analysis. Some of these have even managed to focus on the human impacts on the earth system (cf. Weyant et al. 1996). GCMs in particular were developed first, and lacked much of a human dimension at all. They were largely concerned with climate and atmospheric processes, using a very aggregated scale of analysis that made even large scale units, such as national boundaries, not always relevant to understanding differences, say, in rates of energy consumption. However, a new generation of models has emerged in the last few years that began to have relevance for environmental social scientists. These are a vast improvement from the pioneering work of the Club of Rome models that appeared in the early 1970s in Limits to Growth (Meadows et al. 1972). Despite the many problems with this early-generation effort, it did introduce important concepts like "feedback," "overshoot," and "resource limits" to everyday discourse and scientific debate. The next brave attempt came from the International Institute of Applied Systems Analysis (IIASA) in Austria, with its Finite World model examining global energy flows (Hafele 1980). Criticism of this attempt was very broad in the scientific community and not much happened until the first Intergovernmental Panel on Climate Change (IPCC) published its first assessment in 1990.

New generation models benefited from the progress made by GCMs, from growing evidence of the global nature of environmental problems, and the democratization of computer technology through its wide availability (Alcamo, Leemans, and Kreilerman 1998:262). The next step was clear: both social and physical aspects of the world system had to be coupled in so called "integrated assessment models." Most global modeling groups today acknowledge that to make progress on the accuracy and predictability of modeling efforts at this scale, there must be a simultaneous effort to link them to regionally scaled models that can improve the quality of the spatial resolution and the role of human drivers in global change.

One of the more sophisticated models to date is known as IMAGE 2 (Alcamo, Leemans, and Kreilerman 1998). IMAGE 2 was the first global integrated model with geographic resolution - an important feature that permits improved representation of global dynamic processes including feedbacks, and rapid and efficient ease of testing against new data. It is composed of three fully linked systems of models: the energy-industry system, the terrestrial environment system, and the atmosphere-ocean system. The energy-industry model computes emissions of greenhouse gases and other important emissions in 13 world regions. The terrestrial environmental model simulates changes in global land cover on a grid-scale based on climatic and economic factors. The atmosphere-ocean model computes the build up of greenhouse gases and aerosols and the resulting impact on average temperatures and precipitation. Factors such as population change, economic change, and technical change are particularly important in the terrestrial model - and the ones most in need of good quality regional data to inform the grid-based model. To date, few environmental social scientists have engaged this community of global modelers' efforts, forcing the modelers to make estimates based on very coarse national scale statistics rather than derived from more refined regional studies. This is an important new direction for environmental social scientists and ecologists, given the importance of global simulation models on policies such as carbon trading, setting emission ceilings for carbon dioxide by the beginning of the twenty-first century, and debt-for-nature swaps. The main participants in these exercises have been economists who have relied on the use of optimizing utility functions, rather than the less than optimal, more realistic satisficing behavior of human populations whether in the First or Third World.

The relevance of regionally informed approaches to global models becomes clearly evident when we begin to design a classification system of vegetation types and of land-use classes as a first step toward a good classification of land use/cover. Readers will want to check the recent journal Global Change Biology which was created to promote understanding of the interface between all aspects of current environmental change and biological systems, including rising tropospheric O3 and CO2 concentrations, climate change, loss of biodiversity, and eutrophication. Advances can be achieved through association of bibliography and databases of the study area, analysis of satellite images, fieldwork observation, and ethnoecological interviews with local inhabitants. Different levels of organization are required to define a vegetation cover of a region. In general, levels are organized to fit a specific scale of analysis into the phytogeographical arrangement and into the land-use types present in the area. In other words, one starts with a more aggregated level of major dominant classes (first) adequate to a regional scale and proceeds with increased detail at the next sublevel (second) to inform more detailed scales. For instance, the first level may include major vegetation covers such as forest, secondary succession, and savanna. In the second, more detailed level, forest is subdivided into open forest and closed forest, secondary succession into old secondary succession and young secondary succession, and savanna into grassland savanna and woodland savanna. At the third level of this classification system, still more detailed information needs to be included to account for the variability of vegetation required at this local scale. So, a new subdivision of the forest class may include a third structural variation of the former two and/or a floristic variation of them, such as a forest with a dominant tree species. The importance of developing a detailed classification key is crucial to inform the land-use/land-cover analysis at the landscape level, as well as the sampling distribution at the site-specific level.

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