By Jim Shortle, David Abler, and Ann Fisher, Pennsylvania State University

Climate impact assessments usually begin with climate change scenarios that describe future climate conditions given assumptions about future emissions of greenhouse gases. Climate scenarios are, however, only one type of scenario needed to assess the impacts of human induced climate change. Because of the profound effects that humans have on the environment from local to global scales, socioeconomic scenarios are essential to understanding how climate change will affect not only people, but also other assessment endpoints such as agriculture, water, coasts and forests. Developing socioeconomic scenarios for climate assessment is challenging. Here we discuss development and use of socioeconomic scenarios for the Mid-Atlantic Regional Assessment.

We have distinguished two basic types of socioeconomic scenarios: socioeconomic baseline scenarios and socioeconomic response scenarios. Impact analysis involves comparing conditions (e.g., climate, economy, and population) "with" the stimulus that induces change to conditions "without" the stimulus. For example, in defining the impacts of greenhouse gas emissions on climate, the climate baseline is the naturally evolving climate. The usually slow rate of natural climate change is of no consequence for human society in the foreseeable future (30 to 100 years), so current climate can serve as the baseline future climate.

Similarly, socioeconomic baseline scenarios describe future socioeconomic conditions as they would be without human-induced climate change, and provide the "without" condition for defining the impacts of the climate change stimulus. If socioeconomic systems were like the global climate, we would need only to project current conditions. However, this is not case -- the economy and society are likely to change significantly with or without climate change.

For example, mining, forestry, agriculture, and manufacturing were the largest components of the Mid-Atlantic region's economy at the turn of the century, but today they are much diminished in importance. Similarly, the economy and society of the region will undoubtedly be substantially different in the future than today in terms of their structure, producer and consumer technologies, the range of available goods and services, and public and private institutions. This in turn means that the region may be significantly different in terms of its sensitivity to climate change and its potential for response and adaptation.

Not only must we expect change, but we are also very uncertain of the socioeconomic future even without climate change. Economic and technological forecasting accuracy diminishes rapidly with forecast length. Point forecasts of socioeconomic conditions for the year 2030, to say nothing of the year 2100, would be far more likely to be wrong and misleading than to be useful. In this respect, economic modeling is well behind climate modeling -- though the challenges involved in long-term economic modeling are arguably much greater than those involved in long-term climate modeling. This inability to forecast is more acute at a regional level because many socioeconomic processes and interrelationships are less stable over time and thus less predictable at a regional level than at the national level. For example, population change cannot be predicted accurately at a regional level because the key regional determinants of population growth are regional migration inflows and outflows, which are essentially impossible to predict on a long-term basis. This tremendous uncertainty about the future without climate change means that more than one socioeconomic baseline scenario is essential for climate impact analysis.

Socioeconomic response scenarios describe the responses that society would make to climate change. Climate change, as well as expectations of climate change, will stimulate socioeconomic responses to reduce risks and exploit opportunities. These responses differ from, but have the potential to shape, final impacts. For example, there may be a variety of steps farmers can take in response to climate-induced changes in temperature, precipitation, and pests. The final impact on agricultural production will depend on these responses.

Responses that are feasible given existing technology and institutions can be identified, but it is more difficult to project changes in technology and institutions. It is certain that climate change will stimulate technological and institutional change, but it is very difficult to predict just what those changes would be. Like socioeconomic forecasts of the future without climate change, forecasts of how society would respond and adapt are inherently uncertain. Multiple response scenarios are again essential to understanding impacts.

However, an exhaustive list of all possible futures, or even "probable" futures, quickly becomes unmanageable. Suppose that socioeconomic futures are defined with respect to k variables and that a alternative values are considered for each variable. For instance, when a = 3, one could think in terms of a "high," a "medium," and a "low" value for each variable. The number of possible combinations of values is a^k, which is large even for moderate values of a and k. For example, if a = 3 and k = 5, the number of possible combinations is 3⁵ = 243. If k = 10, the number of possible combinations is 3¹⁰ = 59,049.

Rather than point forecasts or exhaustive lists, we are attempting to construct socioeconomic scenarios that will provide concrete results for present-day public and private decision-making. This goal can be accomplished with a smaller set of scenarios selected to help identify and bound major potential threats and opportunities, and identify critical research and adaptation policy issues in the Mid-Atlantic. Increased vulnerability clearly emerges in scenarios that combine greater future baseline socioeconomic or ecosystem sensitivity with increased climate stresses on socioeconomic or ecological systems and little ecological and/or socioeconomic adaptation.

This category yields upper bounds on adverse impacts and lower bounds on favorable impacts. Similarly, reduced risks clearly emerge in scenarios that combine reduced baseline socioeconomic or ecosystem vulnerability with reduced climate stresses. This category yields lower bounds on adverse impacts and upper bounds on favorable impacts. Combinations of climate and socioeconomic scenarios with offsetting effects may yield greater or smaller risks. The ranges between the upper and lower bounds could be viewed as confidence intervals.

Another crucial issue in socioeconomic scenario design is the selection of subjects (domains) and variables. The list of possible socioeconomic subjects for climate impact assessment is too large for comprehensive coverage. Our choices have been guided by our goal described above. In selecting subjects, the first step was to identify the region's sectors likely to be sensitive to climate change. We are looking at issues related to agriculture, coasts, forests, health and water. The second step was to identify and select among risks within the sectors.

To illustrate, we identified four key societal interests in agriculture: food availability and cost, agricultural income and employment, rural landscape, and environmental impacts of agricultural production. Because food availability and cost are almost entirely determined by factors external to the region, such as global agricultural production and trade, we chose to focus our agricultural assessment on the latter three. We further focused our agricultural assessment by concentrating on the leading agricultural commodities in terms of land use, income and employment and water quality impacts.

Socioeconomic variables have two basic roles in climate impact assessment. One role is as an indicator of economic and social conditions that might be influenced by climate. Examples include income, unemployment, levels of economic activity in particular sectors, and indicators of health status. Climate impacts on society can be described by changes in such variables. The second role of socioeconomic variables is as indicators of socioeconomic drivers that directly or indirectly influence sensitivity and vulnerability to climate change. For example, population and income growth increase the demand for water and water quality, which has implications for the assessment of impacts of climate change on water quantity and quality.

Like the number of socioeconomic topics, the number of socioeconomic variables of potential interest is very large. Because of this and because of problems in making long-term forecasts, the best approach may be to identify variables that are particularly important and construct summary variables that aggregate over sets of interrelated variables. For example, in our assessment of agriculture, key socioeconomic categories are international markets for commodities produced in the region, markets for agricultural inputs imported to the region, regional agricultural land markets, agricultural production technologies available to producers in the region, and agricultural, land use, and environmental protection policies. Rather than constructing scenarios with specific values of each variable included within these categories, we constructed scenarios as heuristic descriptions of conditions across these categories. Examples are presented in Table 1.