Valuing the Potential Impacts of GEOSS: A Systems Dynamics Approach

Abstract

Global earth observations are perceived as instrumental to attaining sustainable development goals. Methods to assess the long-run socioeconomic benefits of the emerging global Earth observation system of systems (GEOSS) as an integrated multisensor infrastructure have been missing to date. This chapter presents a systems dynamics approach to assess the effect of improvements in Earth observations across the nine societal benefit areas of the Group on Earth Observation (GEO). Two types of integration are assessed with the proposed model structure: (1) measuring benefits in an integrated assessment environment (e.g., improved weather forecasting through better measurement of cloud properties could lead to benefits in the agriculture, energy and water sectors); and (2) measuring benefits of an integrated observing system (e.g., in areas with high cloud cover, improvements in the resolution of optical sensors will lead to benefits only if linked to supplementary observing systems such a radar or ground surveys). The benefits from integration relate mostly to economies of scope on both the observation and benefit system sides. Cost reduction from economies of scale are derived from a global or large scale observing system vis-à-vis the currently prevailing patchwork system of national or regional observing systems. Results indicate that the total system benefits of GEOSS are usually orders of magnitude higher than their costs. Benefits are also policy dependent and tend to increase with the degree of implementation of mainly international environmental agreements.

4.Commentary: The Value of a Comprehensive Model

• Molly K. Macauley 

1. Resources for the Future, Washington, DC, USA

   Molly K. Macauley

4.1.1 4.C.1Introduction

In their contribution to this volume, Steffen Fritz, Ian McCallum, Michael Obersteiner, and Felicjan Rydzak use a systems engineering model of the global economy to illustrate how value could be ascribed to information obtained from Earth-observing satellites. Rydzak and coauthors constructed the model in previous research (Rydzak et al. 2010) to characterize the Earth processes and human interactions that are the focus of the Group on Earth Observations (GEO). GEO is a voluntary collaboration of 80 governments, the European Commission, and regional and other organizations. GEO seeks to coordinate the Earth-observing satellites of different countries across nine themes, called societal benefit areas: public health, climate, energy, water, agriculture, ecosystems, weather, disaster management, and biodiversity.

Rydzak and his colleagues modeled the subcomponents of their engineering model on these themes. For example, subcomponents include representation of the global carbon cycle, energy resources, and land use. With this model, Fritz and coauthors show how the model could be used to ascribe value to Earth observations. For instance, if GEO Earth observations data improve disease prevention or air quality, then the Rydzak model would show an increase in life expectancy. The value of Earth observations data in this engineering framework is expressed in changes in the physical outputs of the model (such as years of life expectancy). The examples in their chapter are hypothetical, not based on actual applications of Earth observation data.

4.1.2 4.C.2Choice of the Model

An advantage of using a systems engineering model as a method to ascribe value to Earth observations information is that engineering is the language of the engineers and, although perhaps to a lesser extent, the scientists who design the satellites and their observing instruments. More challenging is the attempt to model the global economy. Discussing the Rydzak model in detail is outside the scope of this commentary, but as with all models of the global economy, specifying all the interrelationships and interactions of industrial sectors, natural resources, and people is difficult. The authors’ example of life expectancy is a good example of the difficulty. Many factors, including existing health of the population, access to clean water and sanitation, and nutrition and diet, influence life expectancy. The “black box” in global models in which these factors combine with agricultural productivity, international trade in agriculture, peoples’ behavior, technological innovation, and government policy—all of which affect life expectancy—is difficult to formulate.

Fritz and his coauthors want to use a global model because one of their objectives is to replicate the interrelationships among the GEO societal benefit areas. They argue that the value in GEO in coordinating Earth-observing systems of different countries is the complementarity of different kinds of data. To continue with their life expectancy outcome as an example, the complementarity is in data about air quality and water, which combine to influence agriculture and, in turn, life expectancy.

Such an approach is ambitious as a basis for identifying a role of Earth observations. The traceability of attribution of the role of Earth-observing information on each of these influences is difficult at best. Moreover, there are other black boxes in which actions are assumed rather than empirically accounted for: the approach doesn’t permit disentangling Earth observations data from other data sources, and it assumes that the Earth observations data are in fact used by people taking action within the various subcomponents of the model.

Alternative modeling approaches are available to characterize the relationships among economic sectors, natural resources, and people. Examples of some of these alternatives include general equilibrium models and integrated assessment models.⁸ These models combine physical and economic relationships of producers, consumers, and the government sector. Unlike systems engineering models and similar input-output models, these alternatives tend to emphasize the role of relative prices and the capacity of consumers and producers to make substitutions in their decisions in response to changes in prices. Depending on their purpose, the models often include international trade, assumptions about technological change, estimates of stocks and flows of natural resources, and demographic data. The models often draw some of their inputs from purely physical models. One example is integrated assessment models that use, as inputs, the outputs of global circulation models, such as centimeters of sea level rise or parts per million of atmospheric concentrations of greenhouse gases.

There are many shortcomings in these alternative models, including the constraints imposed by functional forms used to characterize production and consumption decisions. The characterization of technical change and uncertainty is also problematic. An advantage of the models, however, is that they usually explicitly allow for interactions such as substitution among inputs, the effects of government policy, and as noted, changes in relative prices. Another advantage is that their outputs are usually expressed in economically relevant measures, such as changes in productivity or overall social welfare.

But even these models are subject to the same challenges as the engineering model. In all many global-scale representations, identifying the role and value of information can be a search for a needle in a haystack. In addition, changes in the quality of natural resources (air quality, water availability) or the effects of these changes (on production relationships of industry, on health and quality of life of consumers) is not typically explicit—there are no prices for these resources. This lack of explicit characterization of the role of resources further confounds the ability to identify the value of Earth observations about them.⁹

4.1.3 4.C.3Other Approaches?

For the representations of the GEO societal benefit areas, a smaller-scale approach might be more tractable. Using one of the existing integrated assessment models for climate is an example. Different runs of the models under different assumptions about information would allow for a set of scenarios: “what if the Earth-observing data allow enhanced use of renewable energy” or “suppose the data show trends in allocation of land away from forests to agricultural production.” Even in these models, however, the tractability of the effect of “information” as a model input is difficult, and the effect of Earth observations data, in particular as a subset of information, is also hard to identify.

Perhaps the most important contribution of Fritz and his coauthors in their assessment of benefits from GEO is to point out the desirability of accounting for the complementarity of different types of Earth observations data. The coordination of different Earth-observing systems, owned and operated by different countries, is the overall goal of GEO. The group describes this goal as GEOSS, the global Earth observation system of systems. Fritz and his coauthors seek a comprehensive model in which, for instance, the air quality observations of one country’s satellite system together with the precipitation data of another country’s system can be valued for their joint information content. I commend this effort.