I. McCallum1, S. Fritz1, N. Khabarov1, S. Fuss1, J. Szolgayova1, F. Rydzak1, P. Havlik1, F. Kraxner1, M. Obersteiner1, K. Aoki1, C. Schill2, M. Quinten2, C. Heumesser3, J. Bouma4, B. Reyers5, U. Schneider6, F. Pignatelli7, L. Pavanello7, M. T. Borzacchiello7, M. Craglia7
GEOSS, the Global Earth Observation System of Systems, is envisioned to be a global public infrastructure that generates comprehensive, near-real-time environmental data, information and analyses for a wide range of users. The general assumption regarding GEOSS is that the benefits to society by far outweigh the costs. However, this notion is being increasingly challenged, and it is becoming necessary to provide rational, quantified and persuasive arguments to justify investment of what are often public funds. In particular, the identification of clear benefits is crucial to ensure long term sustained GEOSS operations. Not surprisingly, it is the estimation of many of these benefits which has proven difficult in the past.
Numerous studies have been undertaken to describe and measure the Value of Information (VOI). They typically employ a wide variety of methods and generally find a large range of benefits, from quite small to very large, in part owing to differences in methodologies (Macauley, 2006). The state of the art in understanding the VOI reflects general agreement on how to model an individual’s or a government’s decision and some useful implications about the value of information: when it is most and least valuable, its relationship to subjective prior opinions, and the decision maker’s ability to take action in light of the information (Macauley, 2006).
To date, however, there have been few integrated assessments of the economic, social and environmental benefits of Global Earth Observation (EO). In an effort to address these issues, the European Commission sponsored project ÛÏGlobal Earth Observation ÛÒ Benefit Estimation: Now, Next and EmergingÛ (GEOBENE) developed methodologies and analytical tools to assess the societal benefit areas (SBAs) of GEO in the domains of: Disasters, Health, Energy, Climate, Water, Weather, Ecosystems, Agriculture and Biodiversity. Thus it is the aim of this article to present several of these overarching methodologies as a contribution to the ongoing effort to improve GEOSS. The article concludes with a look to the future via the EuroGEOSS Project.
GEOSS Benefit Assessment
The GEOBENE Project resulted in a variety of tools and methodologies developed for GEOSS benefit assessment which address the various SBAs within GEOSS, along with GEOSS as a whole. These ranged in scope from studies on biodiversity and emerging diseases, optimal vaccination timing and robust energy portfolios through to weather observation for forest fires and wetland conservation. From the large variety of applications resulting from GEOBENE, four overarching methods warrant further explanation here because of their cross-sectoral applicability, not only at the SBA level, but also to assess interoperability among areas, and GEOSS as a whole. These include: the benefit chain concept; Bayesian decision theory; a real options framework; and systems dynamics modeling and are described below.
Benefit Chain Concept
In the course of the GEOBENE Project, a conceptual framework for assessing the benefits of GEOSS via the Û÷benefit chain’ concept was developed (Fritz et al., 2008). The basic notion is that an incremental improvement in the observing system (including its data collection, interpretation and information sharing aspects) will result in an improvement in the quality of decisions based on that information. This will lead, in turn, to beneficial societal outcomes, which have a value. As further elaborated in Jantke et al., (2009), the approximation of benefit improvements requires mapping the decision implications for each data quality to the data set of highest quality. Otherwise, the benefit comparison between societal outcomes under alternative data qualities will be biased.
The incremental value of improved data must also be judged against the incremental cost of the improved observation system. Since in many cases there will be large uncertainties in the estimation of both the costs and the benefits, and it may not be possible to express one or both of them in comparable monetary terms, the Û÷benefit chain’ concept describes how order-of-magnitude approaches and a qualitative understanding of the shape of the cost and benefit curves can help guide rational investment decisions in Earth Observation systems (Fritz et al., 2008).
The example of improved data for biodiversity conservation planning illustrates how the benefit chain concept can be applied in a case where the benefits are non-marketable. This case study demonstrates the benefits of replacing commonly available coarse scale global data (the non GEOSS scenario) with finer scale data used in conservation decision making. These finer scale data are comparable with those expected from GEOSS and can thus be used to estimate the potential benefits of GEOSS data. The coarse scale data led to a 9% overestimate of priority areas identified by the finer national scale data and a 10% underestimate in other areas. A simple proxy to convert these differences into benefit estimates would be the cost consequences of these over or underestimates. Based on this approach, it appears that the benefits of moving from global to national data are large and provide significant savings. Work is in progress to determine at which level these benefits begin to saturate (Fritz et al., 2008).
Bayesian decision theory
A method that explicitly considers the extent to which decision-makers actually use Global Earth Observation for decision-making is Bayesian decision theory. The approach is particularly attractive as it links the value of information to the perceived accuracy of the information system. Bouma et al., (2009) used Bayesian decision theory to study the added value of Global Earth Observation for preventing potentially harmful algal blooms in the North Sea.
Using expert elicitation to assess decision-makers perceptions of the accuracy of the GEO-based algal bloom early warning system, the analysis indicated that the value (i.e. avoided damage) of an early warning system would be 74,000 âÂ/week. Since the costs of establishing and maintaining such an early warning system amount to 50,000 âÂ/week, investing in satellite observation for preventing potentially harmful algal blooms seems to be an economically efficient investment to make. Increasing the accuracy of the information system substantially increases the value of information – the value of perfect information, for example, being estimated at 370,000 âÂ/week (Bouma et al., 2009).
Real Options Framework
Many VOI problems can be addressed in a real options framework, which takes into account investment irreversibility, uncertainty and the flexibility to react when new information arrives. Such a framework is proposed here, and applied to a satellite mission case study, considered to bring about new scientific information potentially leading to lower damage from natural disasters (Fuss et al., 2008).
Satellites are a key source of Earth observation designed to obtain information for improved decision making. Satellite missions are, however, expensive undertakings involving large sunk costs in the face of uncertain benefits. In terms of avoiding damages from natural disasters through, for example, better weather forecasts, early warning or better-informed rescue missions, the benefits are high, but also difficult to quantify. Using real options to optimize the timing of the launch of a satellite enables us to derive the value that such information conveys, when it can be used to reduce the extent of the damage from disasters and their consequences. This technique could be applied towards the NASA Decadal Survey, which provides scientific priorities indirectly through a time sequencing of recommended missions.
Key findings show that large volatility of the benefits from avoided damage or damage mitigation increases the option value, thus leading to postponement of the satellite mission. While rational to wait in the face of uncertainty, higher volatility also implies higher spikes in damages, representing high-impact disasters ÛÒ hence it is important to ex ante assess the benefits that could be obtained through EO. For example, a larger value of the trend parameter has been shown to trigger an earlier launch ÛÒ thus if prior benefit assessment can establish that the trend can be expected to be relatively high, an EO system could be installed earlier (Fuss et al., 2008).
Systems Dynamics Modeling
The approaches described above are typically applied to one SBA or sector, or a specific country or region, conducting a thorough analysis of GEO’s benefits only in that area. In order to illustrate the propagation of GEO benefits across all nine SBAs and to capture the global perspective of such issues as greenhouse gas emissions or climate change, system dynamics modeling and simulation methodology was used to develop the FeliX (Full of Economic-Environment Linkages and Integration dX/dt) model (Rydzak et al., 2010).
The FeliX model provides a systems perspective, where the underlying social, economic, and environmental components of the Earth system are interconnected and constitute a complex dynamic system. A change in one area results in changes in other areas ÛÒ for instance, use of food crops as a source of energy may increase food prices and deforestation rates through land use change. Being a dynamic model it captures change of certain phenomena (e.g. depletion of natural resources, carbon dioxide emissions) or impact of certain policies (e.g. afforestation, emission reductions) over time. Constructed as such, the model allows for analysis of particular policies, actions and interventions in both the short and long term.
The FeliX model was initially calibrated to historical data for the 20th century, constituting a simplified representation of the Earth system. The Business as Usual run for the 21st century was constructed based on projections of historical data. Additionally, a total of six GEO scenarios were constructed: Energy, Disaster, Health, Climate, Agriculture and Water. The Base Run scenario is then compared to the GEO scenarios, the difference indicating the potential impact of GEO across the SBAs. For example, the agriculture GEO scenario demonstrates the ability of the agricultural sector to meet global food demand beyond 2070, compared to the Base Run which shows severe shortfalls. Results demonstrate the significant impacts of combined GEO scenarios over the Base Run in several sectors, namely a significant decrease in CO2 emissions, increased savings of water resources, limitations to deforestation and decreasing amounts of agricultural land required.
Building in part upon the successful achievements of GEOBENE, the European Commission is supporting a new project titled EuroGEOSS. Where GEOBENE focused on the societal benefits of GEO within individual SBAs, EuroGEOSS is tasked with implementing methodologies to assess the added value of Spatial Data Infrastructure (SDI) and interoperability in three SBAs, specifically developing, linking, and making globally available the European information systems addressing forests, drought, and biodiversity. EuroGEOSS therefore focuses primarily on the VOI for integrated assessment, which is critical to support environmental decision-making and policy assessment.
The economic importance of integrated assessment can be gauged by a recent survey of practitioners in Europe undertaking Environmental Impact Assessments (EIAs) and Strategic Environmental Assessments (SEAs). This survey indicates that the current barriers to the discovery, access, and use of the environmental and geographic data necessary to undertake EIAs and SEAs account for an added cost of âÂ 150-200 million per annum in the EU alone, along with reports of lower quality, i.e. greater uncertainty on the environmental impacts of the projects proposed (Craglia et al., 2010). The development of SDIs and of interoperable systems of systems in the GEOSS context can remove these barriers, and therefore provide significant economic benefits, in addition to the all important increased understanding of the complex relationships between environmental processes and human agency. With the implementation of INSPIRE requiring the development of SDIs at multiple levels across Europe, and the development of GEOSS at the global level, it is important to develop a portfolio of studies providing evidence of the benefits of these investments.
As a first step in this process, a database to collect SDI-related benefit assessment research has been established. This builds upon a similar online bibliography established for GEOBENE. The objective is to collect literature which demonstrates measuring benefits qualitatively or quantitatively in relation to SDI, INSPIRE and/or GEOSS. In particular, studies from different approaches which illustrate the potential of SDIs and also the use of standards compliant architectures will be archived. The goal is that this site will evolve into a public domain database of all SDI/INSPIRE/GEOSS related benefit studies. This online bibliography will be used as a platform from which to develop new applications for GEOSS benefit assessment.
A second step towards the evaluation of the benefits of a Global System of Systems is being developed in the context of EuroGEOSS by means of a set of surveys aimed at investigating the current needs and requirements of users of data and information systems belonging to different thematic areas: the comparison between these needs and the actual achievements of the project, in terms of data finding, accessing and integrating, of data and models’ sharing, of interoperability and costs, will give the opportunity to gather evidence on the benefits that the partners of the project and their users will have gained thanks to the efforts made.
This research was supported by the European Community’s Framework Programme (FP6/FP7) via the Projects GEOBENE (No. 037063) and EuroGEOSS (No. 226487).
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1 FOR, International Institute for Applied Systems Analysis, Austria
2 FELIS, University of Freiburg, Germany
3 ISED, BOKU University, Austria
4 IVM, VU University, Netherlands
5 CSIR, South Africa
6 UNIHH, Germany
7 IES, Joint Research Centre, Italy