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Societal Benefits - Methods and Examples for Estimating the Value of Remote Sensing Information

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Remotely sensed data and information (RSDI) is a form of infrastructure for decision-making that supports many societal activities. Remotely sensed data range over a wide array of spatial, temporal, and spectral resolutions and have many applications that can contribute to economic prosperity. Data collected using remote sensors have improved society’s understanding of an array of Earth’s natural and human systems by establishing baselines and monitoring changes. Monitoring spatiotemporal change provides a means to analyze how land use, land cover, air quality and subsurface processes are affected by human and natural activities. Increasingly, these improvements in information have been applied in decision-making processes that take place in both the public and private sectors.

RSDI provides an input to other activities that are considered an intermediate economic good. As an intermediate good, the same RSDI can have many uses simultaneously. Because RSDI is digital, the cost of supplying the data is greatest to the first user; the cost of disseminating information to additional users is much smaller than the cost of obtaining information for the first user. In economics this situation is referred to as jointness-of-supply. This is true for the great majority of information goods and gives rise to the need for studies to demonstrate the socioeconomic benefits of the information. The benefits of RSDI are best demonstrated when an analysis explains how the data can be applied and used to make a particular decision.

This chapter is an overview of the approaches that have been undertaken to estimate the value of information (VOI) of RSDI and other digital geospatial information. The VOI for RSDI depends on what is at stake in a decision and how uncertain decision-makers are. In its simplest form, VOI is defined as the gains that result from making better decisions that are based on additional information in the presence of uncertainty. In addition to providing realized cost savings, RSDI provides new societal benefits from innovative applications.

Section 1 is an introduction that presents an overview of the VOI concept from an economist’s perspective. Descriptions of the microeconomics approach in Section 2 and macroeconomics approach in Section 3 follow the introduction. The microeconomic models focus on quantitative evaluations of individual decisions with uncertain information. Included in this part of the chapter are: a formal development of a Bayesian decision model to determine VOI in Section 2.3, a summary of cost — benefit analysis in Section 2.5.1, and empirical valuation of publicly provided RSDI using stated preference methods in section 2.5.2. In Section 3 macroeconomic approaches are described that include input/output analysis in Section 3.2 and computable equilibrium models in Section 3.3. These models address the VOI problem in a different manner. The models are assumed to represent an economy that is in equilibrium. The economic impact of remotely sensed data can be evaluated at the national, regional, and local scales to evaluate policies and regulations. Following the description of the various approaches to quantifying VOI from RSDI, there are brief summaries of fifteen case studies.

Document Type: Research Article

Affiliations: 1: University of New Mexico, Albuquerque, New Mexico, U.S.A. 2: Resources for the Future, Washington, DC, U.S.A. 3: ACIL Allen, Sydney, New South Wales, Australia 4: Resources for the Future Washington, DC, U.S.A.) 5: United States Geological Survey, Fort Collins, CO, U.S.A.

Publication date: January 1, 2019

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