Management outcomes can be understood and compared with many approaches outside of a monetization or multi-criteria decision analysis (MCDA) framework. These approaches include highly structured approaches, such as cost-effectiveness analysis (CEA), and less structured approaches, such as those in which subsets of stakeholders choose indicators for their relevance, weight those outcomes, and generate aggregate scores for alternatives.
A key distinction between these approaches and traditional benefit assessment methods (i.e., monetization and MCDA) are that these approaches are either less formal approaches to comparing benefits or are formal approaches that cannot give managers a quantitative understanding of social welfare effects. They are not full representations of benefits, either because they focus on maximizing one outcome (e.g., habitat acres) or because they have not used a democratic process to consider public preferences. It is perfectly valid to use these approaches to represent projects’ capacity to achieve an agency mission or meet selected stakeholder goals, but that objective is not equivalent to quantifying improvements in social welfare. (For a more in-depth explanation, see Monetary Valuation.
A key distinction among methods is whether they represent one type of benefit or many. When one type of benefit is being represented, analysts can often use straight-forward empirical approaches to model the relationship between a variable and suggested benefit. For example, a specific improvement in an indicator (e.g., exceeding a quality threshold), can be combined with increased use (e.g., visitor days) to suggest a higher value for changes that are appreciated by more people. Either type of indicator might be used in CEA to compare projects.
When a few benefits are being analyzed, more complex models may be needed to compare tradeoffs among alternatives. Bioeconomic or other types of models have been used to optimize the production of multiple benefits. These models often use monetized benefits,1 but they may also aggregate benefits using benefit weightings.2 In this way, these models build on CEA by creating a system in which decisionmakers can evaluate maximization of multiple services per dollar spent.
Informal methods for analyzing multiple kinds of benefits often include many implicit assumptions about how benefits accrue (e.g., linearly with increases in benefit relevant indicators) and how benefits combine (e.g., all are equal and additive). Different units in the indicators used to represent different benefits are typically made comparable by normalized or standardized scores. These approaches can create biased results and often double count benefits. However, some techniques attempt to overcome some of these biases.
Informal methods of assigning weights to benefits or ranking projects include a wide range of approaches, from simple weighted sums to complex aggregations. Visualizations rather than quantitative scores may be used to present information in easily digestible formats—for example, color-coded graphics that allow the user to make judgments about the importance of different indictors.
Although such approaches are easy and popular, they have also been criticized for failing to consider how the aggregation method may bias the interpretation of results. For example, assignment of scores to “high,” “medium,” and “low” categories can be arbitrary and lead to unintended consequences when data are used to make decisions. Similarly, standardizing scores can lead to very small physical changes being given the same weight as very large physical changes. Perhaps, most importantly, many techniques do not adequately consider thresholds or the context of the indicator changes. A large change in the concentration of a toxic chemical in a stream may not have any social welfare effects if the toxin is still too high to support fish or revive the ecosystem.
Ferraro, P.J. 2004. “Targeting Conservation Investments in Heterogeneous Landscapes: A Distance-Function Approach and Application to Watershed Management.” American Journal of Agricultural Economics 86: 905–918.
This article uses bridging indicators to support decision making by applying a multivariate statistical approach (data envelopment analysis) that has been grounded in an economic framework.
Kienast, F., J. Bolliger, M. Potschin, R.S. de Groot, P.H. Verburg, I. Heller, D. Wascher, and R. Haines-Young. 2009. “Assessing Landscape Functions with Broad-Scale Environmental Data: Insights Gained from a Prototype Development for Europe.” Environmental Management 44: 1099–1120.
This article’s example of ecosystem-services based indicator development also demonstrates an approach to aggregating indicators on the basis of the amount of change occurring in land use scenarios.
Mace, G.M., and J.E.M. Baillie. 2007. “The 2010 Biodiversity Indicators: Challenges for Science and Policy.” Conservation Biology 21: 1406–1413.
This article details considerations for selecting policy-relevant indicators for biodiversity conservation.
Zhao, M., R.J. Johnston, and E.T. Schultz. 2013. “What to Value and How? Ecological Indicator Choices in Stated Preference Valuation.” Environmental and Resource Economics 56: 3–25.
This article discusses characteristics that make ecological indicators useful for understanding and measuring welfare effects.
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