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Cost-Benefit Analysis and the Environment

Further Developments and Policy Use

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This book explores recent developments in environmental cost-benefit analysis (CBA). This is defined as the application of CBA to projects or policies that have the deliberate aim of environmental improvement or are actions that affect, in some way, the natural environment as an indirect consequence. It builds on the previous OECD book by David Pearce et al. (2006), which took as its starting point that a number of developments in CBA, taken together, altered the way in which many economists would argue CBA should be carried out and that this was particularly so in the context of policies and projects with significant environmental impacts.

It is a primary objective of the current book not only to assess more recent advances in CBA theory but also to identify how specific developments illustrate key thematic narratives with implications for practical use of environmental CBA in policy formulation and appraisal of investment projects.

Perhaps the most significant development is the contribution of climate economics in its response to the challenge of appraising policy actions to mitigate (or adapt to) climate change. Work in this area has increased the focus on how to value costs and benefits that occur far into the future, particularly by showing how conventional procedures for establishing the social discount rate become highly problematic in this intergenerational context and what new approaches might be needed. The contribution of climate economics has also entailed thinking further about uncertainty in CBA, especially where uncertain outcomes might be associated with large (and adverse) impacts.

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Discrete choice experiments

Many types of environmental impacts are multidimensional in character. What this means is that an environmental resource that is affected by a proposed project or policy often will give rise to changes in component attributes each of which command distinct valuations. One tool that can elicit respondents’ distinct valuations of these multiple dimensions, and account for trade-offs between them, are (discrete) choice experiments (DCEs). DCEs share strengths and weaknesses with contingent valuation but also have some distinctive characteristics that may differentially affect its performance and accuracy. A number of developments, on the face of it, appear to work against one another. The selection of the experimental design, i.e. the combination of attributes and levels to be presented to respondents in the choice sets, is a key stage and the tendency has been to opt for increasingly complex designs, to improve response efficiency. Yet this creates inevitable cognitive difficulty for respondents, associated with making multiple complex choices between bundles, with many attributes and levels. There is a limit to how much information respondents can meaningfully handle while making a decision, possibly leading to error and imprecision, depending on whether fatigue or learning dominate respondent reactions. The links to behavioural research are again highly relevant such as on heuristics, and filtering rules guiding choices that are “good enough” rather than utility-maximising. The growing sophistication of statistical modelling of responses is another notable characteristic of this work and has enabled far better account for considerations such as preference heterogeneity. While the domain of specialists, this modelling is increasingly accessible more broadly via a growth in training opportunities and free statistical software.

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