Technical Policy Briefing Notes - 1
Summary of Methods and Case Study Examples from the MEDIATION Project
Decision Support Tools
Policy Briefs
Summary Methods and Case Study Examples
Decision Support Tools
There are a number of existing decision
support tools that are widely used in policy and project appraisal, and
which have potential for some conventional adaptation decisions. Three
key techniques are used in European policy and option appraisal,
summarised below.
Cost-Benefit Analysis (CBA) is the method of choice in most Government economic appraisal or impact assessment. Social cost-benefit analysis (CBA) values all relevant costs and benefits to society of all options, and then estimates a net present value or a benefit:cost ratio. In this regard, CBA is an absolute measure providing the justification for intervention, though it is often difficult to value all the costs and benefits of a particular project or policy.
CBA has been used in some adaptation assessment, usually as part of an impact assessment focused analysis. However, the routine CBA applied in economic appraisal does not fully address many of the complex issues of adaptation (UNFCCC, 2009). In general, the more unique and less routine the decision-making context is, the more difficult the use of CBA will be, and the technique will be appropriate only for some adaptation decision-making contexts, though it can be combined with many of the new techniques below.
Cost-Effectiveness Analysis (CEA) is a widely used decision support tool. It compares alternative options for achieving similar outputs (or objectives). In this regard it is a relative measure, providing comparative information between choices (unlike CBA, which provides an absolute measure). It has been widely used in environmental policy analysis, because it avoids monetary valuation of benefits, and instead quantifies benefits in physical terms.
At the technical or project level, CEA can be used to compare and rank alternative options. It does this by assessing options in terms of the cost per unit of benefit delivered, e.g. cost per tonne of pollution abated. This identifies those options that deliver highest benefit for lowest cost (i.e. the most cost-effective). At the project, policy or programme level, where combinations of options are needed, CEA can be used to assess the most cost-effective order of options, and identify the least-cost path for achieving pre-defined policy targets. This is undertaken through the use of marginal abatement cost (MAC) curves, which implement options in order of cost-effectiveness, adding up the cumulative benefits with each additional option. This approach can also identify the largest benefits possible with the available resources, and can be used to help set targets.
Cost-effectiveness analysis has become the main appraisal technique used for climate change mitigation, as it allows a comparison and ranking of alternative options within and across sectors, using the metric of cost per tonne of GHG abated (€/tCO2), and there has also been widespread use of marginal abatement cost curves for mitigation.
However, the lack of a common metric makes a similar cross-sectoral approach impossible for adaptation. Moreover, adaptation is a response to many different local, regional or national level impacts, rather than to a single global burden, and the application of CEA to adaptation is therefore much more demanding, in terms of analysis detail and resources.
CEA also focuses analysis on a single metric, thus omitting a full analysis of all relevant costs and benefits, which reduces the potential for cross-sectoral applications. Nonetheless, costeffectiveness is already used in many sectors that are relevant to adaptation, such as health (using health impact metrics) and flooding (looking at acceptable levels of risk), and it has some potential for appraising options within a sector, though the approach does not easily lend itself to the analysis of uncertainty.
Multi-Criteria Analysis (MCA) is a decision support tool that allows consideration of quantitative and qualitative data together in ranking alternative options. The approach provides a systematic method for assessing and scoring options against a range of decision criteria, some of which are expressed in physical or monetary units, and some which are qualitative. The various criteria can then be weighted to provide an overall ranking of options.
MCA has been widely applied in the environmental domain. It has also been used as a complementary tool to support cost-benefit analysis in appraisal, to consider the performance of options against criteria that may be difficult to value or involve qualitative aspects.
MCA does have considerable potential for adaptation. Criteria can be included to consider uncertainty or various complex elements of good adaptation, and the approach brings the flexibility to work with qualitative information, which is particularly useful given there are often data gaps. As an example, previous adaptation MCAs have considered criteria of robustness, low/no regret characteristics or flexibility, as well as co-benefits and synergies with mitigation (van Ierland et al, 2007). However, the analysis can be somewhat subjective in nature, especially in relation to uncertainty, as it tends to work with individual scenarios, against which options are assessed. This makes it more difficult to incorporate the trade-offs over time and to fully incorporate climate change uncertainty (i.e. how benefits of different adaptation options vary).
Cost-Benefit Analysis (CBA) is the method of choice in most Government economic appraisal or impact assessment. Social cost-benefit analysis (CBA) values all relevant costs and benefits to society of all options, and then estimates a net present value or a benefit:cost ratio. In this regard, CBA is an absolute measure providing the justification for intervention, though it is often difficult to value all the costs and benefits of a particular project or policy.
CBA has been used in some adaptation assessment, usually as part of an impact assessment focused analysis. However, the routine CBA applied in economic appraisal does not fully address many of the complex issues of adaptation (UNFCCC, 2009). In general, the more unique and less routine the decision-making context is, the more difficult the use of CBA will be, and the technique will be appropriate only for some adaptation decision-making contexts, though it can be combined with many of the new techniques below.
Cost-Effectiveness Analysis (CEA) is a widely used decision support tool. It compares alternative options for achieving similar outputs (or objectives). In this regard it is a relative measure, providing comparative information between choices (unlike CBA, which provides an absolute measure). It has been widely used in environmental policy analysis, because it avoids monetary valuation of benefits, and instead quantifies benefits in physical terms.
At the technical or project level, CEA can be used to compare and rank alternative options. It does this by assessing options in terms of the cost per unit of benefit delivered, e.g. cost per tonne of pollution abated. This identifies those options that deliver highest benefit for lowest cost (i.e. the most cost-effective). At the project, policy or programme level, where combinations of options are needed, CEA can be used to assess the most cost-effective order of options, and identify the least-cost path for achieving pre-defined policy targets. This is undertaken through the use of marginal abatement cost (MAC) curves, which implement options in order of cost-effectiveness, adding up the cumulative benefits with each additional option. This approach can also identify the largest benefits possible with the available resources, and can be used to help set targets.
Cost-effectiveness analysis has become the main appraisal technique used for climate change mitigation, as it allows a comparison and ranking of alternative options within and across sectors, using the metric of cost per tonne of GHG abated (€/tCO2), and there has also been widespread use of marginal abatement cost curves for mitigation.
However, the lack of a common metric makes a similar cross-sectoral approach impossible for adaptation. Moreover, adaptation is a response to many different local, regional or national level impacts, rather than to a single global burden, and the application of CEA to adaptation is therefore much more demanding, in terms of analysis detail and resources.
CEA also focuses analysis on a single metric, thus omitting a full analysis of all relevant costs and benefits, which reduces the potential for cross-sectoral applications. Nonetheless, costeffectiveness is already used in many sectors that are relevant to adaptation, such as health (using health impact metrics) and flooding (looking at acceptable levels of risk), and it has some potential for appraising options within a sector, though the approach does not easily lend itself to the analysis of uncertainty.
Multi-Criteria Analysis (MCA) is a decision support tool that allows consideration of quantitative and qualitative data together in ranking alternative options. The approach provides a systematic method for assessing and scoring options against a range of decision criteria, some of which are expressed in physical or monetary units, and some which are qualitative. The various criteria can then be weighted to provide an overall ranking of options.
MCA has been widely applied in the environmental domain. It has also been used as a complementary tool to support cost-benefit analysis in appraisal, to consider the performance of options against criteria that may be difficult to value or involve qualitative aspects.
MCA does have considerable potential for adaptation. Criteria can be included to consider uncertainty or various complex elements of good adaptation, and the approach brings the flexibility to work with qualitative information, which is particularly useful given there are often data gaps. As an example, previous adaptation MCAs have considered criteria of robustness, low/no regret characteristics or flexibility, as well as co-benefits and synergies with mitigation (van Ierland et al, 2007). However, the analysis can be somewhat subjective in nature, especially in relation to uncertainty, as it tends to work with individual scenarios, against which options are assessed. This makes it more difficult to incorporate the trade-offs over time and to fully incorporate climate change uncertainty (i.e. how benefits of different adaptation options vary).
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