Mediation training module: Cost Effectiveness Analysis
- Level: Advanced
- Time commitment: 1-2 hours
- Learning product: Tool/method summary
- Sector: Multi-sector
- Language: English
Certificate available: No
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. 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). As well as ranking different options, such an analysis can be used for benchmarking, see box. 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 so identify the least-cost path for achieving pre-defined policy targets. This is undertaken through the use of marginal abatement cost (MAC) curves. The approach can also identify the largest benefits possible with the available resources, and can even be used to help set targets, by selecting the point where cost-effectiveness falls significantly (i.e. where there are disproportionately high costs for low benefits).
*Download the technical brief from the right-hand column.
When should this tool be applied?
The approach is most useful for short-term assessment, for market and non-market sectors. It is most relevant where there is a clear headline indicator and a dominant impact (and less applicable for cross sectoral and complex risks). It is also most appropriate where climate uncertainty is low, and good data exists for major cost/benefit components. It is a useful tool for consideration of low and no regret option appraisal (short-term), especially for non-market sectors, and as a potential decision support tool as part of an iterative risk management framework.
What are the 5 main things a user should take away from this module
A number of issues are highlighted for the application to adaptation.
- The application of CEA will need to be context and location specific, and will need to use sector and risk specific metrics.
- The application should consider non-technical as well as technical options (noting most applications tend to focus on technical options, because non-technical options are harder to quantify). Further, a key omission in CEA is the lack of iterative planning and portfolios of options. CEA should therefore be undertaken within such an iterative plan, and additional analysis is needed to examine portfolios of adaptation, rather than using the outputs of a CEA as a simple prioritisation for implementation.
- A CEA needs to define a baseline, and this should include starting conditions and existing and planned policy, and for future scenarios, the analysis of climate and socio-economic scenarios.
- The application of CEA to adaptation needs to consider uncertainty. This could involve a sampling (and multiple cost curve analyses) across socio-economic scenarios and climate model projections (even if low/high ranges). The use of single central estimates and single cost curves should be avoided.
- A critical issue is the consideration of time periods. A good starting point is to consider the cost-effectiveness of current measures for current climate variability/vulnerability, and then look to assess the cost-effectiveness in a number of defined future periods.
- CEA optimises to a single metric. However, there is a need to capture multiple attributes and cross-sectoral effects. While a single headline indicator (a single metric) has to be used, it is important to consider other potential issues and to assess whether adaptation options have beneficial or negative effects for these. A further approach might be to consider altering the ranking of options to take account of the various other aspects and/or cross-sectoral issues.
Where has this tool been applied?
CEA has been used in risk-based flood protection assessment, particularly for coastal zones (e.g. RIVM, 2004) assessing the cost-effectiveness of achieving flood protection targets (defined as a level of acceptable risk, such as protection against a 1 in 10000 year return period). Specific applications also exist for adaptation. Boyd et al (2006) undertook a detailed application of cost-effectiveness analysis for the South-East of England, looking at the impact of climate change (including potential scenarios of reduced precipitation) and socio-economic growth (increased demand) on water resource zones and the potential adaptation response to address household water deficits. The study undertook detailed basin modelling for the water catchment (Wade et al, 2006) and assessed baseline 30-year average household water deficits in three future time periods (2011-2040, 2041-2070 and 2071-2100) for four separate climate-socioeconomic scenarios. The cost of addressing the projected water deficits was analysed through a cost-effectiveness analysis, looking at a range of options for managing public water supply (including options that reduced demand and options that increased supply). Detailed cost-yield curves (cost-effectiveness curves) were produced to estimate how to eliminate the household water deficit at minimum cost, providing cost curves for each scenario, for each of the three future time periods in an inter-linked analysis. This addresses many of the issues raised above, by working with multiple projections and multiple time periods. The ECA (2009) study analysed measures to protect against drought-related health risks cases in Tanzania. Measures were classified as prevention (such as cholera vaccinations) or treatment (such as oral rehydration therapy for cholera patients). The costs of each measure were estimated, including the costs of various components of the programme and the likely efficacy of the intervention. The estimated disease burden that would be prevented with each measure was estimated, discounted by the penetration rate – the proportion of the population that could be reached – and the efficacy rate (%). Applying this information, the study ranked a number of potential strategies on the basis of cost per effective case. The study found the educational programmes and water quality improvements (rainwater harvesting) were highly cost effective measures.
Strengths and weaknesses of this approach
The key strength of CEA is that it avoids valuation of economic benefits, enhancing applicability where valuation is difficult or contentious (e.g. ecosystems). The approach is also relatively simple to apply, and the communication of results is concise and easy to understand – helped by the widespread use of CEA in mitigation. The potential weaknesses relate to the need to choose a single common cost-effectiveness metric and the consideration of uncertainty. It can be often be difficult to identify a single common metric for analysis, because there are many types of risks across and even between sectors. In the case of sea level rise for example, using a headline metric of the number of people at risk, or an objective of acceptable levels of risk, will omit consideration of coastal erosion and coastal ecosystems. This means such a CEA will not consider all relevant costs and benefits for coastal adaptation and may not identify the most holistic option. For this reason, CEA is less suitable for complex or cross-sectoral risks. For uncertainty, most previous CEA applications, e.g. in areas such as environmental policy and mitigation, ignore uncertainty, presenting single cost curves. Early applications of CEA to adaptation have also followed this approach, largely presenting individual cost curves, or at best, a few cost curves (each representing a central estimate for a different emission scenario, or a central and high scenario). Indeed, the range of climate model outputs for a given scenario, whether from the degree of temperature change, or for precipitation projections where even the sign of the change is uncertain, will alter the cost-effectiveness of options, their relative CEA ranking, and their total effectiveness and the cost curve. It is possible to address this by sampling across multiple scenarios/model outputs, or using stochastic approaches, but this has resource implications. Finally, CEA tends to focus on technical options, because these can be easily assessed in terms of costs and benefits (effectiveness). However, adaptation is now seen as a process as well as an outcome, and capacity building and non-technical (soft) options are considered an important and early priority. Such non-technical options do not lend themselves easily to the quantitative analysis in CEA, thus they tend to be given lower priorities (or omitted). This issue is compounded by the strict linear sequencing adopted in cost-effectiveness analysis, where options are considered as discrete options implemented in turn: this contradicts the emphasis in the adaptation literature for portfolios of options and the need to explicitly consider inter-linkages.
Why is this method useful for the field of climate adaptation?
Cost-effectiveness is already used in many sectors that are relevant to adaptation, such as health and flooding, and therefore has potential for appraising options to address future climate change. It is particularly useful where there is a need for the analysis of benefits in non-monetary terms, notably in areas that are difficult to value, such as ecosystems or health.