Cost-Effectiveness Analysis (CEA) is a methodology used to compare different approaches to achieve pre-defined adaptation targets. CEA can be used to analyse both technical or project oriented work and policy or programme approaches, providing comparisons and rankings of options with the same adaptation objective, or identifying the least cost combination of options.

Cost-effectiveness analysis is a methodology used to compare different options aiming to achieve similar outcomes. It is particularly attractive in the adaptation context because it allows for benefits to be valued in non-monetary terms, opting for quantification in physical terms instead.

When should Cost-Effectiveness Analysis be used?

Cost-effectiveness analysis is generally most useful for short-term adaptation assessment, for example when ranking low and no regret options. This is because CEA does not explicitly deal with uncertainty and aims to optimise the selection of adaptation interventions against a single objective usually under one climate scenario. This can be addressed by testing across multiple scenarios/model outputs, or using more complex stochastic approaches, but this has resource implications. Because effectiveness does not need to be quantified in monetary terms, CEA is also a helpful tool when dealing with sectors which include significant non-market dimensions such as biodiversity protection.

CEA is less useful when considering non-technical or “soft” options, as their effectiveness is more difficult to evaluate. This can present some issues in the adaptation field, where a large combination of diverse options may be needed to best deal with future conditions and where soft options are important (e.g. in combination with technical adaptation options).

What does it involve?

The methodology for CEA aims to provide a comparison and ranking of the relative cost-effectiveness of various options to achieve pre-determined targets. It involves a series of common methodological steps:

  • Establish the effectiveness criteria, such as the reduction in the number of people at risk of affect by floods
  • Collate a list of options
  • Collect cost data for each option - noting this involves the full costs over the lifetime of the option, including capital and operating costs – and thus requires all values to be expressed on a common economic basis (in equivalent terms using discount rates and either an equivalent annualised cost or a total present value)
  • Assess the potential benefits (effectiveness) of each option in non-monetary metric. Generally, these are expressed as an annual benefit, relative to a baseline or reference case
  • Combine these to estimate the cost-effectiveness, by dividing the lifetime cost by the lifetime benefit (or annualised costs by annualised benefit)

Following these steps, all the options can be expressed in equivalent terms, as a cost per unit of effectiveness. This allows the ranking or prioritising of measures, identifying the most cost-effective options, i.e. those that deliver highest benefits at lowest cost.

This information can then be used as an input to form a marginal abatement cost curve.  In graphical terms, they are often presented as cumulative bar charts. At a basic level, cost curves present all options in order of unit cost-effectiveness analysis, beginning with the most cost-effective. Cost curves also assess the total cumulative effectiveness of each option, as it is added. When considered together, this allows the estimation of the least-cost path to achieve a plan, programme or policy target. 

Key lessons learnt

Key strengths

  • Does not require monetary valuation of benefits. Increases applicability to non-market sectors.
  • Provides easily understandable rankings of measures.
  • Frequently used for mitigation, and thus approach known by policy makers.
  • Can look at the cost implications of progressively more ambitious policies.

Potential weaknesses

  • Optimises to a single metric, which can be difficult to choose. Focus on a single metric may omit important risks, and may not capture all costs and benefits for option appraisal.
  • Less applicable for cross-sectoral or complex risks.
  • May give lower priority to non-technical measures such as capacity building and soft.
  • Does not lend itself to the consideration of uncertainty and adaptive management.
Measure category