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Data envelope decision analysis

Financing Adaptation Sufficient financial resources and capacity to access available resources are critical for adaptation. Also, various financial mechanisms, incentives and budget systems are needed to implement climate adaptation measures. In addition, the process of comparing different measures, including their costs and benefits, sometimes also consulting with stakeholders for their preferences is important. This can be part of risk dialogues, when different climate impacts, and needed strategies are discussed. The economic impacts of potential disasters if not prevented also need to be understood. Understanding the financial implications of climate impacts provides the rationale for making decisions on investments. Financial mechanisms can provide incentives for actors to make adaptive choices. This is for example clear in urban development when space is needed to absorb rainwater and prevent flooding. This requires reserving space for infiltration, via for example nature-based solutions. However, it is not seen as having a direct financial benefit for the developer, it will not be implemented. It is important to consider pooling resources in climate adaptation for water. For example, if a coastal dune protects the coastline from flooding, maintaining this coastal protection also benefits tourism and recreation that provide great financial revenues and socio-cultural values. Investing in the beach could therefore pool resources from these mentioned sectors.
Takeshi Takama
    788 Page views
  • 25th Mar 2011
  • 2 min read
2D plot of DEA - this is used in business

2D plot of DEA – this is used in business

This idea is from the Data Envelope Analysis (DEA), but the purpose is very different. DEA is used to study the efficiency of each Decision Making Unit (DMU). For example, if an input and output is plotted in a 2D plot, it will be like the figure on the right hand. The observed sample ‘B’ is the most efficient for one measured output as it produces more output in relation to an input. So, the DMU becomes the basis to judge the efficiency of other samples., i.e. in this case the efficiency rate is measure by [Output of ‘B’ ] / [Output of other DMUs]. In this case, A’s efficiency can be improved without adding extra input within the boundary of A, A1, A2.

The measurement of efficiency is irrelevant to the climate envelope, so I will not explain this purpose of DEA beyond this. The relevant concept of DEA is non-parametric approach to find the frontier or range of data. In other words, DEA does not discuss the trend in sampled data or the probability of achieving certain level of outcomes. Instead, DEA will talk about the feasibility or possibility of certain outcome by showing the range of observed outcomes. As this does not talk about the probability, does not talk about the distribution of outcomes either.

There are some applications of DEA to ranked voting systems, this can be more relevant in context of choosing adaptation options under climate envelope. We need more studies on this subject to decide if we would like to pursue this tool.

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