D5.2 Toolset for Societal Benefit Estimates

The key motivation behind Arctic PASSION is to contribute to the co-creation and implementation of a coherent, integrated Arctic observing system, dubbed pan-AOSS (pan-Arctic Observing System of Systems). It aims to overcome known flaws in the present observing system by refining its operability, improving, and extending pan-Arctic scientific and community-based monitoring and the integration with Indigenous and Local knowledge. It also aims at streamlining the access and interoperability of Arctic Data systems and services, and contribute to ensuring the economic viability and sustainability of the observing system.
While the previous output of Work Package 5 ‘Assessing Societal Benefits and Economic Impacts‘ (Deliverable 5.1) outlined the basic methods, theories, and data requirements for economic evaluation of service products, this Deliverable 5.2 aims to provide a practical and easy-to-follow Guidebook on the assessment of socioeconomic benefits of service products. Before the
Guidebook, economic theory ─ particularly from the field of environmental economics ─ is presented to complement the content of Deliverable 5.1 and to offer background information that is beneficial when both conducting an economic evaluation and interpreting the results of one. Especially concepts of value and efficiency are essential.

The structure of the Guidebook follows an evaluation hierarchy which is presented as a pyramid, where the base, or Step 1, is Qualitative Assessment. Qualitative analysis is conducted by applying value chain analysis (VCA) method. VCA allows for the inclusion of a wide range of benefits, including intangible ones with no market price, and is made in close collaboration with service developers. Qualitative assessment can also be conducted by means of value tree analysis (VTA), which assesses the information propagation process in relation to particular objectives of a service and locates societal benefit areas that are impacted by the information. Web traffic analysis can be used to both support the VCA and quantify the impacts in prospective steps where applicable. Within the Arctic PASSION project, each (pilot) service is assessed through the means of VCA, and the results support determining whether a quantitative analysis is feasible. Full results of VCA for each service will be provided in Deliverable 5.3.

Proceeding to quantitative assessments, Step 2 – Evaluation, introduces a step-by-step approach for the selection of an economic evaluation method and gives a brief introduction to each of the evaluation methods. Economic evaluation of the services can be conducted by means of, for instance, cost-benefit analysis (CBA), cost-effectiveness analysis (CEA), multi-criteria decision analysis (MCA) or economic impact assessment. Common limitations of each method are made visible, along with additional reading and helpful examples where the methods have been applied in the field of information services or similar. At the top of the pyramid, Step 3 – Valuation, focuses on the quantification and monetisation of the impacts within the economic evaluation. Like in Step 2, each valuation method is presented with information on their limitations and additional reading. Valuation methods applicable include methods such as contingent valuation (CV), travel cost method, and avoided cost method. Valuation methods can be adjusted to suit a specific context, and in some cases, used as an evaluation approach.

Selection of both the evaluation method and valuation method(s) is determined by the availability of data, types of the benefits measured (e.g., intangible, tangible, market value, non-market value), maturity of the service and the scope of the assessment (e.g., which sectors the assessment covers). In Deliverable 5.3 where economic evaluation of selected Arctic services is carried out, the factors limiting applicability of an economic evaluation will be discussed in more detail. Factors affecting the accuracy of the results, uncertainties and standing (i.e. who are impacted by the service) must be clearly stated when reporting the results, and the historic values must be discounted to obtain comparable results.