EIA is the selected technology and location linked consideration. Environmental assessment is specific, concrete, and deep. The endpoint is to determine clearly the environmental changes in terms of their scope, intensity and tolerability. Risks are assessed quantitatively. Very specific indicators of environmental quality may be applied.

Integration of strategic planning and environmental evaluation

Figure 3 provides a synthesis of the desired integration between strategic planning and tiering environmental evaluations. A brief overview of present issues and their possible resolution at different planning stages is also given. One should not overlook the importance of a loop from the fifth planning step (Plan implementation; Licensing) back to the step 2a informing all planning steps between success and issues in the plan implementation. This loop actually acts as a special form of historical monitoring of the plan implementation.

Comparative evaluation approach and its indicators

Multi-objective analysis (MOA) is aimed at facilitating comprehensive and consistent consideration, comparison and trade-offs of economic (financial), supply security, social, health and environmental attributes of selected alternative energy options or systems (could also be technologies for electricity production). These technologies are usually classified as thermal and non-thermal, or renewable and non-renewable, and include nuclear, coal, natural gas, biomass, hydro, PV-Photo Voltaic, and wind systems. MOA is expected to assist in the systematic evaluation of options according to multiple objectives/criteria which are different and which may not be measured on an interval (or even ordinal) scale. It should be understood that MOA is not primarily a method that can be used to derive impacts, but rather a method that places different types of impact on a comparable basis and facilitates comparisons between impacts originally estimated and expressed in different units (IAEA, 2000).

The main objectives of MOA are:

• to provide quantitative information where it is difficult to quantify the impacts directly;

• to display risk-benefit trade-offs that exist between different impact indicators;

• to facilitate comparisons and trade-offs;

• to facilitate understanding of the ‘values’ that need to be placed on different attributes.

The impact of each option under consideration should be represented using the units of measurement appropriate for each indicator or attribute. For example, impact indicators could be:

• The proportion of area utilized in the area (e. g. as a measure of land use impacts associated with each option referring to shares of existing and planned land-use);

• Health determinants affected/changed due to implementation of the alternative energy option.

Table 2 indicates a set of aggregated indicators; these need to be developed further into measurable (possibly quantifiable) sub-indicators, so as to enable clear, verifiable, reproducible, and transparent evaluation. How this could be done in a comprehensive and transparent manner shows the example of Eurelectric RESAP — Renewables Action Plan (Eurelectric, 2011); the WG Environmental Management and Economics of the Eurelectric RESAP was tasked with an evaluation, based on existing literature — 296 selected worldwide studies — of the sustainability of renewable energy sources (RES) and other technologies over their whole life cycle (IPCC, 2011). The quantitative indicators applied in comparative evaluation were, e. g., carbon footprint, health impacts, water use, land use, biodiversity, raw materials, energy payback, etc. No matter the approach of selecting the indicators, caution should be exercised to ensure that the sub-indicators are chosen on the basis of:

• Relevance: indicators should reflect the overall objectives of the study;

• Directionality: indicators must be defined in a manner that ensures that their magnitude can be assessed and interpreted. This can be accomplished by specifying indicator measurement in terms of maximizing or minimizing, increasing or maintaining, etc.;

• Measurability: it should be possible to measure quantitatively or estimate directional impacts of each alternative on each indicator, in the unit of measurement that is appropriate for the indicator. Directionality and measurability together determine interpretability, i. e. they permit an interpretation of impacts as being good/bad or better/worse on each indicator;

• Manageability: in order to make assessments comprehensible and to facilitate effective comparison, the number of sub-indicators should not be too large.

Once the impact analyses have been consolidated, all the data should be expressed in a common metric, or ‘standardized’, so that the indicators can be compared and assessed. For example, impact indicators can be presented on an interval scale (e. g. from 0 to 1). The scale would indicate the relative effect of each fuel chain option being considered, on the basis of the relative magnitude of the impact indicator.

The process can be standardized as follows (adapted from Canter & Hill, 1979 and combined with IAEA, 2000):

Table 2. A list of main indicators to be applied in comparative multi-objective assessment

a. For each indicator, the analyst should identify the best value (e. g. highest contribution to employment) and the worst value (least contribution to employment) from the alternatives under consideration.

b. Then, the impact scale should be arranged on a horizontal axis from the best value (at the origin on the scale) to the worst value (at the extreme of the scale). The scale will depend on the units of measurement used in the impact assessment for each indicator.

c. Then, the standardized values of the impact indicators should be represented on the vertical axis, the same for all indicators and ranging from 0 to 1.

Finally, an indicator value of 1 should be assigned to the best option and 0 to the worst. The other options are then located according to their impact values on the line joining the best and worst.

Once the impact data are standardized, the following three methods could be used for the aggregation of results (IAEA, 2000; Kontic et al., 2006):

• Weighting; weight should be assigned to each indicator on the basis of its relative importance, for instance in a comparison of human health, global environmental impacts and land occupation (land-use impacts). Sensitivity analysis of the weighting should be performed in terms of investigating the difference in final comparative assessment results due to assignment of different weight values to a particular indicator (at least three justified variations should be considered); the final amalgamation method can be weight summation.

• Aggregation rules; based on standardization of the indicator’s values, and a tree structure of the whole set of indicators where a root of the tree represents the ultimate aggregated value; pairs or sets of multiple indicators should be aggregated and evaluated by means of the »if-then« approach. In this way the aggregation rules should be developed as an alternative to weighting. A final score is derived by comparing aggregated values at the tree root for the treated alternatives. This approach is described in detail in (Bohanec, 2003) while an example of a decision tree specifying evaluation indicators is presented in Figure 4.

• Trade-offs; the final product of the analysis should be presented as a description of trade-offs in either tabular or graphical form. Goal programming can employ the amalgamation method which ranks the alternatives on the basis of the deviation from a goal or target that analysts (decision makers) would like to see achieved: the less the deviation, the closer to the goal, and thus the higher the alternative is ranked.

The analysts’ view on the three methods and results achieved should be a part of the conclusions.