A checklist presents a catalogue of issues that might be considered when assessing particular types of plan or programme. Checklists may list:

• Environmental, including health, concerns usually associated with certain plans and programmes
• Relevant environmental, including health, objectives for various development activities
• Indicators or specific guiding questions that can be asked when evaluating a plan or programme in certain fields

• Analysis context and baseline
• Identification of issues and impacts

• Help remember all the information relevant to a task
• Provide a simple way of identifying whether certain issues are relevant to a proposal and help to avoid overlooking potential issues

• Do not offer a very analytical approach to analysis
• Encourage neglect of any important effects that are not present in the checklist
• May cloud judgement with irrelevant information
• Do not specify the nature of cause-and-effect relationships – are prone to pigeon-holing impacts into certain categories whereas, in reality, an impact may be part of a complex system.

[Examples will be provided when supplied by countries to the Espoo Convention secretariat]

Matrices of impacts enable identification and presentation of potential impacts of proposed interventions (e.g. proposed objectives or actions) on the different components of the environment, including health. They are similar to checklists and can best be described as a two-dimensional checklist. They can use symbols, characters and numerical scores, in different scales or colours, to show the nature of the impact or its approximate scale or magnitude. Matrices can also illustrate cumulative and indirect impacts and impact interactions. For example, they may include columns or rows that summarize overall impacts of proposed interventions.

Presented information should be easy to verify, and each matrix may need to be accompanied by a text explaining the nature of specific effects.

Matrices of conflicts or synergies show relationships between proposed interventions (e.g. proposed objectives or actions) and relevant environmental, including health, objectives or on other objectives (e.g. in the case of more comprehensive assessments).

• Identification of issues and impacts
• Assessment of impacts
• Contributing to development and comparison of alternatives

• Provide a good visual summary of impacts, which is easy to interpret
• Can be adapted to identify cumulative impacts as well as impact interactions
• Is a useful tool for presenting results, for example from subjective assessments, or from numerical modelling
• Can be designed to include the potential for interactions and can combine the impacts from various actions or from a number of projects. They can also be used to compare alternative options.

• Matrices often present only direct impacts
• May lead users to overcomplicate the analysis by considering all potential interactions between all proposed actions and all environmental, including health, issues. This is time consuming and may divert attention to minor impacts.

Sample matrix for assessment of the measures of the National Development of the Czech Republic in the Proceedings of the International workshop on Public Participation and Health Aspects in SEA (the REC)

http://www.rec.org/REC/Publications/ Proceedings/SEAproceedings.pdf

Matrix method suggested to screen alternative (in an SEA of carbon dioxide capture and storage)

Collective expert judgments iteratively canvass opinions and perspectives from recognized ‘experts’ in relevant fields.

Specific means that meet this aim may include simple workshops, interviews or questionnaires with a problem-solving focus (for example, to assess possible impacts or risks), as well as more sophisticated techniques. These means are described in Annex A5.2

The Delphi technique represents the systematic and powerful tool for formulation of collective expert judgements. It enables identification of prevailing judgment within a large group of experts who do not directly interact with each other. This technique thus reduces costs and enables participation of experts from geographically dispersed locations. It also defines principles and steps that can be effectively used for formulation of expert judgements using other less time-consuming techniques (e.g. workshops, conferences, etc).

The Delphi technique is based on the following key steps:

• Clarify what information is needed, design the questions and determine the time line of the process.
• Identify the appropriate number of experts to serve on the Delphi panel and explain the tasks.
• Prepare and distribute the initial set of open-ended or closed-ended questions.
• Collect and analyze the first responses and compile the responses. If open-ended questions were used extensively, analyze and present the first set of responses within an appropriate theoretical framework, typology, or outline.
• Send the same question out to the same panellists a second and third time. The process may be repeated with additional waves, if necessary. Include the responses with the question so that panellists can read the other opinions and adjust their own opinions. Respondents will read each other's ideas and answer the question again. As information is exchanged, people incorporate each others’ perspectives and information into their thinking and arrive at a fairly accurate understanding of the critical issues to consider in their decision-making process.

Always prepare and distribute a final report to panellists. One of the motivations for participating in a Delphi panel, particularly for specialists, is to learn firsthand, before others, what the results of the Delphi study are.

• Analysis of context and baseline
• Identification of issues and impacts
• Assessment of impacts

• Can deal with quite technical or complex issues.
• Allows sharing of ideas and consensus in decision-making by a large number of stakeholders who are geographically distanced
• Convenient to participants, as they can contribute from their own office or home.

• Takes time for the organizers (can run for several months)
• Participant commitment may falter if the process takes too long or they have other commitments
• Large amounts of data need to be carefully assessed and distributed, so the process can be expensive to manage

Nehiley, J. M. (2001) How to Conduct a Delphi Study

Dick, B. (2000), Delphi face to face, available at http://www.uq.net.au/action_research/ arp/delphi.html

Overlay mapping and GIS are methods for identifying the spatial distribution of impacts. Both methods involve the preparation of maps or layers of information that are then superimposed on one another. They can:

• Provide a composite picture of the receiving environment, including health (sensitive areas or resources, current pressures, etc.)
• Present impacts of previous developments
• Illustrate potential impacts of future activities
• Map the cumulative impacts, or map the impacts on a number of receptors

An important feature of spatial analysis is its ability to consider topographic data that become essential when planning infrastructure or analyzing certain impacts (e.g. noise, local air quality, visual impacts).

Manual overlay mapping uses a series of transparent maps with different information shown on each layer.

• Analysis of context and baseline
• Identification of issues and impacts
• Assessment of impacts
• Contributing to development and comparison of alternatives

• Both techniques enable visual presentation of past, present and future impacts

• Both techniques can be expensive and time consuming.

British Geological Survey report (2004) on Strategic environmental assessment (SEA) and future aggregates extraction in the East Midlands Region presents a number of GIS usage methods and approaches:

http://www.mineralsuk.com/britmin/ CR_04_003N.pdf

Accurate trend analysis is one of the most important aspects of any strategic assessment. In the context of SEA, its can be defined as an interpretation of environmental pressures and changes in the state of the environment, including health, over time.

Trend analysis uses data sets and helps to trace any trends or patterns. Trends can be linear, exponential or cyclical and they should, where possible, be analyzed over a correct temporal scale. The presentation of trends can be fairly simple, e.g. a line graph, or quite complex, e.g. using three-dimensional graphics or video simulation. There are numerous computer programs that facilitate trend analysis (e.g. the simplest ones being computer spreadsheet software, more advanced ones including RATS, GAUSS, JMP, etc.).

Trend analysis facilitates presentation of the main linkages between environmental pressures and corresponding (sometime delayed) changes in the state of the environment. As such, it can also assist predictions of future impacts. Some trends can be safely extrapolated on the assumption that the trend is going to continue in the same dynamic. When doing so, it is important to realize that virtually every trend has a corresponding counter-trend. Oversimplified extrapolation that does not consider how the trend will evolve once it reaches a key breaking point (e.g. when carrying capacity of the surrounding environment has been reached or exceeded), or once the counter-trend becomes stronger, may be misleading.

Trend extrapolation can thus play an important role in medium-to-short term forecasts when no major counter-trends or breaking points are expected. Long-term trends can be precisely determined only through modelling, if at all.

• Analysis of context and baseline
• Assessment of impacts

• Can greatly assist in the quantification of cumulative impacts in cases where environmental data are available over long periods of time

• There are often situations where it is not possible to obtain relevant or sufficient data on specific environmental pressures.
• In cases where there are gaps in data, it becomes important to use appropriate statistical methods to ensure the proper interpretation of trends. Such analysis may be quite cumbersome.

Different examples of trend analysis are presented in the Transport Analysis Guidance on SEA for Transport Plans and Programmes (2004) by UK Department for Transport, available at

http://www.webtag.org.uk/webdocuments/ 2_Project_Manager/11_SEA/2.11.pdf

Networks and systems illustrate the cause and effect relationship. They identify the pathway of an effect using a series of chains (networks) or webs (system diagrams) between a proposal and the wider environment in which it is proposed to operate. These techniques can help to illustrate implications of the plan or programme on the subsequent decisions and its knock-on effects on other developments (decision-trees) or a gradual progression from direct immediate effects to indirect or longer-term or delayed effects (effect networks).

If sufficient data is available, it is possible to include quantitative measurements in the network diagram. This technique constitutes a simple form of modelling and allows the evaluation of effects and their interactions (see more on modelling below).

In developing a network or system, the steps might comprise:

• Consider and list the measures
• Identify effects of each measure on other developments or on directly affected elements of the environment, including health
• Identify secondary knock-on effects on other developments or environmental, including health, elements – thus illustrating pathways from direct effects to indirect implications
• When doing so, determine whether any cumulative effects on the same development pattern or element of environment, including health, occur
• If appropriate consider a loop to show any feedback
• If appropriate use quantitative techniques as a simple form of modelling to evaluate the effects.

• Identification of issues and effects
• Assessment of effects
• Contributing to development & comparison of alternatives

• Use of flow diagrams can assist with understanding effects
• Network diagrams clearly illustrate the interaction pathways – the mechanism of cause and effect is made explicit
• Although network analysis may not be quantitative, it may still provide a good basis for choosing which processes could be quantified or modelled in further detail

• No spatial or temporal scale can be provided
• Network analysis uses a holistic approach to impact assessment, so it may require a considerable effort to complete
• Diagrams can become too complex

[Examples will be provided when supplied by countries to the Espoo Convention secretariat]

Modelling is an analytical tool that enables the quantification of environmental, including health, effects by simulating environmental, including health, conditions. Often models use computer technology to predict the effects. A mathematical model lends itself to the spatial and temporal analysis of aspects of the environment such as air and water quality, water volume and flows, noise levels and airborne deposition on soils and vegetation. Other types of model include socio-economic models, species habitat models and expert systems that allow the effects of a project to be determined through a programme of decisions.

The most advanced and used models are for air quality, water quality and noise modelling as well as ecological and visual modelling. There are a number of different models available for assessing those effects. They can be used to consider direct and cumulative effects of a number of measures proposed in the plan or programme and enable some assessment of indirect effects resulting from emissions or effects of development proposals.

• Assessment of effects
• Contributing to development & comparison of alternatives

• Noise, air dispersion and hydrodynamic models are well developed and generalised in form and are therefore suited to the analysis of direct and cumulative effects
• Modelling results can be combined with overlay techniques effectively, for example to assess different alternatives
• Modelling is also a particularly useful tool for simulating effects over time and in space

• Models are extremely costly and time consuming
• The accuracy of the model is only as good as the baseline environmental data used to construct, calibrate and run it and the assumptions made in its design
• It is difficult for any model to address realistically every intricacy of the natural system
• Models also have a reputation for being pessimistic in their outcome and data can be manipulated relatively easily
• Developing a new model is generally demanding in terms of cost, expertize, time and possibly data. For this reason it is best suited to larger and more complex projects
• It is therefore often more appropriate to use a model that has been used previously and is therefore established and accepted

[Examples will be provided when supplied by countries to the Espoo Convention secretariat]

Scenario building is a process of designing hypothetical situations that incorporate the most uncertain and important driving forces affecting future development. The technique is aiming at addressing of the following questions:

1. What are the driving forces?
2. What are the uncertainties?
3. What is inevitable?
4. How about this or that scenario?

Scenario building is sometimes associated with forecasting, which is also used to predict future events, but it uses calculations based on historical data. There are many scenario-building techniques. A method based on 8 steps of scenario-building approach described in The Art of the Long View by Peter Schwartz (see reference below) may be of interest in SEA.

1. Identify focal issue or decision: Where having scenarios will be helpful? What do you really want to know?
2. Identify the key forces in the local environment: What factors influence the focal issue or decision? What will decision makers want to know when making their choices?
3. Identify driving forces: What major trends influence the key forces?
4. Rank the key and driving forces on the degree of importance and the degree of uncertainty. Identified key or driving forces should be looked at carefully as they are more critical to providing different scenarios that are important. Select 2-3 to study further.
5. Select scenario logics: Following the ranking, take the information to define the key variables for building scenarios.
6. Flesh out the skeletal scenarios by looking at key factors and driving forces developed in steps 2 and 3. Each key factor and driving force should be given some role in the scenario. For example, if you had two key factors and 2 driving forces, that is 4 possible combinations that can be built into a narrative about the scenarios.
7. Define implications: Once the scenarios are defined, look for implications – what would happen in the different scenarios? Build these into your scenarios.
8. Select the leading indicators and signposts: Relate the scenarios to real situations – some are more likely than the others given the trends underway. Then, identify further indicators (e.g., leading indicators) that could alert you if this scenario plays out.

• Assessment of effects
• Contributing to development & comparison of alternatives

• Scenarios provide a simplified version of reality and a way of creating a shared understanding of complex systems among those that work in them
• They can be used to test ideas and explore consequences

• Scenario development and interpretation requires relatively high technical skill
• Scenario-based analysis is no better than the model itself and the data used. Careful testing and validation are necessary to avoid decisions or actions based on a flawed model.
• Scenarios may involve complex mathematical operations or graphic images that are hard to understand and explain to non-technical audiences and policy makers

• Detailed overviews of various approaches to scenario development can be obtained at: http://www.dit.ie/DIT/built/futuresacademy/ whoweare/ Scenario-Building.doc and http://www.gbn.com/ ArticleDisplayServlet.srv?aid= 27802
• Global Business Network (http://www.gbn.com/)
• Information portals on scenario building can be found at http://www.plausiblefutures.com/ index.php?cat=6691a and www.well.com/~mb/scenario/

Life Cycle Assessment (LCA) is a technique for assessing the potential environmental, including health, effects and potential issues associated with a product (or service), by:

• Compiling an inventory of relevant inputs and outputs
• Evaluating the potential environmental, including health, effects associated with those inputs and outputs
• Interpreting the results of the inventory and effect phases in relation to the objectives of the study

LCA generally addresses at least energy but may also include emissions into air and water, land use and depletion of natural resources.

• Identification of issues and effects
• Assessment of effects
• Contributing to dev­elop­ment & comp­arison of alter­nat­ives

• Comprehensive analysis of effects based on cradle-to-grave approach
• LCA serves as validation for the system boundaries used in the evaluation of the environmental effects

• Apart from energy it is very difficult to quantify emissions from all possible processes, requiring huge emission inventories
• LCA must be used cautiously and, in the interpretation of the inventory, care must be taken with subjective judgments. Certain products do not provide enough information to accurately assess environmental effects (e.g. metals, VOC). Also, production processes and usage might differ from country to country.
• Reliable methods for aggregating figures generated by LCA, and using them to compare the life-cycle effects of different products, do not yet exist
• Preserving the confidentiality of commercially-sensitive raw data without reducing the credibility of LCAs is also a major problem
• LCA does not have spatial and temporal resolution
• In most situations it is impossible to prove conclusively using LCAs that any one product or any one process is better than any other, since many parameters cannot be simplified to the degree necessary to reach such a conclusion. Many LCAs have reached different and sometimes contradictory conclusions about similar products.

• INTERREG III B Project Alp Frail (http://www.alpfrail.com/) Operational Solutions for the transalpine railway freight traffic for sustainable management of connections of the economic areas within the alpine space, available at http://www.deutscher-verband.org/seiten/dv-ev-projekte/downloads/Alp_Frail-Kurzdarstellung-CADSES-en.pdf
• Complete Life Cycle Assessment for Vehicle Models of the Mobility CarSharing Fleet Switzerland Gabor Doka, Doka Life Cycle Assessments Sabine Ziegler, Mobility Car Sharing Switzerland Conference paper STRC 2001 Session Emissions, available at http://www.strc.ch/doka.pdf

• Umberto – software tool to model, calculate and visualize material and energy flow systems, available at http://www.umberto.de/en/
• Gabi 4 – Life Cycle Engineering, Green House Gas Accounting, Benchmarking and Energy Efficiency, available at http://www.environmental-expert.com/ - search for "gabi" under "Software and on-line solutions"
• Greet model, ANL – Fuel-Cycle Model for Transportation Fuels and Vehicle Technologies, available at http://www.transportation.anl.gov/software/ GREET/
• E2database LBST – fuel chain analysis decision aiding tool, E3database for energetic, emissions-related and economic regional evaluation of hydrogen fuel chains, Agator, His, Schindler, available from http://www.lbst.de/
• GEMIS – Global Emission Model for Integrated Systems Germany, available at http://www.oeko.de/service/gemis/ en/index.htm
• SimaPro – collects, analyzes and monitors the environmental performance of products and services, available at http://www.pre.nl/simapro/ simapro_lca_software.htm

CBA seeks to compare the monetary value of benefits with the monetary value of costs. A benefit is defined as anything that increases human well-being, and a cost as anything that decreases human well-being. In turn, human well-being is determined by what people prefer. Preferences are either revealed through choices and market behaviour or are stated through questionnaire (market research) procedures. Measurement of a preference is obtained by finding out the individual’s willingness to pay for a benefit or for the avoidance of a cost, or their willingness to accept compensation for tolerating a cost or foregoing a benefit. These WTP (‘Willingness to pay’ for an environmental gain) and WTA (‘Willingness to accept’ compensation for an environmental loss) concepts provide estimates of what is known as consumers’ surplus. The aim of maximizing benefits minus costs, or of requiring benefits to exceed costs, is fundamental to the concept of economic efficiency which has the overall goal of maximizing the sum of human well-being in a given economy.

In many cases, WTP can be found from market behaviour and damages can be estimated directly. An example might be the effects of air pollution on crop productivity. The relationship between the two is secured from experimental or field observation and is known as a dose response function. The loss in yield can then be measured by the market value of the crops. This combination of a dose-response function and a market value is one instance of a production function approach.

In other cases there is no evident market to refer to. Revealed preference analysis looks at ‘surrogate markets’, markets in goods and services that embody some environmental feature. An example would be a house and the market would be the housing market. Each house is seen as a ‘bundle’ of characteristics or attributes and these attributes contribute to the price of a house. Among the attributes might be peace and quiet or air pollution. By statistically regressing the price of the house on the attributes the ‘hedonic price coefficient’ can be found. Thus, many studies have found significant relationships between air pollution, disamenity and noise and house prices. Further manipulation of the data by then showing how the hedonic prices vary with income etc., produces measures of WTP for noise reduction etc. Stated preference techniques use questionnaires to elicit preferences in contexts where there may be no surrogate market. In principle, the questionnaires are similar to conventional market research for new or modified products. Contingent valuation asks directly what people are WTP, or asks if they are WTP ‘X’ where X is some starting point sum. Contingent ranking (or conjoint analysis) ranks alternatives and anchors one of the alternatives to a money price. Individuals’ WTP is then inferred rather than derived directly from answers about WTP.

• Assessment of effects
• Contributing to development & comparison of alternatives

• CBA is a widely used and recognized technique
• It provides easy-to-understand information (in monetary terms) to the decision maker
• It allows comparison of effects which might otherwise be difficult to compare, e.g. time savings for motorists versus loss of landscape value

• There are many issues of contention in CBA, including appropriate discount rates and the reduction of future costs and benefits to net present values, and the valuation of health, life and environmental goods and services
• There are many technical difficulties and much dispute regarding the methods used within CBA, such as contingent valuation

UK Department of the Environment, Transport and the Regions, Review of Technical Guidance on Environmental Appraisal: A Report by EFTEC (Economics for the Environment Consultancy)

http://www.defra.gov.uk/environment/economics/ rtgea/1.htm

• Boardman A, D Greenberg, A Vining, D Weimer, 1996. Cost-Benefit Analysis: Concepts and Practice, Prentice Hall, Upper Saddle River, USA.
• Dixon J, L Fallon Scura, R.Carpenter and P.Sherman, Economic Analysis of Environmental Impacts, Earthscan, London, 1994.
• Hanley N and C Spash, 1993. Cost-Benefit Analysis and the Environment, Edward Elgar, Cheltenham.
• Mishan E, 1988. Cost Benefit Analysis, Allen and Unwin, London.
• Pearce DW, D Whittington, S Georgiou and D James, 1994. Project and Policy Appraisal: Integrating Economics and the Environment, OECD, 2 rue Andre Pascal, Paris.
• Risk and Policy Analysts Ltd, Guidance on Environmental Costs and Benefits, Report to the Environment Agency, January 1998.
• Winpenny J, 1995. The Economic Appraisal of Environmental Projects and Policies: a Practical Guide, OECD, Paris.

MCA is a method for evaluating alternative options against several criteria, and combining the separate evaluations into an overall evaluation. It can be used to identify a single most preferred option, to rank options, to short-list a limited number of options for subsequent detailed appraisal, or simply to distinguish acceptable and unacceptable options.

MCA helps to manage that complexity by converting the evaluation to a numerical score. All MCA approaches incorporate judgments that are expressed in weights of criteria and in performance evaluations. Usual steps in a multi-criteria analysis are as follow:

1. Identify assessment criteria. Theycan measure key consequences of proposed alternative options based on the relevant objectives or on their likely impacts. Carefully examine the proposed set of criteria to ensure that:

• The set of criteria is complete (no significant criteria is missing)
• There are no redundant criteria (these may include insignificant criteria or criteria where all options perform equally)
• Criteria are measurable (it must be possible to assess - at least qualitatively - how well each option performs in relation to the criterion)
• Criteria are mutually independent (there is no double counting)

2. Analyze relative importance of criteria (weighting). Most MCA techniques enable to determine relative weights of each criteria in the decision -making. Methods of weighting vary from simple techniques (e.g. comparing criteria against each other to determine their relative weight) to complex methods (e.g. sociological surveys to determine importance of each criterion in the affected community).

3. Analyze performance (scoring). Before scoring the performance, determination of what constitutes the best and the worst performance in a given context is required. Scoring performance may be done through three basic means:

• Direct rating through expert judgments by assigning a score to each option (e.g. 0-100 point scale)
• Determining performance against criterion-specific function that defines gradual progression from the worst to the best performance
• Judging performance of options against each other. Methods vary – through simple ranking of options to determine the order of their performance (e.g. on criterion 1 the option A scores best, C second and B third) to complex calculations (based on fuzzy sets)

4. Multiply weights and scores for each of the options and derivation of their overall scores. Each option's performance on a criterion is multiplied by the weight of the respective criterion – this done for all the criteria. The sum yields the overall relative score for the given option. The results for all options are compared and discussed.

5. Analyze sensitivity to changes in scores or weights. Sensitivity shows how changes in the scores or weight affect the results of MCA. Such analysis may be essential if:

• There are serious uncertainties about performance of some options against selected criteria, or
• If decision-makers or stakeholders argue about the relative weights of criteria used in MCA.

• Assessment of impacts
• Contributing to development & comparison of alternatives

• MCA takes into account different criteria at the same time, which is impossible with the usual decision-making process based on a single criterion;
• MCA may be used to bring together the view of the different stakeholders in the evaluation;
• MCA is transparent and explicit (the scores and weights are recorded), easy to audit;
• MCA may facilitate communication with decision maker and sometimes with the wider community.

• MCA reduces rational debate about various pros and cons of proposed alternative options into discussion about abstract numbers (scores and weights)
• MCA cannot facilitate consensus on very controversial decisions;
• By presenting quantitative information (aggregated scores) MCA may create a false impression of accuracy despite the fact that application of MCA heavily depends on a value judgment;
• The results may be manipulated by those who master MCA (i.e. simple sensitivity analyses that are normally performed within MCA show criteria that best influence outcomes and this knowledge can be used to produce different overall scores).

Multi-criteria Analysis Manual of the UK Government, available at http://www.communities.gov.uk/documents/ corporate/pdf/146868

The Journal of Multi-Criteria Decision Analysis (ISSN: 1099-1360). By subscription only. More information can be obtained from the editor val@mansci.strath.ac.uk or at http://www3.interscience.wiley.com/cgi-bin/ journal/5725/home

Department of the Environment, Transport and the Regions, Review of Technical Guidance on Environmental Appraisal: A Report by EFTEC (Economics for the Environment Consultancy) http://www.defra.gov.uk/environment/economics/ rtgea/1.htm