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Appropriate MCDA methods for climate change policy evaluations

6 th International Scientific Conference on ‘Energy and Climate Change”, 9-11 October, Athens, Greece. Dr. Popi KONIDARI Head of Climate Change Policy Unit of KEPA Mrs. Anna FLESSA, Dipl. – Ing. Fellow Researcher of KEPA. Appropriate MCDA methods for climate change policy evaluations.

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Appropriate MCDA methods for climate change policy evaluations

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  1. 6th International Scientific Conference on ‘Energy and Climate Change”, 9-11 October, Athens, Greece Dr. Popi KONIDARIHead of Climate Change Policy Unit of KEPAMrs. Anna FLESSA, Dipl. – Ing.Fellow Researcher of KEPA Appropriate MCDA methods for climate change policy evaluations National and Kapodistrian University of Athens – Energy Policy and Development Centre (KEPA)

  2. Structure • Problem • Approach • Conclusions National and Kapodistrian University of Athens – Energy Policy and Development Centre (KEPA)

  3. Problem National and Kapodistrian University of Athens – Energy Policy and Development Centre (KEPA)

  4. Framework • Evaluation of climate change policy options (instruments or mixtures) using the appropriate tool so as to • Understand aggregate performance • Improve implementation through modification of design characteristics • Develop optimum policy mixture through acceptable policy interactions (types and extent) National and Kapodistrian University of Athens – Energy Policy and Development Centre (KEPA)

  5. Evaluation tools • Monetary approaches • Outcomes of models • Multi-Criteria Decision Analysis (MCDA) methods • Qualitative comparisons National and Kapodistrian University of Athens – Energy Policy and Development Centre (KEPA)

  6. Selection problem • Large number of MCDA methods • Lessons taught • “Not all MCDA methods are appropriate for all types of decision making problems” (Polatidis H. et al., 2004; Joubert A.R. et al., 1997) • “There is no one single-best method for evaluating one type of decision making problems” (Mundaca L., Neij L., 2009) • “Assist an actor to make a decision in conformity with his goals” (Roy B., 1985) National and Kapodistrian University of Athens – Energy Policy and Development Centre (KEPA)

  7. Aim Identification of the most appropriate MCDA methods for climate change policy evaluations Strengths and weaknesses of MCDA methods Need 1 of DMs Needs of DMs Strengths and weaknesses of MCDA methods Strengths and weaknesses of MCDA methods Strengths and weaknesses of MCDA methods Need 2 of DMs Strengths and weaknesses of MCDA methods Strengths and weaknesses of MCDA methods Need 3 of DMs Strengths and weaknesses of MCDA methods National and Kapodistrian University of Athens – Energy Policy and Development Centre (KEPA)

  8. Approach National and Kapodistrian University of Athens – Energy Policy and Development Centre (KEPA)

  9. Approach • Step 1: Identify most frequently used MCDA methods • Step 2: Examination and classification of their strengths and weaknesses according to DMs’ needs • Step 3: Comparison • Step 4: Conclusion with the most appropriate ones for evaluation National and Kapodistrian University of Athens – Energy Policy and Development Centre (KEPA)

  10. Step 1 (1/2) • Search of peer-reviewed papers • Relevant key words • Time period: 2000-2014 • First round: Evaluations on climate change issues using a MCDA method or approach - 65 papers • Second round: Policy oriented evaluations (area of goal, options, criteria, outcomes) – 24 papers/29 cases National and Kapodistrian University of Athens – Energy Policy and Development Centre (KEPA)

  11. Step 1 (2/2) • Frequently used • AHP - Analytical Hierarchy Process • F-AHP - Fuzzy AHP • PROMETHEE - Preference Ranking Organization METHod of Enrichment Evaluation • AMS - Acronym for combination of AHP, MAUT and SMART • ELECTRE - Elimination Et Choix Traduisant le REalite • ELECTRE TRI, ELECTRE III • NAIADE,Swing, Simple MCA 31% 21% 17% 14% 7 MCDA Methods National and Kapodistrian University of Athens – Energy Policy and Development Centre (KEPA)

  12. Step 2 (1/12) • Need 1: Understand performance of all evaluated policy options • Set of criteria/subcriteria • Reflect • multiple attributes of policy options and context • preferences of users in criteria and significance • Same for all policy instruments or countries • Form and information of outputs • DMs’ preferences in form • Comprehensive but adequate, comparable and simple information regardless of user’s background and experience National and Kapodistrian University of Athens – Energy Policy and Development Centre (KEPA)

  13. Step 2 (2/12) • Strengths and weaknesses for “Set of criteria/subcriteria” • AHP, F-AHP • better understanding of problem through decomposition (+) • reduced complexity due to hierarchy of criteria-tree (+) • participation of stakeholders (+) • AMS • complete and confirmed set using AHP hierarchy (+) • reflection of preferences of all involved stakeholders (governments, target groups, researchers) (+) • Same set used for all types of policy instruments/mixtures or countries (+) National and Kapodistrian University of Athens – Energy Policy and Development Centre (KEPA)

  14. Step 2 (3/12) • Strengths and weaknesses for “Set of criteria/subcriteria” • SMART, MAUT • Combined with AHP Hierarchy (+) • PROMETHEE • No possibility of classical tree of decision (-) • Difficulty for user to obtain clear view and evaluate (-) • Usually combined with AHP (+) • ELECTRE • No possibility of classical tree of decision (-) • Usually all criteria or criteria of the same group are of equal importance (-) National and Kapodistrian University of Athens – Energy Policy and Development Centre (KEPA)

  15. Step 2 (4/12) • Strengths and weaknesses for “Form and information of outcomes” • AHP, F-AHP, AMS, MAUT, SMART • identify the “best” alternative out of a set of possible policy options (instruments or scenarios/policy portfolios) (+) • PROMETHEE • Partial or full ranking of evaluated policy options (+) • ELECTRE • Sometimes unable to identify the preferred alternative and produces only a core of leading alternatives (-) • Convenient for few criteria and large number of alternatives (-) • AHP • Often might lead to important losses of information(-) National and Kapodistrian University of Athens – Energy Policy and Development Centre (KEPA)

  16. Step 2 (5/12) • Need 2: Rely with confidence on outcomes based on structural background and main elements of method • Mathematical and procedural background • No time or knowledge to examine it - trust to the knowledge base • Simple and basic procedure • Main elements • consensus among stakeholdersfor defining weight coefficients National and Kapodistrian University of Athens – Energy Policy and Development Centre (KEPA)

  17. Step 2 (6/12) • Strengths and weaknesses for “Mathematical and procedural background” • All methods • Solid scientific background (+) • AMS • Combinations are considered operational synergies (+) • AHP - SMART combination is considered as better suited for quality evaluation(+) • PROMETHEE • More stable than ELECTRE (+) National and Kapodistrian University of Athens – Energy Policy and Development Centre (KEPA)

  18. Step 2 (7/12) • Strengths and weaknesses for “Main elements” • AHP • One consistency test (+) • F-AHP • absence of proven techniques for fuzzy consistency and fuzzy priority vector(-) • MAUT, SMART • difficulty to specify a tradeoff ratio between two different criteria (-) • AMS • Weight coefficients express preferences of three stakeholder groups (+) • Two consistency tests (+) National and Kapodistrian University of Athens – Energy Policy and Development Centre (KEPA)

  19. Step 2 (8/12) • Strengths and weaknesses for “Main elements” • PROMETHEE • No procedure for defining weight coefficients (-) • Difficulty to be completed particularly by an inexperienced DM (-) • ELECTRE • Uncertainty of which values each parameter (thresholds and limits for the categories) should take (-) • Difficulty in defining thresholds (-) National and Kapodistrian University of Athens – Energy Policy and Development Centre (KEPA)

  20. Step 2 (9/12) • Need 3: use of manageable method • Flexibility in inputs • Qualitative and quantitative data • Lack of reliable data • Incorporate outcomes of models • Ease to use • Low requirements in time and efforts • Available software • Facilitates users National and Kapodistrian University of Athens – Energy Policy and Development Centre (KEPA)

  21. Step 2 (10/12) • Strengths and weaknesses for “Flexibility in inputs” • AHP • Allows use of qualitative and quantitative data (+) • AMS • Use of MAUT or SMART depending on data availability (+) • Use of LEAP, Green-X outcomes (+) • ELECTRE • Often lack of detailed knowledge on energy and economic background of developing countries/small island developing states(-) National and Kapodistrian University of Athens – Energy Policy and Development Centre (KEPA)

  22. Step 2 (11/12) • Strengths and weaknesses for “Ease to use” • AHP • More popular due to its simplicity (+) • AMS • User – friendly (+) • PROMETHEE • Complicated, but simpler compared to ELECTRE (-) • Introduction of new criteria/alternatives any time (+) • ELECTRE • Complicated (-) • Introduction of new criteria/alternatives any time (+) National and Kapodistrian University of Athens – Energy Policy and Development Centre (KEPA)

  23. Step 2 (12/12) • Strengths and weaknesses for • Low requirements on time and efforts • AHP • Time consuming and exhausting with pairwise comparisons for criteria and alternatives (-) • F-AHP • Considerable computations (-) • careful handling of fuzzy operations and consistent interpretation of any results obtained (-) • AMS, SMART • Less computations compared to AHP (+) • Available software • AHP • More available software than the others(+) National and Kapodistrian University of Athens – Energy Policy and Development Centre (KEPA)

  24. Step 3 (High (High+, High 0, High -), Moderate (Moderate +, Moderate 0, Moderate -) and Low (Low+, Low 0, Low -)) National and Kapodistrian University of Athens – Energy Policy and Development Centre (KEPA)

  25. Conclusions • AHP and AMS are the most appropriate followed by PROMETHEE • Strengths • Set of criteria/sub-criteria • Form and information • Flexibility in inputs • Ease to use • Weaknesses • Considerable requirements in time and efforts National and Kapodistrian University of Athens – Energy Policy and Development Centre (KEPA)

  26. Thank you National and Kapodistrian University of Athens – Energy Policy and Development Centre (KEPA)

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