Sustainable energy futures: Methodological challenges in combining scenarios and participatory multi-criteria analysis

This paper analyses the combined use of scenario building and participatory multi-criteria analysis (PMCA) in the context of renewable energy from a methodological point of view. Scenarios have been applied increasingly in decision-making about long-term consequences by projecting different possible pathways into the future. Scenario analysis accounts for a higher degree of complexity inherent in systems than the study of individual projects or technologies. MCA is a widely used appraisal method, which assesses options on the basis of a multi-dimensional criteria framework and calculates rankings of options. In our study, five renewable energy scenarios for Austria for 2020 were appraised against 17 sustainability criteria. A similar process was undertaken on the local level, where four renewable energy scenarios were developed and evaluated against 15 criteria. On both levels, the scenario development consisted of two stages: first an exploratory stage with stakeholder engagement and second a modelling stage with forecasting-type scenarios. Thus, the scenarios consist of a narrative part (storyline) and a modeled quantitative part. The preferences of national and local energy stakeholders were included in the form of criteria weights derived from interviews and participatory group processes, respectively. Especially in the case of renewable energy promotion in Austria, the paper systematically analyses the potentials and limitations of the methodology (1) for capturing the complexity of decision-making about the long-term consequences of changes in socio-economic and biophysical systems and (2) for appraising energy futures. The paper concludes that assessing scenarios with PMCA is resource intense, but this methodology captures successfully the context of technology deployment and allows decision-making based on a robust and democratic process, which addresses uncertainties, acknowledges multiple legitimate perspectives and encourages social learning.     

Integrating Geographical Information Systems and Multi-Criteria Methods: A Case Study

This paper presents an application of the integration between Geographical Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) to aid spatial decisions. We present a hypothetical case study to illustrate the GIS–MCDA integration: the selection of the best municipal district of Rio de Janeiro State, Brazil, in relation to the quality of urban life. The best municipal district is the one that presents the closest characteristics to those considered ideal by the decision-maker. The approach adopted is the Multi-Objective Linear Programming (MOLP) and the chosen method is the Pareto Race.     

Structuring an MCDA model using SSM: A case study in energy efficiency

This work presents the use of a problem structuring method, Soft Systems Methodology (SSM), to structure a Multi-Criteria Decision Analysis (MCDA) model, aimed at appraising energy efficiency initiatives. SSM was useful to help defining clearly the decision problem context and the main actors involved, as well as to unveil the relevant objectives for each stakeholder. Keeney’s Value Focused Thinking approach was then used to refine and structure the list of objectives according to the perspective of the main evaluators identified. In addition to describing this particular case study, this paper aims at providing some general guidelines on how SSM may facilitate the emergence of objectives for MCDA models.     

A fuzzy-stochastic OWA model for robust multi-criteria decision making

All realistic Multi-Criteria Decision Making (MCDM) problems face various kinds of uncertainty. Since the evaluations of alternatives with respect to the criteria are uncertain they will be assumed to have stochastic nature. To obtain the uncertain optimism degree of the decision maker fuzzy linguistic quantifiers will be used. Then a new approach for fuzzy-stochastic modeling of MCDM problems will be introduced by merging the stochastic and fuzzy approaches into the OWA operator. The results of the new approach, entitled FSOWA, give the expected value and the variance of the combined goodness measure for each alternative. Robust decision depends on the combined goodness measures of alternatives and also on the variations of these measures under uncertainty. In order to combine these two characteristics a composite goodness measure will be defined. The theoretical results will be illustrated in a watershed management problem. By using this measure will give more sensitive decisions to the stakeholders whose optimism degrees are different than that of the decision maker. FSOWA can be used for robust decision making on the competitive alternatives under uncertainty.     

Using a multi-criteria decision aid methodology to implement sustainable development principles within an organization

The implementation of Sustainable Development (SD) within an Organization is a difficult task. This is due to the fact that it is difficult to deal with conflicting and incommensurable aspects such as environmental, economic and social dimensions. In this paper we have used a Multi-Criteria Decision Aid (MCDA) methodology to cope with these difficulties. MCDA methodology offers the opportunity to avoid monetary valuation of the different dimensions of the SD. These dimensions are not substitutable for one another and all have a role to play. There is an abundance of possible aggregation procedures in MCDA methodology. In this paper we have proposed an innovative method to choose a suitable aggregation procedure for SD problems. Real life case studies of the implementation of an outranking approach (i.e., ELECTRE) and of a mono-criterion synthesis approach (i.e., MAUT approaches based on the Choquet integral) were done to respectively rank 22 SD strategic actions within an expertise Institute and rank 20 practical operational actions to control energy consumption of the Institute’s buildings.     

Adapting RRI public engagement indicators to the Spanish scientific and innovation context: a participatory methodology based on AHP and content analysis

The paradigm proposed by Responsible Research and Innovation in the European Commission policy discourse identifies Public Engagement as a key area for exchange and dialogue among multiple actors following an inclusive and participatory process. Two definite set of indicators have already arisen at European level to monitor Public Engagement activities in the Science and Innovation realm. Our study aims to propose a deliberative participatory process, which involves selected stakeholders, for the adaptation of the European indicators to the specific Spanish scientific and innovation context. The methodological procedure is of exploratory nature and will be based in a combination of, on the one hand, qualitative content analysis techniques for the in-depth study of the deliberative process and the generation of indicators; and, on the other hand, a multi-criteria decision analysis technique such as the Analytic Hierarchy Process for the prioritization of the indicators. The discussion will focus on the procedure to articulate stakeholders’ values and use them as the basis for creating a context-based improved list of indicators. Two types of research questions arise: (i) Is the proposed methodology adequate for the adaptation of the European indicators to the Spanish context? (ii) What are the main indicators to monitor and to expand reflection on the public engagement in the Spanish science and innovation?

     

PROMETHEE based sustainable concept selection

Manufacturing organisations started recognising the importance of sustainability concepts: This is due to increasing government regulations and the consciousness of the sustainable products among the customers. In general, the sustainability concepts are classified into economic, environment and social perspectives. The classification of sustainable concept concerning to manufacturing firms can be material oriented, product design oriented and manufacturing process oriented. Sustainable concept selection is a vital problem for the contemporary manufacturing organisations. The selection process includes multiple criteria, therefore a Multi-Criteria Decision Making (MCDM) method namely, PROMETHEE (Preference Ranking Organisational Method for Enrichment Evaluation) is used in the study to select the best sustainable concept considering the criteria from social, economical and natural perspectives. The study revealed that the change of material as the best orientation and it implies that the material should be done at the first stage to achieve sustainability in the case organisation.     

PROMISE: A DSS for multiple objective stochastic linear programming problems

Most of the multiple objective linear programming (MOLP) methods which have been proposed in the last fifteen years suppose deterministic contexts, but because many real problems imply uncertainty, some methods have been recently developed to deal with MOLP problems in stochastic contexts. In order to help the decision maker (DM) who is placed before such stochastic MOLP problems, we have built a Decision Support System called PROMISE. On the one hand, our DSS enables the DM to identify many current stochastic contexts: risky situations and also situations of partial uncertainty. On the other hand, according to the nature of the uncertainty, our DSS enables the DM to choose the most appropriate interactive stochastic MOLP method among the available methods, if such a method exists, and to solve his problem via the chosen method.     

Transparent prioritisation,budgeting and resource allocation with multi-criteria decision analysis and decision conferencing

Managers in both for-profit and not-for-profit organisations continually face the task of allocating resources by balancing costs, benefits and risks and gaining commitment by a wide constituency of stakeholders to those decisions. This task is complex and difficult because many options are present, benefits and risks are rarely expressed as single objectives, multiple stakeholders with different agendas compete for limited resources, individually optimal resource allocations to organisational units are rarely collectively optimal, and those dissatisfied with the decisions taken may resist implementation. We first explain three current approaches to resource allocation taken from corporate finance, operational research and decision analysis, and we identify a common mistake organisations make in allocating resources. The paper then presents a technical process, multi-criteria portfolio analysis, for balancing the conflicting elements of the problem, and a social process, decision conferencing, which engages all the key players during the modelling process, ensuring their ownership of the model and the subsequent implementation. This socio-technical process improves communication within the organisation, develops shared understanding of the portfolio and generates a sense of common purpose about those projects that will best realise the organisation’s objectives. The paper concludes with lessons we have learned from actual practice. The authors want to thank Allergan and FCT (Portuguese Science Foundation) for their support.     

Decision support models in climate policy

Climate change is considered among the most critical risks that global society faces in this century. So far, climate policy strategies have been evaluated by means of a variety of climate-economy models, or Integrated Assessment Models (IAMs), in the aim of supporting climate-related decision making. However, their inherent complexity, the number and nature of driving assumptions, and usual exclusion of stakeholders from the modelling process raise the issue of the extent to which they can provide fruitful insights for policy makers. Moreover, as with all modelling frameworks, IAMs inevitably fail to incorporate all relevant types of uncertainty and risk when used as stand-alone tools. This exclusion can have a significant impact on the model outcomes, but can be mitigated if experts’ knowledge is elicited in a structured manner and effectively taken into account, towards identifying such factors or reducing respective knowledge gaps. At the same time, a growing number of research publications have been suggesting decision support frameworks for assessing specific aspects in climate policy, based on “bottom-up” approaches and participatory processes. The objective of this paper is to provide a critical review of such frameworks—namely Portfolio Analysis, Multiple Criteria Decision Making and Fuzzy Cognitive Maps—in order to explore their strengths and weaknesses in this area, and propose a new integrative approach, appropriately exploiting blends of these frameworks, to productively complement IAMs, towards enhancing climate policy support.     

Mean value and volume-based sensitivity analysis for Olympic rankings

This article describes two methods of creating Olympic rankings based on number of medals won. One method is based on the weighted mean value, which we will show is equivalent to the compromise programming known in Multi-Criteria Decision Analysis (MCDM). The other method uses volume-based sensitivity analysis. Both methods presented in this paper can also be used to construct rankings that include more than just the three top positions.     

Methodological aspects of multi-criteria decision analysis for policy support: A case study on pharmaceutical removal from hospital wastewater

Decision making in public and political contexts can be complex. Multi-attribute value/utility theory (MAVT/MAUT) can support such decision processes by providing a transparent framework that helps focusing on objectives and corresponding degrees of achievement by different alternatives.     

Mathematical programming and the sensitivity of multi-criteria decisions

We report on the current state of a project whose aim is to implement a framework for sensitivity analysis in Multi-Criteria Decision Making (MCDM). The framework is largely based on mathematical programming. Due to the potentially large number and nature of the mathematical programmes, it is far from trivial to provide solutions to all of them in acceptable computing times. The challenge is even greater when we recognize that much decision analysis is performed in the context of decision conferences where any sensitivity analysis needs to be conducted in near real time (preferably) on a PC. We present a parallel processing approach to this challenge and point to some of the difficulties still to be resolved. Preliminary results obtained on a network of transputers are discussed.     

The analytic hierarchy process with stochastic judgements

The analytic hierarchy process (AHP) is a widely-used method for multicriteria decision support based on the hierarchical decomposition of objectives, evaluation of preferences through pairwise comparisons, and a subsequent aggregation into global evaluations. The current paper integrates the AHP with stochastic multicriteria acceptability analysis (SMAA), an inverse-preference method, to allow the pairwise comparisons to be uncertain. A simulation experiment is used to assess how the consistency of judgements and the ability of the SMAA-AHP model to discern the best alternative deteriorates as uncertainty increases. Across a range of simulated problems results indicate that, according to conventional benchmarks, judgements are likely to remain consistent unless uncertainty is severe, but that the presence of uncertainty in almost any degree is sufficient to make the choice of best alternative unclear.     

Environmental impact assessment and ranking the environmental projects in Jordan

The Nominal Group Technique as a structured group decision process and PROMETHEE as a Multi-Criteria Decision Aid software are utilised to develop a reliable methodology to prioritise environmental projects in Jordan and to evaluate their environmental impacts. This methodology is novel in that it integrates the various analytical decision management techniques in order to increase the flexibility and efficiency of the environmental planning. This methodology is then implemented on a sample of wastewater projects in Jordan, and the results are discussed.     

A strategic decision support system framework for energy-efficient technology investments

Energy systems optimization under uncertainty is increasing in its importance due to on-going global de-regulation of the energy sector and the setting of environmental and efficiency targets which generate new multi-agent risks requiring a model-based stakeholders dialogue and new systemic regulations. This paper develops an integrated framework for decision support systems (DSS) for the optimal planning and operation of a building infrastructure under appearing systemic de-regulations and risks. The DSS relies on a new two-stage, dynamic stochastic optimization model with moving random time horizons bounded by stopping time moments. This allows to model impacts of potential extreme events and structural changes emerging from a stakeholders dialogue, which may occur at any moment of the decision making process. The stopping time moments induce endogenous risk aversion in strategic decisions in a form of dynamic VaR-type systemic risk measures dependent on the system’s structure. The DSS implementation via an algebraic modeling language (AML) provides an environment that enforces the necessary stakeholders dialogue for robust planning and operation of a building infrastructure. Such a framework allows the representation and solution of building infrastructure systems optimization problems, to be implemented at the building level to confront rising systemic economic and environmental global changes.     

Decision under risk as a multicriteria problem

Most of the approaches to decision problems under uncertainty are based on decision paradigms, generally associated to an optimization process that leads to a final solution. For the Decision Maker, the basic decision is thus what paradigm to choose, the rest of the procedure being mainly technical.     

A framework for participatory decision support using Pareto frontier visualization,goal identification and arbitration

There is a growing interest in promoting participation of lay stakeholders in public decision-making processes, possibly with the aid of Internet-based systems. This implies supporting non-sophisticated users and, consequently, developing user-friendly, yet rigorous, participatory decision support methods. We outline a framework to develop such methods based on interactive Pareto frontier visualization combined with expression of preferences in terms of feasible goals and using feasible goal-based arbitration.     

Pricing swing options with regime switching

Gasoline price is highly volatile and exhibits Markov regime-switching process. In the electricity and the natural gas markets, “swing” options, which can provide some protection against day-to-day price fluctuations, are used to incorporate flexibility in delivering acquired energy. We propose a framework for pricing swing options for an underlying variable that follows a regime-switching process. We study the proposed framework in the gasoline industry for pricing swing options under price uncertainty by extracting the gasoline market information, estimating the parameters of the regime-switching process, and then presenting different numerical examples.     

The evidential reasoning approach for multi-attribute decision analysis under interval uncertainty

The evidential reasoning (ER) approach is a method for multiple attribute decision analysis (MADA) under uncertainties. It improves the insightfulness and rationality of a decision making process by using a belief decision matrix (BDM) for problem modelling and the Dempster–Shafer (D–S) theory of evidence for attribute aggregation. The D–S theory provides scope and flexibility to deal with interval uncertainties or local ignorance in decision analysis, which is not explored in the original ER approach and will be investigated in this paper. Firstly, interval uncertainty will be defined and modelled in the ER framework. Then, an extended ER algorithm, IER, is derived, which enables the ER approach to deal with interval uncertainty in assessing alternatives on an attribute. It is proved that the original ER algorithm is a special case of the IER algorithm. The latter is demonstrated using numerical examples.