Portfolio decision analysis methods in environmental decision making

Lahtinen, Tuomas J.; Hämäläinen, Raimo P.; Liesiö, Juuso

doi:10.1016/j.envsoft.2017.04.001

Cited by 34 publications

(16 citation statements)

References 59 publications

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“…In the final stage of the proposed methodology, the project portfolio is created for the strategic planning of OWFs in Greece and its analysis is carried out. The portfolio decision analysis approach combines multi-criteria evaluation and mathematical optimization and is characterized by the following elements: (i) formation of one portfolio of project proposals (basic goal), taking into account multiple objectives, interactions, and resource constraints; (ii) capturing of the decision makers' preferences regarding the objectives by utilizing a multi-attribute value function; (iii) implementation of integer optimization to obtain the feasible portfolio with the greatest overall strategic value and (iv) potential implementation of interactive "what-if" analyses to examine how the optimal portfolio of actions changes in response to changes in the model parameters or constraints [32]. The objective of this particular stage is to calculate the strategic value of the proposed projects based on specific AC and to prioritize the implementation of the portfolio projects.…”

Section: Stage 5 (Stg5)-assessment Of Sa and Portfolio Analysismentioning

confidence: 99%

Strategic Planning of Offshore Wind Farms in Greece

2020

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In the present article, a new methodological framework for the efficient and sustainable exploitation of offshore wind potential was developed. The proposed integrated strategic plan was implemented for the first time at national spatial planning scale in Greece. The methodological approach is performed through geographical information systems (GIS) and Microsoft Project Server Software and includes five distinct stages: (i) definition of vision/mission, (ii) identification of appropriate areas for offshore wind farms’ (OWFs) siting, (iii) determination of the OWFs’ layout, (iv) calculation of the OWFs’ (projects) total investment cost and, finally, (v) portfolio analysis. The final outcome of the proposed strategic planning is the prioritization of the proposed sixteen offshore wind projects based on their strategic value, as well as the estimation of the overall investment cost of the entire portfolio. High economic, socio-political and environmental benefits could be achieved through the implementation of only 60% of the total investment capital of the proposed strategic plan.

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Section: Stage 5 (Stg5)-assessment Of Sa and Portfolio Analysismentioning

confidence: 99%

Strategic Planning of Offshore Wind Farms in Greece

2020

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“…Similar approaches exist in landscape planning, including multiobjective forest planning (Mendoza, Bare, and Campbell 1987; Pukkala 1998; Uhde et al 2015) and multiobjective land-use planning (Krcmar, van Kooten, and Vertinsky 2005). Environmental economists have studied tradeoffs between multiple economic and environmental objectives (Nijkamp 1975; Rodríguez et al 2006; Mantoglou and Kourakos 2007; Kennedy et al 2008; Maringanti, Chaubey, and Popp 2009; White, Halpern, and Kappel 2012; Gramig et al 2013; Lester et al 2013; Lahtinen, Hämäläinen, and Liesiö 2017).…”

Section: Conservation Planning Lessons Learned From Previous Researchmentioning

confidence: 99%

Diversifying to Reduce Conservation Outcome Uncertainty in Multiple Environmental Objectives

Ando¹,

Howlader

Mallory³

2018

Agric. Resour. Econom. Rev.

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In this paper we develop tools and intuition for portfolio optimization for multiple conservation objectives. We show it is more efficient to optimize a conservation portfolio for multiple goods jointly, allowing planners to exploit information about multiple dimensions of correlations between goods. We identified a new type of correlation that is important for optimal conservation planning of multiple objectives under uncertainty: scenario correlation between objectives in a given part of the landscape. The conservation planner faces a different kind of problem if the objectives at hand respond similarly rather than differently to climate shocks in subregions of the planning area.

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“…Given these challenges, a single standardized financial or health-economic value metric is unlikely going to be able to measure the value of investments. In such a context, a multi-criteria value framework could be more appropriate, incorporating stakeholder preferences to inform how criteria should be traded off [17][18][19]. Such a framework would require the elicitation of preferences of relevant stakeholders involved.…”

Section: Introductionmentioning

confidence: 99%

Prioritizing investments in rapid response vaccine technologies for emerging infections: A portfolio decision analysis

Gouglas

Marsh²

2021

PLoS ONE

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This study reports on the application of a Portfolio Decision Analysis (PDA) to support investment decisions of a non-profit funder of vaccine technology platform development for rapid response to emerging infections. A value framework was constructed via document reviews and stakeholder consultations. Probability of Success (PoS) data was obtained for 16 platform projects through expert assessments and stakeholder portfolio preferences via a Discrete Choice Experiment (DCE). The structure of preferences and the uncertainties in project PoS suggested a non-linear, stochastic value maximization problem. A simulation-optimization algorithm was employed, identifying optimal portfolios under different budget constraints. Stochastic dominance of the optimization solution was tested via mean-variance and mean-Gini statistics, and its robustness via rank probability analysis in a Monte Carlo simulation. Project PoS estimates were low and substantially overlapping. The DCE identified decreasing rates of return to investing in single platform types. Optimal portfolio solutions reflected this non-linearity of platform preferences along an efficiency frontier and diverged from a model simply ranking projects by PoS-to-Cost, despite significant revisions to project PoS estimates during the review process in relation to the conduct of the DCE. Large confidence intervals associated with optimization solutions suggested significant uncertainty in portfolio valuations. Mean-variance and Mean-Gini tests suggested optimal portfolios with higher expected values were also accompanied by higher risks of not achieving those values despite stochastic dominance of the optimal portfolio solution under the decision maker’s budget constraint. This portfolio was also the highest ranked portfolio in the simulation; though having only a 54% probability of being preferred to the second-ranked portfolio. The analysis illustrates how optimization modelling can help health R&D decision makers identify optimal portfolios in the face of significant decision uncertainty involving portfolio trade-offs. However, in light of such extreme uncertainty, further due diligence and ongoing updating of performance is needed on highly risky projects as well as data on decision makers’ portfolio risk attitude before PDA can conclude about optimal and robust solutions.

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Portfolio decision analysis methods in environmental decision making

Cited by 34 publications

References 59 publications

Strategic Planning of Offshore Wind Farms in Greece

Strategic Planning of Offshore Wind Farms in Greece

Diversifying to Reduce Conservation Outcome Uncertainty in Multiple Environmental Objectives

Prioritizing investments in rapid response vaccine technologies for emerging infections: A portfolio decision analysis

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