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Structured decision-making for sustainable water infrastructure planning and four future scenarios

  • Original Article
  • Published:
EURO Journal on Decision Processes

Abstract

Water supply and wastewater infrastructures are vital for human well-being and environmental protection; they adhere to the highest standards, are expensive and long-lived. Because they are also aging, substantial planning is required. Climate and socio-economic change create large planning uncertainties and simple projections of past developments are no longer adequate. This paper presents the initial phases of a structured decision-making (SDM) procedure which is designed to increase the sustainability of water infrastructure planning and includes various stakeholders in an exemplary Swiss case study. We evaluate the SDM approach critically based on stakeholder feedback, give general recommendations and provide ample material to make it applicable to other settings. We carried out 27 interviews and two stakeholder workshops. We identified important objectives for water infrastructure planning, including all three sustainability pillars and their respective attributes (indicators, benchmarks) to measure how well the objectives are achieved. We then created strategic decision alternatives, including “business-as-usual” upgrades of the central water supply and wastewater system as well as semi- to fully decentralized alternatives. To tackle future uncertainty, we developed four socio-demographic scenarios. We used these to test the robustness of decision alternatives in a later Multi-Attribute Utility Theory analysis. Additionally, we contribute to the topical discussion of combining scenario planning with multi-criteria decision analysis and demonstrate how various scenarios can stimulate creativity when generating decision alternatives. Their internal consistency is ensured by rigorously specifying them using a strategy generation table. Our SDM procedure can be adapted to inform decisions about sustainable water infrastructures in other contexts.

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Notes

  1. Formally, the linear additive value model is: \( v\left( a \right) = \sum\nolimits_{i = 1}^{m} {w_{i} v_{i} \left( {a_{i} } \right)} \) where: v(a) = total value of alternative a, a i  = attribute level of alternative a for attribute i, v i (a i ) = value for attribute i of alternative a, w i  = weights (or scaling constants) of attribute i, and sum of w i equals 1.

  2. “Sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs” (WCED 1987, p. 43).

  3. “Level 3 uncertainty represents deep uncertainty about the mechanisms and functional relationships being studied. We know neither the functional relationships nor the statistical properties, and there is little scientific basis for placing believable probabilities on scenarios. In the case of uncertainty about the future, Level 3 uncertainty is often captured in the form of a wide range of plausible scenarios. Level 4 uncertainty implies the deepest level of recognized uncertainty; in this case, we only know that we do not know” (Walker et al. 2010, p. 918).

  4. Stewart et al. (2013, pp. 683–684) distinguish “internal uncertainty” from “external driving forces”. Internal uncertainties concern e.g. the imprecision of measurements; probability frameworks can deal with these. Stewart et al. (2013) also classify epistemic uncertainty as internal uncertainty. In epistemic interpretations, probabilities can be used to quantify human (expert) knowledge or belief concerning the probability of something occurring. How to conceptually deal with uncertainties in environmental management with a specific focus on MCDA is discussed by Reichert et al. (2014). In contrast, external uncertainties may much more strongly affect the outcome of decisions we make today. These uncertainties (e.g. future climate, demographic or economic development) can often be better captured by the scenario approach.

  5. These include local practitioners (engineers or operating staff of treatment plants), representatives from administration and politics from the municipalities, the region (e.g. cantonal agency for waste, water, energy and air) and at national level (e.g. environmental protection agency; associations of water professionals).

  6. Essential objectives (without this objective I cannot judge whether a fundamental objective has been reached), important (without this it is difficult…) and nice to have (attainment of the fundamental objective can be judged without this).

  7. Specific questions: “What would be next step and who should do it?”/“What are your expectations, fears or hopes w.r.t. our project and Eawag?” (Eawag is our research institute, i.e. the Swiss Federal Institute of Aquatic Science and Technology)/“Do you have general feedback, also concerning the interview or recommendations?”.

  8. We based the planning on Swiss standards, preserving agricultural land and forests. We used typical building features in dense areas of Swiss cities (Zurich, Geneva), with up to 10-storey houses, and allocated these to areas earmarked for urban development in the current spatial plans of the study region. We added additional building sites for the Boom scenario and increased the population to 200,000 without “building” skyscrapers.

  9. The predictions for water demand are a function of scenario and alternative (e.g. water saving by using rain water or urine-separating toilets). Halving the water demand in the Doom scenario as defined in the workshop, for example, still translates into high water provision for the utilities, since there will likely be large water losses caused by low maintenance (leaky pipes).

  10. “Second, scenario analysis usually tries to identify a set of possible futures, each of whose occurrence is plausible but not assured. This combination of offering more than one forecast, and offering it in form of a narrative, is deemed by advocates to be a more reasonable approach than trying to predict (to four significant decimal places) what will happen in the future” (Schnaars 1987, p. 106).

  11. The sum of the probabilities for the realization of the scenarios is not 1 but can be anywhere between 0 and 1.

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Acknowledgments

We thank the Swiss National Science Foundation for funding within the National Research Program entitled “Sustainable Water Management” (NRP 61; www.nfp61.ch), Project Number: 406140_125901/1. We thank Cristina Fritzsche, Peter Reichert, Florian Schnetzer, Jun Zheng and Anja Zorn for their good collaboration and data collection, and the interview partners and workshop participants for their patience, support and valuable input. We thank Richard Michell for improving our English. We also thank two anonymous reviewers and the editors for their critical reviews which helped to greatly improve the manuscript.

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  1. Swiss Federal Institute of Aquatic Science and Technology, Eawag, P.O. Box 611, 8600, Duebendorf, Switzerland

    Judit Lienert, Lisa Scholten, Christoph Egger & Max Maurer

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  1. Judit Lienert
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Lienert, J., Scholten, L., Egger, C. et al. Structured decision-making for sustainable water infrastructure planning and four future scenarios. EURO J Decis Process 3, 107–140 (2015). https://doi.org/10.1007/s40070-014-0030-0

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