A participatory multi-criteria decision analysis framework reveals transition potential towards non-grid wastewater management.

Many public environmental decisions are wicked problems due to high complexity and uncertainty. We test a participatory value-based framework based on multi-criteria decision analysis (MCDA) to tackle such problems. Our framework addresses two important gaps identified in reviews of MCDA applications to environmental problems: including stakeholders and treating uncertainty. We applied our framework in two complex real-world cases concerning a paradigm shift in the wastewater sector; the transition from centralized wastewater systems to decentralized non-grid systems. Non-grid systems may solve some problems of centralized systems by reducing costs, increasing flexibility, and addressing growing demands on environmental issues, especially in rural areas. But non-grid systems have rarely been implemented in OECD countries, because it is unclear whether a transition is recommendable, and whether stakeholders would accept this shift. This problem allows addressing several fundamental research questions. As theoretical contribution, we found that stakeholder participation in MCDA is necessary, because different preferences of stakeholders can lead to different best-performing options in the assessments. Compared to the typical integrated assessment (IA) approach that excludes stakeholders' preferences, the MCDA process led to clearer outcomes. Results indicate that including the uncertainty of predicted consequences of options with Monte Carlo simulation helped discriminate between options and identify best-performing options. Challenging the uncertainty of elicited stakeholder preferences with sensitivity analyses, we found that best-performing options were especially sensitive to the MCDA aggregation model. Despite the high uncertainty, it was possible to suggest robust consensus options that would perform reasonably well for all stakeholders. As practical contribution, results indicated that a transition from the centralized to decentralized non-grid systems seems feasible. Most stakeholders assigned highest weights to environmental protection objectives in decision-making workshops. These stakeholder preferences implemented in MCDA led to a generally better assessment of innovative non-grid systems, especially when including urine source separation. Stakeholders perceived the MCDA process as beneficial and found results plausible. We conclude that the proposed participatory value-based framework is rigorous, but still feasible in practice. The framework is certainly transferable to any context and is open to testing and refinement in various applications to wicked decision problems.

The Potential of Knowing More: A Review of Data-Driven Urban Water Management.

The promise of collecting and utilizing large amounts of data has never been greater in the history of urban water management (UWM). This paper reviews several data-driven approaches which play a key role in bringing forward a sea change. It critically investigates whether data-driven UWM offers a promising foundation for addressing current challenges and supporting fundamental changes in UWM. We discuss the examples of better rain-data management, urban pluvial flood-risk management and forecasting, drinking water and sewer network operation and management, integrated design and management, increasing water productivity, wastewater-based epidemiology and on-site water and wastewater treatment. The accumulated evidence from literature points toward a future UWM that offers significant potential benefits thanks to increased collection and utilization of data. The findings show that data-driven UWM allows us to develop and apply novel methods, to optimize the efficiency of the current network-based approach, and to extend functionality of today's systems. However, generic challenges related to data-driven approaches (e.g., data processing, data availability, data quality, data costs) and the specific challenges of data-driven UWM need to be addressed, namely data access and ownership, current engineering practices and the difficulty of assessing the cost benefits of data-driven UWM.