Decision analysis under uncertainity for sustainable development

Aplicat embargament des de la data de defensa fins el 31 de desembre de 2019Policy-making for sustainable development becomes more efficient when it is reliably backed by evidence-based decision analysis. Concretely, this is crucial in the planning of public services delivery. By translating "raw" data into information, decision analysis illuminates our judgment, and ultimately the policies we adopt. In the context of public services provision, decision analysis can support the prioritization of policy options and the monitoring of progress. However, most models are deterministic - that is, they do not consider the uncertainty in their evidence. These "incomplete" models, through their impact in policy decisions, can ultimately lead to an inefficient use of resources. The main barriers to a wider incorporation of uncertainty are: (i) the complexity of the approaches currently available, and (ii) the need to develop methods tailored to the specific decision problems faced in public services delivery. To overcome these limitations, this thesis intends to facilitate the incorporation of uncertainty in the evidence into decision analysis for sustainable development. We propose two methods. First, a non-compensatory multi-criteria prioritization under uncertainty model. Given multiple criteria and uncertain evidence, the model identifies the best policy option to improve service provision for sustainable development. The non-compensatory nature of our model makes it an attractive alternative to the widely used composite index approach. Second, a compositional trend analysis under uncertainty model to monitor service coverage. By considering the non-negativity and constant-sum constraints of the data, our model provides better estimates for measuring progress than standard statistical approaches. These two methods are validated in real case studies in the energy, water and health sectors. We apply our prioritization model to the context of strategic renewable energy planning, and the targeting of water, sanitation and hygiene services. Furthermore, we use our trend analysis model to the global monitoring of water and sanitation and child mortality. Our results emphasize the importance of considering and incorporating uncertainty in the evidence into decision analysis, particularly into prioritization and monitoring processes, both central to sustainable development practice.La formulación de políticas para el desarrollo sostenible es más eficiente cuando está respaldada por un análisis de decisiones basado en evidencia. Esto es especialmente crucial en la planificación de la prestación de servicios públicos. Al transformar los datos "brutos" en información, el análisis de decisiones ilumina nuestro juicio y, en última instancia, las políticas que adoptamos. En el contexto de la provisión de servicios públicos, el análisis de decisiones puede apoyar la priorización de las políticas públicas, así como el monitoreo del progreso. Sin embargo, la mayoría de los modelos son deterministas, es decir, no consideran la incertidumbre presente en la evidencia. Estos modelos "incompletos" pueden, a través de su impacto en las decisiones políticas, conducir a un uso ineficiente de los recursos. Las principales barreras para una incorporación más amplia de la incertidumbre son: (i) la complejidad de los enfoques actualmente disponibles, y (ii) la necesidad de desarrollar métodos adaptados a los problemas de decisión específicos a la planificación de los servicios públicos. Para superar estas limitaciones, esta tesis pretende facilitar la incorporación de la incertidumbre presente en la evidencia en el análisis de decisiones para el desarrollo sostenible. Proponemos dos métodos. Primero, un modelo de priorización multicriterio no compensatorio bajo incertidumbre. Dados múltiples criterios y evidencias con incertidumbre, el modelo identifica la mejor política para mejorar la provisión de servicios para el desarrollo sostenible. La naturaleza no compensatoria de nuestro modelo lo convierte en una alternativa atractiva al enfoque de índices compuestos ampliamente utilizado. Segundo, un modelo de análisis de tendencias composicionales bajo incertidumbre para monitorear la cobertura de los servicios. Al considerar las restricciones de no negatividad y de suma constante de los datos, nuestro modelo proporciona mejores estimadores para medir el progreso que los enfoques estadísticos estándar. Estos dos métodos se validan en casos de estudio reales en los sectores de energía, agua y salud. Aplicamos nuestro modelo de priorización al contexto de la planificación estratégica de energías renovables y de los servicios de agua, saneamiento e higiene. Además, utilizamos nuestro modelo de análisis de tendencias para el monitoreo global del accesso a agua y saneamiento, así como de la reducción de la mortalidad infantil. Nuestros resultados enfatizan la importancia de considerar e incorporar la incertidumbre de la evidencia en el análisis de decisiones, particularmente en los procesos de priorización y monitoreo, ambos centrales para la práctica del desarrollo sostenible.Postprint (published version

The Water–Employment–Migration nexus: buzzword or useful framework?

Motivation Critical development studies have overlooked water-related nexuses and frameworks proposed by development agencies that recognize that water and sanitation are linked to other development challenges and identify the synergies and trade-offs between sectors. In particular, critical development studies have ignored these nexus approaches urged upon the governments of the Middle East and North Africa (MENA) region, the world's most water-scarce region. Purpose The article presents a case study of the Water–Employment–Migration (WEM) nexus framework, which has been recently proposed in policy circles. The analysis reflects on the extent to which this new nexus may be either a buzzword or instead a useful framework to improve national policies in the MENA region. Methods and approach We undertook a comprehensive review of the relevant literature on the WEM nexus. We complemented this secondary data with interviews with key informants from the institutions involved in the WEM nexus, as well as from youth organizations active in the Mediterranean region and working in the development sector. Findings What emerged is that there are no concrete examples of how to operationalize the WEM nexus at the policy level. Many respondents in the MENA region highlighted the need to “mainstream WEM in policies and plans” but were vague when asked how. There is a need for more critical evidence to elevate the WEM nexus from a discussion topic among regional organizations, to a concept that can be useful and practical. Policy implications Rather than a new nexus, which would capture only a few sectors relating to water, what is needed is a systems thinking approach, able to encompass the complexity and multifaceted issues relating to water resources

Estimating access to drinking water and sanitation: the need to account for uncertainty in trend analysis

Nationally representative household surveys are the main source of data for tracking drinking water, sanitation and hygiene (WASH) coverage. However, all survey point estimates have a certain degree of error that must be considered when interpreting survey results for policy and decision making. In this article, we develop an approach to characterize and quantify uncertainty around WASH estimates. We apply it to four countries – Bolivia, Gambia, Morocco and India – representing different regions, number of data points available and types of trajectories, in order to illustrate the importance of communicating uncertainty for temporal estimates, as well as taking into account both the compositional nature and non-linearity of JMP data. The approach is found to be versatile and particularly useful in the WASH sector, where the dissemination and analysis of standard errors lag behind. While it only considers the uncertainty arising from sampling, the proposed approach can help improve the interpretation of WASH data when evaluating trends in coverage and informing decision making.Peer ReviewedPostprint (author's final draft

Aquifer contracts for groundwater resources planning in the MENA region: a means to support stakeholder participation?

Groundwater resources play a key role in the water economy of the countries of the Middle East and North Africa (MENA) region. However, groundwater abstraction is estimated to exceed the annual groundwater recharge in 50% of the countries, reaching groundwater stress levels higher than 200% in some cases. This high level of groundwater stress is an indication of unsustainable groundwater consumption in the MENA region, which jeopardizes its socio-economic development. Given the urgent need for the sustainable use of groundwater resources in the MENA region. some countries have attempted to instigate a participatory management of groundwater resources. Morocco, for instance, have been trying to formulate an aquifer management model (‘aquifer contracts’) as a tool to promote stakeholder participation at the aquifer level. The aim of the government through these contracts is to develop a collective dialogue process where all concerned users are involved and engaged in defining sustainable groundwater management policies. However, the implementation of these aquifer contracts is rather a top-down approach and does not reflect the consultative process. This work aims to explore the potential of aquifer contracts as an instrument to support stakeholder participation in groundwater resources planning in the MENA region. The experience of aquifer contracts in Morocco is analyzed from a groundwater governance framework perspective, and compared to other participatory groundwater governance instruments.Postprint (published version

Embracing data uncertainty in water decision-making: an application to evaluate water supply and sewerage in Spain

Analyses of complex water management decision-making problems, involving tradeoffs amongst multiple criteria, are often undertaken using multi-criteria decision analysis (MCDA) techniques. Various forms of uncertainty may arise in the application of MCDA methods, including imprecision, inaccuracy or ill determination of data. The ELECTRE family methods deal with imperfect knowledge of data by incorporating ‘pseudo-criteria’, with discrimination thresholds, to interpret the outranking relation as a fuzzy relation. However, the task of selecting thresholds for each criterion can be difficult and ambiguous for decision-makers. In this paper, we propose a confidence-interval-based approach which aims to reduce the subjective input required by decision-makers. The proposed approach involves defining the uncertainty in the input values using confidence intervals and expressing thresholds as a function of the interval estimates. The usefulness of the approach is illustrated by applying it to evaluate the water supply and sewerage services in Spain. Results show that the confidence interval approach may be interesting in some cases (e.g. when dealing with statistical data from surveys or measuring equipment), but should never replace the preferences or judgments of the actors involved in the decision process.Peer ReviewedPostprint (author's final draft

Multi-criteria decision analysis under uncertainty: two approaches to incorporating data uncertainty into water, sanitation and hygiene planning

The final publication is available at Springer via http://dx.doi.org/10.1007/s11269-018-2152-9In the era of the Sustainable Development Goals, for which one of the aims is to provide universal access to safe water, sanitation and hygiene (WASH) services, it is crucial to target and prioritize those who remain unserved. Multi-criteria decision analysis (MCDA) models can play an important role in WASH planning by supporting priority-setting and policy-making. However, in order to avoid misleading assumptions and policy decisions, data uncertainty — intrinsic to the available collection methods — must be integrated into the decision analysis process. In this paper, we present two approaches to incorporating data uncertainty into MCDA models (MAUT and ELECTRE-III). We use WASH planning in rural Kenya as a case study to illustrate and compare the two approaches. The comparison focuses on the way these two models handle uncertainty in the available data. The analysis shows that, while both methods incorporate data uncertainty in a considerably different manner, they lead to similar prioritization settings.Peer ReviewedPostprint (author's final draft

WASH your data off: navigating statistical uncertainty in compositional data analysis

International monitoring of access to drinking water, sanitation and hygiene (WASH) is essential to inform policy planning, implementation and delivery of services. The Joint Monitoring Programme for Water Supply and Sanitation (JMP) is the recognized mechanism for tracking access and progress, and it is based on household surveys and linear regression modelling over time. However, the methods employed have two substantial limitations: they do not address the compositional nature of the data, nor its statistical uncertainty (Ezbakhe & Pérez-Foguet 2018). While the first issue has been tackled previously in the literature (Pérez-Foguet et al. 2017), the effect of non-uniform sampling errors on the regressions remains ignored. This article aims to address these shortcomings in order to produce a more truthful interpretation of JMP data. The main challenge we try to overcome is how to translate the sampling errors provided in household surveys to the space of compositional data. A Normal distribution is commonly assumed for estimates in household surveys, with a mean and its standard deviation. However, when working with binary data on households - the proportions of households that have access to WASH services - the errors cannot follow normal distributions due to the domain restrictions of proportions, limited to the range 0 to 1. Thus, the Beta distributions seems a better option to characterize the uncertainty around mean access coverage. Yet, as the Beta distribution is defined on the [0,1] interval, the zero values must be dealt with in order to employ the isometric log-ratio (ilr) transformation designed for compositional data. In this article, we investigate the use of two probability distributions (Pearson Type I and Truncated Normal) and Monte Carlo simulations to reinterpret the error in the JMP data so that compositional data analysis is possible. With a specific focus on the WASH sector, our article shows that the importance of including the survey errors of the data - and its compositional nature - when using this information to support evidence-based policy-making. Indeed, given the current levels of statistical uncertainty in WASH, data may lead to misleading results if errors are not acknowledged (or minimized).Postprint (published version

Evaluating the human right to water and sanitation: the use of participatory diagnosis tools at a local level

Participatory analysis frameworks can help realize the Human Right to Water and Sanitation (HRtWS) by helping stakeholders self-assess how equitable is access to water and sanitation services, achieving a better understanding of the rights to water and sanitation and their principles, and raising awareness. This article presents the experiences of Castelló (Spain), Lima (Peru) and Barcelona (Spain) in using the “Equitable Access Score-card” to analyze the equity of access to safe drinking water and sanitation in these three cities. The aim is to compare and discuss the three different ways in which the diagnosis tool was applied. Results show that, although all three approaches served as a first-assessment and overview of the HRtWS by sector stakeholders at local level, developing an effective inter-stakeholder collaboration was pivotal for these participatory diagnosis tools to have significant impact.Postprint (published version

A systematic review of success factors in the community management of rural water supplies over the past 30 years

Community management is the accepted management model for rural water supplies in many low and middleincome countries. However, endemic problems in the sustainability and scalability of this model are leading many to conclude we have reached the limits of an approach that is too reliant on voluntarism and informality. Accepting this criticism but recognising that many cases of success have been reported over the past 30 years, this study systematically reviews and analyses the development pattern of 174 successful community management case studies. The synthesis confirms the premise that for community management to be sustained at scale, community institutions need a ‘plus’ that includes long-term external support, with the majority of high performing cases involving financial support, technical advice and managerial advice. Internal community characteristics were also found to be influential in terms of success, including collective initiative, strong leadership and institutional transparency. Through a meta-analysis of success in different regions, the paper also indicates an important finding on the direct relationship between success and the prevailing socio-economic wealth in a society. This holds implications for policy and programme design with a need to consider how broad structural conditions may dictate the relative success of different forms of community management

Data in transboundary water governance: what is at stake?

In 2017, the Global High-Level Panel on Water and Peace called for strengthening knowledge-based and data-driven decision making and cooperation for water, security and peace. The rationale is that having better data leads to better water management, not only because it allows for a better diagnosis of the problems and development of solutions, but also a common understanding of these data builds trust and constructive dialogues between the involved actors. This is especially true in transboundary waters, where data and information exchange become an important mechanism to develop a mutual understanding about the basin amongst the parties. Yet, the interplay between data, knowledge, and power should not be neglected. Issues regarding data ownership - and the models transforming data into knowledge - merit attention to understand the political dimension of data