Community management is the accepted management model for rural water supplies in many low and middle-income 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.
Achieving equitable access to water, sanitation and hygiene (WASH) services requires paying special attention to the most disadvantaged segments of the population. Yet, despite all the progress made to evaluate the access of vulnerable and marginalized groups, important knowledge gaps still remain with respect to identifying their specific barriers and needs. At the global level, for example, the two monitoring mechanisms for SDG 6 -the Joint Monitoring Programme (JMP) and Global Analysis and Assessment of Sanitation and drinking-water (GLAAS) -face difficulties in understanding how, and to what extent, vulnerable and marginalized groups access WASH services. In this context, this work examines the UNECE/WHO-Europe 'Equitable Access Score-card' for assessing the access to WASH services by vulnerable and marginalized groups. In particular, we: (i) analyse its strengths and limitations as a tool for revealing the needs of these groups in accessing WASH services; and (ii) propose an extended variant of the score-card that addresses these limitations. We test this version in two local-level case studies: Lima (Peru) and Castelló de la Plana (Spain). The score-card diagnosis is found to be particularly useful for collecting information on the level of access of the different vulnerable and marginalized groups, as well as the specific public policies and funding mechanisms in place that address and support their needs. However, the score-card should be complemented with specific assessments of all five normative dimensions of the human rights to water and sanitation (access, availability, quality, acceptability and affordability) in order to have a better understanding of the concerns for service delivery for the different vulnerable and marginalized groups.
In the era of the Sustainable Development Goals, where 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 in the decision analysis process. In this paper we present two approaches to incorporate 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 the uncertainty in the available data. The analysis shows that, while both methods incorporate data uncertainty in a considerable different manner, they lead to similar prioritization settings.
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