Fresh groundwater for Wajir—ex-ante assessment of uncertain benefits for multiple stakeholders in a water supply project in Northern Kenya

Abstract

Decision-making in development rarely considers uncertainty in project benefits and costs and the risk of project failure. Lack of appropriate tools for ex-ante analysis under conditions of data scarcity constrains the ability of decision-makers to anticipate project outcomes. Business analysis techniques can help in such situations, but they have rarely been applied in development contexts. We use the principles of Applied Information Economics to develop a decision model for a water supply intervention. In the proposed Habaswein-Wajir Water Supply Project in Northern Kenya, water is to be extracted from a major aquifer near Habaswein and piped to the city of Wajir. A team of eight experts developed a model including all costs, benefits, and risks considered important for project success. After estimation training, these experts expressed their uncertainty for about 100 variables in the model with probability distributions. We used Monte Carlo simulation to project decision outcomes, and Partial Least Squares (PLS) regression to identify critical uncertainties affecting the decision. The project was found to be risky for most stakeholders, mainly due to the risk of political interference caused by water supply concerns in Habaswein and due to unclear profitability of the water supply business. Uncertainties about how to value decreasing infant mortality and reduction in water-borne disease incidence were also critical. The greatest hydrological risk was salt water intrusion into the aquifer. Careful well design, inclusive project planning and benefit sharing could raise the chance of project success. The analysis improved understanding of the decision by all stakeholders, some of which changed their opinions on the pipeline, requested more measurements, or proposed alternative water supply options. Decision analysis can help clarify decision uncertainties and outcome expectations and thereby improve decision-making processes, especially in data-scarce areas.

Valuing Ecosystem Services in Semi‐arid Rangelands through Stochastic Simulation

“…A sensitivity analysis elucidated which study variables most constrained our ability to provide precise ES value estimates. This calculation was based on the variable importance in the projection (VIP) metric of partial least squares (PLS) regression (Luedeling & Gassner, 2012;Luedeling et al, 2015). The PLS procedure relates all dependent variables (the total ES value of the system) to all input variables.…”

“…This paper develops and applies a Monte Carlo‐based decision model (cf. Rosenstock et al ., ; Luedeling et al ., ), providing empirical insight into the monetary valuation of ES in Botswana's arid and semi‐arid Kalahari rangelands. These rangelands support diverse livelihoods by delivering a wide variety of ES, many of which are vital to the subsistence and survival of the poorest in society (Dougill et al ., ).…”

Probabilistic Decision Tools for Determining Impacts of Agricultural Development Policy on Household Nutrition

“…Defining the context and finding the appropriate parameters to explore with the model is the first step in developing a BN. To achieve this, we use decision modeling approaches (Luedeling et al, ), inspired by the principles of Applied Information Economics (AIE; Hubbard, ). This is then complemented with innovative group work techniques for eliciting expert knowledge to construct a logical framework to describe system interactions and outcomes (i.e., an impact pathway).…”

Valuing Environmental Benefit Streams in the Dryland Ecosystems of Sub-Saharan Africa

“…Valuation of water supply in the drylands can include the avoided costs of disease [125][126][127][128] and replacement expenses such as travel costs to obtain water supplies [129,130] or infrastructure, pumping and treatment costs. The input costs to society to secure water availability are very context-dependent and may be higher in remote or hard-to-reach areas than they are in densely populated areas.…”