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This study presents the first meta-analysis on the economic value of ecosystem services delivered by lakes. A worldwide data set of 699 observations drawn from 133 studies combines information reported in primary studies with geospatial data. The meta-analysis explores antagonisms and synergies between ecosystem services. This is the first meta-analysis to incorporate simultaneously external geospatial data and ecosystem service interactions. We first show that it is possible to reliably predict the value of ecosystem services provided by lakes based on their physical and geographic characteristics. Second, we demonstrate that interactions between ecosystem services appear to be significant for explaining lake ecosystem service values. Third, we provide an estimation of the average value of ecosystem services provided by lakes: between 106 and 140 USD$2010 per respondent per year for non-hedonic price studies and between 169 and 403 USD$2010 per property per year for hedonic price studies.

a b s t r a c tWater, food and energy are at the core of human needs and there is a boundless complex cycle among these three basic human needs. Ecosystems are in the center of this nexus, since they contribute to the provision of each component, making it imperative to understand the role of ecosystems in securing food, water and energy for human well-being. In this study we aimed to map and assess water provisioning services and associated benefits to support the ecosystem-water-food-energy nexus by taking into account environmental flow requirements for riverine ecosystems using the hydrological model Soil and Water Assessment Tool (SWAT). We developed a framework that includes indicators of renewable water (capacity of ecosystem to provide water) and water use (service flow) and we applied it in the Danube river basin over the period 1995-2004. Water scarcity indicators were used to map the possible water scarcity in the subbasins, and analyze the spatial match of water availability and water use. The results show that modelling is instrumental to perform the integrated analysis of the ecosystem-waterfood-energy nexus; and that spatial mapping is a powerful tool to display environmental availability of water provisioning and regulatory services delivered by ecosystems, and can support the nexus analysis.

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HighlightsWe use a CV approach to estimate how households value a green infrastructure.As a case study we consider the Gorla Maggiore water park (Northern Italy).We find a positive willingness to pay for the green infrastructure (16.5 euros/hh/y).Building of the green infrastructure can be rationalized based on a cost-benefit criterion.

A B S T R A C TIn the arid areas of Sub-Saharan Africa, perennial challenges of water scarcity and food insecurity are exacerbated by climate change and variability. The development of robust strategies to cope with the region's climatic challenges requires thorough consideration of uncertainty and risk in decision making. We demonstrate the use of probabilistic decision analysis to compare intervention options to prevent reservoir sedimentation in Burkina Faso. To illustrate this approach, we developed a causal impact pathway model based on the local knowledge of expert stakeholders. Input parameters were described by probability distributions derived from estimated confidence intervals. The model was run in a Monte Carlo simulation to generate the range of plausible decision outcomes, quantified as the net present value and the annual cash flow. We used Partial Least Squares regression analysis to identify the parameters that most affected projected intervention outcomes and we computed the Expected Value of Perfect Information (EVPI) to highlight critical uncertainties. Numerical results show that the preferred intervention to secure agricultural production is a combination of dredging, rock dams and a buffer scheme around the reservoir. The EVPI calculation reveals an information value for the profit per ton of vegetables, indicating that more information on this variable would be useful for supporting the decision. However, without the need for follow-up analysis, the results show high probability of benefits given the combined interventions, which, given the current state of information, should be preferred over inaction.

Governments around the world have agreed to end hunger and food insecurity and to improve global nutrition, largely through changes to agriculture and food systems. However, they are faced with a lot of uncertainty when making policy decisions, since any agricultural changes will influence social and biophysical systems, which could yield either positive or negative nutrition outcomes. We outline a holistic probability modeling approach with Bayesian Network (BN) models for nutritional impacts resulting from agricultural development policy. The approach includes the elicitation of expert knowledge for impact model development, including sensitivity analysis and value of information calculations. It aims at a generalizable methodology that can be applied in a wide range of contexts. To showcase this approach, we develop an impact model of Vision 2040, Uganda's development strategy, which, among other objectives, seeks to transform the country's agricultural landscape from traditional systems to large‐scale commercial agriculture. Model results suggest that Vision 2040 is likely to have negative outcomes for the rural livelihoods it intends to support; it may have no appreciable influence on household hunger but, by influencing preferences for and access to quality nutritional foods, may increase the prevalence of micronutrient deficiency. The results highlight the trade‐offs that must be negotiated when making decisions regarding agriculture for nutrition, and the capacity of BNs to make these trade‐offs explicit. The work illustrates the value of BNs for supporting evidence‐based agricultural development decisions.

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