A Simple Heuristic for Reducing the Number of Scenarios in Two-stage Stochastic Programming

In this work we address the problem of solving multiscenario optimization models that are deterministic equivalents of two-stage stochastic programs. We present a heuristic approximation strategy where we reduce the number of scenarios and obtain an approximation of the original multiscenario optimization problem. In this strategy, a subset of the given set of scenarios is selected based on a proposed criterion and probabilities are assigned to the occurrence of the reduced set of scenarios. The original stochastic programming model is converted into a deterministic equivalent using the reduced set of scenarios. A mixed-integer linear program (MILP) is proposed for the reduced scenario selection. We apply this practical heuristic strategy to four numerical examples and show that reformulating and solving the stochastic program with the reduced set of scenarios yields an objective value close to the optimum of the original multiscenario problem.</p

Large-Scale Hydrological Modeling for Calculating Water Stress Indices: Implications of Improved Spatiotemporal Resolution, Surface-Groundwater Differentiation, and Uncertainty Characterization

Physical water scarcities can be described by water stress indices. These are often determined at an annual scale and a watershed level; however, such scales mask seasonal fluctuations and spatial heterogeneity within a watershed. In order to account for this level of detail, first and foremost, water availability estimates must be improved and refined. State-of-the-art global hydrological models such as WaterGAP and UNH/GRDC have previously been unable to reliably reflect water availability at the subbasin scale. In this study, the Soil and Water Assessment Tool (SWAT) was tested as an alternative to global models, using the case study of the Mississippi watershed. While SWAT clearly outperformed the global models at the scale of a large watershed, it was judged to be unsuitable for global scale simulations due to the high calibration efforts required. The results obtained in this study show that global assessments miss out on key aspects related to upstream/downstream relations and monthly fluctuations, which are important both for the characterization of water scarcity in the Mississippi watershed and for water footprints. Especially in arid regions, where scarcity is high, these models provide unsatisfying results

Large-Scale Hydrological Modeling for Calculating Water Stress Indices: Implications of Improved Spatiotemporal Resolution, Surface-Groundwater Differentiation, and Uncertainty Characterization

Physical water scarcities can be described by water stress indices. These are often determined at an annual scale and a watershed level; however, such scales mask seasonal fluctuations and spatial heterogeneity within a watershed. In order to account for this level of detail, first and foremost, water availability estimates must be improved and refined. State-of-the-art global hydrological models such as WaterGAP and UNH/GRDC have previously been unable to reliably reflect water availability at the subbasin scale. In this study, the Soil and Water Assessment Tool (SWAT) was tested as an alternative to global models, using the case study of the Mississippi watershed. While SWAT clearly outperformed the global models at the scale of a large watershed, it was judged to be unsuitable for global scale simulations due to the high calibration efforts required. The results obtained in this study show that global assessments miss out on key aspects related to upstream/downstream relations and monthly fluctuations, which are important both for the characterization of water scarcity in the Mississippi watershed and for water footprints. Especially in arid regions, where scarcity is high, these models provide unsatisfying results

How To Address Data Gaps in Life Cycle Inventories: A Case Study on Estimating CO₂ Emissions from Coal-Fired Electricity Plants on a Global Scale

One of the major challenges in life cycle assessment (LCA) is the availability and quality of data used to develop models and to make appropriate recommendations. Approximations and assumptions are often made if appropriate data are not readily available. However, these proxies may introduce uncertainty into the results. A regression model framework may be employed to assess missing data in LCAs of products and processes. In this study, we develop such a regression-based framework to estimate CO₂ emission factors associated with coal power plants in the absence of reported data. Our framework hypothesizes that emissions from coal power plants can be explained by plant-specific factors (predictors) that include steam pressure, total capacity, plant age, fuel type, and gross domestic product (GDP) per capita of the resident nations of those plants. Using reported emission data for 444 plants worldwide, plant level CO₂ emission factors were fitted to the selected predictors by a multiple linear regression model and a local linear regression model. The validated models were then applied to 764 coal power plants worldwide, for which no reported data were available. Cumulatively, available reported data and our predictions together account for 74% of the total world’s coal-fired power generation capacity

How to address data gaps in life cycle inventories: A case study on estimating co2 emissions from coal-fired electricity plants on a global scale

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A spatially explicit data-driven approach to assess the effect of agricultural land occupation on species groups

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