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Water Resources Planning Under (Deep) Uncertainty  

Riddhi Singh

Public investments in water infrastructure continue to grow where developed countries prioritize investments in operation and maintenance while developing countries focus on infrastructure expansion. The returns from these investments are contingent on carefully assessed designs and operating strategies that consider the complexities inherent in water management problems. These complexities arise due to several factors, including, but not limited to, the presence of multiple stakeholders with potentially conflicting preferences, lack of knowledge about appropriate systems models or parameterizations, and large uncertainties regarding the evolution of future conditions that will confront these projects. The water resources planning literature has therefore developed a variety of approaches for a quantitative treatment of planning problems. Beginning in the mid-20th century, quantitative design evaluations were based on a stochastic treatment of uncertainty using probability distributions to determine expected costs or risk of failure. Several simulation–optimization frameworks were developed to identify optimal designs with techniques such as linear programming, dynamic programming, stochastic dynamic programming, and evolutionary algorithms. Uncertainty was incorporated within existing frameworks using probability theory, using fuzzy theory to represent ambiguity, or via scenario analysis to represent discrete possibilities for the future. As the effects of climate change became palpable and rapid socioeconomic transformations emerged as the norm, it became evident that existing techniques were not likely to yield reliable designs. The conditions under which an optimal design is developed and tested may differ significantly from those that it will face during its lifetime. These uncertainties, wherein the analyst cannot identify the distributional forms of parameters or the models and forcing variables, are termed “deep uncertainties.” The concept of “robustness” was introduced around the 1980s to identify designs that trade off optimality with reduced sensitivity to such assumptions. However, it was not until the 21st century that robustness analysis became mainstream in water resource planning literature and robustness definitions were expanded to include preferences of multiple actors and sectors as well as their risk attitudes. Decision analytical frameworks that focused on robustness evaluations included robust decision-making, decision scaling, multi-objective robust decision-making, info-gap theory, and so forth. A complementary set of approaches focused on dynamic planning that allowed designs to respond to new information over time. Examples included adaptive policymaking, dynamic adaptive policy pathways, and engineering options analysis, among others. These novel frameworks provide a posteriori decision support to planners aiding in the design of water resources projects under deep uncertainties.


Multi-Objective Robust Planning Tools  

Jazmin Zatarain Salazar, Andrea Castelletti, and Matteo Giuliani

Shared water resource systems spark a number of conflicts related to their multi sectorial, regional, and intergenerational use. They are also vulnerable to a myriad of uncertainties stemming from changes in the hydrology, population demands, and climate change. Planning and management under these conditions are extremely challenging. Fortunately, our capability to approach these problems has evolved dramatically over the last few decades. Increased computational power enables the testing of multiple hypotheses and expedites the results across a range of planning alternatives. Advances in flexible multi-objective optimization tools facilitate the analyses of many competing interests. Further, major shifts in the way uncertainties are treated allow analysts to characterize candidate planning alternatives by their ability to fail or succeed instead of relying on fallible predictions. Embracing the fact that there are indeterminate uncertainties whose probabilistic descriptions are unknown, and acknowledging relationships whose actions and outcomes are not well-characterized in planning problems, have improved our ability to perform diligent analysis. Multi-objective robust planning of water systems emerged in response to the need to support planning and management decisions that are better prepared for unforeseen future conditions and that can be adapted to changes in assumptions. A suite of robustness frameworks has emerged to address planning and management problems in conditions of deep uncertainty. That is, events not readily identified or that we know so little about that their likelihood of occurrence cannot be described. Lingering differences remain within existing frameworks. These differences are manifested in the way in which alternative plans are specified, the views about how the future will unfold, and how the fitness of candidate planning strategies is assessed. Differences in the experimental design can yield diverging conclusions about the robustness and vulnerabilities of a system. Nonetheless, the means to ask a suite of questions and perform a more ambitious analysis is available in the early 21st century. Future challenges will entail untangling different conceptions about uncertainty, defining what aspects of the system are important and to whom, and how these values and assumptions will change over time.


Global and Regional Economic Damages from Climate Change  

Anil Markandya, Elena Paglialunga, Valeria Costantini, and Giorgia Sforna

Economic damage from climate change includes several aspects that need to be considered at the global and regional levels to achieve an equitable common solution to global warming. The economic literature reviewed here analyzes this issue under three general perspectives. First, the analytical estimation of the linkages between damages in monetary terms and climate variables, as projections of temperature, precipitation, and frequency of extreme events, is rapidly evolving. Damage functions are included in complex economic models in order to calculate the economic impact of the climate change on economic output and growth, thus informing the debate on the amount of resources that should be devoted to reducing greenhouse gas (GHG) emissions and limiting climate damages. The choice of the geographical aggregation in this respect is a crucial aspect to be considered if policy advice is to be formulated on the basis of model results. The higher the level of regional detail, the more reliable the results are in terms of geographical distribution of economic damages. Second, the precise estimation of the costs associated with different damages caused by climate change is attracting growing interest. Climate costs present a wide range of heterogeneity for several reasons, such as the different formulation of the damage function adopted, the modeling design of the economic impact, the temporal horizon considered, and the differentiation across sectors. Two broad categories of analysis are relevant. The first refers to the choice of the sectoral dimension under investigation, where some studies cover multiple sectors and their interactions, while others analyze specific sectors in depth. The second classification criterion refers to the choice of the economic aspects estimated, where a strand of literature analyzes only market-based costs, while other analyses also include non-market (or intangible) damages. The most common sectors investigated are agriculture, forestry, health, energy, coastal zones and sea level rise, extreme events, tourism, ecosystems, industry, air quality, and catastrophic damages. Most studies consider market-based costs, while non-market impacts need to be better detailed in economic models. Third, the computation of a single number through the analytical framework of the social costs of carbon (SCC) represents a key aspect of the process of adapting complex results in order to properly inform the political debate. SCC represents the marginal global damage cost of carbon emissions and can also be interpreted as the economic value of damages avoided for unitary GHG emission reduction. Several uncertainties still influence the robustness of the SCC analytical framework, such as the choice of the discount rate, which strongly influences the role of SCC in supporting or not mitigation action in the short term. Although the debate on the economic damages arising from climate change is flourishing, several aspects still need to be investigated in order to build a common consensus within the scientific community as a necessary condition to properly inform the political debate and to facilitate the achievement of a long-term equitable global climate agreement.


Performance Bonding for Environmental Protection  

Olli-Pekka Kuusela

Performance bonds are a widely used enforcement and assurance mechanism in extractive activities and large-scale projects that are associated with clearly defined environmental liabilities. They are especially effective and useful in conditions where the judgment-proof problem is a pervasive challenge. Bonds provide an assurance for the regulator that funds are available to complete the decommissioning activities and the termination of operations under contingencies where the operator fails to follow the contractual obligations or becomes insolvent. Applications of bonding are found in oil and gas drilling, surface mining, renewable energy projects, and timber concessions. However, real bonding systems are not without issues and limitations. Defining a sufficiently high enough bond has been especially challenging in conditions with limited information about costs and environmental risks and where smaller operators may not be able to secure access to required funds or financial services for posting the bond. The use of surety companies, bond pools, and blanket bonds provides a solution to these problems, albeit not without issues of their own. Furthermore, to keep up with the technological developments in extractive industries and with the inescapable uncertainties related to reclamation costs and environmental damages, the regulator should continually review and update bonding instruments based on new available information. Regulatory rules and statistical models that have been developed to adjust the required bonds, based on observable risk factors, provide some encouraging examples for how to design more responsive and effective bonding systems.


A Century of Evolution of Modeling for River Basin Planning to the Next Generation of Models, Methods, and Concepts  

Caroline Rosello, Sondoss Elsawah, Joseph Guillaume, and Anthony Jakeman

River Basin models to inform planning decisions have continued to evolve, largely based on predominant planning paradigms and progress in the sciences and technology. From the Industrial Revolution to the first quarter of the 21st century, such modeling tools have shifted from supporting water resources development to integrated and adaptive water resources management. To account for the increasing complexity and uncertainty associated with the relevant socioecological systems in which planning should be embedded, river basin models have shifted from a supply development focus during the 19th century to include, by thes 2000s–2020s, demand management approaches and all aspects of consumptive and non-consumptive uses, addressing sociocultural and environmental issues. With technological and scientific developments, the modeling has become increasingly quantitative, integrated and interdisciplinary, attempting to capture, more holistically, multiple river basin issues, relevant cross-sectoral policy influences, and disciplinary perspectives. Additionally, in acknowledging the conflicts around ecological degradation and human impacts associated with intensive water resource developments, the modeling has matured to embrace the need for adequate stakeholder engagement processes that support knowledge-sharing and trust-building and facilitate the appreciation of trade-offs across multiple types of impacts and associated uncertainties. River basin models are now evolving to anticipate uncertainty around plausible alternative futures such as climate change and rapid sociotechnical transformations. The associated modeling now embraces the challenge of shifting from predictive to exploratory tools to support learning and reflection and better inform adaptive management and planning. Managing so-called deep uncertainty presents new challenges for river basin modeling associated with imperfect knowledge, integrating sociotechnical scales, regime shifts and human factors, and enabling collaborative modeling, infrastructure support, and management systems.


Economics of Climate Change Adaptation  

Babatunde O. Abidoye

To view climate change adaptation from an economic perspective requires a definition of adaptation, an economic framework in which to view adaptation, and a review of the literature on specific adaptations (especially in agriculture). A focus on tools for applying adaptation to developing countries highlights the difference between mitigation and the adaptation decision-making process. Mitigation decisions take a long-term perspective because carbon dioxide lasts for a very long time in the atmosphere. Adaptation decisions typically last the lifespan of the investments, so the time frame depends on the specific adaptation investment, but it is invariably short compared to mitigation choices, which have implications for centuries. The short time frame means that adaptation decisions are not plagued by the same uncertainty that plagues mitigation choices. Finally, most adaptation decisions are local and private, whereas mitigation is a global public decision. Private adaptation will occur even without large government programs. Public adaptations that require government assistance can mainly be made by existing government agencies. Adaptation does not require a global agreement.


Review of the State of the Art in Analysis of the Economics of Water Resources Infrastructure  

Marc Jeuland

Water resources represent an essential input to most human activities, but harnessing them requires significant infrastructure. Such water control allows populations to cope with stochastic water availability, preserving uses during droughts while protecting against the ravages of floods. Economic analysis is particularly valuable for helping to guide infrastructure investment choices, and for comparing the relative value of so called hard and soft (noninfrastructure) approaches to water management. The historical evolution of the tools for conducting such economic analysis is considered. Given the multimillennial history of human reliance on water infrastructure, it may be surprising that economic assessments of its value are a relatively recent development. Owing to the need to justify the rapid deployment of major public-sector financing outlays for water infrastructure in the early 20th century, government agencies in the United States—the Army Corps of Engineers and the Bureau of Reclamation—were early pioneers in developing these applications. Their work faced numerous technical challenges, first addressed in the drafting of the cost-benefit norms of the “Green Book.” Subsequent methodological innovation then worked to address a suite of challenges related to nonmarket uses of water, stochastic hydrology, water systems interdependencies, the social opportunity cost of capital, and impacts on secondary markets, as well as endogenous sociocultural feedbacks. The improved methods that have emerged have now been applied extensively around the world, with applications increasingly focused on the Global South where the best infrastructure development opportunities remain today. The dominant tools for carrying out such economic analyses are simulation or optimization hydroeconomic models (HEM), but there are also other options: economy wide water-economy models (WEMs), sociohydrological models (SHMs), spreadsheet-based partial equilibrium cost-benefit models, and others. Each of these has different strengths and weaknesses. Notable innovations are also discussed. For HEMs, these include stochastic, fuzz, and robust optimization, respectively, as well as co-integration with models of other sectors (e.g., energy systems models). Recent cutting-edge work with WEMs and spreadsheet-based CBA models, meanwhile, has focused on linking these tools with spatially resolved HEMs. SHMs have only seen limited application to infrastructure valuation problems but have been useful for illuminating the paradox of flood management infrastructure increasing the incidence and severity of flood damages, and for explaining the co-evolution of water-based development and environmental concerns, which ironically then devalues the original infrastructure. Other notable innovations are apparent in multicriteria decision analysis, and in game-theoretic modeling of noncooperative water institutions. These advances notwithstanding, several issues continue to challenge accurate and helpful economic appraisal of water infrastructure and should be the subject of future investigations in this domain. These include better assessment of environmental and distributional impacts, incorporation of empirically based representations of costs and benefits, and greater attention to the opportunity costs of infrastructure. Existing tools are well evolved from those of a few decades ago, supported by enhancements in scientific understanding and computational power. Yet, they do appear to systematically produce inflated estimations of the net benefits of water infrastructure. Tackling existing shortcomings will require continued interdisciplinary collaboration between economists and scholars from other disciplines, to allow leveraging of new theoretical insights, empirical data analyses, and modeling innovations.