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Improving Infrastructure Resilience in Developing Countries

Abstract and Keywords

Increasing the amount of resilient infrastructure investments in developing countries is key to achieving development goals. Two issues need to be addressed to better support investment decisions. First, analysts need to better integrate the social, economic, and environmental dimensions of investment decisions in their quantitative analyses, given the intertwined objectives of climate change adaptation and poverty reduction. Second, analysts and practitioners need to recognize that the future state of those three dimensions is deeply uncertain and that new techniques need to be used that look for robust investments—performing well under multiple future conditions—rather than an optimal solution under a single prediction of the future. Doing so can be achieved by beginning important decision processes with an integrated model representing technical and socioeconomic factors, and exploring various interventions under many possible futures.

Keywords: deep uncertainty, infrastructure, climate change, developing countries, resilient infrastructure, sustainable development

Building a more resilient future to climate change is only one of the objectives of the international community for developing nations. There are still 700 million people living in extreme poverty. Globally, around 1.1 billion people today do not have access to electricity. About 2.9 billion use solid fuels—wood, charcoal, coal, and dung—for cooking and heating. Despite impressive gains over the past several decades, 2.4 billion people lack access to improved sanitation, of which 1 billion practice open defecation. At least 663 million people lack access to safe drinking water. Understandably, climate action is only one of the seventeen broader Sustainable Development Goals (SDGs), which range from eliminating poverty and bringing universal access to basic services, to protecting the environment, to peace and justice.

The World Bank’s Shock Waves report (Hallegatte et al., 2016a) and associated background papers find that climate change will exacerbate development challenges. Higher temperature is likely to reduce agricultural yield, possibly pushing up food prices and threatening food security. Increased water scarcity is likely to worsen water quality in many places and as a consequence, increase diarrheal diseases that affect mostly children and lead to stunting and lower physical and cognitive development—which in turn reduce their earning capabilities as adults. Natural disasters—intensified by climate change in many regions—can wipe out decades of savings for poor people, and often force people to cope by reducing food intake or health care, or by taking children out of school. All these climate-related shocks can keep, or throw back, people in poverty by making it more difficult for households to accumulate assets, regularly wiping out their stock of assets, or even creating irreversible impacts on human capital through health or educational impacts (Carter & Barrett, 2006; Dercon, 2004; Heltberg, Oviedo, & Talukdar, 2014; Krishna, 2006; Krishna, 2007; Moser, 2008; Sen, 2003). Natural disasters also threaten the infrastructure that is necessary to provide basic and essential services such as education, improved drinking water, and access to energy. And sea level rise may force coastal cities to undertake major investments to protect themselves against coastal floods.

But climate change itself remains one piece of the broader puzzle. Hallegatte et al. (2016a) also find that most of the climate change impacts on poverty by 2030 will be determined by development pathways. By 2030, improved socioeconomic conditions, with higher incomes and lower poverty and inequality, combined with universal access to basic infrastructure and targeted adaptation, can prevent most of climate change’s projected effects on poverty, even in bad climate change scenarios. In other words, by increasing people’s adaptive capacity via development, the impacts of climate change can be contained in the short run.

Achieving the SDGs is therefore a good climate change adaptation strategy in the short run. But developing countries face many challenges in doing so, from creating employment, to improving institutions, to building the needed infrastructure. Bringing universal access to basic services and eradicating extreme poverty requires increased infrastructure investments in developing countries, with a shift toward low-carbon and resilient infrastructure.

This opens two converging avenues for improving development projects. First, considerations of climate change need to be incorporated in all development projects and policies, and more particularly in infrastructure projects. A well-thought infrastructure project design can help mitigate climate change impact as well as enhance infrastructure lifespan. Unfortunately, in many places around the world, little attention is given to climate change in infrastructure project design, and yet rapid development is shaping future vulnerability (Fankhauser & McDermott, 2014; Winsemius et al., 2016). For example, in Lima, SEDAPAL, the city’s water utility, had developed a 2.6B$ infrastructure investment plan on the assumption that climate would not change, and with one scenario of 2040 demand. The water utility believed the 2.6B$ investments to provide reliable service to an expected number of people, without any sensitivity analysis around what would happen if the population did not grow as expected, or if climate varied (Kalra et al., 2015) Yet, upstream developments, population growth, and changing precipitation patterns can affect the success of the plan, and the city’s water reliability, Similarly, the rapid paving of cities, coastal development, groundwater overabstraction—which accelerate subsidence—all increase the vulnerability of cities to flooding and sea level rise. Incorporating climate change in infrastructure planning and design requires long-term thinking and new methods to handle deep uncertainty pertaining to future climate changes (Hallegatte, Shah, Brown, Lempert, & Gill, 2012; Haasnoot, Kwakkel, Walker, & ter Maat, 2013; Karvetski & Lambert, 2012; Lempert & Groves, 2010; Lempert, Groves, Popper, & Bankes, 2006; Ray & Brown, 2015). Second, projects and programs aiming at improving the resilience of infrastructure and populations need to better incorporate social and economic dimensions in the decision-making process and in particular in quantitative analyses. It is necessary but not sufficient to address the uncertainty related to climate change. The uncertainty pertaining to socioeconomic dimensions may sometimes be as high or higher than the one pertaining to climate change, and more influential for a project’s viability. The combinations of climate and socioeconomic changes can sometimes have nonlinear catastrophic effects, such that climate change uncertainty considered alone probably underestimates its potential negative impacts. It is the case in the previous SEDAPAL investment example where high population growth combined with a dryer climate could put a lot of stress on SEDAPAL’s ability to provide a reliable service, even with 2.6B$ investments in place.

In recent years, the development community has increased its focus on the future dangers associated with climate change and as a result, climate change is more frequently embedded in decision-making processes. All World Bank projects need to be screened for potential climate change impacts and, if vulnerable, show modifications to the project design that may reduce these impacts. However, climate change impacts are very often looked at in isolation from other future changes, missing the potential combinations of factors that create vulnerabilities.

This chapter discusses the analytical work that is carried out to support decision-making processes for infrastructure investments, and proposes improvements with a focus on developing countries, since this is where the bulk of future investments in infrastructure will take place over the twenty-first century. It identifies two gaps in the way infrastructure plans and projects are assessed, which prevent decision makers from appreciating the full range of future vulnerabilities and the potential for “nonstructural” solutions—that is, those outside of the hard infrastructure realm—to increase resilience. First, most of the models that are used to design a project or a long-term strategy are engineering models that do not represent socioeconomic systems explicitly. Economic analyses are nearly always performed at the end of the decision-making process to validate an investment or choose between a restricted subset of strategies, rather than to inform their design. Second, most analyses use a “predict-and-act” or “agree on assumptions” approach, during which stakeholders have to agree on a vision of the future before designing the best project or strategy for that particular future (Kalra et al., 2014). Such an approach can create gridlocks when climate change must be considered because global climate models point to different future local climates (Smith et al., 2001) and agreement on which one to use is difficult.

Methods have been developed to try and address the second gap by focusing the decision makers’ attention on actions rather than on predictions (“agree on decisions” approaches—see Kalra et al., 2014). To address the two gaps simultaneously, these methods must not confine the analyses of deep uncertainty to uncertain climate change impacts but also need to consider the wider development context in which the infrastructure will be built and test the robustness of projects to many combinations of changes along multiple dimensions.

The remaining of this chapter analyzes these two gaps and proposes different ways forward for the scientific community, building on a growing body of literature that tries to address them, and on the authors’ experience from World Bank projects.

Gap 1: The Design of Infrastructure Projects and Strategies Is Very Often Based Solely on Engineering Models

Decision-making processes for infrastructure projects rest on feasibility studies that are generally mathematically based and model oriented, relying heavily on engineering models like flood models (Anselmo, Galeati, Palmieri, Rossi, & Todini, 1996; Zerger & Wealands, 2004); water-planning models (Yates, Sieber, Purkey, & Huber-Lee, 2005; Yeh, 1992); energy-planning models (Fishbone & Abilock, 1981; Pina, Silva, & Ferrão, 2013; Syri et al., 2008), or infrastructure network models (Barr et al., 2013; Cho, Kim, & Choi, 2012).

While those models are necessary to support investment planning, they seldom include socioeconomic dimensions. The model described in Pant, Hall, and Blainey (2016) for studying the resilience of transport networks to climate hazards, for example, is a very sophisticated representation of infrastructure systems but does not link infrastructure service provision to economic activity. Thus their recommendations for the prioritization of interventions is purely based on infrastructure failure rather than on the social and economic consequences of these failures. Other examples include studies on the future adaptation of the power sector to a low-carbon world, which often rely on bottom-up optimization models in which future energy demand, technology availability, and costs and fuel prices are exogenous parameters (Labriet et al., 2015). Hence, the economic feedback loops that can either improve the sector or degrade its conditions are very rarely modeled, leaving the practitioner with only partial insights.

Naturally, exceptions can be found, especially with models developed for natural disasters risk assessments (Hall, Sayers, & Dawson, 2005; Johnson, Fischbach, & Ortiz, 2013; Rose & Liao, 2005; Rose, Benavides, Chang, Szczesniak, & Lim, 1997; Rose & Wei, 2014). In those models, the damages caused by natural disasters on economic assets are represented, sometimes integrating indirect damages on income and wages (Johnson, Fischbach, & Ortiz 2013); general equilibrium impacts, including trade (Rose et al., 1997; Rose & Liao, 2005; Rose & Wei, 2014); or even recent welfare impacts (Hallegatte, Vogt-Schilb, & Bangalore, 2015; Hallegatte Vogt-Schilb, Bangalore, & Rozenberg, 2016b).

But typically, the socioeconomic dimensions of a project or plan, when they are modeled, are relegated to the end of the feasibility studies, and explored through a cost-benefit analysis (CBA). The CBA is often conducted after the investment has been analyzed with an engineering model and, as such, it is generally used to reassure on the benefits of an investment already decided, rather than to compare technically drastically different alternatives. The CBA is frequently used for public projects to justify the intervention and the allocation of resources against other priorities (Her Majesty’s Treasury, 2003; OMB, 2015; World Bank OPSPQ, 2016). The objective is to value all relevant costs and benefits of a proposed project or strategy to society, allowing comparison of costs and benefits of different options in monetary terms. As noted in World Bank (2010), the CBA is often not used to inform decisions but as an assurance that the project is a good use of scarce resources, and to compare the economic performance of projects across countries and sectors.

In addition, when some benefits are difficult to model—for example, environmental or distributional benefits—analysts tend to disregard them or use a cost-effectiveness framework instead (World Bank, 2010). Cost-effectiveness analysis compares the costs of alternative ways of producing the same or similar outputs and as such avoids the need to quantify the outcome of choices. In water sector–planning exercises for example, analysts often choose to use cost-effectiveness analyses, mostly because of the difficulty of assigning dollar values to the projects’ benefits (i.e., water delivered to citizens by public utilities) and because there is a consensus on the objectives of the project. The question that most of these studies seek to answer is how to ensure water reliability at the lowest cost. Although justifiable, the frequent utilization of the cost-effectiveness analysis also means that most of the social and economic dimensions of the projects are left out of the analysis and decision makers are often unable to compare the full impacts of alternative projects on communities and on the economy.1 This can lead to a focus on infrastructure investments rather than an exploration of softer options in the broader watershed that may equally, or even more reliably, ensure the sustainability of a water system.

There are examples of projects that failed because of the exclusion, in the feasibility studies, of important socioeconomic factors for the viability of the project. In Peru, an economic analysis on the effectiveness of using raised field irrigation in Puno concluded that the benefits of rehabilitating the raised fields outweighed the cost, in this case that yields were higher in irrigated plots in raised fields than along the mountainside and pampas terrain. However, the analysis missed several inefficiencies, such as the tendency of farmers to abandon the management of the raised fields because of competing labor demand, for instance, despite the increase in crop yield. If considered, these inefficiencies would not have justified the promotion of this method in that community, or at least would have led to measures addressing them—for instance, they could have provided farmers with individual incentives for rehabilitating the raised fields. Therefore, the failure of the raised field project in Puno was due to the fact that the economic analysis narrowly considered only the direct outputs of the project and not the ancillary conditions that would have made it sustained in the longer term (Middleton, 2007).

Similarly, “no-tillage” technologies in Morocco are often considered the solution to a changing climate and have been included in an ambitious rural development program by the Government (Agence pour le Développement Agricole, 2012), as they reduce the costs for the farmers while increasing the yields. Yet, the increased yields are usually measured on demonstration fields where all inputs, machineries, and skills are readily available and there is no competition of time and resources. Moreover, the analysis had assumed optimistic rates of adoption, when in reality, these remained extremely low (around 2 percent), again because of constraints farmers faced for the adoption of no tillage and which they had ignored (Bonzanigo, Giupponi, & Moussadek, 2016).

These examples show that it is extremely important to consider the different determinants of adaptive capacity—the political, social, cultural, and economic context—within which adaptation decisions will be made. Economic benefits and costs are important criteria, but they are not always sufficient to determine adequately the appropriateness of all adaptation measures. Already in 2001, Yohe pointed out the lack of research on the dynamics of adaptation in human systems, the processes of adaptation decision making, the conditions that stimulate or constrain adaptation, and the role of nonclimatic factors. According to him, there were also serious limitations in existing evaluations (Yohe, 2001). More than fifteen years later, in the field of climate change resilience in particular, there is still a disconnect between different scientific communities working on the concepts of resilience (which is model-based and quantifies the persistence of relationships within a system after changes), vulnerability (which comes from the study of natural hazards and quantifies the impacts of a natural hazard on infrastructure or human systems), and adaptation of humans to environmental changes (which is a focus of anthropology) (Füssel & Klein, 2006; Janssen, Schoon, Ke, & Börner, 2006). This has led researchers to call again for more integrated approaches, like Taylor et al. (2013) who insist on the importance of being informed by models able to consider the range of interactions between groundwater, climate, and also human activity.

A few authors have addressed these issues by looking at the indirect socioeconomic impacts of natural disasters through trade and supply chains (Rose, 2004; Rose & Liao, 2005; Todo, Nakajima, & Matous, 2015) and by looking at the welfare consequences of disasters ( Hallegatte et al., 2016b). Considering the distributional impacts of infrastructure investment through welfare impacts can change the preferred interventions. Hallegatte et al. (2016b) find that when natural disaster risk is expressed in terms of welfare losses rather than asset losses, implying that it takes into account the differential impact of natural disasters on the poor and the nonpoor, policy recommendations can be significantly different. For example, reducing welfare losses tends to favor infrastructure that protects the poor and social safety net systems over the usual hard protection around rich areas.

Gap 2: Deep Uncertainty is a Barrier to Good Decision Making for Infrastructure Planning and Resilience

In parallel to the need to better model human systems in infrastructure planning, there is a need to better acknowledge and handle uncertainty about future conditions. Knight (1921) distinguished between two kinds of ignorance about our uncertain future: that which we can reliably quantify with probability distributions—called risk—and that which we cannot quantify—called uncertainty, or more recently deep uncertainty (Kalra et al., 2014; Karvetski & Lambert, 2012; Lempert, Popper, & Bankes, 2003) or severe uncertainty (Ben-Haim, 2006). For example, the likelihood of experiencing a car crash is easily calculable from ample historical data and is an example of risk. In contrast, likelihood estimates of long-term land use patterns or global economic growth would be neither reliable nor verifiable. They are uncertainties or deep uncertainties. Deep uncertainty occurs when parties to a decision do not know or cannot agree on (a) models that relate the key forces that shape the future, (b) probability distributions of key variables and parameters in these models, and/or (c) the value of alternative outcomes (Lempert, Popper, & Bankes, 2003).

In many cases, infrastructure-planning exercises address uncertainty using risks, meaning that they quantify the likelihood of things that can go wrong. More precisely, risk assessment in systems engineering aims at objectively answering three questions: What can go wrong, what is the likelihood, and what are the consequences (Kaplan & Garrick, 1981; Karvetski & Lambert, 2012)? When engineering models include only parameters whose values are accurately predictable based on past data, this framework works well (Lambert, Jennings, & Joshi, 2006). Yet there is an increasing recognition that such approaches may not help manage deep uncertainties that arise in many investment decisions (Kalra et al., 2014). For example, climate change is changing the likelihood of extreme weather events in a way which is difficult to predict with precision. In many cases, as it is very difficult to quantify the likelihood of parameters, the analysis of uncertainty is omitted or restricted to simple sensitivity analyses, one variable at a time.

As for economic analyses, because they model variables that are difficult to predict, they rarely handle uncertainty well. At the World Bank the typical approach is to conduct a CBA of the preferred investment option under best estimate predictions of the relevant future conditions (Bonzanigo & Kalra, 2014). When they can, analysts occasionally replace a deterministic parameter by a stochastic one, thereby incorporating risk in the CBA (2014). The objective is then to have a project with a positive net present value on average over all possible values of the parameter (weighted by their likelihood) instead of a positive net present value in the best estimate projection. Thoft-Christensen (2012), for example, uses a stochastic cost-benefit analysis to support infrastructure maintenance and replacement policies, assigning probability distributions to the deterioration, maintenance, and benefits of infrastructures.

But in many cases it is difficult to find or agree on probability distributions for most parameters of an economic analysis (Kalra et al., 2014). The future state of socioeconomic variables like costs, demand, or prices is also difficult to predict. Ansar, Flyvbjerg, Budzier, and Lunn (2014); Flyvbjerg, Skamris Holm, and Buhl (2002); and Flyvbjerg, Skamris Holm, and Buhl (2004) find that cost forecasts are invariably wrong (costs are always underestimated) for infrastructure projects, while Flyvbjerg, Skamris Holm, and Buhl (2006) find that traffic demand is almost always overestimated in transport projects. Silver (2012) also finds that economic forecasts requested by the US Federal Reserve every year are outside of the 90 percent confidence interval one-third of the time (instead of one-tenth if they were reliable forecasts). Craig, Gadgil, and Koomey (2002) explain how energy forecasters during the 1950–1980 period failed to foresee the ability of the United States economy to respond to the oil embargos of the 1970s by increasing efficiency. Not only were most energy demand forecasts of that period systematically high, but forecasters systematically underestimated uncertainties. Morgan and Keith (2008) try to understand why energy forecasts are often so inaccurate and recall a bias well shown by experimental psychologists: in the face of uncertainty, experts persistently demonstrate overconfidence, believing strongly in their ability to predict the future when they cannot (Kahneman, 2011). Since these variables are actually impossible to predict, it is easy to be biased toward values that will support the implementation of a project.

There are many examples of projects that failed to deliver on their initial promises because of overconfidence in one prediction for cost or demand. During the preparation of the Eurostar project, the high-speed train that connects Paris and London, demand was grossly overestimated. In 2009 the Eurostar carried 9.2 million passengers, 60 percent of what forecasters said it would carry fourteen years earlier (Enthoven, Grindley, & Warren, 2010). As a result of this overestimation of demand and of an underestimation of the capital costs, the British and French Governments had to heavily subsidize the project, thereby deepening their public debt while they had promised the Eurostar would be a fully private project (France, Ministère de l’économie & Pébereau, 2006). The Optima Lake Dam in Texas County, Oklahoma, was constructed for flood control and water supply in 1978, but has never been used due to unforeseen economic development upstream, with expanded irrigation and drinking water withdrawals, which significantly reduced the river flow (Wahl & Tortorelli, 1997).

Up until the 1990’s, big limitations existed in the way infrastructure planning exercises dealt with uncertainty in models, possibly because the lack of computation power available only allowed models to be run a few times. Uncertainty was sometimes explored through scenario planning like the famous Royal Dutch/Shell scenario planning exercises which preserved the company from the negative impacts of oil shocks in the 1970’s (Cornelius, Van de Putte, & Romani, 2005).

But over the past decades, there has been a shift in the way scientists and planners approach deep uncertainty. One reason may be the boom in computation power capacity, which enabled methods like stochastic programming, Robust Decision Making, or Monte-Carlo sensitivity analyses to run models a very high number of times and for long computation times, to explore all potential options under thousands of future conditions. Another reason may be that climate change turned variables that used to be predictable into unpredictable ones (Boorman & Sefton, 1997), or that uncertainty is identified as one of the main barriers to the rapid implementation of climate change adaptation actions (Eisenack et al., 2014). There is now an increased recognition, among the scientific community as to future climate change and the need to better comprehend it, to be prepared for unanticipated events, and the need for decision support processes that can engage stakeholders with significantly different visions of the future.

New Methods Developed To Incorporate Climate Change Uncertainty in Decision-Making Could Consider Socioeconomic Uncertainties

As part of the growing recognition of climate change uncertainty, many new methods have been developed for decision support that fall into the broad category of “Decision Making Under Deep Uncertainty” (DMDU). These include for example Dynamic Adaptive Policy Pathways (Haasnoot et al., 2013; Hall et al., 2012); Robust Decision Making (Lempert et al., 2006); Info-Gap (Ben-Haim, 2014; Hall et al., 2012); and Decision Scaling (Brown et al., 2012), which were all developed for or applied to climate change adaptation projects. Instead of looking for optimal strategies or investments in a deterministic future, these methods consider multiple alternative futures and seek strategies that are either insensitive to uncertainty or that can be easily adapted when conditions change. These methods also involve multiple stakeholders in the analysis, to ensure everyone’s view is represented in the scenarios that are analyzed and therefore avoid gridlock. In addition, these methodologies are particularly relevant in developing countries where data is scarce and uncertainty sometimes comes from lack of data rather than the impossibility to predict the future (Bhave, Conway, Dessai, & Stainforth, 2016).

Since these methodologies were mainly developed to address climate change adaptation problems, and since they are applied with engineering models, they often model only climate change uncertainty. For example, Ghile, Taner, Brown, Grijsen, and Talbi (2014) explore all the future climate conditions that can make the Niger River basin’s socioeconomic system vulnerable, but they never explore socioeconomic uncertainty. Kwadijk et al. (2010) explore adaptation strategies for the Netherlands using Dynamic Adaptive Policy Pathways and defining tipping points beyond which a strategy will stop performing well. Those tipping points are only defined in terms of climate change and not in terms of changes in other conditions.

While DMDU methods have triggered tremendous progress on how the climate change community thinks about adaptation and interaction with decision makers, they could have a much bigger impact if they considered more systematically other sources of uncertainty than just climate change. Circumscribing the analysis of uncertainty to climate change impacts focuses analysts’ and practitioners’ attention on one uncertainty when in fact demographic changes, urbanization changes, or economic growth could have a larger impact on the performance of the system they are studying. And importantly, in many cases a project fails because of a combination of climate conditions and socioeconomic factors, the so-called “perfect storm.”

When socioeconomic uncertainty is taken into account in a risk assessment or in the preparation of infrastructure project, it is often the biggest source of uncertainty for the project performance. For example, Herman, Zeff, Reed, and Characklis (2014) compare different methods and robustness metrics for choosing water supply projects that are insensitive to future uncertainty, and while different methods provide slightly different recommendations as to what should be done, they all identify future demand growth as the main uncertainty and potential threat to the system. Winsemius, et al. (2016) find that in some places like Southeast Asia, future increase in flood risk is mostly driven by socioeconomic changes while in the United States it is driven by climate change. Similarly, Hall, Sayers, and Dawson (2005) assess flood risk in the United Kingdom fifty and eighty years into the future and find that the most dangerous scenarios are the result of severe climate change combined with high economic growth and increased economic vulnerability.

In 2014, the World Bank helped Lima’s water utility SEDAPAL evaluate the risks that climate and demand changes might pose to their long-term infrastructure investment plan. When developing this plan, SEDAPAL had considered one scenario of demand increase by 2040, and no climate change impacts (i.e, no streamflow changes). Kalra et al. (2015) evaluated the performance of their water system over a wide (600) range of plausible futures, each of them combining changes in streamflow, demand, and the possibility of implementing all projects. The study, by applying DMDU methods, helped SEDAPAL develop an implementation plan that would be robust across these 600 futures. A key outcome of the study is that, despite initial expectations, the system was vulnerable to an even slightly higher increase in 2040 demand than what SEDAPAL had initially forecasted when developing their plan. And it was more vulnerable to demand than to climate change impacts on the streamflow. The awareness that even the implementation of their full plan would not protect them against a sharp increase in demand helped the water utility focus future efforts on demand side management, pricing, and soft infrastructure, a refocusing that is difficult to achieve in traditional utility companies.

Similarly, the Nepal Energy Agency asked for support in identifying threats posed by climate change to a large hydropower investment that they were considering, the Upper Arun dam. By applying the decision tree framework (Ray & Brown, 2015), the team started by identifying the threats that climate change in isolation would pose. The climate stress test sampled a wide range of changes in climate and variability to reveal the vulnerability of the design to plausible climate changes. The team found that the economic value of the proposed 335 MW design was robust to the wide range of climate changes considered. The design capacity of 1000 MW emerged as an attractive alternative, providing the best combination of robustness to climate change (it was performing well across many future climates) and opportunity (it could take advantage of glacier melting and produce more during the dry season). But this bigger design also appeared more sensitive to increases in capital costs and low electricity prices than the 335MW design. What may thus seem like a robust investment when looking at climate alone may indeed be vulnerable to other variables. These examples show that it is very important to consider several uncertainties together, as otherwise the decision maker may be ill-informed on the risks they are taking when proceeding with the investment.

Today the awareness of the need to combine climate with the proper assessment of the more traditional variables used in economic analyses remains scant. As mentioned above, although many institutions are mainstreaming tools for climate change assessments, and in parallel are trying to improve the use of economic analyses in decision-making processes, to our knowledge no institution is explicitly reviewing the need for an explicit inclusion of climate change combined with other sources of uncertainty in the socioeconomic analysis in project design.

And yet, a careful analysis of the social and economic benefits (and costs) of infrastructure investments under multiple scenarios, including a careful accounting of the distributional impacts of an investment, sometimes strengthens the case for that investment. In Colombo, Sri Lanka, the World Bank compared the costs and benefits of protecting the urban wetlands under climate and socioeconomic uncertainty. The study compared increase in flood risk and loss of ecosystem services when wetlands disappear with rent increases due to land scarcity if wetlands are declared no development zones. The analysis used a social welfare maximization approach and factored in the fact that poor people are the ones mostly affected by floods. Results showed that protecting the wetlands was the most robust strategy since it had the highest welfare gains in most future cases. The only scenarios in which developing the land was a better option were scenarios with extreme assumptions on the cost of constructing higher buildings, combined with optimistic assumptions on future flood occurrence and optimistic assumptions on redistribution policies. The detail of the scenarios and of the analysis convinced policymakers that all potential benefits of land development had been explored and that protecting the wetlands was indeed the best option (Rozenberg, Avner, Bangalore, Bonzanigo, & Lahiru, forthcoming).

The important lesson learned from this example is that consensus was reached because the analysts did not prescribe the best decision, but presented the scenarios under which each option was preferable. As tautological as it sounds, decision makers were the ones who made the decision based on the information available. Too often, feasibility studies conclude on which option is the best in a very normative way, hiding the hypotheses that led to the recommendation. Instead, DMDU methods applied to a wide range of parameters present the decision makers with the risks and opportunities associated with each option, depending on the assumptions that are made on uncertain parameters. Presented in a pedological way, this information is much more useful to decision makers than a strict expert recommendation.

Way Forward: Integrated Models To Combine Several Sources of Deep Uncertainties for Infrastructure Planning in Developing Countries

To improve decision making in infrastructure planning in developing countries, we suggest adding an initial step to the usual decision-making process. This first step would involve the development of an integrated model (from systems mapping to numerical models), which considers both social responses and the economic and environmental impacts of the investment choices under evaluation. Examples of improvement compared to currently widely used models include: decomposing future demand growth into relevant drivers (like population growth, per capita consumption growth, and share of consumption dedicated to the service under analysis); treating vulnerability of assets or people as a dynamic variable; and adding feedback loops between an infrastructure investment and changes in demand (for instance, population in a flood-prone area probably grows faster as a result of investments in flood protection).

DMDU techniques would be used to run the model under hundreds of plausible future conditions and to identify the combinations of factors—climatic, social, or economic—that could create vulnerabilities for the investment plan. The complexity of the model would depend on the time and budget available, but as it was nicely put by Michael Oppenheimer at the 2016 DMDU Society Workshop, “anything worth doing is worth doing superficially.” So, in some cases where models are not available, or data too scarce, in this initial step analysts and stakeholders can also brainstorm of all possible surprises they can think of, or bottlenecks that will hamper a project’s success, around a system map model. This step would take place before the detailed engineering design study and the economic analysis and allow to identify the dimensions to focus on during those studies.

Doing so would naturally require multidisciplinary teams including social scientists like anthropologists, behavioral scientists, political scientists, or economists. The use of DMDU methods in social sciences is fairly new, but examples of social scientists’ attempts to better manage uncertainty do exist. For instance, Auping, Pruyt, and Kwakkel (2015) apply the Robust Decision Making methodology to investigate if and when the Dutch public retirement and healthcare contributions will become unaffordable, and to identify the main causes of the unaffordable societal costs. Another example would be the bank stress tests that became widespread after the 2007–2009 global financial crisis. Hypothetical crises are determined using various factors from the Federal Reserve and International Monetary Fund and are used to quantify the robustness of banks’ balance sheets.

The innovation in this proposal lies in the combination of various sources of uncertainty—environmental, economic, and social—in the assessment of the robustness of a project or program, to look for the “perfect storm” that could make them fail. The value of doing so can be illustrated by recent examples in the literature. Zeff, Herman, Reed, and Characklis (2016) show that when short-term management and cooperation are included in the analysis for long-term planning decisions on when and how to add new water supply capacity, different solutions can be identified compared to traditional optimization methods. These solutions are often cheaper and comprise less overall development than what traditional optimization methods recommend. Buchanan, Kopp, Oppenheimer, and Tebaldi (2016) provide a framework of sea level rise allowances that join factors about decision makers’ preferences in the analysis such as time horizon, risk tolerance, and confidence in SLR projections for coastal adaptation.2 Walker, Loucks, and Carr (2015) propose using agent-based modeling to integrate the social, environmental, and economic dimensions of water resources planning and management. They propose to use the agent based model in conjunction with stakeholder involvement and dynamic adaptation planning.

These few examples show that an effort is being made to try and integrate into the analysis some of the uncertainties not traditionally modeled in engineering models. However, despite the general recognition that economic and political uncertainty are often more determinant for a project’s viability than climate alone, the integration of the full socioeconomic impact of a planning process or an infrastructure project into the “engineering” analysis is far from being mainstreamed. The difficulty lies in the perceived inability to model socioeconomic variables with the same scientific rigor as climate change. However, the economic literature proposes various modeling frameworks in addition to the CBA that can be adapted to infrastructure planning (Bourguignon & Spadaro, 2006; Leontief, 1986; Tesfatsion & Judd, 2006; Thissen, 1998) and other disciplines like sociology, migration studies, and epidemiology, now heavily rely on agent-based models (Gilbert, 2008). In addition, some variables can be introduced qualitatively if necessary (Smithson & Ben-Haim, 2015).

A decision process that starts by stress-testing an infrastructure plan to various sources of uncertainty together is particularly attractive for developing countries, given the amount of infrastructure which is yet to be built and the pace and magnitude of the changes to come. Developing countries face huge risks of social disruption and economic stalling if they ignore future climate changes, but also technological disruptions, possible financial turmoil, and the distributional impacts of development policies. The tools to do so exist, and it is our role as analysts to strive to mainstream them in the decision-making process, and, as practitioners, to create the right institutional incentives that may expand demand for the application of these tools.


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                                                                                                                                                            (1) This is also an institutional issue. The water utility has a narrow mandate (i.e, delivering water reliably to a city), and tends to consider the broader economic or environmental impacts as something beyond their authority.

                                                                                                                                                            (2) This is the height adjustment from historic flood levels that maintains under uncertainty the annual expected probability of flooding.