After a long period of relative stability in the climate system, climate conditions are changing and are projected to continue to change (Ch. 2: Our Changing Climate). As a result, historically successful strategies for managing climate-sensitive resources and infrastructure will become less effective over time. Although decision-makers routinely make complex decisions under uncertain conditions, decision-making in the context of climate change can be especially challenging due to a number of factors. These include the rapid pace of changes in some physical and human systems, long time lags between human activities and response of the climate system, the high economic and political stakes, the number and diversity of potentially affected stakeholders, the need to incorporate uncertain scientific information of varying confidence levels, and the values of stakeholders and decision-makers.^7,1,2 The social, economic, psychological, and political dimensions of these decisions underscore the need for ways to improve communication of scientific information and uncertainties and to help decision-makers assess risks and opportunities.

Extensive literature and practical experience offer means to help improve decision-making in the context of climate variability and change. The decision support literature includes topics such as decision-making frameworks, decision support tools, and decision support processes. These approaches can help evaluate the costs and benefits of alternative actions, communicate relative amounts of risk associated with different options, and consider the role of alternative institutions and governance structures. In particular, iterative decision processes that incorporate improving scientific information and learning though periodic reviews of decisions over time are helpful in the context of rapid changes in environmental conditions.^2,3 Some of the approaches described in this chapter can also help overcome barriers to the use of existing tools and improve communications among scientists, decision-makers, and the public.^23,6

What are the decisions and who are the decision-makers?

Decisions about climate change adaptation and mitigation are being made in many settings (Table 26.1). For example:

• The Federal Government is engaged in decisions that affect climate policy at the national and international level; makes regulatory decisions (for example, setting efficiency standards for vehicles); and makes decisions about infrastructure and technologies that may reduce risks associated with climate change for its own facilities and activities.
• State, tribal, and local governments are involved in setting policy about both emissions and adaptation activities in a variety of applications, including land use, renewable portfolio and energy efficiency standards, and investments in infrastructure and technologies that increase resilience to extreme weather events.
• Private-sector companies have initiated strategies to respond both to the risks to their investments and the business opportunities associated with preparing for a changing climate.
• Non-governmental organizations have been active in supporting decisions that integrate both adaptation and mitigation considerations, often in the context of promoting sustainability within economic sectors, communities, and ecosystems.
• Individuals make decisions on a daily basis that affect their contributions to greenhouse gas emissions, their preparedness for extreme events, and the health and welfare of their families.⁴

Many decisions involve decision-makers and stakeholders at multiple scales and in various sectors. Effective decision support must link and facilitate interactions across different decision networks.^24,25,26

What is decision support?

Figure 26.1: Decision-making Elements and Outcomes Details/Download

Decision support refers to “organized efforts to produce, disseminate, and facilitate the use of data and information” to improve decision-making.² It includes processes, decision support tools, and services. Some examples include methods for assessing tradeoffs among options, scenarios of the future used for exploring the impacts of alternative decisions, vulnerability and impacts assessments, maps of projected climate impacts, and tools that help users locate, organize, and display data in new ways. Outcomes of effective decision support processes include building relationships and trust that can support longer-term problem-solving capacity between knowledge producers and users; providing information that users regard as credible, useful, and actionable; and enhancing the quality of decisions.² Decision support activities that facilitate well-structured decision processes can result in consensus about defining the problems to be addressed, objectives and options for consideration, criteria for evaluation, potential opportunities and consequences, and tradeoffs (Figure 26.1).

Incorporating the implications of climate change in decision-making requires consideration of scientific insights as well as cultural and social considerations, such as the values of those affected and cultural and organizational characteristics. Chapter 28 (Adaptation) addresses how some of these factors might be addressed in the context of adaptation. The importance of both scientific information and societal considerations suggests the need for the public, technical experts, and decision-makers to engage in mutual shared learning and shared production of relevant knowledge.^2,12 A major challenge in these engagements is communicating scientific information about the risks and uncertainties of potential changes in climate.¹¹

Figure 26.2: Boundary Processes Linking Decision-Makers and Scientific/Technical Experts Details/Download

Efforts to facilitate interactions among technical experts and members of the public and decision-makers are often referred to as “boundary processes” (Figure 26.2). Boundary processes and associated tools include, for example, joint fact finding, structured decision-making, collaborative adaptive management, and computer-aided collaborative simulation, each of which engages scientists, stakeholders, and decision-makers in ongoing dialog about understanding the policy problem and identifying what information and analyses are necessary to evaluate decision options.^15,16,27 The use of these kinds of processes is increasing in decision settings involving complex scientific information and multiple – sometimes competing – societal values and goals. Well-designed boundary processes improve the match between the availability of scientific information and capacity to use it and result in scientific information that is perceived as useful and applicable.⁶

Though boundary processes developed to support climate-related decisions vary in their design, they all involve bringing together scientists, decision-makers, and citizens to collaborate in the scoping, conduct, and employment of technical and scientific studies to improve decision-making. Boundary processes can involve establishing specialized institutions, sometimes referred to as boundary organizations, to provide a forum for interaction amongst scientists and decision-makers.²⁸ One such boundary activity is the National Oceanic and Atmospheric Administration’s (NOAA) Regional Integrated Science and Assessment (RISA) Program. Interdisciplinary RISA teams are largely based at universities and engage regional, state, and local governments, non-governmental organizations, and private sector organizations to address issues of concern to decision-makers and planners at the regional level. RISA teams help to build bridges across the scientist, decision-maker, and stakeholder divide.²⁹ Effective engagement may also occur through less formal approaches by incorporating boundary processes that bring scientists, stakeholders, and decision-makers together within a specific decision-making setting rather than relying on an independent boundary organization. Sustained conversations among scientists, decision-makers, and stakeholders are often necessary to frame issues and identify, generate, and use relevant information.^30,31

Courtesy of Lynn Laws, Iowa State University 2013

Some analysts have emphasized the importance of boundary processes that are collaborative and iterative.^13,14 In one example, federal, state, and local agencies, water users, and other stakeholders are using a collaborative process to manage the Platte River to meet species protection goals and the needs of other water users. The Platte River Recovery Implementation Program brings together participants on an ongoing basis to help set goals, choose management options, and generate information about the effectiveness of their actions.³² Scientists engaged in the process do not make policy decisions, but they engage directly with participants to help them frame scientific questions relevant to management choices, understand available information, design monitoring systems to assess outcomes of management actions, and generate new knowledge tailored to addressing key decision-maker questions. The process has helped participants move beyond disagreements about the water-flow needs of the endangered species and move to action. Through monitoring, participants will evaluate whether the water flows and other management practices are achieving the goals for species recovery set out in the Platte River Recovery Implementation Plan.

In a number of other examples, boundary processes involve the use of computer simulation models.²⁷ Scientists, stakeholders, and decision-makers develop a shared understanding of the problem and potential solutions by jointly designing models that reflect their values, interests, and analytical needs. The U.S. Army Corps of Engineers has developed this type of boundary process in their “shared vision planning.”^33,34,35 A comprehensive website provides a history of the process, demonstrations and case studies, and tools and techniques for implementing the process.³⁶

Recently, the International Joint Commission used the shared vision planning process in decisions about how to regulate water levels in both the Lake Ontario-St. Lawrence River system³⁷ and in the Upper Great Lakes.^38,39 Both studies engaged hundreds of participants from the United States and Canada in discussions about water level management options and the impacts of those options on ecosystems; recreational boating and tourism; hydropower; commercial navigation; municipal, industrial, and domestic water use; and the coastal zone. The models used in the studies incorporated information about ecosystem responses, shoreline dynamics, economics, and lake hydrology, and the potential operating plans were tested using multiple climate change scenarios. Although the shared vision planning process did not ultimately lead to consensus on a single recommended plan in the Lake Ontario-St. Lawrence River Study, the process did help improve participants’ understanding of the system and develop a shared vision of possible futures.^37,40 Building on lessons from the Lake Ontario-St. Lawrence River Study, the Upper Great Lakes Study’s use of shared vision planning did result in a single recommended plan.³⁹

The term “adaptive management” is used here to refer to a specific approach in which decisions are adjusted over time to reflect new scientific information and decision-makers learn from experience. The National Research Council (NRC) contrasts the processes of “adaptive management” and “deliberation with analysis.”² Both can be used as part of an “iterative adaptive risk management framework” that is useful for decisions about adaptation and ways to reduce future climate change, especially given uncertainties and ongoing advances in scientific understanding.^4,5 Iterative adaptive risk management emphasizes learning by doing and continued adaptation to improve outcomes. It is especially useful when the likelihood of potential outcomes is very uncertain.

Figure 26.3: Decision-Making Framework Details/Download

An idealized iterative adaptive risk management process includes clearly defining the issue, establishing decision criteria, identifying and incorporating relevant information, evaluating options, and monitoring and revisiting effectiveness (Figure 26.3). The process can be used in situations of varying complexity, and while it can be more difficult for complex decisions,⁴¹ the incorporation of an iterative approach makes it possible to adjust decisions as information improves. Iterative adaptive risk management can be undertaken through collaborative processes that facilitate incorporation of stakeholder values in goal-setting and review of decision options.⁴² Examples of the process and decision support tools that are helpful at its different stages are included in subsequent sections of this chapter.

An initial step in a well-structured decision process is to identify the context of the decision and factors that will affect choices – making sure that the questions are posed properly from scientific, decision-maker, and stakeholder (or public) perspectives (corresponding to the first two steps in Figure 26.3). An important challenge is identifying the stakeholders and how to engage them in decision-making processes. There are often many categories of stakeholders, including those directly and indirectly affected by, or interested in, the outcomes of decisions, as well as the decision-makers, scientists, and elected officials.⁴³ Other important considerations often overlooked but critical to defining the issue are:

• understanding the goals and values of the participants in the decision process;
• identifying risk perceptions and the sense of urgency of the parties involved in the decision;
• being clear about the time frame of the decision (short- versus long-term options relative to current and future risk levels) – and when the decision must be reached;
• acknowledging the scale and degree of controversy associated with the risks and opportunities as well as the alternatives;
• assessing the distribution of benefits or losses associated with current conditions and the alternatives being considered;
• reaching out to communities that will be affected but may lack ready access to the process (for example, considering environmental justice issues);
• recognizing the diverse interests of the participants;
• recognizing when neutral facilitators or trained science translators are needed to support the process; and
• understanding legal or institutional constraints on options.

Identifying and agreeing on decision criteria – metrics that help participants judge the outcomes of different decision options – can be extremely helpful in clarifying the basis for reaching a decision. Based on the relevant objectives, decision criteria can be established that reflect constraints and values of decision-makers and affected parties. Criteria can be quantitative (for example, obtaining a particular rate of return on investment) or qualitative (for example, maintaining a community’s character or culture). If the issue identified is to reduce the risks associated with climate change, decision criteria might include minimizing long-term costs and maximizing public safety. Related sections below provide information on tools for valuing and comparing options and outcomes and provide a basis for using decision criteria.

Decision framing and establishment of decision criteria can be facilitated using various methods, including brainstorming, community meetings, focus groups, surveys, and problem mapping;^2,43 selecting among techniques requires consideration of a number of context-specific issues.^44,45 There are a variety of techniques for organizing, weighting information, and making tradeoffs for the goals that are important for a decision,^46,47 several of which are discussed in more detail in the section “Examples of Decision Support Tools and Methods.”

Developing a solid base of information to support decision-making is ideally a process of matching user needs with available information, including observations, models, and decision support tools. In some cases, needed information does not exist in the form useful to decision makers, thus requiring the capacity for synthesis of currently available information into new data products and formats. For decisions in the context of climate change and variability, it is critical to consult information that helps clarify the risks and opportunities to allow for appropriate planning and management. An example of information systems that synthesize data and products to support mitigation and adaptation decisions is the National Integrated Drought Information System (NIDIS), a federal, interagency effort to supply information about drought impacts and risks as well as decision support tools to allow sectors and communities to prepare for the effects of drought.^48,49,50 Learning from the successes of such efforts, the National Climate Assessment (NCA) is currently developing an indicator system to track climate changes as well as physical, natural, and societal impacts, vulnerabilities, and responses.⁵¹ This effort is building on existing indicator efforts, such as the U.S. Environmental Protection Agency’s (EPA) Climate Change Indicators,^52,53 NASA Vital Signs,⁵⁴ and NOAA indicator products,⁵⁵ as well as identifying when new data, information, and indicator products are needed.

Information technology systems and data analytics can harness vast data sources, facilitating collection, storage, access, analysis, visualization, and collaboration by scientists, analysts, and decision-makers. Such technologies allow for rapid scenario building and testing using many different variables, enhancing capacity to measure the physical impacts of climate change. These technologies are managing an increasing volume of data from satellite instruments, in situ (direct) measurement networks, and increasingly detailed and high-resolution models.⁵⁶ “Information Technology Supports Adaptation Decision-Making” below highlights use of an open platform data system that facilitated collaboration across multiple public and private sector entities in analyzing climate risk and adaptation economics along the U.S. Gulf Coast.

While progress is being made in development of data management and information systems, multiple challenges remain. Specific issues highlighted in the recent USGCRP National Global Change Research Plan⁵⁶ include data permanence, volume, transparency, quality control, and access. For data on socioeconomic systems – important for evaluating vulnerabilities, adaptation, and mitigation –privacy, confidentiality, and integration with broader systems of environmental data are important issues.⁵⁶ Experience with adaptation and mitigation decisions is often an excellent source of information and knowledge but is difficult to access and validate. Several organizations have been developing knowledge management systems for integrating this highly dispersed information and providing it to a network of practitioners (for example, CAKE 2012⁹). Addressing these and other challenges is essential for making progress in establishing a sustained assessment process and meeting the challenge of informing decision-making.⁵⁷

Making effective climate-related decisions requires balance among actions intended to manage, reduce, and transfer risk. Risks are threats to life, health and safety, the environment, economic well-being, and other things of value. Risks are often evaluated in terms of how likely they are to occur (probability) and the damages that would result if they did happen (consequences). As noted by the Intergovernmental Panel on Climate Change,⁶³ human choices affect the risks associated with climate variability and change. Such choices include how to manage our ecosystems and agriculture, where to live, and how to build resilient infrastructure. Choices regarding a portfolio of actions to address the risks associated with climate variability and change are most effective when they take into consideration the range of factors affecting human behavior, including people's perception of risk, the relative importance of those risks, and the socioeconomic context.^63,64 The process shown in Figure 26.4 is designed to help take such factors into consideration.

The next few sections describe the “integrate, evaluate, and decide” steps in Figure 26.3, which aim to help decision-makers choose risk management strategies. While a full quantitative risk analysis is not always possible, the concept of risk assessment coupled with understanding of risk perception provides a powerful framework for decision-makers to evaluate alternative options for managing the risks that they face today and in the future.⁶⁵ As described below, methods such as multiple criteria analysis, valuation of both risks and opportunities, and scenarios can help to combine experts’ assessment of climate change risks with public perception of these risks, both influenced by the diverse values people bring to these questions⁶⁶ and in support of risk management strategies more likely to achieve both public support and their desired objectives.⁶⁴ To illustrate how this framework can be applied to resource management decisions, we use an example of coastal risk management decisions in the context of climate change.⁶⁷

Figure 26.4: Linking Risk Assessment and Risk Perception with Risk Management of Climate Change Details/Download

Risk Assessment

Risk assessment includes studies that estimate the likelihood of specific sets of events occurring and/or their potential consequences.⁶⁸ Experts often provide quantitative information regarding the nature of the climate change risk and the degree of uncertainty surrounding their estimates. Risk assessment focuses on the likelihood of negative consequences but does not exclude the possibility that there may also be beneficial consequences.

There are four basic elements for assessing risk – hazard, inventory, vulnerability, and loss.⁶⁹ This generalized approach to risk assessment is useful for a variety of types of decisions. The first element focuses on the risk of a hazard as a function of climate change, including interactions of climate effects with other factors. In the context of the coastal community example, the community is concerned with the likelihood of future hurricanes and the impacts that sea level rise may have on damage to the residential development from future hurricanes. There is likely to be considerable uncertainty about maximum storm surge and sea level from hurricanes during the next 50 to 70 years. The second element identifies the inventory of properties, people, and the environment at risk. To inventory structures, for instance, requires evaluating their location, physical dimensions, and construction quality.

Evaluating both the hazard and its impacts on the inventory often requires an appropriate treatment of uncertainty. In some cases a probabilistic treatment may prove sufficient. For instance, in the coastal community example, decision-makers may have sufficient confidence in estimates of the return frequency of extreme storms (for example, that the once-in-a-hundred-years storm is and will remain a once-in-a-hundred-years storm) to base their choices largely on these estimates. If such probabilistic estimates are not available, or if decision-makers lack sufficient confidence in those that are available, they may find it useful to consider a range of scenarios and seek risk management strategies robust across these ranges of estimates.^67,70,71,72

Together, the hazard and inventory elements enable calculation of the damage vulnerability of the structures, people, and environment at risk. The vulnerability component enables estimation of the human, property, and environmental losses from different climate change scenarios by integrating biophysical information on climate change and other stressors with socioeconomic and environmental information.⁷³ These assessments typically involve evaluation of exposure, sensitivity, and adaptive capacity for current and projected conditions. Quantitative indicators are increasingly used to diagnose potential vulnerabilities under different scenarios of socioeconomic and environmental change^74,75 and to identify priorities and readiness for adaptation investments.⁷⁶ In the case of a coastal residential development, the design of the facility will influence its ability to reduce damage from hurricanes and injuries or fatalities from hurricane storm surge and sea level rise. Decisions may involve determining whether to elevate the facility so it is above ten feet, how much this adaptation measure will cost, and the reduction in the impact of future hurricanes on damage to the facility and on the residents in the building, as a function of different climate change scenarios.

Risk Perception in Climate Change Decision-Making

The concept of risk perception refers to individual, group, and public views and attitudes toward risks, where risks are understood as threats to life, health and safety, the environment, economic well-being, and other things of value. Risk perception encompasses perspectives on various dimensions of risks, including their severity, scope, incidence, timing, controllability, and origins or causes. The knowledge base regarding risk perception includes research in psychology, social psychology, sociology, decision science, and health-related disciplines (see “Factors Affecting Attitudes Toward Risk”).

As noted in “Factors Affecting Attitudes Towards Risk,” many factors influence risk. Social scientists and psychologists have studied people’s concerns about climate change risks and found that many individuals view hazards for which they have little personal knowledge and experience as highly risky.¹⁰⁴ On the other hand, seeing climate change as a simple and gradual change from current to future values on variables such as average temperatures and precipitation may make it seem controllable.¹⁰⁵

The effects of risk perception on decision-making have also been studied extensively and support a number of conclusions that need to be considered in decision support processes. The decision process of non-experts with respect to low-probability, high-consequence events differs from that of experts.¹⁰⁶ Non-experts tend to focus on short time horizons, seeking to recoup investments over a short period of time, in which case future impacts from climate change are not given much weight in actions taken today. This is a principal reason why there is a lack of interest in undertaking adaptation measures with upfront investments costs where the benefits accrue over a long period of time.¹⁰⁷ In the context of the coastal residential development, elevating the structure will reduce expected damages from hurricanes, resulting in smaller annual insurance premiums. Long-term loans that spread the costs of this action over time can make the option financially attractive, if the savings on the insurance premiums outweigh the costs of the loan payments.

There is also a tendency for decision-makers to treat a low-probability event as if it had no chance of occurring because it is below their threshold level of concern (such as a 1 in 100 chance of a damaging disaster occurring next year). As shown by empirical research, stretching the time horizon over which information is communicated can make a difference in risk perception.¹⁰⁸ In the case of the coastal residential development, community leaders may pay more attention to the need for adaptation measures if the likelihood of inundation by a future hurricane is presented over a 25-year or 50-year horizon (for example, the facility may flood 5 times in 25 years) rather than as a risk on annual basis (for example, there is a 20% chance of flooding in any given year).

Risk Management Strategies

In general, an effective response to the current and future risks from climate variability and change will require a portfolio of different types of actions, ranging from those intended to manage, reduce, and transfer risk to those intended to provide additional information on risks and the effectiveness of various actions for addressing it (see “Value of Information”). For instance, in the coastal community example, decision-makers might better manage risk through changes in building codes intended to reduce the impact of flooding on structures, might share risk by appropriate adjustments in flood insurance rates, and might reduce risk via land-use policies that shift development towards higher ground and via participating in and advocating for greenhouse gas emission reduction policies that may reduce future levels of sea level rise.

To facilitate these strategies given the uncertainty associated with the likelihood and consequences of climate change, “robust decision-making” may be a useful tool for evaluating alternative options and risk management strategies. One study reviews the application of a range of decision-making approaches to assessing options for mitigating or adapting to the impacts of climate change.¹⁰⁹ In the context of the coastal residential development, the choice of adaptation measures to reduce the likelihood of future water-related damage may require using such an approach. To illustrate, consider two adaptation measures, elevating a building and flood-proofing it, to reduce the chances of severe water damage from hurricane storm surge coupled with sea level rise. Measure 1 (elevation) may perform extremely well based on specific estimates of the likelihood of different climate change conditions that will affect storm surge and sea level rise, but it may perform poorly if those estimates turn out to be mistaken. Measure 2 (flood-proofing) may have a lower expected benefit than elevation but much less variance in its outcomes and thus be the preferred choice of the community.⁶⁷

Turning to risk management strategies, public agencies, private firms, and individuals have incentives, information, and options available to adapt to emerging conditions due to climate change. These options may include ensuring continuity of service or fulfillment of agency responsibilities, addressing procurement or supply chain issues, preserving market share, or holding the line on agency or private-sector production costs. Commercially available mechanisms such as insurance can also play a role in providing protection against losses due to climate change.¹¹⁰ However, insurers may be unwilling to provide coverage against such losses due to the uncertainty of the risks and lack of clarity on the liability issues associated with global climate change.¹¹¹ In these cases, public sector involvement through public education programs, economic incentives (subsidies and fines), and regulations and standards may be relevant options. Criteria for evaluating risk management strategies can include impacts on resource allocation, equity and distributional impacts, ease of implementation, and justification.

The implementation phase of a well-structured decision process involves an ongoing cycle of setting goals, taking action, learning from experience, and monitoring to evaluate the consequences of undertaking specific actions, as shown on the left-hand side of Figure 26.3. This cycle offers the potential for policy and outcome improvement through time. Ongoing evaluation can focus on how the system responds to the decision, leading to better future decisions, as well as on how different stakeholders respond, resulting in improvements in future decision-making processes. The need for social and technical learning to inform decision-making is likely to increase in the face of pressures on social and resource systems from climate change. However, the relative effectiveness of monitoring and assessment in producing social and technical learning depends on the nature of the problem, the amount and kind of uncertainty and risk associated with climate change, and the design of the monitoring and evaluation efforts.

While decision frameworks vary in their details, they generally incorporate most or all of the steps outlined above. To support decision-making across these steps, various technical tools and methods, developed in both the public and private sectors, can assist stakeholders and decision-makers in meeting their objectives and clarify where there are value differences or varying tolerances for risk and uncertainty. Many of these tools and methods are applicable throughout the decision-making process, from framing through assessment of options through evaluation of outcomes. Several of the tools and methods – data management systems and scientific assessments – help to expand the relevant information and provide a means of managing large amounts of data. Three other tools described below – comparative tradeoff methods, scenario planning, and integrated assessment models – are particularly useful in assisting stakeholders and decision-makers in identifying and evaluating different options for managing risks associated with climate change. The following discussion describes these approaches; examples are provided in “Example Decision Support Tools.”

Example Decision Support Tools

Many decision support tools apply climate science and other information to specific decisions and issues; several online clearinghouses describe these tools and provide case studies of their use (for example, CAKE 2012;⁹ CCSP 2005;⁸ NatureServe 2012¹⁰). Typically, these applications integrate observed or modeled data on climate and a resource or system to enable users to evaluate the potential consequences of options for management, investment, and other decisions. These tools apply to many types of decisions; examples of decisions and references for further information are provided in Table 26.2.

Table 26.2: Examples of decisions and tools used

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Topic	Example Decision(s)	Further Information and Case Studies
Water resources	Making water supply decisions in the context of changes in precipitation, increased temperatures, and changes in water quality, quantity, and water use	Means et al. 2010;118 International Upper Great Lakes Study 2012; 39 State of Washington 2012;119 “Denver Water Case Study” (below); Ch. 3: Water
Infrastructure	Designing and locating energy or transportation facilities in the coastal zone to limit the impacts of sea level rise	Ch. 11: Urban; Ch. 10: Energy, Water, and Land
Ecosystems and biodiversity	Managing carbon capture and storage, fire, invasive species, ecosystems, and ecosystem services	Byrd et al. 2011;120 Labiosa et al. 2009;121 USGS 2012a, 2012b, 2012c;122,123,124 Figure 26.5
Human health	Providing public health warnings in response to ecosystem changes or degradation, air quality, or temperature issues	Ch. 9: Human Health
Regional climate change response planning	Develop plans to reduce emissions of greenhouse gases in multiple economic sectors within a state	“Washington State’s Climate Action Team” (below)

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Figure 26.5: Land-use Planning Tool for the Upper Santa Cruz Watershed. Details/Download

Many available and widely applied decision-making tools can be used to support management in response to climate extremes or seasonal fluctuations. Development of decision support resources focused on decadal or multi-decadal investment decisions is in a relatively early stage but is evolving rapidly and shared through the types of clearinghouses discussed above.

Valuing the Effects of Different Decisions

Understanding costs and benefits of different decisions requires understanding people’s preferences and developing ways to measure outcomes of those decisions relative to preferences. This “valuation” process is used to help rank alternative actions, illuminate tradeoffs, and enlighten public discourse.⁴⁶ In the context of climate change, the process of measuring the economic values or non-monetary benefits of different outcomes involves managers, scientists, and stakeholders and a set of methods to help decision-makers evaluate the consequences of climate change decisions.^{126,127,128,129,130} Although values are defined differently by different individuals and groups and can involve different metrics – for example, monetary values and non-monetary benefit measures^131,132,133 – in all cases, valuation is used to assess the relative importance to the public or specific stakeholders of different impacts. Such valuation assessments can be used as inputs into iterative adaptive risk management assessments (which has advantages in a climate context because of its ability to address uncertainty) or more traditional cost-benefit analyses, if appropriate.

Some impacts ultimately are reflected in changes in the value of activities within the marketplace and in dollars^134,135 – for example, the impacts of increased temperatures on commercial crop yields.^136,137,138 Other evaluations use non-monetary benefit measures such as biodiversity measures¹³⁹ or soil conservation and water services.¹⁴⁰

Valuation methods can provide input to a range of decisions, including cost-benefit analysis of new or existing regulations¹⁴¹ or government projects;^142,143 assessing the implications of land-use changes;^144,145 transportation investments and other planning efforts;^146,147 developing metrics for ecosystem services; and stakeholder and conflict resolution processes.¹⁴⁸

Comparative Tradeoff Methods

Once their consequences are valued or otherwise described, alternative options are often compared against the objectives or decision criteria. In such cases, approaches such as listing the pros and cons,¹⁴⁹ cost-benefit analysis,¹⁵⁰ multi-criteria methods,¹¹² or robust decision methods^151,152 can be useful. Multi-criteria methods provide a way to compare options by considering the positive and negative consequences for each of the objectives without having to choose a single valuation method for all the attributes important to decision-makers.⁴⁶ This approach allows for consequences to be evaluated using criteria most relevant for a given objective.¹⁵³ The options can then be compared directly by considering the relative importance of each objective for the particular decision.

Integrated Assessment Models

Integrated Assessment Models are tools for modeling interactions across climate, environmental, and socioeconomic systems.^154,155,156 In particular, integrated assessment models can be used to provide information that informs tradeoffs analyses, often by simulating the potential consequences of alternative decisions. Integrated assessment models typically include representations of climate, economics, energy, and other technology systems, as well as demographic trends and other factors that can be used in scenario development and uncertainty quantification.¹⁵⁷ They are useful in national and global policy decisions about emissions targets, timetables, and the implications of different technologies for emissions management.¹⁵⁸ These models are now being extended to additional domains such as water resources and ecosystem services to inform a broader range of tradeoff analyses and to finer resolutions to support regional decision-making.¹⁵⁹

Scenarios and Scenario Planning

Courtesy of NOAA NCDC

Scenarios are depictions of possible futures or plausible conditions given a set of assumptions; they are not predictions. Scenarios enable decision makers to consider uncertainties in future conditions and explore how alternate decisions could shape the futures or perform under uncertainty. One approach to building scenarios begins with identifying any changes over time that might occur in climate and socioeconomic factors (for example, population growth and changes in water availability), and then using these projections to help decision-makers rank the desirability of alternative decision options to respond to these changes.¹⁶⁰ This works well when decision-makers agree on the definition of the problem and scientific evidence.^71,161 A second approach is widely used in robust decision-making and decision-scaling approaches. It begins with a specific decision under consideration by a specific community of users and then poses questions relevant to these decisions (for example, “how can we build a vibrant economy in our community in light of uncertainty about population growth and water supply?”) to organize information about future climate and socioeconomic conditions (for example, Robinson 1988¹⁶²).

Scenario planning often combines quantitative science-based scenarios with participatory “visioning” processes used by communities and organizations to explore desired futures.¹⁶³ It can also facilitate participatory learning and development of a common understanding of problems or decisions. There are many different approaches, from a single workshop that uses primarily qualitative approaches to more complex exercises that integrate qualitative and quantitative methods with visualization and/or simulation techniques over multiple workshops or meetings. Common elements include scoping and problem definition; group development of qualitative (and, optionally, quantitative) scenarios and analyses that explore interactions of key driving forces, uncertainties, and decision options.

Scenario planning has been useful for water managers such as Denver Water, which has also used “robust decision-making” to assess policies that perform well across a wide range of future conditions, in the face of uncertainty and unknown probabilities (see “Denver Water Case Study”). Other examples of the use of scenario planning include:

• National Park Service, to consider potential climate change impacts and identify adaptation needs and priorities in several parks or regions^164,165
• California State Coastal Conservancy, to plan tidal marsh restoration and planning in the San Francisco estuary in the face of climate change and sea level rise¹⁶⁶
• Urban Ecology Research Lab at the University of Washington, for planning adaptation to preserve ecosystem services in the Snohomish Basin¹⁶⁷
• A group of agencies and organizations considering the impacts of climate change on ecosystems in the Florida Everglades¹⁶⁸

The National Climate Assessment has developed and used a number of different types of scenarios and approaches in preparation of this report (see Appendix 5: Scenarios and Models).¹⁶⁹

Denver Water case study

©Photo courtesy Denver Water

Climate change is one of the biggest challenges facing the Denver Water system. Due to recent and anticipated effects of climate variability and change on water availability, Denver Water faces the challenge of weighing alternative response strategies and is looking at developing options to help meet more challenging future conditions.

Denver Water is using scenario planning in its long-range planning process (looking out to 2050) to consider a range of plausible future scenarios (Figure 26.6). This approach contrasts with its traditional approach of planning for a single future based on demand projections and should better prepare the utility and enhance its ability to adapt to changing and uncertain future conditions.

Denver Water is assessing multiple scenarios based on several potential water system challenges, including climate change, demographic and water-use changes, and economic and regulatory changes. The scenario planning strategy includes “robust decision-making,” which focuses on keeping as many future options open as possible while trying to ensure reliability of current supplies.

Figure 26.6: Scenario Planning Details/Download

Scenario planning was chosen as a way to plan for multiple possible futures, given the degree of uncertainty associated with many variables, particularly demographic change and potential changes in precipitation. This method is easy to understand and has gained acceptance across the utility. It is a good complement to more technical, detailed analytical approaches.

The next step for Denver Water is to explore a more technical approach to test their existing plan and identified options against multiple climate change scenarios. Following a modified robust decision-making approach,^170,171 Denver Water will test and hedge its plan and options until those options demonstrate that they can sufficiently handle a range of projected climate conditions.

Scientific Assessments

Ongoing assessments of the state of knowledge allow for iterative improvements in understanding over time and can provide opportunities to work directly with decision-makers to understand their needs for information.¹⁷² A sustained assessment process (Ch. 30: Sustained Assessment)⁵⁷ can be designed to support the adaptation and mitigation information needs of decision-makers, with ongoing improvements in data quality and utility over time. This report represents one such type of assessment. The Intergovernmental Panel on Climate Change (IPCC) has prepared assessments of the state of the science related to climate change, impacts and adaptation, and mitigation since the late 1980s. Numerous additional assessments have been prepared for a variety of national and international bodies focused on issues such as biodiversity, ecosystem services, global change impacts in the Arctic, and many others.

While decision support is not necessarily constrained by a lack of tools, a number of barriers restrict application of existing and emerging science and technology in adaptation and mitigation decisions.^2,4,178 In cases where tools exist, decision-makers may be 1) unaware of tools; 2) overwhelmed by the number of tools; 3) hesitant to use tools that are not appraised or updated and maintained with new information; or 4) require training in how to use tools.^4,179 Recent scientific developments could help address some of these barriers, but are not yet incorporated into decision support tools.⁹⁴ For example, individual climate models can provide very different projections of future climate conditions for a given region, and the divergence of these projections can make it seem impossible to reach a decision. But comparing different models and constructing climate model “ensembles” can highlight areas of agreement across large numbers of models and model runs, and can also be used to develop ranges and other forms of quantification of uncertainty (for further discussion, see Ch. 2: Our Changing Climate and Appendix 3: Climate Science Supplement). While results from these activities can prove difficult to present in formats that could help decision-makers,¹⁸⁰ new approaches to visualization and decision support can make such ensembles useful for decision-making.^181,182

There is also a need for “science translators” who can help decision-makers efficiently access and properly use data and tools that would be helpful in making more informed decisions in the context of climate change.^{2,3,4,17,18,19,20,21} The culture of research in the United States often perpetuates a belief that basic and applied research need to be kept separate, though it has been demonstrated that research motivated by “considerations of use” can also make fundamental advances in scientific understanding and theory.¹⁸³ The U.S. climate research effort has been strongly encouraged to improve integration of social and ecological sciences and to develop the capacity for decision support to help address the need to effectively incorporate advances in climate science into decision-making.²²

There are a number of areas where scientific knowledge needs to be expanded or tools further developed to take advantage of existing insight. The National Research Council (NRC) identifies a research agenda both for decision support (such as identifying specific information needs) and on decision support (such as improving tools for risk assessment and management).² A number of studies assess approaches and identify needed research and development (for example, Arvai et al. 2006¹⁸⁴). A subset of the opportunities and needs identified by the NRC seem particularly relevant in the context of the National Climate Assessment, including:

• A comprehensive analysis of the state of decision support for adaptation and mitigation, including assessment of processes, tools, and applications, and development of a knowledge-sharing platform will facilitate wide public access to these resources.
• Comparisons of different adaptation and mitigation options will be improved by investments in understanding how the effects of climate change and response options can be valued and compared, especially for non-market ecosystem goods and services^{146,185,186,187,188} and those impacts and decisions that have an effect over long time scales.
• Improvements in risk management require closing the gap between expert and public understanding of risk and building the institutions and processes needed for managing persistent risks over the long term.
• Probabilistic forecasts or other information regarding consequential climate extremes/events have the potential to be very useful for decision-makers, if used with improving information on the consequences of climate change and appropriate decision support tools.
• Better methods for assessing and communicating scientific confidence and uncertainty in the context of specific decisions would be very useful in supporting risk management strategies.
• Improvements in processes that effectively link scientists with decision-makers and the public in resource management settings and developing criteria to evaluate their effectiveness would enhance knowledge building and understanding.