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Mitig Adapt Strateg Glob Change (2014) 19:1295–1312 DOI 10.1007/s11027-013-9475-x ORIGINAL ARTICLE

Towards a resilience indicator framework for making climate-change adaptation decisions Nathan L. Engle & Ariane de Bremond & Elizabeth L. Malone Richard H. Moss

Received: 8 January 2013 / Accepted: 26 April 2013 / Published online: 26 June 2013 # Springer Science+Business Media Dordrecht (outside the USA) 2013

Abstract Activities are already underway within the development community to improve climate-change adaptation decision making. In these and related efforts, a focus on building resilience is an important objective, one that resonates with development objectives. Compiling and applying indicators will help development practitioners consider resilience in projects, plans, and decision making. Exactly how to do this is a challenging, but important task. Drawing on diverse methods in the literature, this paper identifies factors important to understanding the evolution of resilience over time and space, and suggests a framework for developing indicators that analysts might select as useful for particular places or sectors. The paper lays the groundwork for an assessment framework that can make future development and adaptation choices more resilient. The framework is intended as a starting point for wider discussions of factors that contribute to building resilience and thus provide the basis to develop a toolkit of metrics and approaches. These discussions will need to bridge research on climate-change adaptation and resilience with practice. Keywords Climate change . Resilience . Adaptation . Integrated decision making . Multiple scales . Indicators . Development

1 Introduction Adaptation is increasingly seen as an essential component of managing the risks unleashed by human-induced climate change. It is recognized that the world is now committed to an N. L. Engle : A. de Bremond : E. L. Malone : R. H. Moss Joint Global Change Research Institute, 5825 University Research Court, Suite 3500, College Park, MD 20740, USA A. de Bremond (*) Department of Geographical Sciences, University of Maryland, 2181 LeFrak Hall, College Park, MD 20742, USA e-mail: [email protected]

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approximately 1–1.5 °C global mean surface temperature increase because of greenhouse gas emissions that have already been released to the atmosphere. Additional emissions, which seem certain to remain above levels required to limit global average surface temperature increases to 2 °C, raise the importance of preparing natural and socioeconomic systems to adapt to, adjust to, and resist the impacts associated with these temperature increases. Recent research suggests that overshoot scenarios (in which atmospheric greenhouse gas concentrations are allowed to exceed their eventual stabilization levels) will not avoid the more serious climate consequences of laxer climate change targets: climate change may be irreversible on time scales of 1,000 years, resulting in illustrative impacts such as irreversible dry-season rainfall reductions in several regions comparable to those of the dust bowl era and inexorable sea level rise” (Solomon, et al. 2009). The potential for impacts to be especially severe in vulnerable developing countries has been identified (e.g., Adger et al. 2007; World Bank 2009, 2012) with increasing recognition that adaptation is important in reducing vulnerability or increasing resilience to changes in both long-term average conditions and extreme events. Observation tells us that similar climate events can produce very different levels of socioeconomic impact, depending not only on the location and timing of occurrence, but also the resources and agility of the societies who experience climate-change impacts. The severity of impact is produced from the interaction of the natural triggering event with the specific characteristics of the society and ecosystem affected. Vulnerability and resilience (and adaptive capacity)1 are the central concepts for determining how to assist countries and communities, because they provide frameworks that link biophysical climate sensitivity to social/economic factors that mitigate or amplify the consequences of environmental changes. Multilateral development bank initiatives such as the Climate Investment Funds, the Pilot Program for Climate Resilience, and the World Bank’s “economics of adaptation to climate change” study demonstrate that the development community is only recently responding to observed and potential future changes in climate by seeking to implement projects that build resilience to a range of future climate conditions. This recent shift to resilience represents an important opportunity to improve adaptation decision making, but it is also a challenging task. Among the decision support tools that could aid development practitioners are assessment frameworks and indicators that evaluate the effectiveness of projects and programs for increasing resilience. We define resilience as the potential to absorb and cope with impacts of climate shocks and extremes in the short-term, and to learn, reorganize, and redevelop, preferably to an improved state, in the longer-term. Strategies to build resilience combine preparedness for immediate response to extreme events with long-term sustainable development objectives that increase socioeconomic and environmental capacity to function under new climate conditions. This paper presents ideas for guiding adaptation decision making in the development community and is intended to contribute to wider discussions regarding the construction of metrics and decision-support tools by offering potential answers to the following questions: Why is resilience an important concept for making climate-change adaptation decisions? and How might one consider resilience in constructing indicators and performing assessments to improve development decisions? We begin by discussing vulnerability and resilience, arguing that resilience can better inform adaptation decision making in the context of development than vulnerability. Then we argue 1

Although there are some differences in definition, we equate adaptive capacity to resilience, and limit references to adaptive capacity from this point forward.

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that a range of indicator options to analyze resilience in different circumstances will be needed, since “vulnerability” and “resilience” have different connotations and meanings in different geographical and socioeconomic contexts, and for different groups of stakeholders. We consider the limits of relying on quantitative information and emphasize the importance of a range of qualitative methods, including case studies, expert elicitation, and participatory scenario processes. All this leads to a proposed hybrid approach for indicator development that includes both qualitative and quantitative information and a set of common indicator options that can be adapted to particular places, settings, or time periods. Such an approach can facilitate comparison and learning in a way that accommodates diversity of situation and context. The paper closes with thoughts on next steps for research and development, including the needs for increased interaction between researchers and practitioners and pilot studies to build and test the indicators.

2 Making climate-resilient development choices A changing climate challenges previous conceptions of sustainability. Climate provides the “envelope” in which other environmental goods and services are available and support human well-being. As a consequence, activities and decisions need to be viewed through a sustainability and climatesmart lens that explicitly considers the implications of climate change for development decisions and the consequences of development decisions for climate change (World Bank 2009). A productive way to think about climate-smart decisions is through the concept of resilience, which represents a framework for understanding and managing social-ecological systems. There have been few attempts to integrate resilience research concepts and findings with actual development, and, thus, practitioners are making development decisions without much guidance from the adaptation research community, while researchers continually fall short of placing resilience insights into operation. This paper offers a framework for bridging these two communities. Strategies to build resilience combine preparedness for immediate response to extreme events with long-term sustainable development objectives that increase socioeconomic and environmental capacity to function under new climate conditions. However, practitioners and researchers should not assume that development and resilience are always positively linked. Although many development programs and projects, if successfully implemented, are likely to improve resilience to climate change, not all decisions will maximize resilience, and some might even diminish it by introducing new vulnerabilities. Therefore, there is a critical need to develop methodologies and indicators for assessing and maximizing climate resilience in development initiatives to reduce the risk of exacerbating existing vulnerabilities or creating new ones. While efforts within the development community to assess resilience are beginning to emerge [e.g., the Strategic Climate Fund (SCF) and the Economics of Adaptation to Climate Change (EACC) Study (World Bank 2010; WRI 2008)], the community could develop, implement, and iteratively improve resilience indicators to better inform adaptation decision making.

3 Resilience and the importance of scale Resilience, as envisioned by Holling (1973) and elaborated upon by natural and social science researchers provides a way to think about developing and managing social-ecological systems as integrated systems rather than isolated social, physical, or ecological components. The human

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elements (e.g., institutions, infrastructure, culture) and the environmental elements (e.g., geological, climatological, biological) create a coupled complex system (Folke 2006; Gallopín 2006; Holling 2001). The programs and projects of development practitioners and agencies are often aimed at improving these complex systems, which include overlapping and interacting geographic, administrative, and environmental factors at different, competing boundaries, and scales. Time scale is an important dimension of resilience. For the purposes of this study (as discussed more completely below), we consider resilience to embody both the ability to cope with rapid onset climate disasters such as floods, droughts, heatwaves, and related events, as well as the ability to adapt to changes in climate (defined as alterations in longterm climate conditions including averages, future variability, and frequency and severity of extremes). As Eriksen and Kelly (2007) point out (p 506), by not distinguishing between coping with immediate impacts and improving through long-term adaptation and redevelopment, “past studies have not captured important actors and processes shaping the way the people secure livelihoods and manage climate stress.” Indicators that focus on only one time scale cannot identify tradeoffs and synergies, which are important in any priority-setting process. Actions to promote adaptation are more likely to be sustained over longer time periods necessary for success if they are mainstreamed into natural resources management, economic development, or social policies/programs, and this is more likely if they have potential to simultaneously promote immediate coping capacity (e.g., disaster management) and longer-term adaptation (e.g., development) objectives. Spatial scale is also critical to resilience. Adaptation and resilience are ultimately regional/locallevel matters, and the impacts of climate change are likely to be different even within any particular society, since rights and resources are unevenly distributed. As with the temporal scale, indicators that look at only one scale (e.g., aggregate national level) will gloss over the factors that determine resilience at other scales (e.g., local community level), and also miss the tradeoffs and synergies across these scales. Identification of human population groups that are least resilient across multiple spatial scales will allow resources to be directed where they are most needed. Evaluating resilience by effectively capturing its multiple scales opens a way to begin considering the issues of feedbacks and interactions among scales. Importantly, developing project and aggregate level indicators across short- and long-term time horizons through stakeholder participation processes will likely be critical to understanding these interactions and feedbacks (Peterson et al. 2010).

4 Assessing resilience 4.1 From vulnerability to resilience Resilience and vulnerability are not simply two sides of the same coin. Each concept has multiple meanings, and the two have been framed and used differently. Although we may know intuitively that vulnerability means a potential for harm and resilience has to do with resisting or coming back from harm, both terms are fairly abstract, characterizing overall states rather than specific sets of characteristics. Because this paper focuses on resilience in the context of climate change, we apply the IPCC definitions of vulnerability and resilience. According to these definitions, vulnerability is the degree to which a system is susceptible to, and unable to cope with, adverse effects of climate change, including climate variability and extremes; it is a function of the character, magnitude, and rate of climate change and variation to which a system is exposed, its sensitivity, and its capacity to adapt. Resilience is the ability of a social or ecological system to absorb disturbances while retaining the same basic structure and ways of

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functioning, the capacity for self-organization, and the capacity to adapt to stress and change. Because the paper focuses on resilience building, we apply the definition in a way that is consistent with IPCC usage to define resilience as the potential to absorb and cope with impacts of climate shocks and extremes in the short-term, and to learn, reorganize, and redevelop, preferably to an improved state, in the longer-term. Different menus of characteristics demonstrate differences in definitions, which lead to different emphases and different research approaches (Adger 2006; Füssel 2007; Gallopín 2006; Folke 2006; Moser 2008). Vulnerability tends to be actor-centric and more easily translatable to specific remedies (Nelson et al. 2007). When vulnerability of an actor or set of actors to a particular climate stress is quantified, the result is often politically and managerially digestible maps, indices, and rankings (Brooks et al. 2005; Kelly and Adger 2000; Schröter et al. 2005; O'Brien et al. 2004b; Cutter et al. 2003; Moss et al. 2001). Sometimes a vulnerability focus is helpful; e.g., for disaster risk reduction, it is often the best approach. However, because vulnerability is frequently conceived of in relation to outcomes, analyses tend to focus on the vulnerability of a specific community to a specific category, magnitude, and timing of climate event. This type of analysis would be helpful in a situation where the precise climate-change impacts were confidently understood, but there are few instances where such confidence exists or is likely to exist in the near future. Moreover, vulnerability analyses often leave out key process variables that represent the dynamics of the systems (O'Brien et al. 2004a; Nelson et al. 2007), and evaluations of vulnerability are often performed with respect to an individual sector of society, at an individual spatial scale, and are merely “snapshots” in time (Vincent 2007). In contrast to vulnerability, a resilience framing opens both discussion and action to improve capacity to meet multiple objectives, including economic, health, educational, and governance outcomes in line with more general development goals. The potential for resilience to capture multiple equilibria and perhaps adapt to changed circumstances by transitioning to a new state is important for developing countries, in which the original state may not be desirable to maintain or restore. In addition, resilience is more centrally focused on interactions between social and ecological systems, including processes and feedbacks at various scales (Nelson et al. 2007). In this respect, resilience is more suited for capturing the complex relationships and interconnectedness inherent in issues the development community faces, such as poverty reduction, sustainability, and multiple social and environmental stresses. One caveat however, as mentioned earlier, is that the context specificity that shapes resilience often results in challenges to systematically measure, assess, and characterize resilience (e.g., through indicator development) (Ostrom et al. 2007). These limitations have created difficulties in translating resilience into practice; a hurdle that the framework developed in this paper attempts to overcome by starting with a focus on multiple spatial and temporal scales. The differing emphases between resilience and vulnerability often lead to different kinds of adaptation decisions and actions. Vulnerability lends itself to adaptations that directly defend against climate-change impacts: such as sea walls, stronger “climate-proof” buildings and infrastructure, provision of drought-tolerant plants, and dams and other infrastructure to support fresh water management. These may protect against shorter-term impacts, but may also work against the need for transformational adaptation where incremental changes are not sufficient for potentially new conditions. Resilience broadens the scope of relevant interventions to adaptations that include livelihood diversification, community development, service and tourist businesses, governance, and public health programs. These two framings present different challenges when the goal is to provide input to decision making. A vulnerability framing works better when the questions relate to specific risks and specific actions that can be taken to directly address the risks. A resilience framing works better in addressing

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a fuller spectrum of impacts and their interrelationships and dynamics. Because climate models cannot well inform specific decisions into the foreseeable future and development decisions are ongoing and immediate, a resilience framework can improve decision making under uncertainty. Hence, we recommend using a resilience assessment framework to inform development decisions in the context of climate-change adaptation. In the sections that follow, we explore how resilience (and vulnerability) have been operationalized using indicators sets, then introduce the elements of a hybrid approach that draws on both quantitative and qualitative sources. We explore how practitioners and researchers could apply the framework to actual development initiatives, and discuss important methodological considerations and next steps. 4.2 Quantitative approaches to evaluating resilience Quantification is attractive to decision makers because numerical estimates provide a means to score, rank, and monitor progress across different programs, communities, or even nations. Researchers have developed quantitative tools, principally indicators and proxies, to measure vulnerability and resilience to support adaptation decision making. Researchers have constructed indicators, mainly with respect to vulnerability, from a wide-range of disciplinary perspectives (e.g., climate-change impacts, hazards and disaster risk management, political economy, human ecology, sustainability, and others), and across different spatial scales to inform multi-criteria adaptation decision making (Brooks et al. 2005; Cutter and Finch 2008; Eakin and Bojorquez-Tapia 2008; Polsky et al. 2007). Few efforts exist to operationalize indicators with respect to resilience, however. To the extent that indicators and proxies provide transparent syntheses of knowledge about representative aspects of resilience, they can assist in making comparisons and in examining the sources of vulnerability or resilience. Moreover, some of the limitations of indicators (discussed briefly below) can be addressed by a broader adaptation community process. Extensive stakeholder participation can help create a tool kit of indicator methods and approaches that combines these quantitative metrics with qualitative data and analyses from case studies and expert judgment (among various qualitative methods). Quantifying and modeling resilience or vulnerability in a comparative frame is desirable but subject to knotty issues that confront indicator use in general: lack of clear, universally agreed definitions, the dependence on several levels of assumptions, incomplete data on important variables, co-linearity, lack of accounting for interactions/feedbacks, and so on (Parson and Fisher-Vanden 1997). Thus, the entire enterprise of defining indicators, developing data and methods to support them, and interpreting the results is subject to normative judgments of the analysts who produce the indicators, as well as the decision makers who use them. Nevertheless, indicators and models are advancing the state of knowledge by providing transparent frameworks for comparative analysis. The sources of scores or rankings can be questioned and examined, and results can be used as the basis for further analyses, leading to policies and measures that can address climate-change resilience. To be effective in these ways requires that analysts adhere to common definitions and methodological standards in developing and applying the indicators, including transparency and stakeholder participation so that all parties can understand the assumptions, variables selected, sources of data used, approaches to aggregation, and interpretation of the results. 4.3 Qualitative approaches to evaluating resilience One way to address some of the challenges that quantitative approaches present for decision making is to combine quantitative and qualitative research methods. In addition to

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addressing some of the limitations described above, qualitative methods are sometimes more appropriate for assessing resilience. For example, an analyst might gain a more nuanced understanding of resilience by engaging local decision makers to tell stories or develop alternative future scenarios. Researchers often use case studies to analyze national, regional, or sectoral vulnerability and resilience. While quantification may be used in case study research, case studies frequently draw from qualitative methods, such as ethnography, focus groups, archival research, interviews, and surveys. Case studies play an important role in the application and development of resilience indicators in three ways. First, the data obtained through case studies are important sources of information on resilience-building strategies in their own right. After an initial measurement of resilience using aggregate level indicators, case studies can help provide the process-related and context-specific information that indicators often miss. Second, if structured as a comparative set, case studies can yield important information about phenomena and hierarchical relationships that can aid in establishing rules and approaches for quantitative research, including indicators. That is, once a critical mass of comparable case studies has been established, themes and “rules” gleaned from these case studies can be reincorporated to improve the composition and weighting of the aggregate indicators. Third, also related to informing the aggregate indicators, analysts can use case studies (and pilot projects) to validate indicators. Specifically, case studies might include a monitoring component that gathers critical information on how the project, community, or region is performing in the face of climate stress. Such on-the-ground and real-time measures of performance against climate events present the opportunities to verify that the indicators are accurately assessing resilience. Validation and monitoring through case studies can also lead to focusing on a reduced set of key factors or an expanded set of variables to emphasize in future resilience assessments. Another qualitative method, expert elicitation, is used in climate-change research and other fields. Elicitation processes are often based on a wide variety of information and data, including observations, model results, insights from research into underlying processes (socioeconomic or natural), and other sources. Expert panels and elicitation are thus particularly valuable in understanding climate resilience, where empirical knowledge of processes and relationships among potential causal factors is limited, minimal baseline data exist, and short-term collection of primary data is difficult. Expert elicitation thus can serve as an important element of case study research, particularly to investigate causal factors at local and regional scales.

5 A hybrid resilience framework Circumstances in which resilience building will take place are diverse. Those assessing resilience in the context of development decision making need to balance comparability in indicator metrics with the locally specific nature of resilience (Ostrom et al. 2007). They should also balance the imperative of collecting and analyzing information on the many factors that contribute to resilience with the need to develop practical and manageable efforts that do not have onerous data collection requirements. In this section, we advance an empirically supported framework for guiding further resilience indicator development, rather than offering rigid technical guidelines or an off the shelf toolkit of indicator applications. Neither theory nor practice justifies selecting a single method or approach that would work in all or even most cases. Therefore, we present

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a hybrid approach that has the most potential to be useful in the wide range of circumstances in which development agencies and organizations operate.2 The approach is hybrid in three respects. First, it focuses on both the short-term coping and long-term adaptation aspects of resilience. Second, it attempts to link national/regional investigations with local/project-level analyses, again capturing the multi-scale nature of resilience. Third, it is open to qualitative and quantitative methodologies, emphasizing the use of data from existing quantitative sources (the usual indicator sources), case studies (limited, empirical research), and stakeholder input (including both expert and local knowledge). The hybrid approach may be divided into three recursive processes: (1) identification of exposure to climate variability and change in the context of multiple stresses, (2) identification and elaboration of categories of indicators, and (3) calibration and verification of indicators for iterative learning and improvement. Identification of possible exposure to climate variability and change in the context of other stresses aids in identifying priority areas and the relevant and appropriate indicators in the subsequent step. Involving stakeholders in identifying these priority areas increases the likelihood that the indicators are relevant and tangible. Stakeholders are also important in calibrating and verifying indicators, as they can aid in reporting and tracking key variables during climate events to evaluate and monitor the indicators and resilience successes/failures. 5.1 Exposure to climate variability and change and multiple environmental stresses Under conditions of climate change, frequency or severity of climate hazards may change, and the hazard events themselves may change. For this reason, characterization of current and likely future exposure to climate hazards is an important component of assessing resilience and monitoring the effectiveness of adaptation measures, addressing the question, What should this society be ready to handle in the future? Efforts to construct and use relevant indicators vary widely from simple use of existing climate-model downscaling exercises to detailed consideration of individual factors. Projections of future climate-change impacts are being developed at increasingly finer scales. Fine-scale climate scenarios are being applied in the United Kingdom (East of England Sustainable Development Roundtable 2003; Jenkins et al. 2008), relatively small farming areas (Luers et al. 2003), California (California Natural Resources Agency 2009), and some cities (e.g., New York City Panel on Climate Change 2009). Scenarios are being used to assess exposure to future climate change and to inform both quantitative model results and qualitative stakeholder processes. Robust analyses use multiple scenarios of future climate change to characterize some of the uncertainties associated with regional and fine-scale climatechange projections. Figure 1 shows examples of exposure to climate variability and change and multiple environmental stresses indicators at short-term coping and long-term adaptation scales and at aggregate and project levels. The US National Research Council (NRC 2010), for example, proposes indicators for monitoring climate change and its potential implications across components of the Earth system.

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As discussed in the brief review of methods and approaches for measuring vulnerability and resilience, there are many definitions and frameworks for constructing indicators and performing assessments. To some extent, “indicator fatigue” may be emerging as a response to this situation of developing a new indicator or method for each application. As a result, we believe a productive approach to developing indicators of resilience is to examine existing metrics and systems, and to adjust these where necessary to assess current and potential future resilience in specific contexts.

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5.2 Categories of resilience indicators Based on analysis of previous resilience and vulnerability research efforts and a review of factors considered important in prominent reports and assessments, including those of the Intergovernmental Panel on Climate Change (IPCC), we identify five categories of indicators in the second part of the process of applying the framework: 1. 2. 3. 4. 5.

Governance and security Natural resource systems Social systems Economic systems Built environment/infrastructure

The categories represent a distillation of the various theorized determinants of resilience across a wide range of studies. However, the categories (and the indicators within the categories) may need to be modified in specific cases. Other categories may be added, some categories may not be necessary in certain decision making contexts, and categories may be joined if indicators span two or more categories (e.g., when there is considerable overlap between indicators within a particular region/project). For these reasons, developing indicators through the hybrid approach–preferably in consultation with stakeholders–begins by looking across as wide a range of categories and indicators as possible. From these five categories, what are most relevant will depend on the adaptation decision and which indicators prove most robust in relation to climate events (i.e., verification). Also, the framework, its five categories, and the indicators within these categories might change over time as other explanatory variables are gleaned from case studies, expert elicitation, and evaluation. The iterative nature of the framework, along with the reliance on qualitative methods, stakeholder participation, and monitoring highlight that the framework also includes a learning component, building understanding of what determines resilience. A challenge in implementing this framework is to identify categories and factors for which indicators and data can be collected at the variety of spatial scales that will be important to assessment and monitoring of changes in resilience as a result of adaptation measures. Thus, choosing indicators will require considerable flexibility and continuous attention. The overall intent of indicators is to better understand factors of resilience relevant to specific cases, and to monitor (or in some cases project) how resilience will evolve in response to policy or project interventions. To achieve this, we propose developing indicators along two different dimensions in each of the categories. First, we separate each of the categories by short-term coping and long-term adaptation indicators to capture both temporal aspects of resilience. Second, we present the indicators at different spatial scales, immediate project objectives (project) and the broader community or spatial scale (aggregate), to illustrate the various potential uses of and linkages among indicators across scales. In most of the five categories, we attribute short-term indicators to current conditions and long-term indicators to changes in the same indicators. Another strategy is to choose different indicators for the short and long terms, as we demonstrate in the governance and security category (Fig. 1). Drilling down to more local and context-specific applications of the framework, one might identify indicators in the other five categories that capture long-term adaptation better than the derivatives of the short-term indicators. This adds another justification for a flexible hybrid approach. Within each category, indicators imply preferred outcomes for increasing resilience (e.g., more biodiversity, access to crop insurance, increases in clean drinking water availability). Those developing the indicators need to make explicit the “desired” outcome for each

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Fig. 1 A hybrid climate change resilience framework

indicator–understanding that what is “desirable” is a normative concept that becomes progressively more subjective at finer spatial scales. Stakeholder participation and case study insights can provide the necessary flexibility and iteration to factor in changing understandings of what is (and is not) desirable for resilience. 5.2.1 Governance and security Evaluations of vulnerability and resilience have affirmed the integral role that governance, institutions, and security play in the ability to cope with and adapt to climate change (Agrawal 2008; Brooks et al. 2005; Eakin and Lemos 2006; Ivey et al. 2004; Yohe and Tol 2002). These studies illustrate governance aspects of resilience (Adger 1999), especially identifying the structures, processes, and mechanisms that might better facilitate coping with immediate disasters and long-term adaptations (Adger 2001; Haddad 2005). For development agency and organization purposes, formal government institutions may be the most important governance actors. Their effectiveness is influenced by their perceived legitimacy. The relevant actors and agencies and the openness to civil society input and new information determine how well the planning is done and the resultant policies carried out. This category is the least straightforward of the five, because indicators may be highly placespecific and integrated and, thus, not easily measured. Therefore, Fig. 1 contains various examples of indicators, some process-oriented, others based on presence/absence of a particular service or product provisioned by governing institutions. For example, the World Bank’s governance indicators are off the shelf measures of processes at the country level (Kaufmann

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et al. 2004), and, the literature (e.g., Engle and Lemos; Baral and Stern 2011) demonstrates how qualitative survey data can represent resilience in governance at a sub-country level. The service/product indicators tend to be related to the presence of policies and plans, but case studies or stakeholder involvement may yield relevant local process-oriented indicators (Eakin 2005). 5.2.2 Natural resource systems Assessing the condition of natural resources and the ways in which people use and depend on them helps characterize the ways in which populations may be vulnerable to climate-change impacts. If the society depends on agriculture (food and cash crops, and livestock for consumption and sale), as many developing countries do, the quality and amount of suitable farmland and rangeland are critical to understanding both the climate risks and the strategies in use to reduce those risks. While short-term coping is important, the potential for maladaptation is very evident. For example, cropping may already be maladapted to the region, maintained by unsustainable water withdrawals and/or government subsidies. In such conditions, a long-term perspective is essential, so that short-term fixes do not set up more disastrous collapses as climate impacts become evident. Although agriculture and forestry receive much of the attention of researchers, other natural resources, such as minerals, renewable energy sources, scenery and wild spaces, and biodiversity may be essential parts of a country’s or region’s economy and culture. Sustainable use and long-term resilience may be challenging to achieve when there is high dependence on finite natural resources. 5.2.3 Social systems The systems of social interactions that knit communities and larger societies together are essential factors in resilience. For each analysis, key demographic indicators tell stories about how people live and are connected, and so how they are likely to respond together in a new climate regime. The number of people is important, but their well-being also depends on how they are distributed (e.g., in urban areas, on differently-sized farms, or crowded along coasts), the land tenure system, people’s health status, the age distribution and dependency ratio, and male/female education rates (Brenkert and Malone 2005). At subnational levels, the status of people relative to the whole country–measures of inequality–is important. Cutter et al. (2004) has shown that in the United States, a large fraction of vulnerability to disasters is explained by poverty, minority status, poor health, and older ages. In other words, people whose choices are constrained by physical limitations or through discrimination are less resilient than those whose choices are not so constrained. Cultural values also have a significant bearing on resilience (Malone and LaRovere 2005; Crane 2010). These values include the meaning and structure of families, their felt obligations to one another (kinship or community networks, mutual self-help), their relationship with nature, and informal influence (e.g., elders, warriors). However, strong cultural identities might lead to maladaptation, which further emphasizes the importance of evaluating resilience over short and longer time scales. For example, Wolf et al. (2010) concluded that by reinforcing individuals’ beliefs that they are adequately prepared, social networks among the elderly may actually make deaths from heat waves more probable. Yet the same network channels may also convey information about the dangers and symptoms of heat stroke and heat poisoning, information that could alleviate some of this vulnerability.

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5.2.4 Economic systems A per capita measure of gross domestic product (GDP) is frequently used for economic analysis, but it does not reflect inequalities, include informal markets, or capture self-help or kinship/network help, and so on. Other candidate measures may include access to credit or poverty levels. Some researchers have chosen to focus instead on livelihoods–the ways families’ activities obtain the goods they need for their way of life–and the extent to which livelihoods are diversified as an indicator of resilience (Downing and Patwardhan 2005). Thus, the household is often examined as the unit of analysis rather than the individual. Shifts in the job market and other labor trends may be evaluated for their potential to enable short-term coping and long-term adaptation. Access to markets and off-farm employment (in the nearest city or as far away as another country), as well as the burden sharing necessary to grow crops, gather fuel and water, and manage common resources may be good economic indicators for resilience to short-term shocks or long-term climate-change impacts. 5.2.5 Built environment/infrastructure The built environment can protect against short-term climate shocks and long-term gradual climate changes. Systems that have this potential include industry, energy (especially electricity transmission systems), transportation, water resources infrastructure, commercial and residential buildings, and communication infrastructure. Climate variability and change can affect markets for goods and services in these sectors, as well as natural resource inputs important to production of the built environment. Settlements in coastal margins and on small islands may be damaged by sea level rise, tornado-force winds, and storm surges. These areas and inland settlements can be affected by weather-related events that act directly on infrastructure and indirectly through effects on other sectors (e.g., water supply, agricultural activity, and human migration patterns). Patterns vary for urban and rural settlements and for highly and less industrialized contexts (Wilbanks et al. 2007). Much analysis of resilience in these systems focuses on their capacity to withstand or recover from the effects of rapid onset climate events. Such strategies often include establishment or improvement of standards and codes, construction of barriers or other added infrastructure, and expansion of water handling systems. In longer-term adaptation decisions, an emphasis on climate proofing infrastructure has increased in the development community. Climate proofing can sometimes mean merely adding building material or strengthening an existing (or planned) project along the margins. Innovative long-term planning and climate proofing would include consideration of more forward-looking adaptation options such as relocation or redesigning/re-planning infrastructure decisions. 5.3 Loss and impact indicators for validation and calibration The third process is to actively test and improve the indicators as understanding of resilience in a given situation improves. This is similar to a process of adaptive management, wherein decision makers set up policy experiments to learn about a system, tracking and monitoring key variables and performance along the way, and adjusting management approaches accordingly (Holling 1978; Lee 1993). One method is to evaluate the success of a country, province, or city in coping with past and recent climate-related events. The adaptation literature has increasingly emphasized using analogues of climate events to better understand vulnerability and resilience (Ford et al. 2010; Nielsen and Reenberg 2010). This approach is

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gaining support (Adger et al. 2007) because it investigates systems and relationships when they are most challenged (Folke et al. 2005) and when resilience is most likely assessable. The basic rationale for using past events is that the fewer losses or impacts that the system experiences, the more climate-resilient it might be. An application of this for the hybrid framework would be to evaluate poorly performing systems (i.e., those suffering greater losses and impacts) to identify the indicators that might capture the processes shaping this poor performance, highlighting benchmarks that could help improve future performance. A more complex application for the hybrid framework would be to verify and calibrate the indicators against these past events. That is, the indicator categories most associated with better performance might be allocated higher future weightings or combined with insights across case studies. Alternative indicators might be introduced that seem to more effectively capture why some systems perform better than others. One very important caveat is that less loss does not necessarily equate to greater resilience, and greater loss does not necessarily equate to less resilience. For example, a country might have performed well with respect to a given climate event because it has become well adapted to dealing with a specific magnitude and intensity of that event over time. However, the system might fail to respond to critical thresholds associated with future climate change, may be climate-resilient to rapid extreme events but not slow-onset events, or may have just “gotten lucky” during that particular situation. Therefore, we strongly discourage the use of past events as a one-off indicator for resilience, and instead recommend treating these data as opportunities to test and improve upon the indicators developed through the hybrid framework. A particularly appropriate use would be to consider these indicators as a snapshot of resilience in action that can then be compared to the five indicator categories to determine if they align in the expected directions. For example, evaluating the responsiveness and effectiveness of the governance system with respect to the given climate event and its associated impacts could help refine the governance indicators. These comparisons will improve the hybrid framework, while a commitment to collect, evaluate, and monitor these losses/impacts data at the aggregate and project levels over time could improve decision makers’ abilities to understand the intricacies of resilience. Specific aggregate measures on losses and impacts might include well-established data on outcomes such as deaths, injuries, number of persons displaced in refugee camps, property loss, or incidence of hunger, to name a few examples.

6 Applying the hybrid framework–principles and examples Application of the hybrid framework to adaptation decision making should adhere to the following principles:

& & & &

Adopt an approach that transparently reveals the definitions, variables, the data sources, and mathematical and other methods employed (particularly with respect to the approaches determined most appropriate for aggregation and weighting of variables) Invest in ongoing interactions with stakeholder groups so that indicators have validity and applicability to those who are most affected by their use Start with and adapt indicators already in use for monitoring development objectives to avoid adding new programmatic or research requirements that may, in fact, add little to ongoing efforts Consider the feasibility of implementation and the extent to which the proposed indicators will be understandable to decision makers and/or the lay public, as appropriate

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Develop and include verification and learning through monitoring and evaluation that tests and revises decision frameworks, solicitation documents, and contracts/grant requirements to ultimately improve representativeness and effectiveness of indicators (particularly with respect to actual climate events)

Including stakeholders and performing analyses in a transparent fashion are probably the most important of these principles and are essential to credibility, usefulness, and interpretation of the results, because of the complexity of processes shaping resilience, the potential differences in the components considered, and the different weights assigned to these components in aggregation processes. Although the framework presented in this paper can be adapted to the specific needs of development decision makers, in this section we illustrate several situations wherein the framework could be beneficially applied to actual choices and processes underway within multilateral development agencies and countries. First, we introduce the potential relevance of the framework by offering two hypothetical examples for how decision makers might answer the following question: how can I use resilience indicators and assessments to inform my project development decisions? Although the two situations below are overly simplified for illustrative purposes and there is likely interaction between these types of decisions, they briefly demonstrate how the framework and indicators might be useful. Situation 1: Evaluating implications of climate change for existing development priorities–Resilience as an added benefit At the present time, any given development agency staff member may never have given priority to the impacts of climate change on project development in a country of expertise. For example, a country manager might be working to follow the strategic plans outlined in his or her country’s poverty reduction strategy papers (PRSP) (priorities for development identified by the country) and the country assistance strategy (CAS) (the World Bank’s response to the country-defined needs and priorities within the PRSP). The staff member or team may have expertise in a specific sector, such as health or agriculture, with limited knowledge of climate change issues. To this individual or team, climate change is yet another factor that needs to be considered when reconciling these two plans into operational programs and projects on the ground. In this situation, the resilience indicators discussed in this paper could be used as a learning tool to provide a preliminary glimpse into whether planned or future priorities and objectives are at risk because of a resilience deficiency, and conversely, which are likely to thrive because of high apparent levels of resilience. Here, the analyst could use “off the shelf” country-level indicators to obtain a coarse understanding of resilience, and, where priority areas of opportunity and concern are identified, the manager could consult with field staff in their country as well as neighboring countries within the region to learn about casestudies and pilot programs that might inform projects within their own country; ultimately helping to improve understanding of resilience within their country or region. For example, those launching a program to improve nutrition in a country as a whole or in rural areas within the country will first establish a set of metrics focused on improvements in nutrition, such as increased food availability and health. To broaden the analysis to include climate change considerations, they may wish to combine information from the hybrid framework’s first category (e.g., projected increases in drought under climate change) with the second category (e.g., on drought preparedness efforts), in order to assess likely governance effectiveness under future climate change exposure. Indicators or other measures in three of the remaining categories (natural resource, social, and economic systems) are

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likely already addressed in other non-climate related evaluation criteria, but need to be examined to make sure they account for the longer-term resilience potential needed for climate change. The final category, built environment/infrastructure, may or may not be relevant to agricultural systems in the country or areas assessed. The program’s design will then have at its core nutrition-focused metrics but also include broader implications of the climate change context. Situation 2: Evaluating climate change adaptation and resilience building strategies– Resilience as a primary motivation In selected instances, most likely in highly climate-sensitive countries and sectors, development practitioners have thought considerably about the impacts of climate change on development projects. Furthermore, practitioners in this situation are also likely interested in thinking systematically about climate-change resilience to include as an integrating theme in planning and daily on-the-ground programmatic work. In this situation, the individual or team will already have a certain level of understanding regarding climate-resilience, and may have also begun identifying which specific projects are most important to prioritize in the face of a host of potential climate changes and which are currently resilient (or not) to these changes. The resilience framework and indicator approach put forth in this paper could help the analyst more confidently identify these priority areas by viewing projects through a resilience-enhancing lens. Also, these early adopters of resilience indicators will be critical in establishing baselines and serving as pilot projects for evaluating and improving upon resilience indicators as their effectiveness and representativeness is tested over time. For example, programs that focus on climate-change impacts on coastal development intended to draw ecotourists could use at least one indicator in each of the six categories: for exposure to variability and change, projections of sea-level rise and/or hurricane occurrence, based on at least 30 years of historical data and available regional projections; for governance and security, disaster preparedness and response and governance of freshwater systems (which are often stressed by tourism); for natural resource systems, the presence and nature of protections for systems (e.g., coral reefs, rainforests) that will be accessed or used by tourists; for social systems, assessment of the impact on indigenous populations; for economic systems, the potential for local employment and livelihood diversification; and for built environment/infrastructure, the effect of constructed hotels and beach infrastructure on vulnerability to disasters. Thus, general development goals are nested within such a climateresilient-focused project.

7 Research & development needs and next steps A development decision maker might use this hybrid framework for analyzing resilience as outlined here. However, several unresolved practical, methodological, and theoretical issues should be considered high priority research and development areas. First and foremost is the need to build a strong indicator and monitoring program for resilience decision making, drawing on past and current development agency and organization experiences. The program should include:

&

Identifying adaptation needs and broad priorities in decision making processes, particularly at multilateral development bank/agency/organization and country levels

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Conducting pilot indicator studies, and assembling a manual/toolkit of cases and examples, sampling across various objectives, sectors, regions, and exposures Evaluating indicator experience (i.e., what has proven useful for different systems, spatial scales, and temporal scales) Developing scenario processes for planning under circumstances with high uncertainty (e.g., participatory scenario planning exercises, or engaging end-users with visual learning tools) Challenging data and conceptual issues also need to be addressed, including:

& & & &

Determining the most appropriate methodologies for aggregating and weighting indicators for comparable metrics (which would include stakeholder involvement) Establishing protocols, databases, and infrastructure for evaluating and monitoring resilience to guide the revision of indicators, and to improve knowledge of resilience within a particular system Reconciling the need for flexibility and dynamism in resilience indicators with program evaluation needs, such as avoiding shifting baselines and measuring resilience without the benefit of “counterfactuals” Identifying approaches to understand and measure other resilience concepts through development decision making indicators, such thresholds, regime shifts, and transformations

Climate change has the potential to affect all aspects of development, but the specific location, timing, and magnitude of the impacts on development remain uncertain. Given this uncertainty, it is important to discover approaches to examine whether decisions with long-term implications are robust to a range of future conditions, and whether efforts to adapt and improve resilience are likely to have their intended effects. This paper aims to contribute to discussions within the development and research communities by providing initial ideas for placing empirically informed resilience concepts into operation and stimulating discussion of climatechange resilient development and ways in which it could be monitored. Allocating resources to climate-change adaptation requires comparative analysis, which can be accomplished through the use of indicators. Furthermore, we recognize that piloting and refining the framework and indicators also face funding challenges. However, the long-term benefits of understanding resilience in the context of development and adaptation decision making could far outweigh the costs of gaining this knowledge. We recommend further examination of the potential options for the development of resilience indicators and their application, focusing specifically on the next steps discussed above. We propose that this would be best accomplished through more organized dialogue within and between the adaptation and development communities and additional reviews of indicators and case studies that evaluate resilience. Pilot applications would also be useful, especially since learning through evaluation is important to revise the hybrid framework and build out climate-resilient options. The ultimate promise of the hybrid approach is the development of an indicators tool kit that multilateral development banks, agencies, and other organizations might use to further integrate development and resilienceenhancing adaptation options. Fully placing resilience concepts into operation (i.e., consideration of multiple cross-scale interactions and learning and feedbacks) will require the development community to consider the relevance and feasibility of including other aspects of resilience (e.g., flexibility, redundancy, thresholds and regime shifts, poverty and rigidity traps) in future iterations of the hybrid approach.

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