Knowledge Coproduction for Transformative Climate Adaptation: Building Robust Strategies

Abstract

Adaptation is a process of adjustment to actual or expected climate and its effects in order to moderate harm or exploit beneficial opportunities. Most adaptation options are scalable and applicable but may result in inequitable tradeoffs stemming from maladaptation. Thus, climate adaptation and maladaptation are inseparable and are equally likely. Adaptation has been commonly envisioned as coping mechanisms or incremental adjustments from existing strategies. However, both coping and incremental adaptations have failed in explicitly address the underlying drivers of systemic inequalities. Enabling and catalyzing conditions for transformative adaptation, both locally and regionally (i.e. strengthening collaborative governance, building capacities, promoting iterative multi-stakeholder engagement), is, therefore, crucial in building robust climate change adaptations under deep uncertainty. However, the lack of approaches entailing decision analytics, stakeholder engagement/deliberation, and interactive modeling and evaluation may hinder transformative adaptation success. Combining robust decision-making approaches with collaborative research and co-production processes can be constructive in illuminating the decision-rule systems that undergird current adaptation decision-making. This chapter offers some insights into how knowledge coproduction can be used to inform robust climate adaptation strategies under contexts of deep uncertainty while facilitating transformative system change.

1. Introduction

Climate change is a multicausal, technically complex, controversial, and highly uncertain problem [1]. The ability of coupled human-earth systems to adapt to the direct and indirect impacts of climate change is, therefore, critical in order to achieve sustainability [2, 3, 4]. Climate change adaptation became a popular concept among scholars after the United Nations climate change convention in the 1990s. From that point, climate change adaptation has been implemented as coping mechanisms that tend to focus only on proximate causes, as well as incremental adjustments of existing institutional, financial, and technological adaptation strategies [5, 6, 7]. As climate change intensifies, fundamental shifts in existing resource systems, policies, power dynamics, and stakeholders’interests and mindsets will be required if we are to keep the average rise in temperature below 2°C [8, 9]. However, both coping and incremental adaptation strategies are not sufficient to promote these long-lasting system transformations [9, 10, 11]. Transformative climate adaptation, on the other hand, has the potential to respond to the magnitude of cascading climate risks by facilitating radical shifts in coupled human-earth systems.

In coupled human-earth systems, characterized by interlocked multisector interactions and feedbacks (e.g. environmental, socio-economic, technological, governance, and institutional), climate change adaptation planning is further complicated by high degrees of uncertainties [12]. Uncertainty emerges from the limited and contested knowledge among stakeholders regarding (i) the appropriate models to describe the key drivers of the system (e.g. population growth, urban sprawl, and water demand), (ii) the probability distributions about key variables and parameters, and (iii) the relative importance of alternative outcomes (e.g. trade-offs among goals) [12, 13]. According to Ref. [13], uncertainty also arises from human actions, which are taken in response to unpredictable situations over time. In order to manage uncertainty in an efficient way, adaptation planning should be able to confront and navigate alternative adaptation strategies in order to choose robust ones that perform well over a wide range of plausible futures.

Adaptation planning cannot be successfully addressed with traditional linear analytical approaches. In this regard, the field of Decision Making Under Deep Uncertainty (DMDU) has emerged as a promising framework that supports and informs climate change adaptation planning under uncertainty [7, 12, 13]. DMDU includes a set of approaches including Robust Decision-Making (RDM), Dynamic Adaptive Planning (DAP), Dynamic Adaptive Policy Pathways (DAPP), and Info-Gap Decision Theory (IG). These approaches accentuate the transition from classical “predict then act” risk management to exploratory modeling. In particular, RDM explicitly follows a learning process called deliberation with analysis that supports decision-makers and stakeholders to iteratively and collaboratively frame the adaptation problem, specify performance metrics and modeling methods, design the experimental framework, evaluate the performance of strategies across multiple futures, and choose or modify robust adaptation strategies [12, 14].

However, climate change adaptation planning is generally built on divergent stakeholder interests and disparate problem framings, meaning that planners do not always agree on common problem definitions and plausible pathways to adaptation [1, 14, 15]. Moreover, adaptation planning is also embedded within political, social, and institutional contexts that shape how networks of actors interact through formal and informal relationships, rules, and norms [15]. In this perspective, collaborative research and coproduction processes can be constructive in illuminating the decision-rule systems that undergird current stakeholder decision-making and revealing how they are, or are not, functioning to deliver desired results, helping stakeholders interrogate what their preferences are and how those preferences can or cannot be met under a wide variety of conditions [13]. Knowledge coproduction has been acknowledged as an action-oriented practice that enables consensus, coordination, and transparency among stakeholders, thus enhancing policy-relevant climate knowledge [16, 17, 18].

This chapter exposes the readers with a synthesis of the state-of-the-art theory and practice associated with climate change adaptation planning under deep uncertainty. The text consists of three subsections. The first subsection presents a review of the different adaptation strategies and their scope in addressing key drivers of systemic inequality. The second subsection presents an overview of robust decision-making (RDM) approaches illustrated through a hypothetical case study for transformative adaptation planning. The last subsection presents some insights into how knowledge coproduction can be used to inform robust climate adaptation strategies under contexts of deep uncertainty.

2. Knowledge coproduction for climate change transformative adaptation: building robust strategies

2.1 Coping, incremental, and transformative climate change adaptations

Adaptation to climate change is an ongoing process by which coupled human-earth systems adjust in response to the observed and expected climatic stimuli in order to better manage the risks posed by climate change [2, 3]. Adapting to climate change involves cascading decisions that transverse multiple sectoral – infrastructure (i.e freshwater, energy, food, and health), governing institutions (public and private), and regulatory agencies – and geographical boundaries (i.e. local, regional, and global), underscoring the complexity of this global phenomenon [19, 20, 21]. Adaptation takes place across multiple temporal and spatial scales and usually entails vulnerability and risk assessments, identification of strategies, planning, monitoring, evaluation, and review [4]. In the climate-change literature, adaptation has been commonly envisioned as a mechanism to cope with risks (e.g. borrowing money to repair houses) and as increments of existing adaptation strategies (e.g. building higher dams or resistant buildings) aimed at accommodating change [10, 21]. However, both coping and incremental adaptation strategies have generally failed to directly address the underlying drivers of systemic inequalities in climate change impacts, that is, to deliberately and fundamentally change systems to achieve equitable distribution of adaptation outcomes (Figure 1) [10, 11, 21].

Figure 1.

Coping, incremental, and transformative climate change adaptation.

Climate change adaptation planning takes place in a context of multiple uncertainties including epistemic (i.e. imperfection of knowledge), normative (i.e. impossibility of knowing the evolution of ethical values), political-induced (i.e. deliberative ignorance of public agencies), knightian (i.e. impossibility ok knowing all the information), and deep uncertainty (i.e. disagreement about the adequacy of models) [22]. Moreover, as a process, climate change adaptation is highly controversial since it usually entails the participation of multiple stakeholders with asymmetric power and competing knowledge [1, 15, 22]. Thus, planning for transformative climate change adaptation must explicitly address the historical power struggles and imbalances, the goals and mindsets of powerful actors, and the structure and rules-in-use that shape system dynamics [1, 10, 11]. However, transformative adaptation planning often encounters multiple barriers that can dampen efforts to create long-term climate-robust adaptation strategies [14].

For example, Ref. [3] and Ref. [14] identified a set of external and internal barriers to transformative adaptation development and implementation. External barriers include, for instance, the uncertainties related to climate change projections, future distribution of extreme meteorological events, expected risks, and vulnerability outcomes, as well as potential adaptation costs and benefits of climate change policy instruments. Internal barriers relate to the interaction and feedback across the existing institutional arrangements and governance structures underpinning system’s dynamics and which operate within specific political, cultural, and social contexts. Moreover, a number of internal barriers to effective decision-making have also been highlighted by Ref. [1] and Ref. [16] including, for instance, analytical difficulties in explicitly incorporating multiple stakeholders’ values, interests, and attitudes into socially-accepted and climate-resilient adaptation plans.

Transformative adaptation entails challenging the status-quo of the current system by fundamentally changing the material (e.g. policies and practices), procedural/relational (e.g. power dynamics), and conceptual/cognitive (e.g. values and preferences) dimensions of human-earth systems (Figure 2) [8, 11]. These changes include, for instance, modifying economic paradigms and development patterns, decolonizing knowledge systems, reforming governance institutions, and transforming the relationships and power dynamics among actors, as well as enabling individual and collective empowerment through learning and knowledge coproduction [11, 15, 16].

Figure 2.

Transformative climate adaptation entails radical changes in the material (e.g. policies and practices), procedural/relational (e.g. power dynamics), and conceptual/cognitive (e.g. values and preferences) dimensions of human-earth systems through knowledge coproduction under conditions of deep uncertainty.

2.2 Robust decision-making (RDM) for transformative climate adaptation

Complex and uncertain sustainability issues, such as the climate change crisis, require the adoption of robust decision-making (RDM) approaches that help decision makers identify adaptation strategies that perform well over a wide range of uncertain futures given socio-economic, environmental, political, and technological future trends [12, 13]. Following [13], “RDM explicitly follows a learning process called deliberation with analysis that promotes learning and consensus-building among stakeholders.” In this perspective, deliberation with analysis entails the coproduction of policy-relevant and legitimate knowledge.

In the context of climate change, the RDM deliberative process starts by eliciting stakeholders’ priorities, preferences, and assumptions underpinning adaptation planning [9, 15]. As noted by Ref. [15], transparency concerning the norms and procedures for deliberation is critical to maximize consensus and minimize conflicts among the stakeholders. Deliberation with analysis requires participants to actively engage with sharing their knowledge in order to collectively frame the decision problem, specify performance metrics and modeling methods, design the experimental framework, evaluate performance of strategies across futures, and choose or modify robust strategies [7, 12, 13]. Table 1 presents a hypothetical case study for climate change adaptation planning. Through an RDM deliberative process, stakeholders identify relevant sources of uncertainties, system parameters, variables and relationships, analytical methods, alternative adaptation strategies, and performance metrics regarding an adaptation planning problem. This information is organized in a four-quadrant matrix called XLRM as follows: (1) exogenous uncertainties (X) including, for example, temperature and precipitation projections under multiple global climate models and radiative forcing scenarios, urban sprawl scenarios, socio-economic future trends, and changes in risk perceptions; (2) alternate adaptation strategies (L), which are driven by stakeholders; (3) causal relationships (R) to simulate interactions and dynamics of human-earth systems; and (4) performance metrics (M) to assess strategy performance and characterize robustness.

Uncertainties (X)	Levers (L)
Climate change projections; Urban sprawl; GDP /socioeconomic trends; Risk perceptions	Current adaptation strategy; Strategy including tech. Inn.; Strategy including carbon incentives; Strategy including tech. Inn. & agency
Relationships (R)	Metrics (M)
Spatially-explicit simulation model (MEGADAPT) to evaluate urban socio- hydrological vulnerability	Reduction in socio-hydrological vulnerability; Increase in adaptive capacities; cost

Table 1.

XLRM matrix showing the main elements of a hypothetical RDM deliberative process.

However, coordinating and implementing RDM deliberative processes may be challenging due to divergent and competing stakeholders` climate risk perceptions and attitudes [15]. Moreover, mainstreaming community, indigenous and local climate change risk perceptions and attitudes into climate change adaptation planning has generally been uncommon in RDM processes [23, 24, 25]. Enabling tools such as computer platforms, computer-based scenarios, and visualization techniques (i.e. boundary objects [16]) offer some potential to facilitate RDM deliberative processes. These boundary objects can be used to iteratively integrate stakeholders’ values, interests, and knowledge examine the main processes and uncertainties affecting human-earth system’s dynamics, and collectively identify potentially vulnerable zones to climate change impacts [12, 13]. Interaction with these decision support tools helps stakeholders to formalize their value judgments regarding risk thresholds and uneven outcomes across alternate adaptation strategies [1, 15, 26].

As an “agree-on-decision” approach, RDM next uses simulation models (R) to evaluate selected adaptation strategies (L) over a wide range of uncertain futures (X) (Table 1). This step usually generates large databases of simulation model results [12, 13]. RDM advances the use of multi-objective optimization algorithms to trace out a range of potentially robust solutions [12, 13, 14]. Analysts and decision makers then apply visualization and data mining techniques including Patient Rule Induction Method (PRIM) and Classification and Regression Tree (CART) algorithms on these large databases to explore and characterize uncertain factors that define vulnerabilities. The vulnerability analysis is an iterative and interactive exercise that allows stakeholders to better understand the system’s conditions and uncertainties under which systemic failures in adaptation can take place [13, 27]. The goal of these analyses is to establish a basic plan that dynamically adapts to signposts over time [7, 28].

As a result of the vulnerability analysis, new adaptation strategies emerge and are reexamined. Alternative strategies can be crafted using expert knowledge, thus conveying a collective strategic vision of a desired future state and the ways to get there [28, 29]. As stated by Ref. [28], policy-making becomes then an essential component of the storyline. Nevertheless, these top-down strategies are usually constrained in their ability to represent disruptive social and technological innovations and imaginaries, meaning they are relatively unresponsive to the underlying drivers of systemic inequalities [29]. Knowledge coproduction processes have the potential to inform robust transformative climate adaptations by engaging with diverse knowledge systems in order to ground alternative adaptation strategies in local realities, perspectives, and visions [15, 29].

2.3 Knowledge co-production for climate change adaptation planning

In the last decades, academic scholars have recognized the urgency to transform traditional science-practice relationships into action-oriented collaborative research in order to effectively address the most pressing challenges of society [17, 18]. Knowledge coproduction is part of this evolving set of action-oriented approaches defined by Ref. [30] as an “iterative, interactive and collaborative process involving diverse types of expertise, knowledge, and actors to produce context-specific knowledge.” This definition underscores the normative aspirations underpinning collaborative scientific practices aimed at transcending the narrowness of disciplinary worldviews through the inclusion of diverse societal actors’ perspectives, discourses, expertise, beliefs, and interests to solve complex and uncertain problems [1, 17, 30, 31, 32, 33, 34].

Knowledge coproduction is a context-based, pluralistic, goal-oriented, and iterative process that bridges knowledge to action [9, 18, 33]. Thus, coproduction entails multi-stakeholder (i.e. academic and nonacademic) engagement and participation to generate impact-driven information, which is sufficiently credible, legitimate, and salient. In accordance with Ref. [16], credibility concerns the technical adequacy of information, legitimacy refers to the perception that the production of information has been respectful for stakeholders’ interests and needs, and salience refers to the relevance of the information. By directly connecting science, policy, and action, coproduction not only generates salient, credible, and legitimate knowledge to define adaptive interventions but also capacities (e.g. technical, analytical procedural, and evaluative), actor network partnerships, and inter-institutional organization fundamentally required for transformative climate change adaptations [10, 11, 21, 22].

Coproduction has been widely incorporated in the fields of health, education, development, and environmental planning [32]. Though the rate of use of coproduction processes in decision-making remains below expected needs, increased public participation in climate change adaptation planning has been recently reported in peer-reviewed scholarly publications [32, 33, 34, 35]. In a comprehensive literature review of more than a hundred scientific publications on climate change, Ref. [35] found no common notion of coproduction but rather a broader collection of conceptual lenses from which coproduction is conceived and implemented. In this perspective, the lenses shed light on the diversity of coproduction goals, theories, practices, capacities, and outcomes providing key insights for policy making [33, 34, 35]. Despite differences in how the coproduction lenses interweave knowledge and action, the concept of boundary work was shown to serve as a common tool to both account for conflicting interests between political, social, and environmental externalities and to systematically reflect on the normative and participatory dimensions of the decision-making process (Table 2) [26, 30, 33, 35].

Lense	Description	Application
Iterative interaction	Relative to the usability of climate information products in a decision-making context	Transform climate science into value-added “climate services”
Extended science	Looks at ways of doing science differently by including the knowledge and values of nonscientists	Democratizing practices of transdisciplinary science to generate robust climate knowledge
Public services	Joint production of public goods and services by government agencies and citizens	Institutional economics and multilevel governance
Institutional	Looks at how the processes of knowledge coproduction build adaptive capacities within governance institutions	Political ecology and environmental science
Social learning	Looks at how coproduction facilitates social learning about climate issues	Organizational studies, policy research, and management theory
Empowerment	Looks at the ways coproduction recognizes and empowers traditional knowledge systems	Anthropology, philosophy of science, and resource management

Table 2.

Normative lenses of coproduction, descriptions, and applications according to Ref. [35].

Climate change adaptation planning is generally built on uncertainty, divergent stakeholder interests, disparate problem framings, and dynamic socio-environmental interactions [1, 15, 19, 20]. Given the “wicked” nature of climate change adaptation planning, deterministic approaches are highly inappropriate since they can give rise to maladaptation and increase climate change vulnerabilities [27]. Vulnerability refers here to the susceptibility to being harmed by climate change, and it dynamically differs within communities and across regions and countries [36]. Research suggests that vulnerability stems from historic structural inequalities, power dynamics and legacies of past interventions, uneven resource distribution (e.g. water, housing, land), centralization of political power, systemic racism, and preexisting social and cultural norms that reinforce unsustainable paradigms [1, 11, 15, 20]. Consequently, advancing climate change transformative adaptation planning demands effectively responding to the magnitude of climate hazards while addressing the drivers of inequality in order to achieve successful outcomes under a wide range of uncertainties and different operational planning periods [7, 11, 28, 37].

International initiatives such as the World Bank Climate Change Action Plan and the NOAA Regional Integrated Science Assessment (RISA) have the potential to foster transformative adaptation planning through interdisciplinary research and engagement [34]. The former represents a global effort to support participation in key partnerships and forums aimed at improving climate change adaptation and resilience while addressing, to some, the root causes of vulnerability. The latter, on the other hand, represents a regionally-focused initiative aimed at building sustained partnerships to support equitable and collaborative adaptation to climate change risks [34, 37]. Other examples pertaining the Global South include the binational laboratory on sustainability, vulnerability, and adaptation to Climate Change (SVACC), a university-based node of collaboration among the US, Mexico, Central America, and the Caribbean aimed at strengthening regional technical and institutional capacities for effective climate change collaborative governance [26]. Despite institutional efforts to drive climate actions by actively and interactively engaging with stakeholders, the climate change research community has not yet achieved a shared conceptual and methodological decision support framework for collaboratively identifying sources of uncertainty, assessing (weight and appraise) risks and vulnerabilities, navigating and prioritizing risk–benefit adaptation trade-offs, choosing among adaptation strategies, and finally evaluating outcomes throughout the decision-process [3, 15].

3. Conclusions

Knowledge of coproduction processes is required if we are to navigate climate change uncertainty and support evidence-based adaptation policy-making. Large-scale systemic thinking at the material (e.g. policies and practices), procedural/relational (e.g. power dynamics), and conceptual/cognitive (e.g. values and preferences) dimensions of human-earth systems is increasingly promoted as a means of enhancing transformative climate change adaptation. Robust decision-making approaches are grounded on a learning process called deliberation with analysis. Deliberation with analysis entails the coproduction of knowledge to support decision-makers and stakeholders to frame the decision problem, specify performance metrics and modeling methods, design the experimental framework, evaluate performance of strategies across futures, and choose or modify robust strategies. Yet, competing knowledge and perceived injustice can dampen efforts to bring together academic and nonacademic actors in the process. Knowledge coproduction can help to navigate these challenges by making tangible and tractable issues of equity and justice in climate change adaptation planning.

Acknowledgments

This work was supported by the Universidad Nacional Autónoma de México (UNAM) [PAPIIT- Proyecto IN223321 Modelación de la resiliencia de servicios ecosistémicos en el Suelo de Conservación de la Ciudad de México]. We also want to thank the collaborative team SEDEMA-LANCIS for their support.