**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 [2–4]. 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 [5–7]. 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 [9–11]. 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 [16–18].

#### *Knowledge Coproduction for Transformative Climate Adaptation: Building Robust Strategies DOI: http://dx.doi.org/10.5772/intechopen.107849*

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.
