Tackling key challenges around learning in environmental governance

1. Introduction

Environmental and ecological systems, along with the social, technological, and institutional systems that shape them, are complex, dynamic, interactive, and continually evolving. To effectively govern and sustain such complex systems, decision-makers, policy actors, managers and stakeholders need to continually learn and infuse learning into governance processes. Although learning is part of human nature, learning within environmental governance processes can often be blocked or impeded, or may result in harmful learning outcomes. Thus, environmental governance scholars are increasingly interested in contexts, structures, and tools that can foster learning in environmental governance, and what types of learning processes are associated with sustainable governance outcomes. This special issue of the Journal of Environmental Policy and Planning (JEPP) focuses on this topic. While this is not the only special issue of JEPP to address questions of learning (e.g. see Wolfram, van der Heijden, Jukola, & Patterson, 2019), it is the first to broadly examine learning processes and outcomes in environmental governance.

Editing this special issue on learning has given us an opportunity to reflect on an academic journey that we began more than fifteen years ago – starting with our joint research on large-scale collaborative environmental governance processes (Gerlak & Heikkila, 2006; Heikkila & Gerlak, 2005). Our original intentions in studying environmental governance was to assess comparatively the institutional design, but in studying these processes, we were struck by the interest and desire among decision-makers and stakeholders in learning about learning. These individuals wanted to find ways to design processes that facilitate sharing of information, to improve their collective understanding of the biophysical systems they were trying to manage, to draw insights from similar governance process, and establish new tools that enhance their capacity to make collective decisions. It struck us that despite many of the assumptions in the literature that collaborative governance processes can enhance learning, there was no guarantee that participants were learning individually or collectively.

From comparative research, we also realized that the design of environmental governance processes and institutions could either foster or inhibit learning. We jumped right in, somewhat naively, to try to tackle this question. We have been learning together about learning ever since – about the ways in which learning can be built into or embedded in processes to improve environmental governance and how as scholars we can study it. We have done so with a little adventure (developing a comprehensive framework on learning) and with humility (realizing that learning about learning could take a lifetime). We have found that our learning has been enhanced tremendously through expanding our own professional networks on learning and reaching out to other colleagues who are exploring similar questions, challenging each other, and collectively pushing the boundaries of the literature. It has also forced us to provide greater clarity in how we conceptualize learning as both a process (e.g. of acquisition, translation, and dissemination of new ideas), and a product (e.g. changes in cognition and behaviors), but also to remain open to conversing with the diversity of conceptual and theoretical approaches scholars are taking in studying learning in environmental governance.

This special issue continues in that tradition. We invited scholars who have been exploring learning from diverse perspectives, and who could speak to some of the perennial challenges facing scholars of learning in environmental governance. In doing so, the goal of this special issue is not merely to provide snapshot of cases of learning across diverse geographies and issue areas, but also to advance the next generation of scholarship on learning in environmental governance. To illustrate the contributions of this special issue, we first highlight the diversity in contexts, issues, and approaches presented in this special issue. We then explore how the contributions to the special issue tackle three fundamental challenges in the literature on learning, which we describe below. We then identify lessons for both scholars and practitioners that cut across these papers. To conclude our introduction to this special issue, we offer some advice on how we can continue to learn as a community of environmental governance scholars.

2. Setting the stage: diverse perspectives on learning

The seven papers in this special issue address a diverse set of contexts and issues where learning is occurring in environmental governance. In doing so, this special issue provides a nice complement to the recent JEPP special issue on learning that focuses specifically on the context of urban climate governance (Wolfram et al., 2019). For example, two of the papers in this special issue look at urban flood risk in European cities (De Voogt & Patterson, 2019; Mukhtarov, Dieperink, Driessen & Riley, 2019). Other researchers examine learning in planning processes in forestry management (Ricco & Schultz, 2019), new collaborative mechanisms around water management (Koebele, 2019), and learning in disaster recovery processes (Crow & Albright, 2019). In addition, researchers examine learning in climate negotiations resulting in the 2015 Paris Agreement (Rietig, 2019) and across a broader set of case studies from publications of environmental governance processes involving stakeholder participation (Newig, Kochskämper, & Challies, 2019). Despite the diversity of contexts studied, there is some notable overlap in the core questions explored in this special issue. For instance, all the papers study either how learning processes emerge and occur or what facilitates or constrains learning in environmental governance.

The papers in this special issue further illustrate how to apply a range of theoretical and methodological approaches to study questions about learning in environmental governance. Several follow a synthetic and integrative theoretical approach to the learning scholarship (De Voogt & Patterson, 2019; Newig et al., 2019; Rietig, 2019), while others apply a collective learning framework that we developed to guide analyses of the social, structural, and technical factors that can influence learning processes and products (Heikkila & Gerlak, 2013). Methodologically, some of the more common approaches used to study learning in this special issue include document analysis, interviews, and surveys (Crow & Albright, 2019; De Voogt & Patterson, 2019; Koebele, 2019; Ricco & Schultz, 2019; Rietig, 2019). We also see the use of process tracing and ethnographic approaches (Mukhtarov et al., 2019), participant observation (Ricco & Schultz, 2019; Rietig, 2019), and a meta-analysis of existing literature (Newig et al., 2019).

3. How do the papers in this special issue help us address key challenges in the study of learning?

Pulling together a set of articles that cover diverse questions, theories, and methods is was one of the goals of this special issue, but not the primary goal. Our primary goal, after spending many years diving into this literature, was to confront some fundamental challenges in the literature. As some of our recent assessments of the literature have shown, opportunities abound for advancing empirical, conceptual, and theoretical insights on learning in environmental governance (Gerlak, Heikkila, Smolinski, Huitema, & Armitage, 2017; Gerlak et al., 2019). For instance, the scholarship would benefit from a deeper understanding of the specific design features of governance institutions that enhance or constrain learning across different contexts, as well as the factors external to a governance setting that shape learning. We also have limited evidence of how learning processes can lead to better or more sustainable environmental outcomes.

Specifically, we need to address three fundamental challenges that are widely recognized in the learning literature on environmental governance (e.g. Armitage, Marschke, & Plummer, 2008; Gerlak et al., 2017; Gerlak et al., 2019; Muro & Jeffrey, 2012; Reed et al., 2010; Rodela, 2013; Rodela, Cundill, & Wals, 2012). First, we need clarity and precision in conceptualizing learning. Second we need more rigorous methods and research designs for measuring and assessing learning. Third, we need to build and test theories of learning. We discuss how the papers in this special issue address these challenges in greater detail below.

3.1. Meeting challenge 1: clarity and precision in conceptualizing learning

Learning in environmental governance often involves complex, multi-level processes (sometimes intentional and sometimes unintentional), such as acquiring information or transferring ideas, among individuals, groups, organizations, and networks (Argote, 2011; Gerlak & Heikkila, 2011; Huber, 1991; Lipshitz, Popper, & Friedman, 2002; Zollo & Winter, 2002). These processes can occur through a variety of mechanisms and venues. At the same time, learning involves different types of outcomes our outputs, such as changes in strategies, beliefs, or policy positions (or ‘what’ is learned). Further, what we learn can occur at different levels of social or human organization, and to different degrees of depth (e.g. single loop versus double loop learning) (Argyris & Schon, 1978; Armitage et al., 2008; Flood & Romm, 1996; Pahl-Wostl, 2009). Given the wide range of ways of learning and what constitutes learning, how can scholars of learning define it and conceptualize it in a way that is valid? How do we know what is learning versus ‘non-learning’? And how do we communicate our understanding of concepts in a way that allows us to compare consistently across studies of the same phenomenon?

As with many social science concepts that are difficult to observe, it is often useful to start by unpacking the component constructs or sub-features of the broader concept, such as through typologies (Gerring, 2012). Three of the papers in this special issue offer typologies of what or how learning occurs, and build these typologies from existing learning theories. For instance, Newig and colleagues define learning as learning as deliberation; as knowledge- and capacity-building; and as informing environmental outputs. For Rietig (2019), learning cannot occur with reflection. She differentiates between three basic archetypes of learning from the public policy literature: factual, experiential, and belief change, and how these types differ from the individual to organizational levels. Crow and Albright (2019) define learning around both processes and products, and recognize that it is important to link learning products to evidence from individuals involved in learning processes. As shown in their Supplemental Table 1, Crow and Albright further recognize the wide diversity of types of learning in the literature (e.g. social, political, policy-oriented, instrumental) and how the indicators and products of learning vary across these types. One of the benefits of differentiating among types of learning is that it can reveal or lead to different theoretical insights about the drivers of learning, as demonstrated by Newig et al. (2019), or provide ways to look at differing levels or intensity of learning, as shown by Crow and Albright (2019).

Similarly, one can draw from existing definitions within a specific theory or framework. Using definitions and concepts from a shared theory or framework has the advantage of speaking to and helping build a shared research community (Ostrom, 2005). For instance, several papers that employ elements of the collective learning framework we developed (Heikkila & Gerlak, 2013) adopt our definition that differentiates processes and products (e.g. Koebele, 2019; Mukhtarov et al., 2019; Ricco & Schultz, 2019). In doing so, the authors are able to talk about learning in a way that can transcend different contexts, while also clarifying some of the nuances in learning that can arise within a specific type of governance process (e.g. Ricco and Schultz’s analysis of learning during policy implementation or Koebele’s assessment of learning during a collaborative process).

3.2. Meeting challenge 2: rigorous methods and research designs for measuring and assessing learning

Building more rigorous methods for measuring learning is critical according to several recent assessments of the learning literature (e.g. Crona & Parker, 2012; Gerlak et al., 2017; Ison, Blackmore, & Iaquinto, 2013; Rodela et al., 2012). The challenge is that when studying governance processes, we cannot tap into individual’s brains where learning actually occurs. As a result, measurement is often indirect. Finding methods for measuring learning across a group or collective becomes an added challenge when learning starts at the individual level. At the same time, learning takes time and many studies take a snapshot of learning. Related to these challenges, researchers often struggle to find ways of measuring learning across cases that are comparable, or to effectively measure non-learning. Several recommendations have been put forth in the literature for how can we make it more precise and rigorous (e.g. multiple methods; longitudinal; large-n). Of course, ‘form follows function’ with methods. That is, methods should be tailored appropriately to the question at hand.

The papers in this special issue provide some useful examples of how to build from the recommendations in the literature. For example, Koebele’s article illustrates how to measure individual learning and then aggregate indicators of individual learning to measure collective-level learning. De Voogt and Patterson (2019) use a most-similar case design to zoom into theoretically relevant variables that can help explain different learning products associated with mitigating flood risks facing two similar municipal governments. Newig et al. (2019) employ a large cross-case dataset and common coding framework using data from published research, which provides a novel approach to theory testing. To illustrate how to examine the temporal features of learning, Rietig (2019) leverages an in-depth case study of climate negotiations over a decade.

The authors in this special issue further illustrate the importance of triangulation and transparency of methods. Multiple methods, with transparency in protocols, for building rich case studies can be seen in the research by Crow and Albright (2019), Mukhtarov et al., (2019), and Ricco and Schultz (2019). Mukhtarov et al., (2019), for instance, undertake 18 semi-structured interviews; process tracing; directly asking interviewees about products and process factors associated with learning; and what external issues facilitated or hindered collaborative learning. These methods are supplemented by ethnographic observations and NVivo coding of interview transcripts, field journals, and policy documents.

3.3. Meeting challenge 3: building and testing theories

While many excellent examples of learning research have sought to describe learning processes, there is still a dearth of theorizing about what factors are known to support or hinder under particular environmental governance contexts (Gerlak et al., 2017; Gerlak et al., 2019; Plummer et al., 2017). In the social sciences, theory typically involves some effort to explain why or how a particular phenomenon arises or what it leads to. Therefore, theories of learning should include explanations of what factors drive learning or what the effects of learning are on governance outcomes. We do have a sense of many of the contextual factors associated with a given governance process or setting (e.g. opportunities for discourse, trust, etc.), but we are still lacking a more robust understanding of the conditions under which particular factors matter, or how the structure of a broader governance process matters, or the types of exogenous factors that can spur learning. As the empirical literature on learning in environmental governance is still relatively new and evolving, it is also incumbent upon scholars to continue to test existing theoretical propositions in new contexts and understand the boundaries or extent of our theories at different levels or scales of action or behavior (Gerring, 2012). For instance, what may be theoretically relevant at the individual level or group level may not be relevant at the group level or vice-versa. The papers in this special issue take this challenge seriously. Many authors build upon existing theoretical arguments and test those arguments in new contexts, some seek to enhance theoretical insights by identifying new variables of governance contexts that have not been adequately tested or explained in relation to learning, while some also improve our understanding of the interrelationships among theoretically relevant factors that can shape learning.

Among these contributions in the special issue are new insights into the elements internal to the context of the governance system that shape learning. Crow and Albright (2019) look at the role of relationships and resources in fostering or constraining learning, which have been recognized in the literature, but not tested in a post-crisis (e.g. flooding) setting. Newig et al. (2019) also identify key contextual factors such as trust and stakeholder cooperativeness, and the level of value conflict as factors that play an important role in learning. Mukhtarov et al., (2019) highlight the social dynamics as important factors, as well as policy issue salience, while Ricco and Schultz (2019) explore internal networks and information sharing as critical features of the context to study. Both sets of authors pay attention to levels within the governance setting in their theorizing (e.g. individual versus collective or top-down versus bottom up). Koebele (2019) also pays attention to how various features of the governance process (e.g. leadership, opportunities for deliberation) affect what people learn.

In addition to building theoretical insights about what drives learning, a handful of the papers develop novel insights about how learning processes shape outcomes of governance. Rietig (2019) builds theoretical insights about how learning can help overcome deadlocks in international negotiations in combination with policy entrepreneurs. Koebele (2019) tests the underlying theoretical assumption from the collective learning framework that learning processes are tied to learning outcomes. In doing so, she finds that at least in collaborative environmental governance settings, participants’ engagement in a learning process (e.g. acquiring more info) influences the extent to which they perceive learning products among the collective (e.g. more consensus across the group). However, Newig et al. (2019) also test the implicit assumption that learning processes necessarily lead to better governance outcomes and do not find significant relationships. What this contributes to theory is perhaps a recognition of the potential for indirect effects of learning on outcomes, or that these effects are inconsistent across such a diverse array of cases that they employ in their study.

4. Four key lessons: insights for theory and practice

In light of the contributions that these papers collectively make toward addressing these key challenges in the study of learning, we distill four key lessons that can inform our understanding of learning in environmental governance.

The first lesson relates to the broader exogenous context – such as the extent of an environmental crisis – that can trigger, or interact with learning processes and outcome. For instance, Crow and Albright (2019) illustrate the degree of damage of floods as a factor associated with learning. Exogenous events may come up quickly as in the case of a large-scale flood, but as De Voogt and Patterson (2019) find, slow moving factors such as demographic trends can also have a substantial impact on learning within policymaking processes when they build up and reach a threshold point. As such, they argue for the importance of paying close attention to the temporal dimensions of exogenous factors, and not simply looking at crises that occur at a given point in time, while Crow and Albright (2019) underscore the importance of the magnitude of exogenous events.

The second lesson across the papers is the importance of social factors in learning processes and outcomes. This aligns with the extensive body of scholarship on social learning that highlights participatory processes and sharing of experiences and ideas (Keen, Brown, & Dybal, 2005; Muro & Jeffrey, 2012; Reed et al., 2010). It also supports the overall findings of the recent special issue on learning in urban climate governance in JEPP where researchers highlighted the role of socialization and informed personal exchange contributing to deeper learning (Wolfram et al., 2019). Several of the papers in our special issue speak of the importance of leadership and entrepreneurs for learning. On a more global scale, in terms of climate change negotiations, Rietig (2019) finds that non-state actors and their entrepreneurial activities played a pivotal role in opening up the window of opportunity for the Paris Agreement to emerge. Social networks are seen to help to promote learning (Mukhtarov et al., 2019; Rietig, 2019), and relationships between different levels of governments influence learning (Crow & Albright, 2019). To their surprise, Newig et al. (2019) found that conflict helped to foster learning.

Further, several papers highlight the interaction or mutual dependence of social factors with the structure of the governance setting as critical in shaping learning. Mukhtarov et al., (2019) note that even when individual learning happens, mechanisms both structurally and socially are needed to allow for collective learning, especially procedures for collective reflection on experiences. Newig et al. (2019) find that structured methods of communication, facilitation, and knowledge-exchange fostered all the modes of learning they examined. From Koebele (2019), we learn that when people feel more confidence in and comfort with a collective process, they are more open to learning. Additionally, learning modes or practices can build upon and support one another creating a learning multiplier effect (Newig et al., 2019). To enhance learning in government agencies, Ricco and Schultz (2019) recommend a suite of steps actors can take that speak to this social-structural linkage. These include ensuring capacity is in place during critical times for learning, identifying individual leaders and units that are likely to facilitate learning during policy implementation, and thinking strategically about when, and for what, learning and innovation are desirable and feasible.

The third lesson that we can distill from this special issue is that the factors that drive learning processes and outcomes are often interactive. For instance, Rietig (2019) shows how timing and learning from past failures served as important external pressures to bolster learning in climate negotiations and ultimately, to overcome serious deadlocks. Sometimes learning may simply not be enough, however, to foster improved governance outcomes. In the case of sustainable urban drainage systems (SuDS) in the UK, Mukhtarov et al., (2019) show us that despite evidence of learning, policy change appeared to be impeded by several barriers such as the reluctance of drainage engineers to embrace SuDS, uncertainties related to costs of adoption and maintenance of SuDS, and the lack of an effective institutional and legal framework on SuDS at the national level. Crow and Albright (2019) also highlight how several factors, including greater intergovernmental engagement, resources, and knowledge, all work together in shaping learning and learning outcomes.

Fourth, the special issue underscores the need to be careful around the tendency toward the halo effect that can arise with learning. Koebele (2019) warns about the propensity to see outcomes as better than they might actually be. Similarly, in their review of coded 300+ case studies from publications of environmental governance processes, Newig et al. (2019) conclude that learning in participatory environmental governance may not lead to better policy outcomes. Although it is a common assumption in the learning literature that learning results in improved products or outcomes, researchers also recognize that such learning can result in worse outcomes or the reinforcement of existing beliefs or practices that are incorrect (Dunlop & Radaelli, 2018). Although there is some indication of new research attempting to better connect learning processes to outcomes, overall, the learning literature as it relates to the assessment of environmental and natural resource management outcomes remains disconnected theoretically and conceptually, and with room to enhance empirical rigor (Gerlak et al., 2019). The recent special issue on learning published in JEPP on urban climate governance, for example, finds only incremental adjustments as opposed to deeper social learning in the eight empirical papers that examined learning in this context (Wolfram et al., 2019)

Taken together, these research findings can help the scholarship push our theoretical boundaries by looking at the interactive effects of learning factors, the dynamics of the exogenous setting, and checking our assumptions about learning outcomes. To continue to build the literature, we recognize that we have to both push for more depth within our theories and for continued conversations across theories. In applying these lessons to our own framework, for instance, we have deeper insights now on the interactive effects between the internal social networks of a governance setting and the tools for information sharing within governance organizations (Ricco & Schultz, 2019). Yet, given the interrelationships among the contextual features that shape learning, we further recognize how studying learning processes more holistically is worthwhile (Koebele, 2019). The research presented here illustrates the feasibility of employing more synthetic theoretical approaches that draw insights from well-established literatures, such as policy learning and social learning (e.g. Crow & Albright, 2019; Newig et al., 2019).

Overall, we are pleased to see the growth and evolution of the learning scholarship in environmental governance over the past two decades. As this special issue illustrates, however, we need to continue to learn as a scholarly community about our conceptual, theoretical, and methodological approaches to understanding learning in environmental governance (Gerlak et al., 2019). This involves building from the examples in this special issue on how to apply, test, and when appropriate, refine our theories and frameworks. It also involves employing rigorous methods, as these studies have employed, and a willingness to stretch boundaries, learn from related disciplines, and continue to communicate clearly and transparently with each other. There are also important topics around learning that are not covered in this special issue, such as how institutional rules of venues or collective settings matter for facilitating the types of social interactions that are important, or how cognitive biases or competing values constrain collective learning in environmental governance, and how to address this. Delving into specific contexts or types of learning, and comparing across cases, as our colleagues have done in the case of urban climate governance (Wolfram et al., 2019) are also important steps in advancing the learning literature. Broadening the community of scholars and practitioners interested in learning in environmental governance can only help serve to mitigate these limitations and further contribute to the study of learning and its role in environmental governance, especially as we can only expect environmental challenges to grow and become more intense in the coming years.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Andrea K. Gerlak is an Associate Professor in the School of Geography and Development and Associate Research Professor at the Udall Center for Studies in Public Policy. Her work addresses institutions and governance of environmental challenges.

Tanya Heikkila is a Professor in the School of Public Affairs at the University of Colorado-Denver. Her research and teaching focus on policy processes and environmental governance.

ORCID

Tanya Heikkila http://orcid.org/0000-0002-2619-4320