Revealing mechanisms in a transdisciplinary Community Reforestation Research Programme

Abstract

Background: The Community Reforestation Research Programme is a joint initiative between eThekwini Municipality and the University of KwaZulu-Natal in South Africa. Addressing complex sustainable development issues requires collaboration across disciplines and between researchers, practitioners and policy-makers. Understanding how this occurs has the potential to inform and improve transitions to sustainability.Objectives: A summative evaluation of one project within the broader programme was commissioned to develop in-depth understandings into the underlying systemic influences or mechanisms that supported or hindered transdisciplinary research and practices.Method: A realist evaluation method informed the analysis of questionnaires, documents, interviews, focus group discussions and participation in programme reporting and planning processes. This approach supported the development of a contextual profile within which underlying mechanisms were identified. These mechanisms were considered in relation to the outcomes of the project.Results: The main mechanisms identified in the evaluation process related to orientations to research, orientations to education, orientations to value creation, orientations to environmental management and orientations to organisational leadership. These orientations either enabled or hindered particular outcomes in the research on, and environmental management of, community reforestation in the context of climate change. The mechanisms also had significant implications for understanding ‘what worked well for whom in what circumstances and how’.Conclusion: Transdisciplinary research and practice across institutional boundaries are enabled or hindered by underlying mechanisms. By identifying and understanding these mechanisms, insights were developed that have the potential to enhance transdisciplinary sustainability initiatives at the local level.

Knowledge integration in transdisciplinary sustainability science: Tools from applied critical realism

“…First, critical realism can bring a critical lens to sustainability science. The critical orientation of critical realism enables engagement with structural mechanisms in social‐ecological sustainability issues related, for example, to political, economic and historical drivers of change in a more radical way than many other traditions in systems thinking (Huckle, 2012; Price, 2014; Togo & Lotz‐Sisitka, 2013; Ward & Rosenberg, 2020). For example, in Price's (2014) critical realist analysis of violence against women, she identified historical, global and unconscious aspects of the problem related to oppressive structures such as colonialism, sexism and racism which a systems‐oriented, interdisciplinary study conducted by the World Health Organisation had overlooked.…”

“…These examples therefore illustrate the value of CR in operationalising key transdisciplinary concepts including collaboration, mutual learning, knowledge integration and reflexivity (Jahn et al, 2012) in a variety of contexts. For example, Ward and Rosenberg (2020), applied critical realism as an underlabourer and conducted their evaluation of a community reforestation initiative through a retroductive mode of inference. This enabled them to integrate a wide range of knowledge forms and information from a transdisciplinary initiative to identify key mechanisms which were driving change in an urban reforestation project.…”

“…The stratified and differentiated ontology of critical realism enables the identification of generalizable, underlying mechanisms and generative structures to explain social-ecological phenomena across contexts and situations (Togo & Lotz-Sisitka, 2013;Ward & Rosenberg, 2020).…”

The Contribution of Trees and Green Spaces to Household Food Security in eThekwini Metro, KwaZulu-Natal

“…The model is widely used to model non-negative outcome variables and to represent count outcomes. The instrumental variable model estimates the parameters of a Poisson regression model with endogenous regressors [34], to estimate the model's parameters, instrumental variables that do not correlate with the error term are used in this case. These instrumental variables are related to the endogenous variables but not the model's error term.…”

“…Following Ndlovu et al [34], three indices, cultivated trees and green spaces, uncultivated trees and green spaces, and local trees, were developed for analysis. The outcome demonstrates that the indicators' dependability is suitable for examining how trees and green spaces affect households' food security.…”