This case study refers to research conducted by the CGIAR Research Program on Agriculture, Climate Change and Food Security (CCAFS). In the research, a methodology was developed for multi-level stakeholder influence mapping (MSIM) that elucidates the power dynamics between actors in complex system regimes. MSIM relies on individual interviews, conducted across multiple actor levels, and utilizes a participatory mapping process for shared system boundary critique. MSIM was piloted in Nepal’s agricultural climate change adaptation regime with actors from the central, regional, and local operational levels.
Relationship to CSA
Without proper consideration of the role which power plays in aiding and hindering in agricultural adaptation, interventions will likely struggle to catalyzing adaptation pathways and moderate the negative impacts of climate change. Furthermore, power analyses produced from the perspective of a single actor level or respondent type can risk sub-optimization of CSA-based adaptation outcomes and can misdirect the lobbying efforts of agencies that utilize mapping outputs.
The MSIM power mapping tool is a useful tool to explore power dimensions and relations before engaging in any CSA project – whether national policy guidance or community-based work. For instance, it has been used in preparatory stages for scenario processes in Case study 1.
Impacts and lessons learned
The MSIM power mapping tool has been successfully used to identify appropriate participants for a multi-level policy design process in Ghana, for the development of a new stakeholder network in Nepal, and for scenario-guided policy development in Southeast Asia.
A key lesson has been that power mapping holds the potential to identify powerful but ‘marginalized’ actors and bridging agents, such as local traders or officials, who may be facilitating key adaptation processes, but who are not recognized by central-level actors and institutions. Ultimately, without proper consideration of the role of power in agricultural climate change adaptation programmes, the resulting interventions run the risk of being insufficient and potentially contradictory in moderating the negative impacts of climate change, as well as highly contested, less equitable, and ultimately less sustainable and effective. A related lesson is that in order to be effective, power mapping should be multi-level and multi-dimensional. Power mapping helps bridge power gaps across governance levels, making sure that both the powerful and the powerless are involved in processes, and it avoids political capture in the development of strategies and plans.
Sova CA et al. 2015a. Multi-level Stakeholder Influence Mapping: Visualizing Power Relations Across Actor Levels in Nepal’s Agricultural Climate Change Adaptation Regime. Systemic Practice and Action Research 28(4):383-409.http://dx.doi.org/10.1007/s11213-014-9335-y Where power lies and how it is conceived in studies of governance and institutions is often not discussed. This is due to the ubiquitous nature of the topic. Power is shaped by a variety of institutional factors, including the architecture of governing structures, questions of scale and level, and access to key resources including knowledge and capital, among other factors. To date, there are relatively few tools available that allow policy makers, researchers, and development practitioners to render these power dynamics explicit and thus take steps to mitigate the potentially deleterious effects of power orientations. This paper proposes a methodology, multi-level stakeholder influence mapping (MSIM), for elucidating power dynamics between actors in complex system regimes. MSIM departs from existing power mapping techniques in that it relies on individual interviews conducted across multiple actor levels and utilizes a participatory mapping process for shared system boundary critique. MSIM was piloted in Nepal’s agricultural climate change adaptation regime with actors from the central, regional, and local operational levels. The results suggest that without proper consideration of the role of power in agricultural adaptation regimes, the resulting interventions will likely be insufficient in catalyzing adaptation pathways and moderating the negative impacts of climate change. Furthermore, power analyses produced from the perspective of a single actor level or respondent type can risk sub optimization of adaptation outcomes and can misdirect the lobbying efforts of those agencies utilizing mapping outputs.