Multi-stakeholder Partnerships’ Potential to Govern Climate-SDG Interactions: Evidence from the SIDS Action Platform
David Horan
Abstract
Recent scholarship in global sustainability governance suggests that transnational multi-stakeholder partnerships (MSPs) have the potential to effectively govern synergies and trade-offs among Sustainable Development Goals (SDGs). However, there is limited empirical evidence on the extent to which MSPs address key interconnections among SDGs and the factors that shape MSPs’ effectiveness to address these connections. This paper develops a methodology to assess and explain the effectiveness of a sample of MSPs to address key SDG nexuses in an international development context through their output activities, by studying 50 MSPs registered in the SIDS Action Platform in 2019 that aimed at addressing climate change (SDG13) in Pacific SIDS (PSIDS). Using a novel measure of partnership’s output-SDG fit and descriptive statistics, the paper links scientific evidence on climate-SDG interactions with the demand for climate-SDG nexus governance in PSIDS, and compares it to the supply of MSPs with SDG-aligned outputs. Results suggest the sample of MSPs (which consisted mostly of transnational adaptation initiatives) addressed a narrow set of climate-SDG nexuses with a clustering of MSPs with outputs focused on the climate-ocean (SDG13-14) nexus and relatively few MSPs with outputs focused on the climate-development nexus (in particular, SDG13-1,3,5,9,10). The paper further adds by discussing factors that likely shaped MSPs' (in) effectiveness to address these nexuses at the meta-governance level, identifying supply-side factors such as UN conferences and (PSIDS limited) access to finance, and demand-side factors such as geography and development needs that help to explain observed supply and demand imbalances.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.