Abstract
Over recent decades, substantial funding from a variety of sources has been directed towards climate change adaptation projects in Pacific Island countries. There remains, however, considerable uncertainty about which factors influence adaptation project completion, as a pre-cursor to effective adaptation. In this study, we empirically establish the links between project attributes (duration, funding, cash co-financing, in-kind contributions, location, and adaptation approach) and whether a project is likely to complete or be terminated. We examine this issue by developing a logistic regression model to predict the probability of completion for small-scale climate change adaptation projects using a new dataset of 190 projects in the South Pacific (with end dates ranging from November 1995 to May 2016) that were financed through the Global Environment Facility Small Grants Programme. Empirical results suggest that all else equal, such a project was more likely to complete if it was shorter, received more co-financing cash input and in-kind support from other donors and project partners, was explicitly targeted towards climate change adaptation, focused on a single adaptation approach, and was undertaken in Micronesia or Fiji. Our results can be used to help funders and project proponents design projects to mitigate the risks of non-completion, particularly in high-risk settings. These findings should not be misinterpreted to undermine the importance of continued investment in adaptation projects across the whole of South Pacific region.
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Notes
Available online: https://stats.oecd.org/Index.aspx?datasetcode=CRS1, date accessed 10 February 2021.
Projects belonging to the same allocated group may have correlated errors, thus violating the conditionally uncorrelated errors assumption in regression. This potential problem is addressed by estimating Eq. 1 using cluster-robust standard errors that allow for intragroup correlation.
Co-financing contributions for GEF-SGP CCA projects in PICs are lower than the co-financing contributions for GEF-SGP projects over the period 1992–2014, as reported earlier. This may reflect differences in funding capacity and/or the CCA focus.
Further sub-division of projects by, for example, individual countries or cultural groupings would have been problematic because such inclusion would further erode degrees of freedom in the statistical estimation.
The variance-covariance matrix of the parameter estimates is specified to allow dependence between observations from the same country group. This produces robust standard errors that are considered more appropriate for clustered datasets (Cameron, A. C., Trivedi 2009).
For example, Infrastructure Australia’s project Assessment Framework devotes a complete chapter towards understanding and assessing risk (Infrastructure Australia 2018).
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We are grateful for the assistance of C. Saint-Ange in producing the map.
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Hasan, S., Fleming, C.M., Smart, J.C.R. et al. Identifying and mitigating risks to completion of small grant climate change adaptation projects: evidence from the Pacific. Reg Environ Change 21, 55 (2021). https://doi.org/10.1007/s10113-021-01781-3
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DOI: https://doi.org/10.1007/s10113-021-01781-3