Effects of integrated conservation–development projects on unauthorized resource use in Volcanoes National Park, Rwanda: a mixed-methods spatio-temporal approach

Abstract

This study supplements spatial panel econometrics techniques with qualitative GIS to analyse spatio-temporal changes in the distribution of integrated conservation–development projects relative to poaching activity and unauthorized resource use in Volcanoes National Park, Rwanda. Cluster and spatial regression analyses were performed on data from ranger monitoring containing > 35,000 combined observations of illegal activities in Volcanoes National Park, against tourism revenue sharing and conservation NGO funding data for 2006–2015. Results were enriched with qualitative GIS analysis from key informant interviews. We found a statistically significant negative linear effect of overall integrated conservation–development investments on unauthorized resource use in Volcanoes National Park. However, individually, funding from Rwanda's tourism revenue sharing policy did not have an effect in contrast to the significant negative effect of conservation NGO funding. In another contrast between NGO funding and tourism revenue sharing funding, spatial analysis revealed significant gaps in revenue sharing funding relative to the hotspots of illegal activities, but these gaps were not present for NGO funding. Insight from qualitative GIS analysis suggests that incongruity in prioritization by decision makers at least partly explains the differences between the effects of revenue sharing and conservation NGO investment. Although the overall results are encouraging for integrated conservation–development projects, we recommend increased spatial alignment of project funding with clusters of illegal activities, which can make investment decision-making more data-driven and projects more effective for conservation.

Proximate Causes of Forest Degradation in the Democratic Republic of the Congo Vary in Space and Time

“…With the inclusion of the spatial neighborhood, we find a weak positive impact of protected area, with a greater negative effect from surrounding protected area. This could show that in the context of a larger area, protected areas might displace disturbances to 25-50 km beyond their borders, where they can attract development and similar activities when local communities benefit from protected areas, or use its resources, indicating a potential leakage effect (Sabuhoro et al, 2017;Bernhard et al, 2020).…”

“…Although deforestation and degradation can be closely correlated (Defourny et al, 2011), they differ fundamentally in terms of definition and impacts on ecosystem services. The quantification of drivers of deforestation and degradation is not only important for targeting national strategies to reduce the emissions from deforestation and degradation (REDD+), but have wide applications to sustainable development initiatives supporting local economies as well as conservation efforts seeking to reverse or slow the significant downward trends in forest cover and quality (Bernhard et al, 2020). A proper understanding of the proximate causes and determinants of degradation is essential for aligning policies with the appropriate actors (Tegegne et al, 2016), but available quantitative information on degradation drivers and how they interact at various scales is still quite limited.…”

“…As forest degradation is dynamic, so must be the proximate causes and spatial determinants to capture the variations in time and space. Spatial econometrics techniques and their application to conservation and development enable research controlling spatial and temporal components via spatial panel data, which are a spatial cross-section of observation repeated over time (Baylis et al, 2011;Bernhard et al, 2020). The observations in a spatial panel can be correlated in time (repeated observations that may be dependent on a previous date) but also in space (neighborhood interaction; (Molinario et al, 2020).…”

Understanding and predicting socioeconomic determinants of deforestation in Vietnam’s Central Annamites Landscape (CAL): Pilot study implementing a spatial econometric approach

“…We modeled underlying macroeconomic determinants of deforestation using the following simple linear OLS estimation with spatially lagged explanatory variables: (Bernhard et al, 2020;Gibbons et. al, 2012;Angelsen and Kaimowitz, 1999) where '(## #$ is tree cover loss by district ) and year *.…”

“…FG(( &' = Q & + R ' + S &+' + P &+' (Bernhard et al, 2020;Hsiang and Sekar, 2016) 4 # is the sector fixed effect, 5 $ is time (year). 6 #%$ is a vector of time-varying socioeconomic conditions at district level ), and provincial level 7, including poverty rates, education levels, proxy for off-farm wages (proximity to urban area), proxy for human development (nighttime lights) at time *.…”