Using community monitoring to decrease deforestation in Uganda
April 2017 - Present
Deforestation is an important contributor to world-wide greenhouse gas emissions: it is estimated that deforestation is responsible for 12% of anthropogenic carbon emissions (van der Werf et al., 2009). As such, reducing deforestation is considered one of the most cost-effective ways of reducing carbon emissions (Stern, 2006).
Deforestation is an important problem in Uganda, with some coining the deforestation rate in Uganda as the “highest in East Africa” (Banana, Bukenya, Arinaitwe, Birabwa, & Ssekindi, 2012). 38 per cent of the land area in Uganda is covered by forest, and the annual deforestation rate in Uganda averaged 5.7% between 2001 and 2014. Deforestation and land use change contribute 37.6 percent to Uganda’s Greenhouse Gas emissions in 2011 (FAO data).
Busara, along with PIs from Oxford University won a grant from Evidence in Governance and Politics (EGAP) via a DFID funded call for proposals on how to use community monitoring to improve resource use.
We are currently engaged in a nation-wide study to create interventions to decrease forest use through monitoring, and to rigorously assess the impact.
There are two primary channels which our initial qualitative work identified as drivers of forest conservation.
Social norms -- Visibility of norms of other community members, and shared expectations of the community as a whole can drive behavior, either explicitly through sanctioning or implicitly. Empowering monitors to aggregate and enforce social norms are a powerful tool for forest conservation
Salience -- Making harvesting levels more salient can add certainty to information and help to reinforce social norms. Our intervention is designed to maximize salience and awareness of forest stocks, harvesting levels, and transgressions of harvesting norms.
Approach and Intervention
Community management of forests not covered by private property rights is subject to common pool problems (Hardin, 1968): it may be collectively rational for communities to harvest responsibly and maintain the forest stock, but individually rational to over-harvest. Key to maintaining the collectively rational level of harvesting are rules and rule enforcement, consisting of monitoring and sanctioning (Gibson, Williams, & Ostrom, 2005). As a result of our qualitative work, we focus on enforcement by the community of harvesters itself, rather than by an external party.
The planned community monitoring intervention trains and pays community members to assess aggregate forest use and makes this information available to the community. As such, the intervention is designed to:
- Decrease the cost of observing collective harvesting levels;
- Increase certainty about collective harvesting levels;
- Increase salience of collective harvesting levels.
This effect may occur through various channels. First, community monitoring decreases the cost of monitoring collective harvesting levels. As such, it may improve forest conservation practices, if before the intervention, these costs were the single binding constraint on the community’s ability to enforce a level of harvesting closer to the collective optimum. A second channel through which the community monitoring intervention may affect forest conservation is by increasing certainty about and the salience of the aggregate harvesting level.
Other potential channels link community monitoring to improved community forest management. Literature suggests that community monitoring may lead to more inclusive decision-making or increased learning (Evans & Guariguata, 2008), community monitoring may build social capital (Evans & Guariguata, 2008), which may affect enforcement costs by enabling stronger non-pecuniary sanctions. Lastly, through communicating the results of the community monitoring of forest use, it is possible that the proposed study might generate a prescriptive effect.
We're currently launching a Randomized Controlled Trials in forest areas around Uganda to evaluate the effect of the interventions we've designed. Results will be available late 2018 / early 2019.