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Coffee Certification and Forest Quality: Evidence from a Wild Coffee Forest in Ethiopia

Ryo Takahashi () and Yasuyuki Todo

World Development, 2017, vol. 92, issue C, 158-166

Abstract: Shade coffee certification programs that aim to conserve the forest and to prevent forest degradation have attracted an increasing amount of attention. However, such programs’ impact on forest degradation remains unclear because of the absence of empirical evidence. In addition, there is heated debate about whether certification programs create an incentive for producers to expand their coffee-growing areas, which may accelerate forest degradation in the surrounding natural forest. This study, which was conducted in Ethiopia, aimed to evaluate the impact of a shade coffee certification program on forest degradation. Additionally, to provide empirical evidence for the debate, we examined the spillover effects of certification to surrounding forest areas and used remote sensing data of 2005 and 2010 to classify forest areas based on their density. We applied matching methods, such as the propensity score matching with different algorithms, to compare forest coffee areas with and without the certification. We found that the certified forest coffee areas slightly increased in forest density. By contrast, we observed drastic forest degradation in the forest coffee areas without certification. We checked the sensitivity of our results and found that our results are robust to potential hidden bias. Furthermore, our empirical results revealed that the natural forest areas within a 100-m radius from the forest coffee boundary area exhibited significantly reduced forest degradation compared with forest areas under similar environmental conditions but that such positive and significant impact diminished after 100-m distance. These results indicate that the certification program is effective in alleviating forest degradation in the certified area and in the surrounding natural forest.

Keywords: shade coffee; coffee certification; impact evaluation; remote sensing; Ethiopia; Africa (search for similar items in EconPapers)
Date: 2017
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