European Union’s Regulation 2023/115, commonly known as the European Union Deforestation Regulation (EUDR), promises to be a watershed event in global deforestation governance. A significant example of the hardening of soft law, spurred by major corporations committing to zero-deforestation supply chains, the EUDR is also a substantial wager on the efficacy of satellite-based remote sensing technologies for effective global forest governance. As remote sensing becomes more deeply embedded into global environmental governance, it is necessary to pay attention to the possibility that misclassification errors - mistaking one type of land cover for another - could become institutional errors with real consequences for those targeted by these initiatives. If compliant producers were to be excluded from zero-deforestation markets due to uncertainties resulting from misclassification errors, this would raise questions about the initiative’s access equity. To develop recommendations for a strategy for avoiding this eventuality, we examine how classification errors could shape the EUDR’s effects in the coffee sector. Coffee, a commodity predominantly cultivated for export by smallholders under tree shade, faces heightened susceptibility to the legislation, given the European market’s significant influence on global consumption. Using ground-truth points collected in coffee-growing regions in Ethiopia and Tanzania, combined with other open datasets, we assess the rate at which five global land cover datasets identify coffee production as forest, finding high rates of misclassification in some geographies, particularly for shade-grown and agroforestry cultivation. Then, following a systematic review of remote sensing studies designed to detect the presence of coffee, we use quantile regression analysis to identify strategies that could be used to reduce classification accuracy for coffee to unproblematic rates. Based on these assessments, we argue that, even in a hard case like coffee, access inequities due to misclassification errors could be mitigated substantially by starting with a global dataset and then building regional, commodity-specific datasets. We suggest that finding ways to compensate and include smallholders, cooperatives, and other producer groups in a project of building monitoring datasets as a public good may be an appropriate strategy for the EUDR and similar zero-deforestation initiatives.

中文翻译：

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避免零森林砍伐价值链中分类错误导致的获取不平等：咖啡和欧盟森林砍伐法规

欧盟的第 2023/115 号法规，通常称为欧盟森林砍伐条例 （EUDR），有望成为全球森林砍伐治理的分水岭事件。在大公司承诺零森林砍伐供应链的推动下，EUDR 是软法硬化的一个重要例子，它也是对基于卫星的遥感技术对有效全球森林治理的有效性的重大赌注。随着遥感越来越深入地嵌入全球环境治理，有必要注意错误分类错误（将一种类型的土地覆盖误认为另一种类型）可能成为制度错误的可能性，对这些倡议的目标产生实际后果。如果由于错误分类错误导致的不确定性而将合规生产者排除在零森林砍伐市场之外，这将引发对该倡议的获取公平性的质疑。为了制定避免这种可能性的策略建议，我们研究了分类错误如何影响 EUDR 对咖啡行业的影响。咖啡是一种主要由小农户在树荫下种植出口的商品，鉴于欧洲市场对全球消费的重大影响，咖啡面临更高的立法敏感性。使用在埃塞俄比亚和坦桑尼亚咖啡种植区收集的地面实况点，结合其他开放数据集，我们评估了五个全球土地覆盖数据集将咖啡生产识别为森林的速度，发现在某些地区错误分类率很高，特别是对于遮荫种植和农林业种植。 然后，在对旨在检测咖啡存在的遥感研究进行系统评价之后，我们使用分位数回归分析来确定可用于将咖啡分类准确性降低到没有问题的比率的策略。基于这些评估，我们认为，即使在像 coffee 这样的困难案例中，也可以通过从全球数据集开始，然后构建区域性的、特定于商品的数据集，大大缓解由于错误分类错误而导致的访问不平等。我们建议，寻找补偿的方法，将小农、合作社和其他生产者团体纳入构建监测数据集作为公共产品的项目中，可能是 EUDR 和类似的零森林砍伐倡议的适当策略。