Understanding compliance with voluntary sustainability standards: a machine learning approach

Abstract

Voluntary sustainability standards are quickly gaining ground. Whether and how they work in the field, however, remains largely unclear. This is troubling for standards organizations since it hinders the improvement of their standards to achieve a higher impact. One reason why it is difficult to understand the mechanics of VSS is heterogeneity in compliance. We apply machine learning techniques to analyze compliance with one particular VSS: Rainforest Alliance-for which we have detailed audit data for all certified coffee and cocoa producers. In a first step, we deploy a k-modes algorithm to identify four clusters of producers with similar non-compliance patterns. In a second step, we match a large array of data to the producers to identify drivers of non-compliance. Our findings help VSS to implement targeted training or risk assessment using prediction. Further, they are a starting point for future causal analyses.