Predictive sustainability in agriculture: Machine learning analysis of active ingredient restrictions and bans

Developing active ingredients for the global market requires substantial investment, often exceeding 300 million euros. This process takes an average of 12 years from initiation to commercialization. Despite this lengthy timeline, the industry frequently encounters significant restrictions and bans on active ingredients due to stringent international regulations and evolving environmental safety requirements. In this context, the analysis of regulatory lists using advanced machine learning and statistical modeling techniques becomes crucial for identifying the key parameters that influence the restriction and banning of active ingredients. This study aims to provide insights that enhance decision-making processes, thereby contributing to sustainability by reducing unnecessary environmental research and development efforts. The findings indicate that Governmental and Non-Governmental Organizations, as well as Blacklists, are key influencers in the restriction and ban of active ingredients for agricultural use, with Codex Alimentarius acting as a regional influencer depending on the specific country. Ultimately, the insights derived from this research can assist industries and policymakers in developing more effective regulatory strategies, promoting sustainable practices, and ensuring that new active ingredients are selected based on comprehensive and informed criteria that consider both safety and environmental impact.