Regenerative Supply Chain: An Analytical Model for Balancing Capital, Ecosystem and Social Community in Coffee and Sugar Cane

The agricultural sector in Mexico, specifically the coffee and sugarcane supply chains, faces the critical challenge of reconciling economic profitability with environmental sustainability and rural social progress. This study presents a critical literature review and conceptual framework that evaluates existing analytical models and proposes methodological integration pathways to simultaneously optimize Triple bottom line (TBL) dimensions in vulnerable smallholder systems. Unlike prior reviews that focus on generic Sustainable Supply chain management (SSCM) practices, this work explicitly addresses the suitability and limitations of multi-objective optimization (MOO) and Life cycle assessment (LCA) for regenerative supply chain objectives in the Mexican coffee and sugarcane context. A critical review of 76 core articles published between 2020 and 2025 was conducted, employing comparative evaluation criteria and narrative synthesis to assess trade-offs, data requirements, and scalability constraints. The review reveals that while agricultural intensification often exacerbates environmental degradation, the adoption of sustainable practices can impose significant financial burdens on vulnerable smallholders. However, analytical models like MOO and LCA serve as robust decision-support systems that effectively evaluate trade-offs and balance competing economic, environmental, and social objectives by identifying optimal production scenarios. The contribution of this work is threefold: (1) a critical synthesis distinguishing regenerative from sustainable supply chain paradigms, (2) a comparative assessment of analytical model applicability to smallholder contexts, and (3) a conceptual framework integrating local socioeconomic realities, traditional knowledge, and modern technological approaches. Fostering resilient supply chains in Mexico requires customized analytical frameworks that explicitly operationalize social indicators, address data limitations, and enable cross-sector collaboration. Ultimately, localized models are essential to simultaneously enhance rural livelihoods, reduce carbon footprints, and maintain economic viability.