This study explores the judicial process involved in criminal cases of protected forest destruction, specifically focusing on the differences in how first-level and cassation-level judges approach the decision-making process. It identifies the factors that influence judicial considerations at each level of the judicial hierarchy and seeks to understand how these differences impact the effectiveness of legal outcomes in environmental cases. The analysis is grounded in environmental law and highlights the implications of legal interpretation, judicial consistency, and legal precedent in the context of criminal cases involving deforestation and illegal logging activities. Based on a qualitative review of court cases, interviews with judges, and legal scholars, the research provides insights into the role of cassation courts in shaping national environmental policy.

The study concludes that first-level courts tend to focus more on the facts of individual cases, delivering more immediate consequences such as fines or imprisonment. In contrast, cassation courts prioritize legal interpretation, ensuring uniformity in the application of the law, which influences the long-term development of environmental jurisprudence. These insights are crucial in understanding the broader role of judicial systems in the enforcement of environmental laws and the protection of natural resources.

The rapid evolution of Artificial Intelligence (AI) has profoundly reshaped how supply chains are conceptualized, managed, and optimized. This paper synthesizes extant literature to propose a unified, hierarchical framework for AI-driven supply chain planning that integrates demand forecasting, real‑time visibility, inventory and logistics optimization, and sustainability considerations. Drawing on empirical and conceptual studies—including hierarchical neural‑network planning, supply‑chain visibility models, and systematic reviews of AI adoption—the framework aims to address critical research gaps in current practices. Through a detailed, structured literature review, this study examines how AI techniques such as artificial neural networks (ANNs), machine learning (ML), and advanced analytics contribute to base‑level outcomes (e.g., demand forecasting, inventory control), mid‑level orchestration (e.g., logistics routing, replenishment scheduling), and high-level strategic objectives (e.g., sustainability, resilience, service-level optimization). Key findings reveal that AI-driven supply chain management (SCM) enhances responsiveness, reduces waste, and improves resource utilization, but also faces barriers including data quality, system interoperability, organizational readiness, and social considerations. The discussion explores theoretical implications, practical challenges, and future research directions—highlighting the need for longitudinal empirical validation, hybrid human–AI decision processes, and standardization of performance metrics. This paper contributes to supply chain theory by offering a comprehensive, multi-layered conceptual model that bridges short-term operational gains and long-term strategic sustainability goals via AI adoption.