Methodological approaches to monitoring the environmental efficiency of logistics in the agri-food sector based on the use of digital technologies

Анотація

Introduction. The article substantiates that modern agri-logistics is transforming into a complex digital ecosystem, where monitoring environmental efficiency becomes a critical task due to climate challenges and stringent environmental constraints. It is proven that logistics operations are a significant source of anthropogenic impact, particularly through food losses and greenhouse gas emissions. It is argued that for Ukraine, as one of the leading players in the global market, the development of methodological approaches to monitoring the environmental efficiency of logistics acquires strategic importance. This is обусловлено both the need to restore logistics chains in the post-war period and the requirements of European integration, in particular adaptation to the European Green Deal and the Carbon Border Adjustment Mechanism (CBAM). It is demonstrated that substantiating methodological approaches to assessing the environmental efficiency of agri-logistics on the basis of digitalization is a relevant task that will contribute to increasing the competitiveness of domestic agribusiness. Purpose. The purpose of the study is to substantiate methodological approaches to monitoring the environmental efficiency of logistics processes in the agri-food sector through the implementation of digital tools to ensure sustainable development and transparency of supply chains. Methods (Methodology). To achieve the research objectives, a set of methods is applied, including analysis and synthesis for studying the conceptual foundations of digitalization, a systemic approach for forming a multi-level monitoring system, and methods of induction and deduction for developing a system of environmental efficiency indicators. Results. It is substantiated that the basis for monitoring the environmental efficiency of agri-logistics is the creation of an integrated digital platform that combines data flows from Internet of Things (IoT) sensors, vehicle telemetry systems, and external analytical services. It is demonstrated that route optimization using machine learning algorithms (in particular Random Forest and Genetic Algorithms) allows reducing transport costs, which directly leads to a decrease in CO₂ emissions. The methodology for the quantitative assessment of the carbon footprint of logistics operations in accordance with the requirements of the international standard ISO 14083 is systematized. The algorithm for calculating the energy intensity of transportation is refined, which makes it possible to differentiate emissions by categories (Scope 1, 2, and especially Scope 3). It is established that for agri-logistics companies it is essential to organize monitoring of indirect emissions (Scope 3) generated by third-party logistics operators.

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