A Framework for Integrating Carbon Accounting Standards into Decision Support Structures in Logistics

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:This paper provides a formal method to link carbon accounting standards with decision models, suggesting avenues for further optimization and stochastic extensions.

🏢実務担当者:Logistics managers can use this framework to design carbon-aware decision support systems that align with GHG Protocol and ISO 14064 requirements.

🏛政策担当者:Policymakers may consider this approach for standardizing logistics emissions reporting and ensuring consistency with verification requirements.

This paper proposes a methodological framework for linking standardized carbon footprint reporting with structured decision support in logistics. The approach integrates the GHG Protocol framework and the ISO 14064 standard in order to formalize emissions inventories, reporting requirements, and verification constraints into a coherent, transparent, and auditable analytical structure. While existing standards provide robust guidance for the quantification and reporting of greenhouse gas emissions, their systematic integration into decision support representations remains limited. The main contribution of the paper consists of the formal operationalization of carbon accounting processes into decision variables, constraints, and performance indicators that preserve traceability, transparency, and compatibility with external verification requirements. A simplified linear programming formulation is employed as a standard-driven decision support abstraction, illustrating how emissions-related data derived from standardized reporting can be consistently translated into operational constraints and analytical indicators. The mathematical formulation is not intended to replace detailed logistics optimization models, but to demonstrate the methodological linkage between emissions reporting, verification requirements, and structured decision-oriented analysis. The proposed framework is illustrated through a logistics hub case study using average emission factors and estimated consumption data. The numerical results serve an illustrative purpose and highlight the functioning of the framework, rather than providing fully calibrated operational solutions. The methodology is designed to be reproducible and auditable and may be extended to other industrial sectors, as well as to more advanced modeling settings incorporating dynamic or stochastic elements.

• semanticscholar https://doi.org/10.3390/su18031542first seen 2026-05-06 00:46:07