The transition toward climate-neutral development in the European Union has intensified the need for structured and data-driven approaches to sustainability assessment in agriculture. Despite extensive research on environmental performance in livestock systems, empirical models explicitly linking managerial cost structure to ESG sustainability outcomes at a cross-country level remain limited. This study develops an integrated analytical framework to examine whether cost allocation patterns and energy structure systematically determine sustainability profiles in the EU livestock sector. The empirical analysis is based on a harmonized panel dataset covering Germany, France, the Netherlands, Spain, and Poland for the period 2000-2023. A multi-stage modelling approach combining k-means and fuzzy clustering, regression analysis, and Random Forest classification was implemented in MATLAB. The clustering stage identified three stable ESG structural profiles. Regression results indicate that energy share and environmental investment allocation significantly explain variation in GHG intensity (R² = 0.9173). Machine learning classification further confirms the structural importance of energy-related cost components in differentiating sustainability classes. The findings demonstrate that sustainability performance in livestock systems is not random but structurally embedded in managerial cost architecture. The study contributes to sustainability accounting by integrating ESG metrics with sector-level cost indicators and provides a scalable analytical framework for sustainability benchmarking and policy evaluation in the EU agri-food sector.
K. R. Lucas and E. Kebreab, “Modeling livestock ecosystem services through carbon footprint,” Ecological Indicators, vol. 181, art. no. 114406, 2025, [Online]. Available: https://doi.org/10.1016/j.ecolind.2025.114406.
X. Kang et al., “Livestock methane emissions in China: Spatiotemporal dynamics and mitigation strategies,” Resources, Conservation and Recycling, vol. 226, art. no. 108695, 2026, [Online]. Available: https://doi.org/10.1016/j.resconrec.2025.108695.
C. Duffy, C. Doedens, R. Moriarty, H. Daly, D. Styles, and M. Meinshausen, “National temperature neutrality, agricultural methane and climate policy: Reinforcing inequality in the global food system,” Environmental Research Letters, vol. 20, no. 9, art. no. 094019, 2025, [Online]. Available: https://doi.org/10.1088/1748-9326/adf12d.
F. Zahm et al., “Assessing farm sustainability: The IDEA4 method, a conceptual framework combining dimensions and properties of sustainability,” Cahiers Agricultures, vol. 33, art. no. 10, 2024, [Online]. Available: https://doi.org/10.1051/cagri/2024001.
T. Deng and Z. F. Mohamad, “Context-sensitive agricultural sustainability assessment: A systematic review of frameworks and local adaptation criteria,” Agricultural Systems, vol. 231, art. no. 104532, 2026, [Online]. Available: https://doi.org/10.1016/j.agsy.2025.104532.
S. R. Ndemewah, K. Menges, and M. R. Hiebl, “Management accounting research on farms: What is known and what needs knowing?,” Journal of Accounting & Organizational Change, vol. 15, no. 1, pp. 58-86, 2019, [Online]. Available: https://doi.org/10.1108/JAOC-05-2018-0044.
G. Sariyer, M. Sarioglu, and B. Ramtiyal, “Machine learning-driven clustering based environmental, social, and governance performance prediction model,” in The Unified International Conference on Emerging Technologies in Cyber-Physical Systems and Industrial AI, Cham: Springer Nature Switzerland, 2024, pp. 661-675, [Online]. Available: https://doi.org/10.1007/978-3-031-95963-9_46.
J. Zhang and Z. Zhao, “Corporate ESG rating prediction based on XGBoost-SHAP interpretable machine learning model,” Expert Systems with Applications, vol. 295, art. no. 128809, 2026, [Online]. Available: https://doi.org/10.1016/j.eswa.2025.128809.
A. Yenkikar, V. P. Mishra, M. Bali, and T. Ara, “An explainable AI-based hybrid machine learning model for interpretability and enhanced crop yield prediction,” MethodsX, art. no. 103442, 2025, [Online]. Available: https://doi.org/10.1016/j.mex.2025.103442.
Y. W. Li, “Spatiotemporal investigation into water, land, energy, and carbon footprints of broiler and correlations among them in China,” Discover Sustainability, vol. 6, no. 1, art. no. 794, 2025, [Online]. Available: https://doi.org/10.1007/s43621-025-01760-2.