Logistics Performance and ESG Outcomes: An Empirical Exploration Using IV Panel Models and Machine Learning

Magaletti, Nicola and Notarnicola, Valeria and Di Molfetta, Mauro and Mariani, Stefano and Leogrande, Angelo (2025): Logistics Performance and ESG Outcomes: An Empirical Exploration Using IV Panel Models and Machine Learning.

Abstract

This study investigates the complex relationship between the performance of logistics and Environmental, Social, and Governance (ESG) performance drawing upon the multi-methodological framework of combining econometric with state-of-the-art machine learning approaches. Employing IV panel data regressions, viz. 2SLS and G2SLS, with data from a balanced panel of 163 countries covering the period from 2007 to 2023, the research thoroughly investigates how the performance of the Logistics Performance Index (LPI) is correlated with a variety of ESG indicators. To enrich the analysis, machine learning models—models based upon regression, viz. Random Forest, k-Nearest Neighbors, Support Vector Machines, Boosting Regression, Decision Tree Regression, and Linear Regressions, and clustering, viz. Density-Based, Neighborhood-Based, and Hierarchical clustering, Fuzzy c-Means, Model Based, and Random Forest—were applied to uncover unknown structures and predict the behaviour of LPI. Empirical evidence suggests that higher improvements in the performance of logistics are systematically correlated with nascent developments in all three dimensions of the environment (E), the social (S), and the governance (G). The evidence from econometrics suggests that higher LPI goes with environmental trade-offs such as higher emissions of greenhouse gases but cleaner air and usage of resources. On the S dimension, better performance in terms of logistics is correlated with better education performance and reducing child labour, but also demonstrates potential problems such as social imbalances. For G, better governance of logistics goes with better governance, voice and public participation, science productivity, and rule of law. Through both regression and cluster methods, each of the respective parts of ESG were analyzed in isolation, allowing to study in-depth how the infrastructure of logistics is interacting with sustainability research goals. Overall, the study emphasizes that while modernization is facilitated by the performance of the infrastructure of logistics, this must go hand in hand with policy intervention to make it socially inclusive, environmentally friendly, and institutionally robust.

• Item Type: MPRA Paper
• Language: English
• Keywords: Logistics Performance Index (LPI), Environmental Social and Governance (ESG) Indicators, Panel Data Analysis, Instrumental Variables (IV) Approach, Sustainable Economic Development.
• Subjects: C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C33 - Panel Data Models ; Spatio-temporal Models; F - International Economics > F1 - Trade > F14 - Empirical Studies of Trade; M - Business Administration and Business Economics ; Marketing ; Accounting ; Personnel Economics > M1 - Business Administration > M14 - Corporate Culture ; Diversity ; Social Responsibility; O - Economic Development, Innovation, Technological Change, and Growth > O1 - Economic Development > O18 - Urban, Rural, Regional, and Transportation Analysis ; Housing ; Infrastructure; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics > Q56 - Environment and Development ; Environment and Trade ; Sustainability ; Environmental Accounts and Accounting ; Environmental Equity ; Population Growth

References

Akram, M. W., Hafeez, M., Yang, S., Sethi, N., Mahar, S., & Salahodjaev, R. (2023). Asian logistics industry efficiency under low carbon environment: policy implications for sustainable development. Environmental Science and Pollution Research, 30(21), 59793-59801.

Al Bony, M. N. V., Das, P., Pervin, T., Shak, M. S., Akter, S., Anjum, N., ... & Rahman, M. K. (2024). COMPARATIVE PERFORMANCE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR BUSINESS INTELLIGENCE: A STUDY ON CLASSIFICATION AND REGRESSION MODELS. Frontline Marketing, Management and Economics Journal, 4(11), 72-92.

Ali, H., & Zafar, M. B. The ESG Code: A Multi-Method Review of Ai in Sustainable Finance. Available at SSRN 5205753.

Altın, F. G., Gürsoy, S., Doğan, M., & Ergüney, E. B. (2023). The Analysis of the Relationship Among Climate Policy Uncertainty, Logistic Firm Stock Returns and ESG Scores: Evidence from the TVP-VAR Model. İstatistik Araştırma Dergisi, 13(2), 42-59.

Auliani, S. N., Novita, R., & Afdal, M. (2024). Implementation of Density-Based Spatial Clustering of Applications with Noise and Fuzzy C–Means for Clustering Car Sales. The Indonesian Journal of Computer Science, 13(4).

Baciu, L. E. (2023). The impact of governance upon sustainable development. Empirical evidence. Studia Universitatis Babes Bolyai-Oeconomica, 68(2), 73-86.

Barykin, S. E., Strimovskaya, A. V., Sergeev, S. M., Borisoglebskaya, L. N., Dedyukhina, N., Sklyarov, I., ... & Saychenko, L. (2023). Smart city logistics on the basis of digital tools for ESG goals achievement. Sustainability, 15(6), 5507.

Bicego, M., & Escolano, F. (2021, January). On learning random forests for random forest-clustering. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 3451-3458). IEEE.

Binzaiman, F., Edhrabooh, K. M., Alromaihi, M., & AlShammari, M. (2024, October). Predicting Environmental, Social, and Governance Scores with Machine Learning: A Systematic Literature Review. In 2024 5th International Conference on Data Analytics for Business and Industry (ICDABI) (pp. 117-122). IEEE.

Błaszczyk, A., & Le Viet-Błaszczyk, M. (2024). The role of social media marketing of ESG in warehouse logistics. Zeszyty Naukowe. Organizacja i Zarządzanie/Politechnika Śląska.

Bo, P. (2024). The Impact of Digital Technology Application on Logistics Enterprise ESG Performance in VUCA Environment: Base on the Moderated Mediation Model. Journal of Roi Kaensarn Academi, 9(11), 1530-1548.

Borisova, V., & Pechenko, N. (2021). Sustainable Development of Logistic Infrastructure of the Region. In E3S Web of Conferences (Vol. 295, p. 01042). EDP Sciences.

Boukrouh, I., Tayalati, F., & Azmani, A. (2024, August). Comparative SHAP Analysis on SVM and K-NN: Impacts of Hyperparameter Tuning on Model Explainability. In 2024 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) (pp. 194-198). IEEE.

Bowden, J., Bornkamp, B., Glimm, E., & Bretz, F. (2021). Connecting Instrumental Variable methods for causal inference to the Estimand Framework. Statistics in medicine, 40(25), 5605-5627.

Burcă, V., Bogdan, O., Bunget, O. C., Dumitrescu, A. C., & Imbrescu, C. M. (2024). Financial Implications of Supply Chains Transition to ESG Models. Exploring ESG Challenges and Opportunities: Navigating Towards a Better Future, 127-143.

Cheng, D., Xu, Z., Li, J., Liu, L., Liu, J., & Le, T. D. (2023). Conditional instrumental variable regression with representation learning for causal inference. arXiv preprint arXiv:2310.01865.

Chiang, K. L. (2024). Delivering Goods Sustainably: A Fuzzy Nonlinear Multi-Objective Programming Approach for E-Commerce Logistics in Taiwan. Sustainability, 16(13), 5720.

Chien, F. (2023). The role of corporate governance and environmental and social responsibilities on the achievement of sustainable development goals in Malaysian logistic companies. Economic research-Ekonomska istraživanja, 36(1), 1610-1630.

CONSTĂNGIOARĂ, A., & Florian, G. L. Is Logistics Mediating The Relationship Between Pollution And Economic Complexity?.

Coto-Millán, P., Paz Saavedra, D., de la Fuente, M., & Fernandez, X. L. (2024). Integrating Logistics into Global Production: A New Approach. Logistics, 8(4), 99.

Das, A. (2024). Predictive value of supply chain sustainability initiatives for ESG performance: a study of large multinationals. Multinational Business Review, 32(1), 20-40.

Dos Santos, M. C., & Pereira, F. H. (2022). ESG performance scoring method to support responsible investments in port operations. Case Studies on Transport Policy, 10(1), 664-673.

Dou, X., & Yin, S. (2024). The impact of ESG on corporate financial performance: Based on fixed effects regression model. Journal of Computational Methods in Science and Engineering, 24(4-5), 2719-2731.

Effiong, U. E., Udofia, L. E., & Garba, I. H. (2023). Governance and economic development in West Africa: Linking governance with economic misery. Path of Science, 9(6), 2009-2025.

Fan, M., Tang, Y., Qalati, S. A., & Ibrahim, B. (2025). Can logistics enterprises improve their competitiveness through ESG in the context of digitalization? Evidence from China. The International Journal of Logistics Management, 36(1), 196-224.

Fatimah, Y. A., Kannan, D., Govindan, K., & Hasibuan, Z. A. (2023). Circular economy e-business model portfolio development for e-business applications: Impacts on ESG and sustainability performance. Journal of Cleaner Production, 415, 137528.

Filassi, M., Oliveira, A. L. R. D., Elias, A. A., & Braga Marsola, K. (2022). Analyzing complexities in the Brazilian soybean supply chain: a systems thinking and modeling approach. RAUSP Management Journal, 57, 280-297.

Fu, X., Feng, L., & Zhang, L. (2022). Data-driven estimation of TBM performance in soft soils using density-based spatial clustering and random forest. Applied Soft Computing, 120, 108686.

Gagolewski, M., Bartoszuk, M., & Cena, A. (2021). Are cluster validity measures (in) valid?. Information Sciences, 581, 620-636.

Gebreyesus, Y., Dalton, D., Nixon, S., De Chiara, D., & Chinnici, M. (2023). Machine learning for data center optimizations: feature selection using Shapley additive exPlanation (SHAP). Future Internet, 15(3), 88.

Ghezelbash, R., Daviran, M., Maghsoudi, A., & Hajihosseinlou, M. (2025). Density based spatial clustering of applications with noise and fuzzy C-means algorithms for unsupervised mineral prospectivity mapping. Earth Science Informatics, 18(2), 217.

Gholami, H., Mohammadifar, A., Bui, D. T., & Collins, A. L. (2020). Mapping wind erosion hazard with regression-based machine learning algorithms. Scientific Reports, 10(1), 20494.

Göçer, A., Özpeynirci, Ö., & Semiz, M. (2022). Logistics performance index-driven policy development: An application to Turkey. Transport policy, 124, 20-32.

Govindan, K., Karaman, A. S., Uyar, A., & Kilic, M. (2023). Board structure and financial performance in the logistics sector: Do contingencies matter?. Transportation Research Part E: Logistics and Transportation Review, 176, 103187.

Gündoğdu, H. G., Aytekin, A., Toptancı, Ş., Korucuk, S., & Karamaşa, Ç. (2023). Environmental, social, and governance risks and environmentally sensitive competitive strategies: A case study of a multinational logistics company. Business Strategy and the Environment, 32(7), 4874-4906.

Guo, J., Dong, R., Zhang, R., Yang, F., Wang, Y., & Miao, W. (2025). Interpretable machine learning model for predicting the prognosis of antibody positive autoimmune encephalitis patients. Journal of Affective Disorders, 369, 352-363.

Gupta, A., Sharma, U., & Gupta, S. K. (2021, December). The role of ESG in sustainable development: An analysis through the lens of machine learning. In 2021 IEEE international humanitarian technology conference (IHTC) (pp. 1-5). IEEE.

Gürler, H. E., Özçalıcı, M., & Pamucar, D. (2024). Determining criteria weights with genetic algorithms for multi-criteria decision making methods: The case of logistics performance index rankings of European Union countries. Socio-Economic Planning Sciences, 91, 101758.

Hasanah, U., Soleh, A. M., & Sadik, K. (2024). Effect of Random Under sampling, Oversampling, and SMOTE on the Performance of Cardiovascular Disease Prediction Models. Jurnal Matematika, Statistika Dan Komputasi, 21(1), 88-102.

Hossen, M. B., & Auwul, M. R. (2020). Comparative study of K-means, partitioning around medoids, agglomerative hierarchical, and DIANA clustering algorithms by using cancer datasets. Biomedical Statistics and Informatics, 5(1), 20-25.

Ilyas, M. (2024). Unveiling the education paradox: Conflict, pandemic and schooling in Kashmir. International Review of Education, 1-23.

Jenifel, M. G., Jasmine, R. A., & Umanandhini, D. (2024, June). Bitcoin Price Predictive Dynamics Using Machine Learning Models. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-6). IEEE.

Jomthanachai, S., Wong, W. P., & Khaw, K. W. (2022). An application of machine learning regression to feature selection: a study of logistics performance and economic attribute. Neural Computing and Applications, 34(18), 15781-15805.

Juvvala, R., Sangle, S., & Tiwari, M. K. (2025). Post-Covid challenges and opportunities: rethinking ESG performance in the logistics sector. International Journal of Production Research, 63(4), 1256-1274.

Kanno, M. (2023). Does ESG performance improve firm creditworthiness?. Finance Research Letters, 55, 103894.

Kara, K. (2023). Clustering of Developing Countries in Terms of Logistics Market Development with Fuzzy Clustering and Discriminant Analysis. Yaşar Üniversitesi E-Dergisi, 18(69), 19-40.

Karaduman, H. A., Karaman-Akgül, A., Çağlar, M., & Akbaş, H. E. (2020). The relationship between logistics performance and carbon emissions: an empirical investigation on Balkan countries. International Journal of Climate Change Strategies and Management, 12(4), 449-461.

Kim, D., Na, J., & Ha, H. K. (2024). Exploring the impact of green logistics practices and relevant government policy on the financial efficiency of logistics companies. Heliyon, 10(10).

Kim, J., Kim, M., Im, S., & Choi, D. (2021). Competitiveness of E Commerce firms through ESG logistics. Sustainability, 13(20), 11548.

Kocabaş, M. B., Tashan, W., Shayea, I., & Alibek, M. (2024, December). Comparative Analysis of One-Dimensional Regression Techniques. In 2024 IEEE 16th International Conference on Computational Intelligence and Communication Networks (CICN) (pp. 1365-1370). IEEE.

Kudryavtseva, T., Rodionova, M., & Skhvediani, A. (2022, April). Event Study on the Stock Performance: The Case of US Logistics Companies. In International Scientific Conference “Digital Transformation on Manufacturing, Infrastructure & Service" (pp. 218-229). Cham: Springer Nature Switzerland.

Kurniawan, F., Musa, S. N., Nurfauzi, B., Ferdian, R., & Khair, F. (2024). Container Terminal Performance: System Dynamic Approach with Port Capacity Constraints and ESG Integration. Jordan Journal of Mechanical & Industrial Engineering, 18(1).

Lee, E. S. (2024). Evaluation of the Impact of ESG Practices on Financial Performance in Korean Small and Medium Logistics Companies. Asia-pacific Journal of Convergent Research Interchange (APJCRI), 237-248.

Lee, J. W., & Lee, H. S. (2022). An Analysis of ESG keywords in the logistics industry using SNA methodology: Using news article and sustainable management report. Korea Trade Review, 47(2), 121-132.

Lee, J., Lee, J., Lee, C., & Kim, Y. (2023). Identifying ESG trends of international container shipping companies using semantic network analysis and multiple case theory. Sustainability, 15(12), 9441.

Leogrande, A. (2024). Integrating ESG Principles into Smart Logistics: Toward Sustainable Supply Chains.

Li, W., & Wang, Y. (2024). A procurement advantage in disruptive times: New perspectives on ESG strategy and firm performance. Available at SSRN 4817562.

Li, X., Sohail, S., Majeed, M. T., & Ahmad, W. (2021). Green logistics, economic growth, and environmental quality: evidence from one belt and road initiative economies. Environmental Science and Pollution Research, 28, 30664-30674.

Liang, Y., Ge, X., Jin, Y., Zheng, Z., Zhang, Y., & Jiang, Y. (2024). Economic optimization of fresh logistics pick-up routing problems with time windows based on gray prediction. Journal of Intelligent & Fuzzy Systems, 46(4), 10813-10832.

Long, J. P., Zhu, H., Do, K. A., & Ha, M. J. (2023). Estimating causal effects with hidden confounding using instrumental variables and environments. Electronic journal of statistics, 17(2), 2849.

Magazzino, C., Alola, A. A., & Schneider, N. (2021). The trilemma of innovation, logistics performance, and environmental quality in 25 topmost logistics countries: A quantile regression evidence. Journal of Cleaner Production, 322, 129050.

Maheshwari, A., Malhotra, A., Hada, B. S., Ranka, M., & Basha, M. S. A. (2024, September). Towards an Improved Model for Stability Score Prediction: Harnessing Machine Learning in National Stability Forecasting. In 2024 IEEE North Karnataka Subsection Flagship International Conference (NKCon) (pp. 1-7). IEEE.

Maier, R., Hörtnagl, L., & Buchmann, N. (2022). Greenhouse gas fluxes (CO2, N2O and CH4) of pea and maize during two cropping seasons: Drivers, budgets, and emission factors for nitrous oxide. Science of the Total Environment, 849, 157541.

Martto, J., Diaz, S., Hassan, B., Mannan, S., Singh, P., Villasuso, F., & Baobaid, O. (2023, October). ESG strategies in the oil and gas industry from the maritime & logistics perspective-opportunities & risks. In Abu Dhabi International Petroleum Exhibition and Conference (p. D041S129R004). SPE.

Masuarah, Y., Suhendra, I., & Umayatu Suiroh, S. (2021). The Impact of Economic and Social Factors on ASEAN Logistics Performance. Jurnal Ekonomi dan Studi Pembangunan, 13, 1.

Mazzuto, G., Antomarioni, S., Ciarapica, F. E., & Bevilacqua, M. (2021). Health Indicator for Predictive Maintenance Based on Fuzzy Cognitive Maps, Grey Wolf, and K‐Nearest Neighbors Algorithms. Mathematical Problems in Engineering, 2021(1), 8832011.

Miklin, N., Gachechiladze, M., Moreno, G., & Chaves, R. (2022). Causal inference with imperfect instrumental variables. Journal of Causal Inference, 10(1), 45-63.

Modak, S. (2023). A new measure for assessment of clustering based on kernel density estimation. Communications in Statistics-Theory and Methods, 52(17), 5942-5951.

Mohanty, P. K., Francis, S. A. J., Barik, R. K., Roy, D. S., & Saikia, M. J. (2024). Leveraging Shapley Additive Explanations for Feature Selection in Ensemble Models for Diabetes Prediction. Bioengineering, 11(12), 1215.

Moreira, O. J., & Rodrigues, M. C. M. (2023). Sourcing third party logistics service providers based on environmental, social and corporate governance: a case study. Discover Sustainability, 4(1), 36.

Mukhtar, M. (2023). Unravelling Structural Underdevelopment: Is Governance Quality the Key? (Doctoral dissertation).

Mutambik, I. (2024). Digital Transformation as a Driver of Sustainability Performance—A Study from Freight and Logistics Industry. Sustainability, 16(10), 4310.

Nagy, G., & Szentesi, S. (2024). Green logistics: Transforming supply chains for a sustainable future. Advanced Logistic Systems-Theory and Practice, 18(3), 29-42.

Nakhjiri, A., & Kakroodi, A. A. (2024). Air pollution in industrial clusters: A comprehensive analysis and prediction using multi-source data. Ecological Informatics, 80, 102504.

Nawurunnage, K. R., Prasadika, A. P. K. J., & Wijayanayake, A. N. (2023, February). TQM and Green Supply Chain Management Practices on Supply Chain Performance of Third-Party Logistics Services in Sri Lanka: A Systematic Review of Literature. In 2023 3rd International Conference on Advanced Research in Computing (ICARC) (pp. 274-279). IEEE.

Nenavani, J., Prasuna, A., Siva Kumar, S. N. V., & Kasturi, A. (2024). ESG measures and financial performance of logistics companies. Letters in Spatial and Resource Sciences, 17(1), 5.

Niu, B., Dong, J., & Wang, H. (2024). Smart port vs. port integration to mitigate congestion: ESG performance and data validation. Transportation Research Part E: Logistics and Transportation Review, 191, 103741.

Noviandy, T. R., Hardi, I., Zahriah, Z., Sofyan, R., Sasmita, N. R., Hilal, I. S., & Idroes, G. M. (2024). Environmental and economic clustering of indonesian provinces: insights from K-Means analysis. Leuser Journal of Environmental Studies, 2(1), 41-51.

Okanda, T. L., Zhang, J., Sarfo, P. A., & Amankwah, O. (2025). Exploring the Nexus between Debt Financing and Firm Performance: A Robustness Analysis Using Instrumental Variables. International Journal of Advanced Engineering Research and Science, 12(02).

Onukwulu, E. C., Agho, M. O., & Eyo-Udo, N. L. (2022). Advances in green logistics integration for sustainability in energy supply chains. World Journal of Advanced Science and Technology, 2(1), 047-068.

Park, B. (2023). The Impact of ESG Frameworks on Economic Performance: The Mediating Role of Logistics Performance and Liner Shipping Connectivity. Journal of Korea Port Economic Association, 39(4), 163-190.

Pehlivan, P., Aslan, A. I., David, S., & Bacalum, S. (2024). Determination of Logistics Performance of G20 Countries Using Quantitative Decision-Making Techniques. Sustainability, 16(5), 1852.

Peiman, F., Khalilzadeh, M., Shahsavari-Pour, N., & Ravanshadnia, M. (2023). Estimation of building project completion duration using a natural gradient boosting ensemble model and legal and institutional variables. Engineering, Construction and Architectural Management.

Pham, T. N., Tran, P. P., Le, M. H., Vo, H. N., Pham, C. D., & Nguyen, H. D. (2022). The effects of ESG combined score on business performance of enterprises in the transportation industry. Sustainability, 14(14), 8354.

Pinjaman, S., Thani, M. A. M., Bakar, M., & Hadi, S. (2025). The Nexus between Governance Quality and Economic Growth of Malaysia: Short-And Long-Run Analyses. International Journal of Research and Innovation in Social Science, 9(15), 115-129.

Qu, Z., & Kwon, Y. (2024). Distributionally Robust Instrumental Variables Estimation. arXiv preprint arXiv:2410.15634.

Rapdecho, C., & Aunyawong, W. (2024, March). THE RELATIONSHIP AMONG OPERATIONAL EFFICIENCY, ESG IMPLEMENTATION, GREEN SUPPLY CHAIN MANAGEMENT, AND SUSTAINABLE SUPPLY CHAIN PERFORMANCE. In INTERNATIONAL ACADEMIC MULTIDISCIPLINARY RESEARCH CONFERENCE IN FUKUOKA 2024 (pp. 186-193).

Rawat, D. S. (2025). Political Governance and Stock Market Performance: An Autoregressive Distributed Lag Analysis of the Nepalese Market. KMC Journal, 7(1), 272-294.

Rodionova, M., Skhvediani, A., & Kudryavtseva, T. (2022). ESG as a booster for logistics stock returns—evidence from the us stock market. Sustainability, 14(19), 12356.

Runhua Xiao, I., Jaller, M., Phong, D., & Zhu, H. (2022). Spatial analysis of the 2018 logistics performance index using multivariate kernel function to improve geographically weighted regression models. Transportation research record, 2676(2), 44-58.

Sadriu, M., & Balaj, D. (2024). ASSESSING THE ROLE OF GOVERNANCE INDICATORS ON FOREIGN DIRECT INVESTMENT: INSIGHTS FROM SOUTHEASTERN EUROPEAN COUNTRIES. Journal of Governance and Regulation/Volume, 13(4).

Safouan, S., El Moutaouakil, K., & Patriciu, A. M. (2024). Fractional Derivative to Symmetrically Extend the Memory of Fuzzy C-Means. Symmetry, 16(10), 1353.

Samy, S., Jaini, K., & Preheim, S. (2024). A Novel Machine Learning-Driven Approach for Predicting Nitrous Oxide Flux in Precision Managed Agricultural Systems. Available at SSRN 4976901.

Sarmas, E., Fragkiadaki, A., & Marinakis, V. (2024). Explainable AI-Based Ensemble Clustering for Load Profiling and Demand Response. Energies, 17(22), 5559.

Shakil, M. H., Munim, Z. H., Zamore, S., & Tasnia, M. (2024). Sustainability and financial performance of transport and logistics firms: Does board gender diversity matter?. Journal of Sustainable Finance & Investment, 14(1), 100-115.

Shang, Y. J., Mao, Y. H., Liao, H., Hu, J. L., & Zou, Z. Y. (2023). Response of PM 2.5 and O 3 to Emission Reductions in Nanjing Based on Random Forest Algorithm. Huan Jing ke Xue= Huanjing Kexue, 44(8), 4250-4261.

Sharawi, H., Alsaadi, L., & Alsagri, M. (2025). The impact of LPIs’ indicators on the global logistics performance index: Global perspective. Multidisciplinary Science Journal, 7(7), 2025361-2025361.

Shen, Y., Ma, J., & Wang, W. (2024). Supply chain digitization and enterprise ESG performance: a quasi-natural experiment in China. International Journal of Logistics Research and Applications, 1-23.

Singh, J., & Gosain, A. (2024). Revolutionizing Missing Data Handling with RFKFCM: Random Forest-based Kernelized Fuzzy C-Means. Procedia Computer Science, 233, 66-76.

Skhvediani, A. E., Gutman, S. S., Rodionova, M. A., & Perfilova, J. A. (2024). Being green as an instrument for increasing firm value: case of US transport and logistics companies. International Journal of Logistics Systems and Management, 47(1), 105-124.

Slezák, J. (2023). Relations between Development of E-Government and Government Effectiveness, Control of Corruption and Rule of Law in 2010–2020: a Cluster Analysis. Acta VŠFS-ekonomické studie a analýzy, 17(2), 161-187.

Srisuradetchai, P., & Suksrikran, K. (2024). Random kernel k-nearest neighbors regression. Frontiers in big Data, 7, 1402384.

Stan, S. E., Țîțu, A. M., Mănescu, G., Ilie, F. V., & Rusu, M. L. (2023). Measuring supply chain performance from ESG perspective. Available at SSRN 5093491.

Šulentić, T., Rakić, E., & Kavran, K. M. Z. (2022, May). ESG management-the main factors of sustainable business in the postal logistics sector. In FIRST INTERNATIONAL CONFERENCE ON ADVANCES IN TRAFFIC AND COMMUNICATION TECHNOLOGIES (p. 9).

Sun, X., Kuo, Y. H., Xue, W., & Li, Y. (2024). Technology-driven logistics and supply chain management for societal impacts. Transportation Research Part E: Logistics and Transportation Review, 185, 103523.

Sun, Y., Li, Y., Jia, Y., Yang, J., Peng, Y., & Guo, X. (2024, October). A Random Forest-based Model for Cargo Volume Prediction and Personnel Scheduling in Logistics Sorting Centers. In 2024 3rd International Conference on Data Analytics, Computing and Artificial Intelligence (ICDACAI) (pp. 289-294). IEEE.

Syed, M. (2021). Neighborhood density information in clustering. Annals of Mathematics and Artificial Intelligence, 90, 855–872.

Syed, M. N. (2022). Neighborhood density information in clustering. Annals of Mathematics and Artificial Intelligence, 90(7), 855-872.

Tao, Y., Wang, S., Wu, J., Zhao, M., & Yang, Z. (2022). Logistic network construction and economic linkage development in the Guangdong-Hong Kong-Macao Greater Bay Area: An analysis based on spatial perspective. Sustainability, 14(23), 15652.

Taskin, D., Sariyer, G., Acar, E., & Cagli, E. C. (2025). Do past ESG scores efficiently predict future ESG performance?. Research in International Business and Finance, 74, 102706.

Thummala, G. S. R., & Baskar, R. (2023, May). Prediction of Heart Disease using Random Forest in Comparison with Logistic Regression to Measure Accuracy. In 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (pp. 1-5). IEEE.

Tian, L., Tian, W., & Guo, M. (2025). Can supply chain digitalization open the way to sustainable development? Evidence from corporate ESG performance. Corporate Social Responsibility and Environmental Management, 32(2), 2332-2346.

Troncoso, J. A., Quijije, Á. T., Oviedo, B., & Zambrano-Vega, C. (2023). Solar Radiation Prediction in the UTEQ based on Machine Learning Models. arXiv preprint arXiv:2312.17659.

Tsang, Y. P., Fan, Y., & Feng, Z. P. (2023). Bridging the gap: Building environmental, social and governance capabilities in small and medium logistics companies. Journal of environmental management, 338, 117758.

Ulkhaq, M. M. (2023). Clustering countries according to the logistics performance index. JATISI (Jurnal Teknik Informatika dan Sistem Informasi), 10(1).

Varkiani, S. M., Pattarin, F., Fabbri, T., & Fantoni, G. (2025). Predicting employee attrition and explaining its determinants. Expert Systems with Applications, 126575.

Wan, B., Wan, W., Hanif, N., & Ahmed, Z. (2022). Logistics performance and environmental sustainability: Do green innovation, renewable energy, and economic globalization matter?. Frontiers in Environmental Science, 10, 996341.

Wang, F., Geng, Y., & Zhang, H. (2021). An improved fuzzy C-means clustering algorithm based on intuitionistic fuzzy sets. In Proceedings of the 9th International Conference on Computer Engineering and Networks (pp. 333-345). Springer Singapore.

Wang, T., Qin, L., Dai, C., Wang, Z., & Gong, C. (2023). Heterogeneous Learning of Functional Clustering Regression and Application to Chinese Air Pollution Data. International Journal of Environmental Research and Public Health, 20(5), 4155.

Wu, M., & Xie, D. (2024). The impact of ESG performance on the credit risk of listed companies in Shanghai and Shenzhen stock exchanges. Green Finance, 6(2), 199.

Xie, T. (2021). ESG transparency on firm performance: an empirical research of Covid-19 in global logistics firms.

Xuan, T. T. T., Quach, P. H., Van Thinh, N., Hoa, T. T., & Tu, N. T. (2023). The efficiency and the performance of the logistics global supply chain activities to Vietnam exportation: An empirical case study. International Journal of Professional Business Review: Int. J. Prof. Bus. Rev., 8(4), 48.

Yang, F., Chen, T., Zhang, Z., & Yao, K. (2024). Firm ESG Performance and Supply-Chain Total-Factor Productivity. Sustainability, 16(20), 9016.

Yıldırım, M. (2023). Cluster Analysis on Supply Chain Management-Related Indicators. İnsan ve Toplum Bilimleri Araştırmaları Dergisi, 12(5), 2499-2520.

Yu, K., Wu, Q., Chen, X., Wang, W., & Mardani, A. (2024). An integrated MCDM framework for evaluating the environmental, social, and governance (ESG) sustainable business performance. Annals of Operations Research, 342(1), 987-1018.

Zeng, H., Li, R. Y. M., & Zeng, L. (2022). Evaluating green supply chain performance based on ESG and financial indicators. Frontiers in Environmental Science, 10, 982828.

Zhang, M., Yang, W., Zhao, Z., Pratap, S., Wu, W., & Huang, G. Q. (2023). Is digital twin a better solution to improve ESG evaluation for vaccine logistics supply chain: An evolutionary game analysis. Operations Management Research, 16(4), 1791-1813.

Zhang, Y., Li, Y., & Che, J. (2024). Optimal weight random forest ensemble with Fuzzy C-means cluster-based subsampling for carbon price forecasting. Journal of Intelligent & Fuzzy Systems, 46(1), 991-1003.

Zhao, L., Yu, Q., Li, M., Wang, Y., Li, G., Sun, S., ... & Liu, Y. (2022). A review of the innovative application of phase change materials to cold-chain logistics for agricultural product storage. Journal of Molecular Liquids, 365, 120088.

Zheng, D., & Wang, T. (2025). Supply chain resilience, logistics efficiency, and enterprise competitiveness. Finance Research Letters, 79, 107335.

Zhu, C., & Liu, Z. (2024, April). Semi-supervised clustering of PM2. 5 pollution. In International Conference on Computer Application and Information Security (ICCAIS 2023) (Vol. 13090, pp. 427-432). SPIE.