Logistics Performance and ESG Outcomes: An Empirical Exploration Using IV Panel Models and Machine Learning
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DOI: 10.31219/osf.io/huxcs_v1
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- Nicola Magaletti & Valeria Notarnicola & Mauro Di Molfetta & Stefano Mariani & Angelo Leogrande, 2025. "Logistics Performance and ESG Outcomes: An Empirical Exploration Using IV Panel Models and Machine Learning," Working Papers hal-05067753, HAL.
- Magaletti, Nicola & Notarnicola, Valeria & Di Molfetta, Mauro & Mariani, Stefano & Leogrande, Angelo, 2025. "Logistics Performance and ESG Outcomes: An Empirical Exploration Using IV Panel Models and Machine Learning," MPRA Paper 124746, University Library of Munich, Germany.
References listed on IDEAS
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More about this item
JEL classification:
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
- F14 - International Economics - - Trade - - - Empirical Studies of Trade
- M14 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Corporate Culture; Diversity; Social Responsibility
- O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
- Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth
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