Exploring N₂O Emissions at World Level: Advanced Econometric and Machine Learning Approaches in the ESG Context
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DOI: 10.31219/osf.io/4r8ux_v1
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- Drago, Carlo & Arnone, Massimo & Leogrande, Angelo, 2025. "Exploring N₂O Emissions at World Level: Advanced Econometric and Machine Learning Approaches in the ESG Context," MPRA Paper 124006, University Library of Munich, Germany.
- Carlo Drago & Massimo Arnone & Angelo Leogrande, 2025. "Exploring N₂O Emissions at World Level: Advanced Econometric and Machine Learning Approaches in the ESG Context," Working Papers hal-04994903, HAL.
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More about this item
JEL classification:
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
- Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
- Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
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