Explaining Meta-learner’s Predictions: Case of Corporate CO2 Emissions

This publication has 14 references indexed in Scilit:

• Method and evaluations of the effective gain of artificial intelligence models for reducing CO2 emissions
  Journal of Environmental Management, 2023
• Predicting corporate carbon footprints for climate finance risk analyses: A machine learning approach
  Energy Economics, 2021
• Statistical stability indices for LIME: Obtaining reliable explanations for machine learning models
  Journal of the Operational Research Society, 2021
• Determining China's CO2 emissions peak with a dynamic nonlinear artificial neural network approach and scenario analysis
  Energy Policy, 2019
• Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
  IEEE Access, 2018
• The Relevance to Investors of Greenhouse Gas Emission Disclosures
  Contemporary Accounting Research, 2017
• Estimating Corporate Carbon Footprints with Externally Available Data
  Journal of Industrial Ecology, 2016
• "Why Should I Trust You?"
  2016
• Carbon dioxide emission prediction using support vector machine
  IOP Conference Series: Materials Science and Engineering, 2016
• Prediction of CO₂ Emissions Using an Artificial Neural Network: The Case of the Sugar Industry
  Advanced Science Letters, 2015