A neural network approach to carbon emission prediction in industrial and power sectors

This publication has 39 references indexed in Scilit:

• PR-FCNN: a data-driven hybrid approach for predicting PM2.5 concentration
  Discover Artificial Intelligence, 2024
• Assessment of sectoral greenhouse gas emission effects of biomass, fossil fuel, and (non)metallic ore utilization of the Nordic economy
  Mineral Economics, 2023
• A novel deep learning carbon price short-term prediction model with dual-stage attention mechanism
  Applied Energy, 2023
• Evolutionary game- based optimization of green certificate- carbon emission right- electricity joint market for thermal-wind-photovoltaic power system
  Global Energy Interconnection, 2023
• A critical review of microplastic degradation and material flow analysis towards a circular economy
  Environmental Pollution, 2022
• Electricity-carbon modeling of flat glass industry based on correlation variable
  Energy Reports, 2022
• Towards achieving eco-efficiency in top 10 polluted countries: The role of green technology and natural resource rents
  Gondwana Research, 2022
• Influence of growth and urbanization on CO₂ emissions: The moderating effect of foreign direct investment on energy use in BRICS
  Sustainable Development, 2021
• Rethinking energy use in distillation processes for a more sustainable chemical industry
  Energy, 2020
• Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia
  Advanced Engineering Informatics, 2018