Abstract
Tariq Ali, Sheikh Muhammad Saqib, Tariq Shahzad, Yasser Alharbi, Tehseen Mazhar, Muhammad Ayaz, Mohammad Hijji, Habib Hamam
Abstract
Authors
Institutions
No local reference links have been materialized yet.
No local citing links have been materialized yet.
Unresolved referenced work
2021
China’s carbon emission prediction from the perspective of shared socioeconomic pathways and machine learning
2025
Is CO2 an indoor pollutant? Direct effects of low-to-moderate CO2 concentrations on human decision-making performance
10.1289/ehp.1104789 · 2012
Unresolved referenced work
Kept as external metadata until matched
“CO2 emission prediction using machine learning,”
10.1007/978-981-97-6681-9_28 · 2024
Forecasting the Potential Scenarios of CO2 Emissions in Kazakhstan Using Deep Learning (DL) Predictive Models
10.1016/j.procs.2025.10.264 · 2025
Machine learning approaches for predictions of CO2 emissions in the building sector
10.1016/j.epsr.2024.110735 · 2024
Advanced machine learning schemes for prediction CO2 flux based experimental approach in underground coal fire areas
10.1016/j.jare.2024.10.034 · 2025
Machine learning-based time series models for effective CO2 emission prediction in India
10.1007/s11356-022-21723-8 · 2023
Prediction of CO2 emission from greenhouse to atmosphere with artificial neural networks and deep learning neural networks
Provenance
crossref
Confidence 100%
ror
Confidence 99%
ror
Confidence 99%
ror
Confidence 99%
ror
Confidence 99%
ror
Confidence 99%
ror
Confidence 99%
openalex
Confidence 95%
datacite
Confidence 0%
10.1007/s13762-020-03079-z · 2021
Predicting CO2 Emissions from Traffic Vehicles for Sustainable and Smart Environment Using a Deep Learning Model
2023
Deep learning model based prediction of vehicle CO2 emissions with eXplainable AI integration for sustainable environment
10.1038/s41598-025-87233-y · 2025
Prediction of CO2 solubility in Ionic liquids for CO2 capture using deep learning models
2024
“Comparing CO2 Storage and Utilization: Enhancing Sustainability through Renewable Energy Integration
10.3390/su16156639 · 2024
“Exploring alternative internal gas recirculating designs of multi-stage adiabatic reactors for direct CO2 methanation from thermodynamic insights,”
10.1016/j.fuel.2026.138643 · 2026
Catalytic CO2 conversion to C1 value-added products: Review on latest catalytic and process developments
10.1016/j.fuel.2023.128178 · 2023
Hydrogen energy systems: A critical review of technologies, applications, and future perspectives
10.1016/j.rser.2021.111180 · 2021
Hydrogen-based systems for integration of renewable energy in power systems: Achievements and perspectives
10.1016/j.ijhydene.2021.06.218 · 2021
Cross-sectoral assessment of CO2 capture from U.S. industrial flue gases for fuels and chemicals manufacture
2024
An overview of CO2 capture and utilization in energy models
10.1016/j.resconrec.2021.106150 · 2022
Integrating hydrogen utilization in CO2 electrolysis with reduced energy loss
2024
“A perovskite fuel electrode for efficient and sustainable CO2 reduction in solid oxide electrolysis cells via A-site entropy engineering”
10.1016/j.fuel.2026.138612 · 2026
Assessment of integrated energy systems for the production and use of renewable methanol by water electrolysis and CO2 hydrogenation
10.1016/j.fuel.2020.119160 · 2021
Unresolved referenced work
Kept as external metadata until matched
An examination of daily CO2 emissions prediction through a comparative analysis of machine learning, deep learning, and statistical models
10.1007/s11356-024-35764-8 · 2025
An examination of daily CO2 emissions prediction through a comparative analysis of machine learning, deep learning, and statistical models
10.1007/s11356-024-35764-8 · doi-reference
Assessment of integrated energy systems for the production and use of renewable methanol by water electrolysis and CO2 hydrogenation
10.1016/j.fuel.2020.119160 · doi-reference
“A perovskite fuel electrode for efficient and sustainable CO2 reduction in solid oxide electrolysis cells via A-site entropy engineering”
10.1016/j.fuel.2026.138612 · doi-reference
An overview of CO2 capture and utilization in energy models
10.1016/j.resconrec.2021.106150 · doi-reference
Hydrogen-based systems for integration of renewable energy in power systems: Achievements and perspectives
10.1016/j.ijhydene.2021.06.218 · doi-reference
Hydrogen energy systems: A critical review of technologies, applications, and future perspectives
10.1016/j.rser.2021.111180 · doi-reference
Catalytic CO2 conversion to C1 value-added products: Review on latest catalytic and process developments
10.1016/j.fuel.2023.128178 · doi-reference
“Exploring alternative internal gas recirculating designs of multi-stage adiabatic reactors for direct CO2 methanation from thermodynamic insights,”
10.1016/j.fuel.2026.138643 · doi-reference
“Comparing CO2 Storage and Utilization: Enhancing Sustainability through Renewable Energy Integration
10.3390/su16156639 · doi-reference
Deep learning model based prediction of vehicle CO2 emissions with eXplainable AI integration for sustainable environment
10.1038/s41598-025-87233-y · doi-reference
Prediction of CO2 emission from greenhouse to atmosphere with artificial neural networks and deep learning neural networks
10.1007/s13762-020-03079-z · doi-reference
Machine learning-based time series models for effective CO2 emission prediction in India
10.1007/s11356-022-21723-8 · doi-reference
Advanced machine learning schemes for prediction CO2 flux based experimental approach in underground coal fire areas
10.1016/j.jare.2024.10.034 · doi-reference
Machine learning approaches for predictions of CO2 emissions in the building sector
10.1016/j.epsr.2024.110735 · doi-reference
Forecasting the Potential Scenarios of CO2 Emissions in Kazakhstan Using Deep Learning (DL) Predictive Models
10.1016/j.procs.2025.10.264 · doi-reference
“CO2 emission prediction using machine learning,”
10.1007/978-981-97-6681-9_28 · doi-reference
Is CO2 an indoor pollutant? Direct effects of low-to-moderate CO2 concentrations on human decision-making performance
10.1289/ehp.1104789 · doi-reference