Research graph
References from CO₂ emission modeling and synthetic fuel energy recovery for hydrogen-integrated sustainable fuel systems. Local targets link to admitted publications; unresolved targets remain external evidence.
Unresolved reference
2021 · External reference
China’s carbon emission prediction from the perspective of shared socioeconomic pathways and machine learning
2025 · External reference
Is CO2 an indoor pollutant? Direct effects of low-to-moderate CO2 concentrations on human decision-making performance
10.1289/ehp.1104789 · 2012 · External reference
Unresolved reference
External reference
“CO2 emission prediction using machine learning,”
10.1007/978-981-97-6681-9_28 · 2024 · External reference
Forecasting the Potential Scenarios of CO2 Emissions in Kazakhstan Using Deep Learning (DL) Predictive Models
10.1016/j.procs.2025.10.264 · 2025 · External reference
Machine learning approaches for predictions of CO2 emissions in the building sector
10.1016/j.epsr.2024.110735 · 2024 · External reference
Advanced machine learning schemes for prediction CO2 flux based experimental approach in underground coal fire areas
10.1016/j.jare.2024.10.034 · 2025 · External reference
Machine learning-based time series models for effective CO2 emission prediction in India
10.1007/s11356-022-21723-8 · 2023 · External reference
Prediction of CO2 emission from greenhouse to atmosphere with artificial neural networks and deep learning neural networks
10.1007/s13762-020-03079-z · 2021 · External reference
Predicting CO2 Emissions from Traffic Vehicles for Sustainable and Smart Environment Using a Deep Learning Model
2023 · External reference
Deep learning model based prediction of vehicle CO2 emissions with eXplainable AI integration for sustainable environment
10.1038/s41598-025-87233-y · 2025 · External reference
Prediction of CO2 solubility in Ionic liquids for CO2 capture using deep learning models
2024 · External reference
“Comparing CO2 Storage and Utilization: Enhancing Sustainability through Renewable Energy Integration
10.3390/su16156639 · 2024 · External 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 · 2026 · External reference
Catalytic CO2 conversion to C1 value-added products: Review on latest catalytic and process developments
10.1016/j.fuel.2023.128178 · 2023 · External reference
Hydrogen energy systems: A critical review of technologies, applications, and future perspectives
10.1016/j.rser.2021.111180 · 2021 · External reference
Hydrogen-based systems for integration of renewable energy in power systems: Achievements and perspectives
10.1016/j.ijhydene.2021.06.218 · 2021 · External reference
Cross-sectoral assessment of CO2 capture from U.S. industrial flue gases for fuels and chemicals manufacture
2024 · External reference
An overview of CO2 capture and utilization in energy models
10.1016/j.resconrec.2021.106150 · 2022 · External reference
Integrating hydrogen utilization in CO2 electrolysis with reduced energy loss
2024 · External 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 · 2026 · External 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 · 2021 · External reference
Unresolved reference
External reference
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 · External reference
“CO2 emission prediction using machine learning,”
10.1007/978-981-97-6681-9_28 · ExternalCitation · doi-reference
Machine learning-based time series models for effective CO2 emission prediction in India
10.1007/s11356-022-21723-8 · ExternalCitation · doi-reference
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 · ExternalCitation · 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 · ExternalCitation · doi-reference
Machine learning approaches for predictions of CO2 emissions in the building sector
10.1016/j.epsr.2024.110735 · ExternalCitation · 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 · ExternalCitation · doi-reference
Catalytic CO2 conversion to C1 value-added products: Review on latest catalytic and process developments
10.1016/j.fuel.2023.128178 · ExternalCitation · 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 · ExternalCitation · 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 · ExternalCitation · doi-reference
Hydrogen-based systems for integration of renewable energy in power systems: Achievements and perspectives
10.1016/j.ijhydene.2021.06.218 · ExternalCitation · 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 · ExternalCitation · doi-reference
Forecasting the Potential Scenarios of CO2 Emissions in Kazakhstan Using Deep Learning (DL) Predictive Models
10.1016/j.procs.2025.10.264 · ExternalCitation · doi-reference
An overview of CO2 capture and utilization in energy models
10.1016/j.resconrec.2021.106150 · ExternalCitation · doi-reference
Hydrogen energy systems: A critical review of technologies, applications, and future perspectives
10.1016/j.rser.2021.111180 · ExternalCitation · doi-reference
Deep learning model based prediction of vehicle CO2 emissions with eXplainable AI integration for sustainable environment
10.1038/s41598-025-87233-y · ExternalCitation · doi-reference
Is CO2 an indoor pollutant? Direct effects of low-to-moderate CO2 concentrations on human decision-making performance
10.1289/ehp.1104789 · ExternalCitation · doi-reference
“Comparing CO2 Storage and Utilization: Enhancing Sustainability through Renewable Energy Integration
10.3390/su16156639 · ExternalCitation · doi-reference
