/fields/informatics-modeling
Informatics & Modeling
Forecasting, optimization, and ML for the GX stack.
30 papers from your field · Snapshot from gxceed corpus on 2026-05-11
Why this field matters in GX
CS, OR, and statistics PhDs are reshaping GX implementation: ML emulators that compress climate-model runs by orders of magnitude, optimization layers behind day-ahead market clearing, anomaly detection in scope-3 reporting, and digital twins for steel and chemical plants. The leverage is enormous because every other field's models become tractable.
GX implementation map
Top results from your field, clustered by GX implementation theme. Each cluster shows where doctoral methods from your field actually meet decarbonization work.
Carbon accounting
7 papers in this clusterFrom Missing Data to Climate Action: Machine Learning for Carbon Accounting in Wastewater Systems
Joseph Sánchez-Balseca, Monserrat Ramírez-Melgarejo, Agustí Pérez Foguet
This paper proposes a machine learning approach for carbon accounting in wastewater systems, addressing missing data and improving emission estimates. Using real-world data, it demonstrates that ML models can enhance accuracy and support climate action in the water sector.
via Earth Systems and Environment
AI-Enabled Smart Carbon Farming System for Climate-Positive Agriculture
Kailash Chand Choudhary, Sakshi Rajput, Atul Choudhary, …
Agriculture accounts for about 23% of global GHG emissions, primarily from CO2, methane, and nitrous oxide. This paper proposes an AI-enabled smart carbon farming system to achieve climate-positive agriculture by optimizing carbon sequestration and reducing emissions.
via International Journal of Science Strategic Management and Technology
Greenhouse gas accounting in urban digital twins
Kimmo Lylykangas, Fabian Dembski, Anssi Joutsiniemi, …
This study investigates the use of urban digital twins (DTs) for subnational spatial greenhouse gas (GHG) accounting. Interviews and case studies from frontrunner cities reveal that current GHG inventories and DT initiatives are largely disconnected. However, DTs offer untapped…
via Environmental Research Infrastructure and Sustainability
AI × ESG
6 papers in this clusterArtificial Intelligence Techniques for Monitoring Carbon Emissions and Supporting Green Investment Decisions
Emmanuel Ohimai Ojo, Prince Michael Akwabeng, Gloria Opoku Darkoh, …
This systematic review examines AI applications in carbon emission monitoring and green investment decisions, analyzing 55 studies from 2021-2025. It finds that machine learning, deep learning, and hybrid algorithms significantly improve emission estimation accuracy, enable…
via International Journal of Environment and Climate Change
Artificial Intelligence in Climate and Sustainable Finance: A Blessing or a Curse?
Filippo di Pietro, Pilar Giráldez‐Puig, P. Palos-Sanchez, …
This study uses a systematic literature review and bibliometric analysis to examine machine learning applications in climate-related financial challenges. It finds growing AI adoption in emissions forecasting, green investment, and sustainability reporting, while highlighting…
via Journal of economic surveys (Print)
Augmented Finance for Climate Action: A Systematic Review of AI, IoT, and Blockchain Applications in Sustainable Finance
Nadia Mansour
This systematic review of 42 studies (2018–2025) examines AI, IoT, and blockchain in sustainable finance. It identifies three application areas: enhanced MRV of environmental impacts, improved physical and transition risk control via predictive modeling, and better ESG analysis…
via International Journal of Financial Studies
Renewables
4 papers in this clusterPower generation forecasting and system technology for renewable energy
This paper examines power generation forecasting and system technology for renewable energy. It discusses methods for predicting the output of variable renewables and technologies for grid stabilization.
via J-STAGE
Artificial intelligence in renewable energy technologies: Advancing optimization, integration, and carbon neutrality
Aamir Sohail
This review comprehensively examines AI applications in renewable energy technologies, covering resource assessment, forecasting, monitoring, control, and grid integration using machine learning and optimization. It highlights challenges such as data uncertainty and model…
via Next Energy
Call for Papers for Special Issue on 'Technologies for Responding to Output Fluctuations of Renewable Energy'
This special issue calls for papers on technologies addressing output fluctuations of renewable energy, including energy storage, demand response, and grid integration.
via J-STAGE
CCUS
4 papers in this clusterAccelerating amine-based CO2 capture with machine learning: From molecular screening to process optimization
Ping Yang, Xiaoman Yu, Kyriakos C. Stylianou, …
This paper reviews machine learning applications for amine-based CO2 capture, from molecular screening to process optimization. Key breakthroughs include improved prediction accuracy in liquid systems and identification of novel adsorbents via virtual screening. Industrial…
via Fundamental Research
Multi-Scale Digital Twin Framework with Physics-Informed Neural Networks for Real-Time Optimization and Predictive Control of Amine-Based Carbon Capture: Development, Experimental Validation, and Techno-Economic Assessment
Mansour Almuwallad
This study develops a multi-scale digital twin framework integrating physics-informed neural networks for real-time optimization and predictive control of amine-based CO2 capture. Validated against pilot-scale data, it achieves temperature RMSE of 1.2 K, CO2 loading RMSE of…
via Processes
Carbon Capture, Utilization, and Storage (CCUS) for Clean Energy
Grazia Leonzio
This paper examines the role of CCUS in the clean energy transition, covering capture methods, utilization pathways, and storage solutions. It discusses integration with energy systems, cost challenges, and scalability, evaluating its potential contribution to net-zero goals.
via Sustainability
Scope 3
3 papers in this clusterCarbon Emission Quantification via Explainable Deep Learning Demand Forecasting in Retail Supply Chains
Yuxuan Wu, Haowen Dai, Hengyi Zhang, …
This paper proposes a method to quantify carbon emissions in retail supply chains using explainable deep learning for demand forecasting. This enables visualization and reduction of scope 3 emissions.
via Research Square
A Machine Learning Framework for Enhancing Scope 3 Emissions Measurement through Integrated Product and Industry Classifications
Ajay S. Jadhav, Shiva Abdoli
Addressing data scarcity in Scope 3 measurement, this paper proposes a machine learning framework integrating CPC, ISIC, and NAICS classifications. Using supply chain emission factors as target, Gradient Boosting Decision Trees achieved R² 0.9108, demonstrating improved…
via Modern Applied Science
ERP-Powered ESG Intelligence: Measuring Carbon Footprint of Medical Device Supply Chains
Bhimalinga Reddy Bangaru
ERP has evolved into sustainability intelligence software for monitoring environmental performance, enabling real-time carbon footprint tracking in medical device supply chains. It offers Scope 1/2/3 emissions management, with AI improving emission factors and automating carbon…
via International Journal of Computational and Experimental Science and Engineering
Energy storage
1 papers in this clusterMachine learning in energy storage optimization for carbon neutrality: A review
P. Balakrishnan
This review comprehensively examines machine learning applications in optimizing energy storage systems for carbon neutrality. It categorizes methods that improve battery operation efficiency and renewable energy integration, and outlines future research directions.
via Renewable Energy
Climate finance
1 papers in this clusterFinancial Intelligence and Environmental Sustainability: A Literature Review on Economic Modeling for Clean Energy Manufacturing
Tonimi Rotimi-Ojo, Juliana Kissiwah Somuah, Ndidi Ezeakunne, …
This review examines the application of financial intelligence and economic modeling to clean energy manufacturing in solar, wind, EV batteries, and green hydrogen. It evaluates tools like LCOE, DCF, real options, Monte Carlo simulations, and ESG-integrated forecasting,…
via Middle East Research Journal of Economics and Management
Electric vehicles
1 papers in this clusterAI-Based Carbon Emissions Monitoring for Electric Vehicles: A Technical Review
Raghavendra Kurva
This technical review examines AI and machine learning approaches for real-time carbon footprint monitoring of electric vehicles (EVs). By integrating data from sensors, vehicle systems, and recycling facilities, dynamic emission profiles are generated. The platform supports…
via Journal of Information Systems Engineering & Management
Other
1 papers in this clusterMore than reporting: enterprise resource planning as enabler of business model transformation for climate change mitigation
Sînziana-Maria Rîndașu, Adalberto Rangone, Liliana Ionescu-Feleagă, …
This study analyzes ERP-related patents from 2020-2024 to investigate how ERP systems enable business model transformation for climate change mitigation. Three main areas are identified: production optimization, sustainability management and monitoring, and supply chain…
via Journal of Business Economics and Management
Carbon pricing
1 papers in this clusterMeasurement, Typology, and Multi-Scenario Forecasting of Urban Marginal Abatement Costs
Jing ye Lyu, ren di song, Xiu Feng Fan
This study develops a framework for measuring and classifying urban marginal abatement costs (MAC), and provides multi-scenario forecasts to inform cost-effective emission reduction policies. It contributes to climate mitigation planning.
via Research Square
Transition finance
1 papers in this clusterGraph Neural Networks for Green Finance: A Spatiotemporal Assessment of Energy Transition Policies
Chuhui Zhong, Zimeng Zhang, Xing-gui Wang, …
This study develops a dual graph neural network (GNN) framework to spatiotemporally assess the role of green finance in China's energy transition towards carbon neutrality. Using a GATv2 model, it identifies influential regions and factors, and a spatiotemporal GNN forecasts…
via Proceedings of the 2026 International Conference on Artificial Intelligence and Fintech
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