GX & Decarbonization Research

Feiyu Cai, Jing Qiu, Yi Yang +4

This paper proposes a proactive spatial-temporal carbon response framework combining deep learning and LLM-based multi-agent systems to accurately forecast day-ahead nodal carbon intensity (NCI). It integrates geographically dispatchable lo…

Zheng Liang, Li Shenshen, Hu Xuefei +2

This dataset provides monthly carbon emission predictions at 0.01 degree resolution for Beijing and surrounding areas from 2019-2024, generated using a weakly supervised neural network (CCFI-Net) that integrates remote sensing, meteorologic…

(著者不明)

This paper proposes a bidirectional temporal convolutional network combined with exogenous variable enhancement for carbon market price forecasting, aiming to improve prediction accuracy. It uniquely merges machine learning techniques with …

Hu B.

This paper empirically investigates the relationships between attitudes toward emission trading schemes (ETS), acceptance of artificial intelligence (AI), climate entrepreneurship, and sustainable performance. Using AI-driven analysis, it r…

Mishra L.

This paper presents a document question-answering (DocQA) system that simplifies ESG accountability. Users can query ESG-related documents in natural language and receive accurate answers, enhancing reporting and compliance efficiency.

Ritu Gaur, Archana Jain, Deepak Kumar Gupta +3

This paper proposes a climate-aware valuation framework that integrates multimodal LLMs, geospatial APIs, and Neo4j knowledge graphs to inject physical climate risk signals into property valuation. It achieves 5-8% improvement in extraction…

Kateryna Chekriy, Rüdiger Kiesel

This paper uses state-of-the-art NLP to evaluate corporate net-zero transition plans. It integrates this data into a Bayesian net that combines past emissions reduction and future plans to compute an adjusted probability of staying within n…

Guowei Wu, Manxuan Mao, Jie Zhi +4

This study analyzed spatiotemporal patterns and drivers of county-level agricultural carbon emissions in Guangdong, China (2000-2022), using an interpretable machine learning framework (Random Forest + SHAP). Emissions decreased by 22.9%, w…

Damrongsak Naparat, Erboon Ekasingh

This paper proposes an agentic AI framework to assess the credibility of corporate SDG claims by analyzing sustainability reports. It automatically verifies alignment between claims and actual actions, contributing to greenwashing detection…

Rüdiger Otto, Juan José García‐Alvarado, Elena Rocafull +6

Presents the first high-resolution forest carbon map for the Canary Islands integrating field data, ALS, Sentinel-2, and climatic variables with machine learning. Estimates 10.26 Tg carbon; laurel forests have exceptional densities. Structu…

Yuanyi Niu, Liang Zheng, Yajuan Chang +7

This paper proposes an intelligent red-blue supplemental lighting control system for greenhouses that balances photosynthesis and carbon footprint. Using AI/machine learning, it optimizes the light environment to reduce energy consumption w…

Shi Zheng, Min Xu, Ziyu Fu +5

This study applies safety-constrained deep reinforcement learning to optimize the coordinated operation of green data centers, integrating renewables, grid, batteries, cooling, and computing loads. It formulates the problem as a constrained…

YINKA ADERIBIGBE

This paper proposes a cloud-native, serverless ML pipeline on AWS for real-time carbon accounting. It uses NLP and XGBoost to analyze corporate sustainability reports, cross-reference with supply chain telemetry, and compute a Disclosure In…

Kaushik P.

This paper explores the use of AI and high-performance computing to improve the efficiency of green finance, likely involving automation of ESG ratings, carbon accounting, and green bond screening. No abstract is available, but the title cl…

Vogel M.

This paper investigates whether data mining can improve methane correction factors (MCFs) for urban nonsewered sanitation systems. By applying machine learning to empirical data, it aims to derive more accurate emissions estimates, enhancin…

Edo G.I.

This paper explores the application of artificial intelligence (AI) in wastewater treatment and resource recovery to achieve net-zero water resource recovery facilities. AI techniques optimize energy consumption and enhance resource recover…

Edralene M. Toñacao, Anik Yuesti, J. Alve

This integrative literature review examines how digital technologies (AI, big data, IoT, blockchain) influence CSR impact measurement, distinguishing it from sustainability disclosure and ESG ratings. Five interdependent dimensions are iden…

Yantong Zhao, Gaoxiu Qiao

This paper introduces a heterogeneous ensemble framework for European carbon price forecasting, integrating LASSO for variable selection, MEMD-ARIMAX-mLSTM for temporal dynamics, and GWnet-attn for spatial dependencies. Empirical results sh…

Temidayo Alex‐Oke, Olusola Bamisile, Joseph Junior Nkou Nkou +3

This study develops a comparative framework using SARIMAX, Random Forest, Transformer Encoder, and Attention-Gated GRU to forecast CO2 emissions for 16 West African countries. Attention-based models achieve the best performance (MAPE 6.14%-…

Aditya Narayan, Arghya Chakraborty, Santosh Kumar Mishra +1

This paper proposes a framework-guided approach using ClimateBERT and Sentence Transformer embeddings to assess corporate climate disclosure quality, defining a Climate Disclosure Quality Index (CDQI). It provides a dataset for automated sc…