GX & Decarbonization Research

Lu Wang, Chunyan Sun, Xiaoyu Wang

This review surveys AI-enabled low-carbon technologies in the oil and gas industry, covering core needs, applications across key processes, comparisons of AI methods, challenges, and future trends. It serves as a reference for researchers a…

Jiamin Zhang, Yang Qian, Christina W.Y. Wong +1

This study examines how configurations of institutional pressures affect firms' low-carbon behaviors (hard and soft) and the moderating role of AI capability. Using panel data from Chinese A-share listed firms (2007-2022) with cluster analy…

Sheikh Ali Tanzil, Kishor Chandra Meher

This qualitative study examines how Fijian listed companies navigate global ESG mandates and local institutional constraints, using managerial sensemaking. It reveals a 'digital decoupling' where firms use digital infrastructures as a symbo…

Thi Loan Bui, Oanh Thi Tran, Nguyen Thi Kim Oanh +2

This paper proposes an NLP-based method to automatically classify ESG-related information from Vietnamese corporate annual reports, aiming to streamline disclosure analysis and automate ESG assessment. It sits at the intersection of AI and …

İlker Mert, Hüseyin Yağlı, J. Costa +1

This study combines NLP and stochastic techno-economic modeling to analyze Türkiye's national hydrogen strategy. Baseline LCOH is 4.89 €/kg with electricity price as dominant driver. Proactive policy intervention raises the probability of r…

Shaofei Wang, Ruyi Wei, Yuqi Li +4

This study proposes ReDP-VT, a novel deep learning model for forecasting greenhouse gas concentrations using TCCON observations. It leverages frequency-domain inter-gas dependencies to guide Transformer self-attention, capturing dynamic rel…

Shoichiro Hosomi, Soichiro Yamamoto

This study examines the association between CDP scores (climate change, water security, forests) and firm value (Tobin's Q) and profitability (ROA, ROE) using panel data of Japanese manufacturing firms from 2018 to 2023. Results show that t…

Michelle Doorasamy, O. Fapetu, Pelumi Abdulmalik Adewumi

Using panel data from Nigeria and South Africa (2010-2022), this study examines how climate finance (CF) and environmental risk accounting (ERA) affect firm value. Employing AI-powered textual analysis to measure climate financial exposure …

Oskars Bagants, Raita Rollande, Juris Klonovs

The construction sector accounts for ~34% of global energy-related CO₂ emissions, yet carbon accounting is not integrated into cost estimation and procurement. This study proposes the AI-driven 'EEBOQ' framework, combining LLMs, ontology-ba…

Tuğba Yeğin

This study examines how GenAI-based carbon footprint feedback (AI-CFB) influences consumer purchase intentions for renewable energy-powered products (REPPs). Analyzing 841 Turkish participants with PLS-SEM, it finds AI-CFB as an antecedent …

Shawn Wang

Using panel data from 266 Chinese cities (2011-2020), this study finds an inverted U-shaped relationship between AI development and urban carbon emission intensity (CEI): AI initially increases CEI but reduces it after a turning point. By 2…

Chen Song, Zhihao Gao

This paper empirically examines how CEO-chairperson dialect connections affect corporate carbon emissions. Findings show that such connections significantly increase emissions, especially in competitive industries, non-state-owned firms, an…

Wen Xiang, Jianzhong Gao

Using survey data from 10,057 kiwifruit growers in China, this study applies machine learning (random forest, SHAP) to analyze how farmers' digital capabilities affect initial and sustained adoption of low-carbon agricultural technologies. …

Helena M. Ramos, Chetan Rishi, Óscar E. Coronado-Hernández +3

This study proposes an MCDA framework combining machine learning (EL-SVM, decision tree, logistic regression) with AHP to evaluate candidate sites for hybrid renewable energy systems (HRES) integrating solar, wind, hydropower, and storage. …

Zeeshan, Mohammad

Scope3Scout is a 16-agent platform that automates ESG and CSRD supply chain compliance verification using a multi-agent architecture. It automates verification of supplier data and was a TinyFish Accelerator finalist. It demonstrates a nove…

Umesh Ganesh Iyer, Sukhpal Singh Gill

This paper explores how AI-driven cloud resource management can contribute to net-zero carbon emission goals. It proposes methods to optimize energy consumption and reduce carbon emissions through AI, enhancing sustainability of cloud data …

Sunday Idowu

An IoT platform using ESP32 and PZEM-004T sensors monitors household electricity usage and CO2 emissions in real time. Tested with 100W incandescent and LED bulbs, it achieved <3% error and <2s response time. CO2 is calculated using an emis…

I. P. Okwuosa, O. J. Odejobi

This study optimizes biogas steam reforming for low-carbon hydrogen production using ANN integrated with GA. The ANN was trained on Aspen HYSYS simulation data, and GA optimized operating conditions to achieve a hydrogen mole fraction of 0.…

Tao Wu, Xinyu Li, Jie Li +2

This paper proposes a multi-agent system enhanced by large language models (LLMs) to optimize low-carbon energy scheduling under load and renewable uncertainties. It applies AI to energy transition, improving grid operation efficiency and s…

Xin Huang, Yixin Li, Keteng Jiang +3

This paper proposes a low-carbon optimal dispatch strategy that models dynamic carbon emission factors of thermal power units using spatiotemporal attention and absolute position encoding, integrated into a power-carbon coupled scheduling f…