GX & Decarbonization Research

Tong Qian, Yang Liu, Yunlin Huang +2

This review examines how AI enables electricity–computing synergy to address data center energy demands and renewable energy variability. It highlights deep learning for renewable forecasting and deep reinforcement learning for flexible wor…

Leyi Yan, Shuangning Li, Sihang Liu

This paper characterizes carbon emissions of AI agent workloads and proposes carbon-aware execution scheduling that exploits deadline flexibility. AgentDecarbonizer, running alongside OpenClaw, estimates task duration and schedules executio…

Wiem Dridi, Saliha Theiri

Using French SBF120 firms from 2018-2024, this study finds that higher carbon emissions are associated with higher audit fees, with effects amplified in concentrated audit markets and regions with high environmental carbon exposure. Carbon …

Jicheng Du

This study develops a machine learning benchmark and life-cycle screening database for low-carbon alkali-activated materials from coal gangue and steel slag. It compiles 237 mixture records, benchmarks Random Forest and XGBoost under variou…

Nhat‐Luong Nhieu, Hoang‐Kha Nguyen

This study develops a T-Spherical Fuzzy-Valued Neutrosophic MEREC-EDAS framework to evaluate low-carbon cooling and energy management technologies for data centers. Based on assessments from 30 experts, nine technologies are ranked against …

Zhengjiao Qi, Yukuan Wang, Jingxian Liu +1

This study applies a wind-constrained Pareto multi-objective reinforcement learning (Pareto MO-PPO) to vessel traffic organization in port approach channels, incorporating wind as both an emission driver and scheduling constraint. In a Caof…

Tariq Muhammad Hussein Al-Zoubi, Odai Al-Hailat, A. Alomar +1

AI is transforming ESG data collection and disclosure but raises transparency, verification, and AI-enabled greenwashing concerns. This doctrinal legal study proposes an integrated governance framework combining transparency, human oversigh…

Su W, Wang J, Tu M +1

This study bridges academic theory and corporate practice by analyzing the intersection of carbon emissions, carbon taxation, and supply chain inventory models (SCIM). Using Main Path Analysis on 3,359 papers (1995-2025), it identifies 41 r…

Meiqin Jiang, Jinxing Che, Yaoxin Tan +1

This paper proposes CarbonTimer, a large time series model to address data scarcity in carbon price forecasting. It enables accurate predictions with limited data, potentially aiding carbon market risk management and policy formulation.

Xiaobin Wang, Yuning Jiang, Zhihua Zhang +4

This paper proposes a contextual information-based distributionally robust optimization method for decarbonizing oil and gas field power systems. It integrates renewable energy while managing uncertainty, aiming to reduce operational costs …

Fan Zhang, Bo Wen, Ditao Niu +4

This study integrates life-cycle carbon accounting, statistical analysis, Monte Carlo simulation, and machine learning to assess embodied carbon in RC buildings. Using data from 124 buildings in Shaanxi Province, it finds material productio…

Murali Krishna Pasupuleti

This monograph presents an integrated research architecture for planetary resilience, combining Earth-system AI, ClimateTech modeling, and sustainable energy intelligence. It covers uncertainty-aware design, physics-guided ML, MLOps, and se…

Ömer Algorabi, Ersin Namlı

This study compares deep learning (GRU) and boosting models for forecasting electricity generation in Türkiye and Germany, then evaluates 2030 BAU, ST, and EC scenarios. GRU performs best for Türkiye, boosting for Germany. Scenario analysis…

Yang Lu, Joston Gary

This study develops an environmental systems engineering framework for carbon-management decision support in prefabricated construction, integrating production and transport decisions under dynamic energy prices and carbon signals. A tri-ob…

Junhao Ren, Honglin Gao, Sijie Wang +5

This paper proposes a multi-agent reinforcement learning approach to jointly design a low-carbon peer-to-peer (P2P) energy market and bidding strategies in microgrids. It optimizes economic and environmental outcomes, promoting renewable in…

Will Alpine, Nathan B. Geldner, Holly Alpine +1

This study models AI as a bidirectional productivity amplifier in a global CGE model, finding that AI-driven productivity gains in fossil fuel sectors lead to net CO2 emission increases of 0.47-1.8 Gt annually, outweighing reductions from r…

Joseph Sanderson

Climate change intensifies food safety risks; digital technologies (AI/ML, IoT, blockchain) strengthen risk analysis via monitoring, prediction, and traceability. Three case studies (AI sorting, blockchain supply chain, IoT cold chain) show…

Shi Yang, Polina Ellina, Kyriakos Christofi +2

This study develops an ESG disclosure quality evaluation framework tailored to the agricultural chemicals industry, based on GRI 2021 Standards and Chinese regulatory requirements. Using qualitative content analysis and AHP, it constructs a…

Lu Gong

This study analyzes how digital monitoring and ESG disclosure affect the assetization efficiency of agricultural carbon sinks. Using data from typical agricultural regions, it demonstrates that high-quality monitoring and ESG disclosure sig…

Ammar Ali Gull, Irfan Haider Shakri, Faizul Haque +1

This study examines the impact of external sustainability assurance and Big-4 providers on GHG emissions using a global sample of 7,968 firm-year observations from 34 countries. It finds a negative association, stronger in stakeholder-orien…