This page provides an English interface to the gxceed GX paper corpus. The corpus aggregates papers from 14 contributing scholarly metadata sources and uses AI-assisted classification to identify signals related to measurement, policy narratives, outcomes, implementation, industrial adoption, and verification.
The goal is not only to discover papers, but to observe how GX research is distributed across research substance, implementation narratives, external expectations, implementation substance, and judgment formation.
Summaries are AI-assisted. Always refer to the original paper for authoritative conclusions.
Showing 241–260 of 1414 papers
Chandrani Ray Chowdhury
This chapter reviews generative models for probabilistic forecasting of renewable energy production, addressing the limitations of traditional ML/DL in handling stochastic, nonlinear data. It demonstrates how probabilistic forecasts support…
Jinliang Wang, Xiang Ma, Z. Ding
This paper proposes a method using large language models (LLMs) to automatically extract and analyze ESG disclosure information from corporate sustainability reports. Compared to traditional manual analysis, it enables efficient and compreh…
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…
Y. D. Bao, Yiyi Lu, C. Liu +5
This paper proposes an LSTM-based forecasting framework for data center energy consumption, integrated into a dual-carbon data management pipeline. The model, featuring attention pooling and multi-task PUE prediction, reduces RMSE by up to …
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…
Rosella Castellano, Federico Cini, Annalisa Ferrari +1
This paper proposes a methodological framework to detect overreporting and underreporting in corporate sustainability disclosures by leveraging divergences among ESG rating agencies. It interprets rating gaps as signals, contributing to rel…
Ya-Ting Jhan, Sheng-Feng Hsieh
This paper examines whether ESG narratives are financially informative by using LLM-based summarization and sentiment analysis. It empirically tests the value of ESG disclosure text for investment decisions.
Jiayi Zhu, Lei Yang, Wenhui Zhou
This paper explores integrating AI into remanufacturing supply chains to enhance sustainability and intelligence diffusion. AI-driven demand forecasting and quality control can reduce waste and improve resource efficiency, supporting the tr…
Gatty M.S.
This paper examines the role of FinTech and AI in advancing green finance. It likely discusses applications such as AI-driven ESG assessment, climate disclosure analysis, and green bond selection. Specific methods and findings are unclear, …
Silpa M.
This paper proposes a federated learning approach to analyze ESG disclosure data from NSE sectoral indices. It aims to improve ESG assessment accuracy while preserving data privacy. The methodology offers technical insights applicable to co…
Moojin Park
This preprint asks whether ESG regulatory events mandating independent third-party assurance produce larger asset-price reactions than mere disclosure expansions. It formalizes a signaling game where verification intensity enters the cost f…
Dominic Ayegba Okoliko, Mehita Iqani, Mehita Iqani
This living repository provides supplementary materials for a multi-phase project analyzing the salience of climate change in electoral discourse in South Africa's 2024 national election and Nigeria's 2023 presidential election, using BERTo…
Newton. Tyler James
AquaContam uses over 4 million samples from 95,223 U.S. water systems to show that ML models learn monitoring processes as much as contamination. Apparent lead-model performance is largely a target-leakage artifact, and monitoring is inequi…
Bushra Ansari, Amjid Khan, Raja Muhammad Usman
This study examines how AI contributes to resilient and sustainable supply chains via multiple case studies. Optimization-driven AI cut emissions or resource waste in 5 of 6 global cases, and predictive analytics/digital twins halved disrup…
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.
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