This page provides an English interface to the gxceed GX paper corpus. The corpus aggregates papers from 13 open 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 1–20 of 987 papers
A. Wesley, T. Kangwansura
This paper reports a drilling contractor's transformation from annual sustainability disclosure to an operational, AI-enabled emission management system. Based on a 2021 GHG baseline, operational control boundaries, and Net Zero 2050 commit…
Futi Liu, Zian Zhao
This paper addresses ESG-aware portfolio optimization under decision-dependent return uncertainty. It constructs a joint polyhedral uncertainty set and formulates a two-stage robust optimization with CVaR constraints, solved via column-and-…
Idham Idham, Chusnul Rofiah
This study examines how cyber-auditing and the conceptual foundation of digital assurance redefine auditors' roles in multinational firms. Using a systematic literature review, it finds that integrating AI and data analytics into audit prac…
Yingwen Guo, Miao He, Weiyin Zhang
This study examines the impact of geographic proximity to cancer villages on firm valuation using Chinese listed firms from 2004 to 2022. A one-standard-deviation increase in proximity reduces Tobin's Q by about 1.7%, driven by higher compl…
S. Amoo, Ali Attajer, Ismahen Zaid +1
This study develops an optimization framework for sustainable AAC production using discrete-event simulation and machine-learning surrogates. It generates 116,640 production scenarios and trains ML models to predict CO2e emissions, cost, an…
Ruolan Zhang, Wei Shen, Dejian Wei +2
This paper proposes a dynamic network framework integrating AIS and MRV data to assess shipping traffic resilience and green operations. Using real AIS and full-year DGX data, it computes functionality curves and Resilience-Green Index valu…
K. Sonawane, P. Saini, U. Biradar +3
This paper proposes an AI-driven integrated digital framework for CCUS to enhance containment assurance and carbon credit credibility. It integrates multi-modal data (DAS/DTS, microseismic, satellite) and uses anomaly detection, Bayesian in…
Yilin Wang, Xujing Dai, Xueyan Li
This study examines whether China's Green Finance Reform and Innovation Pilot Zones (GFRIZ) policy improved urban supply chain resilience, using panel data for 285 cities from 2012-2024 and a partially linear Double Machine Learning approac…
Nehad Gaši, Kemal Spahić, Bekir Fulan
This paper applies AI to optimize smart intersections powered by renewable energy, using machine learning on traffic, energy, and weather data for dynamic signal control. It reduces congestion, improves safety, and cuts CO₂ emissions, contr…
Bo Zhao, Jafar Hussain, Zhenyu Qiu +2
This study analyzes the impact of adopting Smart Emission Control Systems and Energy Security on CO2 reduction and profitability in a supply chain of 500 manufacturers, agents, and retailers using game theory and simulation-based optimizati…
Jian Xu, Jie Song, Yifan Yan +3
This study constructs an energy production revolution index (EIT) for the Yellow River Basin and uses stacking ensemble and Lasso-STIRPAT models to project carbon emissions and EIT from 2025-2045. Scenario analysis reveals optimal pathways …
Chuanchao Li, Yuanhe Du, Shuangyang Zhai
Using data from 284 Chinese cities (2012-2023) and a quasi-natural experiment of climate-resilient city pilots, this study applies double machine learning and spatial lag models to show that climate risk governance significantly enhances ur…
Xiao Zhang, Zhaoqiao Ding
This paper proposes a smart cold chain system integrating IoT sensors and machine learning to enable dynamic monitoring and evaluation of full-chain carbon footprint. Real-time data collection and predictive models optimize energy consumpti…
Shaohuai Feng
This repository provides supplementary data for the manuscript on the SCOPE framework, a reproducibility-driven variable screening method for carbon price forecasting. It includes raw and processed data, additional analyses, supplementary f…
Anonymous
Replication data for a two-period study of CSRD disclosure quality around the 2026 Omnibus scope reduction, covering 188 EU firms and 376 reports coded with a frozen LLM prompt on a ten-dimension instrument. Includes validation outputs (hum…
H. T. Dinh, Hà Thị Thu Bùi, H. Nguyen +3
Using survey data from 853 hotel employees in Vietnam, this study employs PLS-SEM to examine how AI and CSR influence employee green behavior (EGB), green finance (GF), and green performance (GP). Findings show AI positively impacts GF and …
Mohamed Vajeebu Mohamed Hussain Pareeth
This review proposes an AI-driven optimization framework for green hydrogen production networks in Saudi industrial cities (NEOM, Yanbu, Jubail, etc.). It integrates machine learning, digital twins, stochastic optimization, and explainable …
Liu Na, Wan Nordin Wan‐Hussin, Chin Sok Fun
This study examines whether ESG-linked executive compensation influences carbon disclosure quality in China's A-share listed firms, and whether board climate oversight mediates this relationship. Using panel data from 2021-2023, it finds th…
Luana Serino, Manlio Del Giudice, Francesco Campanella +1
This study empirically examines the impact of AI-related disclosure on ESG outcomes in U.S. firms. It investigates whether AI adoption and disclosure are associated with improved ESG performance, contributing to the emerging literature on A…
Sanjana Kewalramani, Sharvari Nimje, Tanishq Rajpuria +2
This review systematically organizes research applying AI/ML to Kubernetes scheduling for improving energy efficiency and reducing carbon emissions. It shows that ML-based workload placement optimization can effectively reduce data center e…
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