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 401–420 of 1070 papers
Hamidah Nurhayati, Muslimin Muslimin, Fajrin Satria Dwi Kesumah
This study analyzes panel data from 30 firms listed on the IDX ESG Leaders Index (2021-2025) to examine the impact of AI and ESG disclosure on firm value. Using fixed-effects regression, it finds no significant standalone effects, but firm …
Abdulazeez Y.H. Saif-Alyousfi
This study proposes a hybrid framework integrating TVP-VAR connectedness with LSTM deep learning to analyze and forecast volatility spillovers in green financial markets, including green bonds, renewable energy stocks, and carbon markets. U…
Yuxiang Xiao, Xiong Zheng
This study develops a reproducible EnergyPlus-based framework combining parametric simulation, interpretable sensitivity analysis, and machine learning surrogates to analyze operational energy and carbon coupling for Beijing hotels. From 20…
Francisco Climent, Pedro Carmona, Alexandre Momparler
This dataset comprises European global equity retail mutual funds with five-year performance records (2020-2024) from Refinitiv Eikon. It includes variables such as fund return, ESG scores (environmental, social, governance), expense ratio,…
Aiting Xu, Senhao Fang, Jiapeng Chen +1
This study proposes a carbon emission forecasting framework integrating STIRPAT economic theory with an adaptive neural architecture (GSGDN). It captures regional dependencies via geographically weighted regression and reduces operational c…
Hadia Arshad
This review comprehensively analyzes AI's environmental footprint (energy, water, carbon, e-waste, rare earths) and its climate mitigation potential. Data center electricity reached 415-470 TWh in 2024-25, with AI workloads projected to rea…
Tony Newton
This paper addresses the paradox that AI systems providing ecosystem services also consume water through their infrastructure. It proposes the AI–Nature Reciprocity Gate, a local, seasonal, full-scope standard to verify whether nature-based…
Nisha Shankar, K. Sagar
Proposes a framework integrating AI and blockchain for carbon credit trading tailored to Indian tourism firms. Introduces the Emission Reduction Progress Factor, uses Entropy Optimal Transport for P2P trading, and particle swarm optimizatio…
Xie, Tengyue
An intelligent emergency decision-support system for transnational renewable energy projects is proposed. It fuses structured and textual evidence via conflict-aware Dempster-Shafer and uses cost-sensitive case-based retrieval to diagnose r…
Michael Addai
This study investigates AI adoption as a driver of sustainability reporting among Ghana Stock Exchange-listed firms. It explores how AI transforms reporting practices, highlighting challenges and opportunities for disclosure in an emerging …
Nawal Louzi, Mohammad Q. Al-Jamal, Mahmoud AlJamal +2
This study proposes a framework integrating AnyLogic simulation and graph-based multi-agent deep reinforcement learning (Graph-MAPPO) to optimize pavement maintenance for carbon reduction and renewable energy use. It manages pavement condit…
Dr. Ayalapogu Ratna Raju, T. Akila, Dr.K Umapathy +3
This study proposes a data-centric framework using a Multilayer Perceptron (MLP) to predict carbon emission reduction potential across the lifecycle of residential buildings. Analyzing 1,640 projects, the model classifies reduction levels w…
Vincenzo Barrile, Emanuela Genovese, Clemente Maesano +2
This study integrates machine learning, remote sensing, and ecosystem modeling to assess carbon dynamics in a semi-arid region of Tunisia. It maps land use/land cover, estimates carbon stocks and emissions using InVEST, and simulates future…
Musa Lefagana, H. Suleiman
This study proposes a conceptual model where cloud accounting moderates the relationship between audit quality and sustainability disclosure. It identifies five audit quality dimensions (anomaly detection, cost efficiency, risk assessment, …
J. Chinna
This study analyzes the impact of AI on ESG reporting from 2019 to 2025, showing adoption surged from 15% to 63%. AI evolved from basic data collection to machine learning, NLP, and generative AI, improving transparency, accuracy, and timel…
Bochen Zhang, H. Huang, Zhili Han +1
This paper empirically analyzes the impact of digital-real integration (DRI) on green total factor productivity (GTFP) using panel data from Chinese industrial enterprises. Fixed-effects and mediation analyses show that DRI significantly im…
Yuxuan Guo, Linxin Yin, Ruihan Xiong +2
This study addresses the severe risk of capital chain rupture in China's construction industry, focusing on vulnerable subcontractors. It proposes an AI-assisted risk control platform and innovative insurance products, using gradient boosti…
Zahra Yaghoubi, Mahyar Mansouri, Hosein Alimardani +2
This study applies explainable machine learning to optimize methane tri-reforming (TRM) for low-emission hydrogen production and CO2 utilization. Using 46,464 equilibrium data points, a neural network achieved the best predictive performanc…
Jiyoung Ko, Yong-Woon Kim, Y. Byun
This study proposes a deep learning framework for accurate greenhouse detection and segmentation from high-resolution aerial images. By integrating class-weighted loss and class-aware sampling into YOLOv8, it achieved mAP@0.5:0.95 of 0.566.…
Laura Elena Jiménez-Casillas, Román Rodríguez-Aguilar, Marisol Velázquez-Salazar +1
This study measures the individual impact of CSR and sustainable operations on financial performance for Mexican listed firms using Random Forest and explainable ML (ICE, PDP, SHAP). Environmental scores show the most consistent contributio…
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