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 221–240 of 986 papers
Simon Utsu-Ingwu, Lloyd Endurance Ogbondamati
This study proposes a hybrid offshore energy system integrating wind, wave, and floating solar, controlled by AI-based predictive control (MPC and reinforcement learning). Simulations show smoothed output averaging 1567 kW, LCOE of 13.84-39…
Ewees A.A.
This paper proposes an optimized dendritic neural network (DNN) to predict the viscosity of CO2-N2 gas mixtures, providing accurate thermophysical data crucial for CCUS process design and operation. It demonstrates superior prediction accur…
Kim H.
This paper proposes a BERT-based text analysis method to assess the alignment of corporate ESG disclosures with the UN SDGs. By automatically classifying disclosure texts, it quantitatively measures corporate contributions to the SDGs, enha…
Shu Li, Yu Song, Zhongli Bai
This paper proposes a flexible arbitrage framework for co-located renewable-battery systems (RES-BESS) bidding in multiple electricity markets, using Soft Actor-Critic (SAC) and Temporal Convolutional Network (TCN) to autonomously decide ch…
Olamide Efosa-Austin, Olajuwon Ifalade, C. Asuquo +2
Aqua Intel™ is a digital optimization layer for produced water treatment in Nigeria's oil and gas sector, using linear programming to dynamically allocate water across treatment, reuse, and disposal pathways. A pilot simulation on a 25,000 …
Kalyani Singh, Vaibhavi Shahane, Mrunalini Khandare
This study quantitatively analyzes the impact of blockchain-enabled green finance on corporate sustainability and performance using panel data from Indian firms across sectors. Integrating stakeholder, institutional, and TOE frameworks, it …
Xirong Gao, Shiwei Wei
This study empirically examines whether supply chain finance (SCF) promotes corporate green innovation using data from Chinese A-share listed firms. Using patent data and text analysis, it finds a positive impact of SCF on green innovation,…
Wai Yie Leong
This paper proposes integrating ESG and GBI (Green Building Index) rating indicators into a BIM-based automated sustainability reporting system for Malaysian property developers. It aims to streamline reporting processes and improve data co…
Weina Zhu, Jiaxin Shen, Yixin Li +2
This study uses LDA topic modeling and fsQCA to identify pathways for carbon emission reduction in green buildings in China. It analyzes configurations of factors that lead to effective carbon reduction, offering insights for policy and des…
Nicholas Dietrich, David McShannon, Merel Huisman +2
This study quantified the impact of deep learning training policies on CO2 emissions and performance for chest radiograph classification. Prospective early stopping preserved performance while reducing emissions by up to 38% and improving c…
Murali Krishna Pasupuleti
This monograph proposes an integrated architecture for AI-verified carbon credit intelligence, combining uncertainty-aware measurement, causal additionality analysis, ML, geospatial analytics, digital MRV, and market governance. It treats v…
Gyamfi B.A.
This paper uses causal machine learning and Bayesian methods to analyze the impact of green financing on corporate leverage and dividend policy. It estimates the causal effects of green bonds and sustainability-linked loans on firm financia…
Van Tam N.
This paper maps the causal relationships of generative AI adoption for net-zero built environments using expert insights and paradox theory. It reveals complex interactions between opportunities and challenges, offering implications for pol…
Jing Song, Xiaoyu Xu, Qi Li +2
This study proposes an improved AHP-Ridge Regression hybrid model for evaluating consumer trust in cross-border B2C e-commerce, integrating expert knowledge with consumer data. Validated on 387 survey responses, it achieves better predictiv…
F. Kelechi, A. Aribisala
This paper proposes a blockchain-based (Hyperledger Fabric) automated system for tracking Scope 1 and 2 emissions in indigenous Nigerian oil and gas companies. It reduces manual reporting time by 75%, eliminates errors, and ensures IFRS S2-…
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 …
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…
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 …
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…
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