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 141–160 of 991 papers
M. Kim, J. Yoo
This study re-examines the conflicting findings on ESG and tax avoidance, attributing them to methodological issues: measurement differences, ESG index heterogeneity, and endogeneity. Using random forest and double machine learning, it show…
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…
Marco Aurélio dos Santos Bernardes
This study introduces Adaptive Energy Stations (AESs) that integrate marginal grid emissions, fuel life-cycle carbon intensities, wholesale prices, and vehicle constraints into station-level dispatch optimization. A one-week winter CAISO pr…
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…
Anh Bui-Tuyet, Nhat Duy Lai, Quyen Hua-Thi-Ngoc
AI has a dual role in energy transition: productivity gains in heavy industry reduce energy intensity, while data centers surge electricity demand. Using a 23-sector CGE model for Vietnam through 2035, Green AI boosts GDP by +1.79%, while B…
Hussain K. Alagele, Nagham Hussein Neama
This study analyzes the impacts of economic, ESG, and AI factors on energy transition in three OECD countries via skills enhancement, using CS-ARDL on 2022-2026 panel data. Economic factors negatively impact transition, but skills enhanceme…
Guoqun Ma, Zhiyu Qin, Lihao Yao +1
Using China's major grain-producing areas designation as a quasi-natural experiment, this study employs DID and double machine learning to show the policy significantly reduces agricultural carbon emissions and pollution, with crop structur…
Xiao Xiao, Miaomiao Zhang, Yueyi Wei +1
This study proposes a green innovation framework combining permissioned blockchain with adaptive monitoring to reduce waste and decarbonize food supply chains. A 12-month pilot in a Chinese dairy supply chain reduced data transmission by 90…
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…
Ikhlas Laasri, Vaibhav Shrivastava
This review examines the 'Biostimulants 4.0' paradigm, converting agro-industrial waste into high-value biostimulants via nanotechnology and AI. Green extraction methods like enzymatic hydrolysis cut carbon emissions by up to 23.2 times vs.…
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 …
Mei Tao, Hongchen Liu
This paper proposes a multi-objective optimization method for runway-taxiway systems to enhance airport surface sustainability. Integrating CTOT compliance, taxiing time, and runway workload balance, the hybrid SSA-SCA-NSGA-II algorithm is …
HUAN Jiajia, LIU Jiawen, LIU Guozhong, WEN Fushuan, CHEN Biyun, GUO Shaoqing
This paper proposes a coordinated dispatching strategy for interconnected data centers to participate in low-carbon power grid operation by leveraging the spatio-temporal flexibility of computing loads. A high-precision dynamic carbon accou…
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…
Aso Abdullah, Abdullah Omar Yassen, Mohammad Talha
Using panel data from 5,378 firms across Asia, Europe, and North America (2015-2024), this study examines how board governance structures affect climate governance effectiveness and transition performance. Independent and gender-diverse boa…
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…
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