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 281–300 of 943 papers
Kokubu, Hiroyuki
A quarterly snapshot of GX disclosure metrics machine-extracted from integrated reports of TSE Prime-listed companies using AI. Covers Scope 1/2/3, SBT, TCFD, CDP, renewable ratio, internal carbon price, and purchased carbon credits. This v…
Xinyi Mu, Dujuan Yang, Qi Han
This study proposes a Bayesian learning agent-based model to analyze heating system decarbonization decisions in homeowner associations. It models the learning process of members and evaluates how policy and economic factors affect the adop…
Rai A.
This study combines techno-economic assessment with deep learning methods to evaluate wind-driven green hydrogen fuel production in Scandinavia. It uses deep learning models to predict wind output and hydrogen production costs, assessing fe…
Leng J.
This study applies data analytics and machine learning to site screening for CO2 geological storage in depleted oil and gas reservoirs in the Gulf of Mexico. It proposes a method to improve the accuracy and speed of storage site evaluation,…
Lin K.
This review comprehensively examines the application of machine learning (ML) to CO2 geological storage. It covers predictive modeling, site selection, monitoring, and risk assessment, highlighting how ML enhances storage efficiency and saf…
Kumar R.
This paper reviews recent trends and future prospects of AI and sustainable finance using Natural Language Processing (NLP) models. It focuses on applications in ESG assessment and greenwashing detection, discussing how AI can enhance decis…
Zhipeng Han, Liguo Wang, Yongling Wang
Using an interpretable machine learning framework (LASSO, random forest, SHAP) on 7,791 firm-year observations of Chinese A-share industrial firms (2016-2022), this study reveals a threshold-dependent ESG-carbon total factor productivity (C…
Mira Kopp
This study develops a typology of European cities for climate mitigation and adaptation using automated text extraction and clustering. It integrates a systematic review to link city types with tailored strategies, supporting evidence-based…
Šribar, Jernej, Mohorcic, Mihael, Čampa, Andrej
This paper proposes CoMAD V2G, a multi-agent deep reinforcement learning framework for coordinated management of V2G-enabled EVs and shared energy storage in Positive Energy Districts. Validated with real-world datasets, it reduces grid rel…
KONE Bakary, DOSSO Mouhamadou, DIARRA Mamadou +1
This study applies recurrent neural networks (RNN) to temporally optimize greenhouse gas (GHG) emissions from a hybrid energy system. By leveraging the time-series prediction capability of RNN, it derives operation schedules that dynamicall…
Gotoh R.
This paper proposes a hierarchical GA-LP framework with explainable AI and clustering to generate and interpret diverse feasible solutions for net-zero energy systems. It integrates optimization with interpretability to aid decision-makers.…
Sayyed Sadaqat Hussain Shah, Arshad Javed, Muhammad Mahboob Khan +2
This study examines how AI adoption influences green bond issuance and corporate ESG scores using panel data from 54 economies (2019-2024) and multiple models (fixed-effects, quantile regression, TVP-VAR-SV). It finds that a one-standard-de…
D. Samontaray, Randheer Kokku, N. M. Nasir +1
This study examines the relationship between FinTech disclosure and ESG reporting among non-financial firms listed on the Saudi Stock Exchange from 2021-2024 using textual analysis. An ESG Disclosure Index and a FinTech adoption measure wer…
Meng-Sian Chen, Jing Wang, Mengfei Peng
This paper proposes a bi-objective optimization model for virtual power plants (VPPs) integrating wind, PV, storage, and gas units, using the NSGA-II algorithm to minimize total cost and carbon emissions. A case study reveals a trade-off be…
张荪理, Jian Yin
This paper empirically analyzes the impact of intelligent technology penetration on the green transition of urban energy systems in Chinese cities using double/debiased machine learning, suggesting contributions to emission reduction and re…
Ahmet Eser, M. Sadrettin Zeybek, Ayşe Dinçer +2
This study uses machine learning to analyze the rheological properties of alginate/Fe3O4 composite suspensions and simultaneously assesses the carbon footprint of the process. ML models predict rheological parameters such as viscosity and e…
Ziming Xue, Jun Zhou
This paper proposes a Q-learning-based feedback optimization method for low-carbon flexible job shop scheduling. It uses type-2 fuzzy sets to handle uncertainty and optimizes for reduced energy consumption and carbon emissions.
Watheq J. Al‐Mudhafar, Ahmed Alsubaih, Kamy Sepehrnoori
This review systematically examines ML applications in the CCS lifecycle, covering pre-injection evaluation, injection optimization, and post-injection monitoring. It covers methods like Random Forest, SVR, XGBoost, and deep learning for an…
Kanungo R.P.
This paper critically examines the integration of sustainable finance, AI, and responsible investing. However, no abstract is available, so detailed findings are unknown.
Rine Dewi Mustikasari, Maria Ulpah
Using XGBoost and SHAP on 1,362 global green bonds (2014-2023), this study finds that structural and macroeconomic factors dominate yield formation, while ESG attributes—especially the social pillar—matter after controlling for macro-financ…
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