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 81–100 of 217 papers
Yubao Wang, Zhenzhong Liu
This study proposes a four-dimensional evaluation system with 22 indicators and a Fuzzy DEMATEL-MultiMOORA-Borda model to assess synergy between digitalization and decarbonization in coal power. Evaluating seven transition scenarios, it fin…
Xing Bao, Xu Zhang
This paper constructs a climate risk perception index from Chinese A-share firms' annual reports (2003-2023) and examines its impact on corporate green innovation. Stronger climate risk perception significantly promotes both quality and qua…
Nabeela Ehsan
This study examines whether AI-driven ESG compliance monitoring reduces corporate sustainability risk using panel data from 320 listed firms (2016-2024). GLS regression shows AI monitoring significantly lowers risk (coefficient -6.84), and …
Jiehui Chen, Dexing Sun, Feiwei Li +4
This paper proposes a dynamic multi-objective optimization framework for industrial electricity consumption, integrating high-precision load forecasting and optimal scheduling. An improved LSTM model (DGO-TA-LSTM) is used for forecasting, a…
Santosh Kumar, Neha Gupta, Swarnima Gorani
This paper discusses the importance of policy frameworks and incentive design for applying AI in green finance. It argues that without proper governance, risks such as algorithmic discrimination and data bias could undermine social goals, a…
Monica Canepa
This paper proposes an explainable multi-criteria decision framework for maritime and port decarbonization, integrating AHP, TOPSIS, and SHAP. It incorporates cyber-physical security as a core criterion and is validated with real-world data…
Yunhua Zhang, Jia Wu
This paper examines the impact of China's green finance reform policy on corporate sustainable innovation, drawing on institutional and stakeholder theory. Using DID analysis on Chinese A-share listed firms from 2012-2022, it finds the poli…
Sendy Dwi Haryanto, Dian Agustia, Iman Harymawan
This study analyzes 205 European oil and gas firms (2018-2023) to show that risk management committees (RMCs) mitigate 37% of emissions-related value destruction, and GRI compliance mediates 12% of the emissions-value relationship, but only…
Nanlan Du
This paper empirically analyzes the impact of green finance policies on corporate low-carbon transition in China using a multilayer perceptron (MLP) algorithm and difference-in-differences (DID) method. Using panel data from 30 provinces fr…
Aiying Zhao, Qian Chen, Yang Zhao +3
This paper proposes a hybrid Transformer-sLSTM model for carbon price forecasting, integrating VMD for multi-scale decomposition and WOA for parameter optimization. Achieving R2=0.9862 and MAPE=0.56% on real datasets, it enhances prediction…
Svenja Hauck, Lucas Greif
This article reviews the role of knowledge graphs and AI in sustainability assessment, enabling comparison of environmental impacts during product development. A case study demonstrates carbon footprint analysis using these technologies, hi…
YINKA ADERIBIGBE
This paper proposes a cloud-native, serverless ML pipeline on AWS for real-time carbon accounting. It uses NLP and XGBoost to analyze corporate sustainability reports, cross-reference with supply chain telemetry, and compute a Disclosure In…
Jingyi Xu, Minghui Cheng, Anchen Sun
This paper introduces BeDA, a multimodal large-language-model instrument, to audit corporate building-decarbonization disclosure. Applied to a global panel of 2,246 firms (2003-2023), it finds that only about one in five firm-reports disclo…
Peixoto E.
This paper proposes an architecture for monitoring and reporting ESG indicators in AI-driven value chains. It automates data collection and analysis across the supply chain, facilitating stakeholder reporting. By leveraging AI, it enables m…
Iqra Mubeen, Samina Rauf
This paper investigates whether ESG sentiment generated by LLMs can predict portfolio risk. It finds that positive ESG sentiment correlates with lower volatility and downside risk, while negative sentiment increases risk exposure. LLMs outp…
Shuai Li, Pei Yu, Z J Yang +1
This supplement accompanies research applying machine learning (XGBoost, Bayesian optimization) to low-carbon concrete mix design. It includes material-stage emission factors, optimized hyperparameters, minimum-embodied-carbon candidate mix…
Ummesara ., Chaitanya Kumar Sahu, R Ranjith +5
This review integrates trends in AI-enabled regenerative agriculture (RA) to promote climate resilience. AI tools (ML, remote sensing, IoT) enable dynamic monitoring of soil/crop health, carbon accounting, and adaptive farm management, acce…
D. R, Santhosh Kumar A G
This secondary data analysis (2020-2025 sources including Bloomberg, MSCI, World Bank) examines AI's role in ESG integration and sustainable finance. It finds that NLP and ML improve ESG data coverage by up to 40% and enhance climate risk p…
Murali Krishna Pasupuleti
This paper proposes the Generative Climate-Aware Deep Hedging (G-CADH) framework, which combines deep hedging with generative market models to manage portfolio risk and financial resilience under climate change. It integrates a conditional …
Anuj Pal
This study evaluates the ability of large language models (LLMs) to distinguish between symbolic and substantive sustainability communication in ESG disclosures. It constructs a dataset from annual and sustainability reports of publicly lis…
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