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 101–120 of 986 papers
Avant Muhammad Armadan, Fitriaty, M. H. Saputra
This study examines ESG disclosure in sustainability reports of IDX ESG Leaders index companies, evaluating patterns that may signal greenwashing. Using a mixed-methods approach with an ESG Disclosure Index based on GRI standards and NVivo …
Yaodan Chi, Xinyu Ding, Qingcai Zhou +2
This paper proposes a novel multi-objective artificial lemming algorithm for optimal operation of a microgrid integrating carbon capture and power-to-methanol. The method balances operational cost and CO2 emissions, deriving efficient Paret…
Mohammed Aldawsari, Saeed Tiari, Sultan Aldossary +1
This paper proposes AI-based prediction and optimization of energy and exergy efficiency in thermal systems. Improving energy efficiency directly contributes to decarbonization and emission reduction in industrial sectors. The application o…
Kaisheng Yang, Ximan An
This study constructs a climate risk attention indicator for Chinese funds using Word2Vec text analysis of annual reports. Applying the Brinson model, it finds that climate risk attention boosts excess returns, with a 1% increase in acute p…
Alfa Yohannis, Alexander Waworuntu
This paper compares the carbon footprint of document-oriented, relational, and hybrid object-relational databases for image-based time-series workloads in Green IoT, proposing a carbon-aware storage selection method. It leverages AI/ML tech…
Shan Wang, Yanze Wang, Yue Gao +1
This paper empirically analyzes the effect of 'AI-washing'—corporate overstatement of AI capabilities—on actual energy transition efforts. It proposes methods to detect discrepancies between AI claims and real green actions, assessing the c…
Qi Chen
This paper proposes RADR-MO, a dynamic re-routing framework for supply chain resilience that jointly optimizes cost, resilience, and carbon emissions using graph attention networks and meta-reinforcement learning. It adapts to novel disrupt…
Manoj D. S, Sankari M, Aruneswar Meera S +1
Greenscan-AICO2R is a framework for evaluating and optimizing website carbon emissions. It performs multi-page crawling, rule-based image classification, and NLP-driven tailored recommendations. Simulations show 30-40% emission reduction vi…
Shiju Li, Hongjie Lan, Hanying Jiang +5
This paper proposes a CEEMDAN-SE-Transformer model to improve carbon price forecasting accuracy. Using data from China's Guangdong carbon market (2015-2022) and incorporating factors like oil prices and EU carbon prices, the model outperfor…
Abbas N, Hao L, Ayoubi M
This study proposes a secure control framework using a reliability-gated GRU to detect and correct false data injection (FDI) attacks on floating offshore wind turbines (FOWT). The method evaluates sensor prediction reliability and compensa…
Athira K.
This paper proposes an AI-based method to predict green bond performance, contributing to climate finance and ESG evaluation. Details are limited due to missing abstract, but it represents a key intersection of AI and sustainable finance.
Adelina Zeqiri
This paper proposes a conceptual framework linking AI-driven marketing to sustainable tourism outcomes, mapping four pathways: smart operations, environmental intelligence, demand shaping, and governance. It advances eight testable proposit…
V. Vairagade
This paper presents an AI-assisted, data-driven approach for designing cement-based hybrid materials and forecasting their lifecycle performance to enhance environmental sustainability. While specific findings are not available, the work li…
N. Subhan, Nouman Ali, H. Hanif
This study analyzes contagion effects of green (Cardano, Stellar) and non-green (Bitcoin, Monero) emerging cryptocurrencies on energy markets (WTI, Brent, Natural Gas) using Quantile VAR and quantile connectedness frameworks. Using daily da…
Zilin Wang, Meili Feng, M. Johnson +6
Using machine learning models trained on published datasets, this study projects greenhouse gas emissions from hydropower reservoirs under climate change. Under a net-zero scenario, total emissions from installed and planned projects could …
Gautam Shivaraj
This chapter explores the role of AI in enhancing resource efficiency and reducing carbon emissions, covering applications such as energy optimization, waste reduction, and resource management. Through case studies in manufacturing, agricul…
Jiwei Song, Yang Bin Huang, Lianmei Zhu
Using panel data of Chinese A-share listed firms from 2013 to 2024, this paper finds a significant negative association between the disclosure-implementation mismatch in digital transformation and corporate ESG ratings. The mismatch proxy c…
Rajeswari Kannan, Raj Firke, Shreya Bengle +1
This paper presents GreenBench, a benchmarking framework to evaluate energy efficiency and carbon footprint of open-source LLM inference on Apple Silicon. Measurements show 30-40x better energy efficiency per token than datacenter GPUs for …
Sheetal Thapa, Asha Rani N R
This chapter reviews AI's role in reducing carbon footprints, covering real-time emissions monitoring, predictive modeling, and sustainable decision-making. It presents case studies (Google data centers, IBM Green Horizon, Climate TRACE) an…
Rui Zhao, WenXuan Sun, Xiao Zhou +3
This study constructs a standardized, traceable global carbon emission factor database for road construction. It leverages large language models to extract key data from unstructured documents, ensuring reliability through manual verificati…
Browse by Topic