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 716 papers
Jiaran Liu, Yuheng Huang
This study proposes GBMP-LCA, a gradient boosting–based framework for performance prediction and life-cycle optimization of green building materials. It applies LightGBM to multi-source features including material composition, durability, a…
Dilba Rayaru Kandiyil, M. Sadique, Denise Lee +2
This study proposes a hybrid ML-LCA framework to estimate cement embodied carbon using only publicly available macroeconomic data, predicting clinker-to-cement ratio from GDP per capita and other indicators. The Gradient Boosting model is i…
Ruxiao Li, Bo Zhang, Jiayan Dong +1
Using text analysis on annual reports of Chinese A-share listed firms (2010-2023), this study measures corporate biodiversity attention and finds it significantly enhances sustainable development performance. Mechanisms include alleviating …
Anh Bui-Tuyet, Anh Nguyen-Ngoc-Lan, Nhu Hung Duong +1
Vietnam's ESG disclosure is fragmented and report-based data scarce. This study develops an ML pipeline to classify Vietnamese ESG news on the environmental pillar and maps each to GRI criteria via a ChatGPT-based interpretability agent. Be…
Jaehyun Park
This study compares human coding, rule-based, and LLM semantic scoring architectures for ESG disclosure quality assessment using Korean listed firms' sustainability reports from 2020–2021. LLM-based scoring shows greater convergence with hu…
Mohammed W. A. Saleh, Marwan Mansour, Zaid Jaradat +1
This study examines how ESG performance reduces firm risk (leverage) and how AI disclosure strengthens this effect, using 2020-2024 panel data from 1,310 European firms. Robust regressions show AI disclosure acts as a complementary transpar…
H. Jung, Shaopeng Che, Haein Lee
This study constructs ESG disclosure quality indicators using LLM-assisted content analysis of sustainability reports from S&P 500 Energy firms. It finds limited positive associations between individual indicators and S&P Global ESG Scores,…
Anh Nguyen-Ngoc-Lan, Anh Bui-Tuyet, Nhu Hung Duong +1
This study develops an Artificial Neural Network combined with NLP to classify Vietnamese corporate news into Environmental, Social, and Governance pillars. Using TF-IDF and grid search, the Environmental model achieves 90.48% accuracy and …
Tianli Huang, Grace T. R. Lin
Proposes an Interpretable Quantile Temporal Network (IQTN) for the Chinese carbon allowance (CEA) market, integrating feature gating, causal temporal convolution, and non-crossing quantile layers to forecast multi-horizon VaR and CVaR. Achi…
Alick Andrew Sakala, Yu Chen
Proposes RL-ACO, a reinforcement learning (DQN) framework for adaptive consensus optimization in blockchain-based GHG monitoring. By dynamically tuning cluster size, block interval, and alert priority, it achieves 3,625 TPS at 400 validator…
J J Liu
Using panel data of 281 Chinese cities (2011–2022), this study applies Double Machine Learning and Spatial Durbin Models to examine digital economy's impact on urban green energy efficiency. Findings show digital economy enhances local effi…
Qing Liu
This paper proposes a multi-agent reinforcement learning framework (MADDG-ICS) to minimize carbon emissions across the product lifecycle. PCA is used for dimensionality reduction to stabilize learning. The model achieves 24% emissions reduc…
Inchara R, Madhu M. Nayak
This paper introduces CarbonIQ, a system integrating IoT sensor data (ESP32) and user activity logs for carbon footprint prediction using a Random Forest regression model with hybrid sensor fusion. A generative AI module provides personaliz…
Heyuan Wang
This repository implements Carbon Mind, a global spatiotemporal AI system for reconstructing 120-year multiscale changes in the land carbon sink. It includes all analyses and visualizations from the associated manuscript.
Yanhua Zhang, Gongguo Wu, Chujun Zou +4
This study developed the first coupled LSTM framework to predict bidirectional relationships between blue carbon stocks and fishery abundance in tropical coastal wetlands. Using 96 monthly field observations from 15 sites in China (2018-202…
Xiaoyu Yang, Liyong Yan, Haichao Zheng
This paper proposes a method to quantify community carbon offsets using point cloud segmentation of buildings and environment. By extracting vegetation and structures, it estimates carbon storage and emissions, supporting local GHG manageme…
Xuejun Wang, Qibin Han
This paper proposes a BIM-CIM integrated decision-making method combining NSGA-II and energy simulation for low-carbon urban renewal. Experiments show convergence distance decreasing from 165.40 to 105.20 over 50 generations, with clear syn…
Demet Ozturk, Nuh Erdoğan, Reza Vatankhah Barenji +1
Proposes a dynamic knowledge graph framework to assess hosting capacity for low-carbon technologies (solar PV, EVs) in low-voltage distribution networks, enabling efficient grid planning and operation.
Bin Li, Weilong Yang
This paper develops an AI framework combining symbolic regression, reinforcement learning, and LLMs to optimize photothermal performance of low-carbon building materials. The method is validated in urban microclimate simulations, showing si…
Cheng Feng, Fengqi You
This dataset supports the study showing that existing hydropower can provide firm low-carbon capacity for datacenters. It includes SWAT+ models for 81 global regions, machine-learning streamflow predictions using LSTM-Transformer, and a dat…
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