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 21–40 of 398 papers
Xuelong Zhang, Xiaoling He, Mei Li +3
Using panel data of Chinese listed energy firms (2010-2023), this study shows AI adoption significantly reduces carbon emission intensity. Executive digital background strengthens the effect, and low-carbon city pilots provide complementary…
Saad Khan, Ahmed A. Aboushady, Firdous Nazir +1
This study proposes a privacy-preserving framework to detect, classify, and localize unregistered behind-the-meter low-carbon technologies (PV, EVs, heat pumps) in low-voltage networks. Using GDPR-compliant measurements, it combines LSTM au…
Aurélien Saussay, Misato Sato, Francesco Vona
Using U.S. online job vacancy data, this paper develops a skill-based NLP method to identify low-carbon jobs within occupations. Low-carbon job creation is prevalent in low-skilled occupations but requires more complex skills, yielding a mo…
Issam Benhayoun
This study analyzes factors determining ISSB adoption intention among 335 Moroccan accounting professionals. It demonstrates that institutional pressures influence adoption intention only through perceived usefulness and ease of use. Using …
Kishore Kumar, Susela Devi Suppiah, Preeti Bhaskar +1
This paper examines policy architectures for governing AI to facilitate circular economy transitions, focusing on challenges and opportunities in emerging economies. It discusses how AI technologies can support circular economy principles a…
João Oliveira, Tiago Santos, Fernanda Brito Correia +3
This study proposes a Digital Twin-inspired simulation framework to support renewable energy community (REC) planning. It integrates high-resolution smart-meter data, demographic filtering, and an AI-based N-HiTS forecasting model, applied …
Shangjie Liu, Agyemang Kwasi Sampene, Hazrat Hassan
This study analyzes nonlinear effects of digital technologies, clean energy transition, and industrial upgrading on climate change in five major robot-adopting economies (China, Japan, Germany, US, South Korea) using a multilayer neural net…
Radosveta Krasteva-Hristova
This study analyzes how the UK Big Four audit firms disclose safeguards for human oversight and accountability in AI-assisted work within their 2024 transparency reports. Developing an AI-Judgment Governance Disclosure Index (AI-JGDI) acros…
Summaiya Unnisa Begum, Mohammed Nadeem Ullah, Mohammed Abdul Ghani Khan
Carbon price forecasting is hard; existing work clusters on EU/China, compresses policy text into sentiment, and lacks calibration or explanation stability. This paper distills ten gaps into an impact-feasibility matrix and proposes EPA-Car…
Huanbei Zhao, Changhong Yu, Guobin Fu +3
This paper proposes a multi-stage low-carbon planning method for data center clusters that coordinates construction sequencing and load growth to avoid redundant early investment while ensuring supply reliability. It integrates a tiered car…
Abdulrahman H. Ba-Alawi, Abdo Abdullah Ahmed Gassar
This review examines AI's role in enabling net-zero buildings (NZBs) through HVAC efficiency, renewable integration, and intelligent control. AI techniques, especially reinforcement learning and digital twins, improve energy forecasting (R2…
Mingyang You, Xiaohui He, Xinyu Pu +2
Using panel data from 286 Chinese cities (2011-2021), this study examines the spatial spillover effects of AI technology innovation (AITI) on carbon inequality. Findings show AITI significantly reduces carbon inequality and has spatial spil…
Dandan Song, Q. Truong, Ha-Oanh Vu +2
Using U.S. firm data from 2002-2023, this paper examines the causal impact of climate risk on corporate financial constraints, combining text-based climate risk measures with carbon intensity. Transition risks significantly tighten financin…
Radosveta Krasteva-Hristova, Vanya Georgieva
This conceptual paper systematically examines AI's role in sustainability assurance, identifying five value-adding domains: evidence extraction, criteria mapping, anomaly/greenwashing screening, external-data triangulation, and documentatio…
ANJALI CHAUDHARY, Hebah Shalhoob, Kholoud Y. Bajunaied +5
This study provides a comprehensive analysis integrating AI optimization for biofuel production on degraded lands with climate mitigation and green finance mobilization. Based on 152 studies and international datasets, it shows AI technique…
Amel Mahmood Ali Alobaidi
This study examines the impact of voluntary climate anxiety disclosure on investor behavior using mixed methods: textual analysis, archival data, and experiments. Disclosure significantly predicts investor behavior, moderated by firm size a…
Sinhika Deshmukh, S. Karve
This study proposes and evaluates a blockchain-enabled traceability framework for Tata Steel's supply chain. Scenario modeling suggests traceability coverage could rise from 42% to 98%, scrap reuse from 60% to 88%, enabling an estimated 3.0…
Wang-Chen Yang, H. Arian, Cui-Cui Luo
This paper proposes a constrained residual learning framework for portfolio optimization in China and Europe's carbon markets. It applies machine learning to capture carbon price dynamics and optimize risk-return trade-offs, offering improv…
Hannes Cordes
This systematic review maps NLP tasks used to analyze sustainability reports, categorizing 160 studies hierarchically. It finds keyword search, content analysis, and topic modeling are most common, and highlights limitations such as linguis…
Rongyu Ji
This qualitative case study of Microsoft examines how ESG information disclosure affects capital market performance in AI enterprises. It finds that completeness, openness, and AI-specific governance of ESG disclosure are high, with respons…
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