GX & Decarbonization Research

Samruddhi Pandit, Anuja Mukherjee, Rugved Dani +1

This paper proposes a five-layer AI-enabled smart agriculture framework for climate-resilient and sustainable food systems. It integrates climate inputs, sensing, predictive analytics, decision support, and sustainability outcomes. Case stu…

Shijie Ma, Jingzhi Hou, Haoran Niu +1

Using a debiased machine learning framework and causal forest analysis on Chinese high-emission listed firms (2009–2024), this study reveals a 'threshold-trigger' mechanism: once climate pressures exceed firm endurance, companies shift to s…

Xiaolong Zhang, Congjun Rao, Abdulrahman Almandeel +2

This paper proposes a spatiotemporal graph-based grey model incorporating fractional calculus to improve greenhouse gas emission forecasting, potentially outperforming traditional methods.

Feng Gao, Maoying Deng, Xingdong Deng +6

This paper proposes a framework integrating explainable AI and clustering to decouple passenger and freight transport emissions in urban areas. It enables precision governance by identifying distinct emission patterns, supporting targeted d…

Zhuokai Zhou, Sizhuo Wang

This study uses a hybrid forecasting framework incorporating machine learning to analyze predictive linkages between carbon markets and coking coal futures. It provides implications for market efficiency and highlights the interconnection b…

Jun Lyu, Yu Shu, Shuo Wang

Using unsupervised machine learning (LDA topic modeling, semantic network analysis, and hierarchical clustering) on subtitle transcripts from 60 YouTube green energy documentaries, this study identifies three framing communities: Technologi…

Noah Lund Syrdal, Anders Vestrum, Jörgen Bergh

This paper proposes a carbon-aware re-ranking strategy for e-commerce recommendations. It estimates missing product carbon footprints using a retrieval-augmented pipeline with LLM and semantic similarity, then applies a tunable re-ranking o…

Harshal Gavali, Apurva Jangle, Asiya Attar +2

This paper systematically reviews the application of AI in climate change mitigation and adaptation, covering climate modeling, explainable AI, and regulatory compliance. It proposes the Climate Intelligence Integration Model (CIIM), a gove…

Dwi Nova Wijaya, Rommy Mochamad Ramdhani, Dewi Fitrotus Sa’diyah

Using panel data of 3,794 Indonesian listed firms (2018-2024), this study analyzes how narrative and quantitative ESG disclosures affect firm value (Tobin's Q). Quantitative disclosure significantly increases firm value, while narrative alo…

Samia Elattar

This paper proposes an AI framework for carbon-aware production scheduling that integrates environmental and public health co-benefits, enabling efficient emission reductions for industrial decarbonization.

A. Sunitha, K. Srinivas, T.Radhika, B.Chandrakala Naik, P. Sandya Rani

This study empirically analyzes the impact of AI, big data, and fintech adoption on sustainability reporting quality and corporate transparency using PLS-SEM on a sample of 312 professionals from India, UAE, and UK. All three digital constr…

Manoj Govindaraj, S. Shafik, Jenifer Lawrence

This paper proposes a conceptual framework integrating AI with climate action strategies for decarbonization across sectors including energy, transportation, industry, agriculture, and urban systems. Drawing on systems theory, socio-technic…

Percy Antonio Vílchez Olivares, Barbara Braga Cruz

This PRISMA-compliant systematic review of 45 peer-reviewed articles (2020-2025) maps AI and data analytics in ESG disclosure amid expanding CSRD and ISSB mandates. Four themes emerge: NLP/text mining, ML for ESG scoring, AI-enabled assuran…

Chi Wei Su, Weiyi Liu, Meng Qin +1

This paper empirically analyzes the impact of artificial intelligence (AI) on achieving carbon neutrality in China. It finds that AI can contribute to energy efficiency and emission reduction, but also poses risks of increased energy consum…

Zhang L.

This paper empirically analyzes the impact of AI on green finance using evidence from China's national pilot zones. It suggests that AI technologies can enhance the adoption and efficiency of green finance, with policy implications.

Jian W.

This paper proposes AI-driven green finance innovation using NLP under the ESG framework. It conceptually discusses the potential of combining AI and finance to promote decarbonization, but lacks specific methods or empirical data.

Bawack R.E.

This paper conducts a bibliometric analysis of AI in sustainable finance and accounting, mapping research trends and proposing a future research agenda.

Xu T.

This paper proposes an AI-driven knowledge graph approach to analyze ESG disclosures at the action level, moving beyond traditional rating methods to reveal granular insights into corporate sustainability practices.

Olawade D.B.

This paper systematically reviews the potential of artificial intelligence (AI) to help achieve net-zero targets. It assesses how AI technologies can contribute to carbon emission reduction, energy efficiency, and renewable energy integrati…

Naveed K.

This study examines the impact of AI adoption on ESG disclosure quality using Chinese firm data. It finds that AI adoption improves disclosure quality, and heterogeneity of sustainability committees strengthens this effect.