GX & Decarbonization Research

Xueqing Wang, Lang Wang

Using Chinese A-share listed firms from 2010-2023, this study shows climate risk exacerbates green innovation bubbles—a divergence between patent quantity and quality. Managerial myopia and financing constraints are key channels, while digi…

Md Qamruzzaman

Using panel regressions and a deep learning (MLP) model on 62 listed Bangladeshi financial institutions (2005-2023, 1,178 firm-years), this study finds that intellectual, human, and natural capital disclosures each positively and significan…

Alfredo Merlet

This replication package constructs a Say-Do Gap Index for IBEX 35 firms (2019-2025) using a fine-tuned Spanish BERT (BETO) to detect unverified ESG commitments from media sources. It links the index to firm value (Tobin's Q, ROA, ROE) via …

Sina Rostami, Muhammad Bilal, Mehrdad Taghipour

This scoping review systematically maps technologies and strategies to reduce energy use and carbon footprints in operating rooms, evaluating methods from HVAC optimization to AI-driven management. It identifies a gap in kinetic energy harv…

Pengfei Zhang, Pan Liu, Li Jiang +1

This paper proposes a cooperative game-based low-carbon operation strategy for multi-microgrids using multi-agent deep reinforcement learning. It introduces an energy-carbon-green certificate P2P trading mechanism and Nash bargaining for fa…

Venkata Krishna Reddy, Aadam Quraishi, G. C. Prashant +1

This paper proposes a deep learning-based decision support system (DSS) for renewable energy (RE) investment and deployment. It uses CNNs to extract features from weather, energy, and economic data, RNNs to forecast energy demand and supply…

Lila Simon, Abino Simon, Aalbin Simon

This chapter explores the integration of AI and renewable energy for a sustainable energy ecosystem. It reviews the renewable landscape in India and globally, detailing AI applications in solar, wind, hydro, biomass, and geothermal, includi…

Michelangelo Misuraca, Luca D’Aniello, Maria Spano

This study analyzes renewable energy social representations in Reddit climate discourse (2018-2026, 91,817 comments) using a computational adaptation of the Hierarchical Evocation Method. Combining theory-informed lexical anchoring with dat…

Sharif M, Seker H

This paper proposes Agentic CAMA-DRL, a context-aware multi-agent deep reinforcement learning framework for optimizing charging infrastructure of last-mile delivery e-bikes. Four stakeholder agents coordinate via DQNs conditioned on real-ti…

Vrcelj Z, Sandanayake MS

This paper addresses the gap between AI technical performance and actual project decision changes in construction. Through a structured review of 34 studies, it proposes a Governed AI Project Controls Framework that separates data foundatio…

Bérard, Cedric

This snapshot demonstrates that NAM-classification-filtered portfolio allocation outperforms five naive baselines in expected avoided-loss NPV for climate adaptation of 725,462 substations across 39 OECD countries. At the reference cell, al…

Jianxun Zhao, Mingxuan Huang, Changqing Tian +1

This study optimizes GHG emissions in vegetable handling processes using Deep Deterministic Policy Gradient (DDPG). Sensitivity analysis identifies key parameters, and a Markov decision process model with multi-objective reward design minim…

Bingler J.A.

This paper uses ClimateBert, a specialized language model, to analyze corporate climate risk disclosures, focusing on 'cheap talk' (substantively empty claims) and 'cherry-picking' (selective disclosure of favorable information). It offers …

Zhang Bokai, Chengjie Huang, Xiao Qiang

Using panel data of Chinese listed firms from 2012-2022, this study exploits the establishment of environmental courts as a quasi-natural experiment with a multi-period DID approach. It finds that environmental courts significantly promote …

Xiao Qin, Quan Fang, Yuan Virtanen

This study empirically examines the impact of climate policy uncertainty on corporate sustainability (ESG scores) using Chinese A-share listed firms from 2016 to 2025. Employing a two-way fixed-effects model, it finds that climate policy un…

Jinqi Jiang, Zhijing Wu, guosen zhang +9

WaterMAP is a scalable ML framework for predicting GHG emissions from wastewater treatment plants. Using data from 5155 Chinese WWTPs, it estimates Scope 1/2/3 emissions and suggests 9.6-34.0% reduction potential by 2060.

Mengqi Yuan, Wenfei Zang, Guangchong Chen

Using provincial data from China (2012-2022), this study analyzes the coupling coordination between AI and high-quality energy development (AHCC). Both advanced, but AI lagged and regional disparities persisted, with intensifying spatial au…

Michael Padget, Gregg Furie, Dionne Kringos +2

This paper is the first to systematically evaluate environmental sustainability measures for healthcare using quality measurement criteria. Six measures were assessed, and a minimum set of greenhouse gas emissions (scopes 1 and 2), water us…

Mehrdad Taghipour, Sina Rostami, Muhammad Bilal

This scoping review systematically maps technologies and strategies to reduce energy use and carbon footprints in operating rooms, including HVAC optimization and AI-driven management. It identifies a methodological gap in kinetic energy ha…

Rufaizal Che Mamat, Azuin Ramli, M. N. A. Ghani +3

This study compares fine tree regression and ANFIS for predicting carbon footprints across four stages of residential construction. Using 2000 observations, ANFIS outperformed Rtree in all stages, achieving lower RMSE values, especially in …