GX & Decarbonization Research

Chuanchao Li, Yuanhe Du, Shuangyang Zhai

Using data from 284 Chinese cities (2012-2023) and a quasi-natural experiment of climate-resilient city pilots, this study applies double machine learning and spatial lag models to show that climate risk governance significantly enhances ur…

Anonymous

Replication data for a two-period study of CSRD disclosure quality around the 2026 Omnibus scope reduction, covering 188 EU firms and 376 reports coded with a frozen LLM prompt on a ten-dimension instrument. Includes validation outputs (hum…

Liu Na, Wan Nordin Wan‐Hussin, Chin Sok Fun

This study examines whether ESG-linked executive compensation influences carbon disclosure quality in China's A-share listed firms, and whether board climate oversight mediates this relationship. Using panel data from 2021-2023, it finds th…

Luana Serino, Manlio Del Giudice, Francesco Campanella +1

This study empirically examines the impact of AI-related disclosure on ESG outcomes in U.S. firms. It investigates whether AI adoption and disclosure are associated with improved ESG performance, contributing to the emerging literature on A…

Jian-yun Lu, Qing-Yi Liu, Yu-Qi Wang +5

This study develops a Bayesian multiobjective optimization framework with Gaussian process surrogates and uncertainty-aware search to map trade-offs in pharmaceutical wastewater treatment. Optimized operating windows improve removal efficie…

Qian Ma, Chunxiao Liu, Kui Huang +6

This paper proposes D-PGNet, a deep projected gradient network that accelerates low-carbon economic dispatch with energy storage and carbon trading costs. It embeds physical constraints as differentiable projection layers, achieving optimal…

Le Sun, Jia Liu, Kuanhou Tian

Using Chinese A-share listed firms from 2008-2024, this study finds that low-carbon transition strategy disclosure increases analyst following. The effect is driven by enhanced investor relations and richer information environment, but mana…

Cristian Valentin Hapenciuc, Daniela Mihaela NEAMTU, Teodora Cajvan +1

This study applies FinBERT to financial news text to analyze the impact of ESG narratives on market volatility. Focusing on tech, energy, and banking sectors in Eastern Europe, it finds signs of a 'regime shift' where ESG transitions from a…

D. Barro, A. Basso, M. Corazza +1

This study uses machine learning to analyze the impact of the energy transition on Eurozone sovereign credit risk. It evaluates how the shift to renewable energy affects government bond yields and CDS spreads, offering insights into the imp…

Simona Epasto, Maria Lúcia, Masiel Melissa Pereira

This paper examines AI integration in agrivoltaic systems, focusing on socio-territorial implications for rural landscapes. Through case studies including the European SYMBIOSYST project and GIS/UAV geospatial monitoring, it analyzes how pr…

Minyou Qing, Suhaiza Zailani

This review systematically analyzes the application of AI-driven practices in road freight transport (RFT) decarbonization. Following the SPAR-4-SLR protocol, 227 articles were reviewed using bibliometric analysis and science mapping. Publi…

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…

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 …

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…

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…

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 …

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.