The financial sector faces mounting challenges in processing vast volumes of high-velocity data to support intelligent, real-time decision-making. Traditional machine learning models often fall short in accuracy, scalability, and responsiveness when dealing with large, dynamic financial...

Purpose This study explores the potential of artificial intelligence (AI) in advancing sustainability within business practices. It focuses on three key areas: optimising energy efficiency, developing sustainable products and services, and improving waste and resource management. By integrating...

Reasonably pricing data resources contributes to the development of the digital economy. However, in the early stage of the development of the data market, the data transaction volume is small, and the resulting small sample problem makes it difficult to accurately model and forecast the price...

Climate-driven market interdependencies critically influence emission reduction strategies, yet the causal mechanisms linking carbon, fossil, and clean energy markets remain poorly quantified -a knowledge gap impairing risk-aware policymaking and sustainable investments. This study uses the...

This document describes the design process, definitions, and algorithmic implementation for a pan-European assessment of forest fragmentation. This study is a preceding presentation of the indicator 4.7 in the State of Europe's Forests 2025 report, published by FOREST EUROPE. Forest cover is...