This study investigates the pathways through which data factor agglomeration (DFA) facilitates the green development of traditional firms in the digital economy. First, we construct a micro-theoretical framework to systematically analyze the mechanisms by which data factor agglomeration influences firms’ green and sustainable development. Second, exploiting the establishment of China’s National Big Data Comprehensive Pilot Zones as a quasi-natural experiment, we employ a difference-in-differences (DID) approach using a panel of A-share listed traditional manufacturing firms from 2011 to 2022. The empirical results indicate that data factor agglomeration significantly promotes green development in traditional firms by accelerating IT and improving capacity utilization (CU) and energy efficiency. These findings remain robust after a battery of robustness checks, including double machine learning (DML) and instrumental-variable approaches. Heterogeneity analyses reveal that the positive effects of data factor agglomeration are more pronounced for state-owned enterprises, firms led by technologically skilled executives, heavily polluting industries, and firms located in regions with stronger government support and stricter environmental regulation. Further analysis uncovers substantial spatial heterogeneity: while the direct effect of data factor agglomeration on local firms’ green development is significantly positive, it generates a “siphoning effect” on geographically adjacent regions, whereas no significant spillover effects are observed among economically similar regions. Overall, this study elucidates the mechanisms and key determinants of green development for traditional firms in the digital era, providing important theoretical and practical implications for global economic growth and sustainable development.

In emerging markets, logistics systems play a critical role in shaping economic integration, the attractiveness of investment, and the potential of development. Differences in logistics performance across countries often reflect in-depth structural conditions related to institutional quality, business environment, and infrastructural capacity, which in turn create distinct development-related opportunities and challenges. This study aims to comparatively assess the logistics performance of emerging markets, in order to identify such structural conditions and their implications for development pathways. To achieve this objective, an integrated “CRiteria Importance Through Intercriteria Correlation Opportunity Losses‐Based Polar Coordinate Distance” (CRITIC–OPLO-POCOD) Multi-Criteria Decision-Making (MCDM) framework was applied to evaluate the logistics performance of 49 emerging markets with four indicators derived from the Agility Emerging Markets Logistics Index (AEMLI). The empirical results indicated that business fundamentals were the most influential determinant of logistics performance. The importance of regulatory stability, governance effectiveness, and investment climate has been highlighted. Contrasting structural opportunities and constraints were reflected by the fact that China emerged as the highest-performing country whereas Venezuela consistently ranked lowest. Robustness analysis confirmed a high degree of consistency between the proposed approach and several established decision-making methods, thus supporting the reliability of the findings. Overall, the study provided evidence-based insights into how logistics performance affected the opportunities and challenges in the development of the emerging markets, in order to offer practical implications for policy prioritization and strategic planning.