Meta-Analysis on Sustainable Development Challenges: Lessons From Asia-Pacific Industrial Estates

Abstract

This study addresses the critical challenge of implementing sustainable development practices in industrial estates across the Asia-Pacific region, where rapid industrialization has yielded both economic growth and environmental degradation. To systematically examine this issue, we integrate advanced natural language processing (NLP) techniques with the technology-organization-environment (TOE) framework. Specifically, NLP is employed to extract and categorize key sustainability factors from a large corpus of academic article abstracts, enabling the identification of technological, organizational, and environmental determinants of sustainable practice. These extracted insights are then structured within the TOE framework to assess the interplay among influencing factors and their associated sustainability outcomes. Using both statistical and causal analysis, including regression and ensemble learning techniques, we quantify the relationships between these dimensions. Our findings highlight that while technological advancements play a pivotal role in promoting sustainability, they are often accompanied by trade-offs such as reduced economic efficiency or innovation inertia. Similarly, external environmental factors, such as regulatory frameworks, enhance sustainability performance but may introduce operational constraints. The study advances methodological approaches by demonstrating the utility of NLP in large-scale sustainability meta-analyses and offers nuanced, context-aware recommendations for managers and policymakers aiming to balance economic and environmental priorities.