From waste to data: AI and circular construction waste management in Zimbabwe

Abstract

Construction waste remains a major environmental challenge in developing economies, including Zimbabwe, where weak infrastructure, limited waste data, and poor regulatory enforcement constrain sustainable management. This study examines how artificial intelligence (AI) could enhance material waste management in Zimbabwe’s construction industry. Using a quantitative-dominant mixed-methods design, the study draws on survey and interview data from construction firms in Harare and Bulawayo. Frequency index analysis ranked waste management practices and barriers, while Spearman’s rank correlation assessed associations between waste management practices and environmental sustainability outcomes. The findings show that reuse and source reduction are the most widely adopted practices, while recycling, waste audits, and prefabrication remain underutilised. Major barriers include financial constraints, limited awareness and skills, weak waste data systems, and inadequate recycling infrastructure. Based on these findings, the paper develops an evidence-informed AI-enabled circular waste management framework to support improved forecasting, waste tracking, and compliance reporting. The study contributes a context-sensitive conceptual model for linking circular-economy goals, environmental evaluation, and digital decision support in resource-constrained construction sectors.