AI-DRIVEN PASSIVE HOUSING DESIGN FOR ECONOMIC AND ENVIRONMENTAL SUSTAINABILITY IN EMERGING ECONOMIES: EVIDENCE FROM PAKISTAN

Abstract

Sustainability issues in the housing sector of emerging economies like Pakistan are exacerbated by rapid urbanization, rising energy consumption and environmental degradation. To overcome these challenges, passive housing design has been developed, which aims to optimize the orientation and design of buildings, use efficient building materials and improve the insulation and ventilation performance to minimize reliance on traditional energy systems. But, conventional passive design approaches are not always accurate, flexible and scalable in complex environments. In this context, Artificial Intelligence (AI) can be an important solution as it can optimize the data and improve the performance of passive houses. This study employs a quantitative research method to examine the role of AI driven passive house design for the economic and environmental sustainability in Pakistan. The data was gathered by using a structured questionnaire from 384 construction, architectural and urban planning professionals. Structural Equation Modeling (SEM) using SmartPLS was used to analyze the relation between integration of AI, passive house efficiency and economical and environmental sustainability. The results show that the adoption of AI technologies has a very positive impact on passive house efficiency and this is the most influential factor for sustainability results. Additionally, the results show that AI's influence on environmental sustainability is more significant than its influence on economic sustainability, especially in terms of energy efficiency, carbon reduction and optimized design processes. The structural model shows moderate to strong explanatory power, supporting the robustness of the proposed model and identifying a mediating mechanism, namely the contribution of Passive House efficiency to the improved sustainability outcomes. The study is an empirical validation of the integration of AI and passive design in a context of a developing country, adding to the literature. The practical implications indicate that it is essential to advocate for the use of AI in design tools, revise building codes, and invest in capacity development for sustainable and climate-smart housing construction, facilitated by AI.