The Development of Models to Identify Relationships Between First Costs of Green Building Strategies and Technologies and Life Cycle Costs for Public Green Facilities

Public buildings and other public facilities are essential for the functioning and quality of life in modern societies, but they also frequently have a significant negative impact on the natural environment. Public agencies, with their large portfolios of facilities, have faced considerable challenges in recent years in minimizing their negative environmental impacts and energy consumption and coping with shortages of financial capital to invest in new facilities and operate and maintain existing ones, while still meeting their mission goals. These range from the need to provide a quality workplace for their staff to providing a public service and long term benefits to the public. The concept of green building has emerged as a set of objectives and practices designed to reduce negative environment impacts and other challenges while enhancing the functionality of built facilities. However, the prevailing belief related to implementing green building is that incorporating Green Building Strategies and Technologies (GBSTs) increases the initial cost of constructing a facility while potentially reducing its life cycle costs. Thus, this research deals with optimizing the design of individual facilities to balance the initial cost investment for GBSTs versus their potential Life Cycle Cost (LCC) savings without the need to conduct detailed life cycle cost analysis during the early capital planning and budget phases in public sector projects. The purpose of this study is to develop an approach for modeling the general relationship between investments in initial costs versus savings in LCCs involved in implementing green building strategies in public capital projects. To address the research question, this study developed multiple regression models to identify the relationships between GBSTs and their initial cost premiums, operating costs, and LCCs. The multiple regression models include dummy variables because this is a