Identification of Significant Attributes for the Development of an Assessment Tool Applicable to Green Campuses in India

¹ Mahale Janhavi, Symbiosis Institute of Technology (SIT), Symbiosis International (Deemed University) (SIU), Pune, Maharashtra, India, 412115; Assistant Professor at Civil Engineering Department, RMD Sinhgad School of Engineering, Warje, Pune; e-mail: janhavi.mahale.phd2020@sitpune.edu.in
² Khare Kanchan, Civil Engineering Department, Pimpri Chinchwad College of Engineering, Pune, India, email: kanchan.khare@pccoepune.org , ORCID Id: 0000-00021102-4396

^* Corresponding author: kanchan.khare@pccoepune.org

Abstract

Green campuses play a significant role in transforming societies and fostering a sustainable future as they are anticipated to make a significant contribution toward environmental sustainability. The evaluation and prioritization of the green campus initiatives are likely to be underresearched. The global movement toward green building initiated following the development of various green building rating tools (GBRT) for assessment. These tools require modification to account for sustainability considerations. When applied to educational campuses in India, the limitations of the eight existing GBRTs were revealed. To address these limitations, a scientific study of eight GBRTs including two Indian GBRTs such as Indian Green Building Council (IGBC), Green Rating for Integrated Habitat Assessment (GRIHA) and other six international GBRTs like Building Research Establishment Environmental Assessment Method (BREEAM), Leadership in Energy and Environmental Design (LEED) was conducted. This study identified significant green attributes for green campuses, with a particular focus on green campuses in India. A questionnaire survey was conducted to gather data on these attributes. Principal component analysis (PCA) was applied to 249 valid responses from the survey, resulting in the extraction of 56 significant attributes of green campuses under nine components. This study discusses the significant attributes identified through this analysis.