Analysis of Rainwater Harvesting, Energy Efficiency, and Waste Management Based on Digital Applications to Support Green Building Assessment

Abstract

The building and construction sector remains a major driver of global environmental degradation, accounting for approximately one-third of final energy use and energy-related carbon emissions. In response, green building rating systems (GBRS) such as Greenship Indonesia and the statemandated Bangunan Gedung Hijau (BGH) regulations have been widely implemented. However, conventional GBRS audits rely heavily on static, manual, checklist-based processes and spreadsheet calculations. This traditional approach creates a significant performance gap between designstage projections and actual operational resource consumption and often allows subjective buffer points to mask critical inefficiencies in energy, water, and waste management. To resolve these limitations, this study aims to (1) formulate mathematical algorithms and calculation methodologies for rainwater harvesting (RWH), energy efficiency, and waste management that precisely align with GBCI Greenship and BGH benchmarks, and (2) design a web-based digital platform architecture capable of integrating real-time operational data. Using a quantitative, system-development methodology, we developed a web-based evaluation platform to automate environmental calculations and process dynamic building parameters, and demonstrated it using a hydro-climatological record for West Jakarta covering 2019 to 2025. For a reference 6,000 m² building, the platform computes an annual harvestable rainwater volume of 1,959 m 3 , equivalent to 54.4 % of nonpotable demand, with a storage saturation point at 300 m 3 ; a photovoltaic contribution of 157.9 MWh yr −1 , corresponding to a renewable fraction of 13.2 %; and a waste diversion rate of 60.7 %. Automated dimensional checking further showed that micro-wind generation at a mean wind speed of 0.87 m s −1 is effectively non-viable, contradicting a previously reported value by three orders of magnitude and demonstrating the error class that manual spreadsheets conceal. The results show that the proposed digital platform successfully automates complex sustainability calculations, eliminates manual spreadsheet errors, and substantially reduces compliance audit duration. More importantly, integrating real-time data inputs significantly enhances the objectivity and transparency of green building performance verification compared with conventional static methods. Ultimately, this research provides a scalable digital tool that bridges the gap between static green certification and dynamic building operation, supporting robust, data-driven sustainable development policies.