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The interpretation of the energetic signature of a city, taken as an integrated urban energy system, has been attempted in this study. Instead of the customary bottom-up approach, we successfully identified an average gray-box dynamic model by inversion of measured hourly system load and weather data. The third-order lumped-parameter thermal network model is derived by applying energy conservation principles to major nodes representing buildings, paved surfaces and urban canopy. This powerful data-driven approach offers deep insights into the thermo-physical properties of the built structures as well as the coupling of buildings and the urban microclimate. In particular, the Urban Heat Island (UHI) intensity has been determined to be close to 2 °C while the corresponding electricity cooling load penalty is 12%. [ABSTRACT FROM AUTHOR]

Due to its significant carbon footprint and cost-effectiveness for upgrades, the commercial property sector is important for climate change mitigation. Although barriers to energy system changes, such as funding, financing and information, are well recognized, Energy Star property owners and managers are successfully overcoming these barriers and instigating energy efficiency upgrades, renewable energy installations, and behavior and management programs. To examine the decision-making process that leads to energy performance improvements, a national survey of property owners and management organizations of buildings that earned an Energy Star score of 75 or higher was conducted. The extent to which energy upgrades were considered strategic investments motivated by environmental sustainability or corporate social responsibility, or financial investments motivated by payback period or return-on-investment criteria, was contingent upon the property type and type of energy project. Environmental sustainability was found to be an important motivation for energy projects in office spaces in general, but in the case of smaller office spaces was often combined with motivations for corporate social responsibility. Energy projects on education properties were motivated by financial investment. Building envelope and mechanical efficiency upgrades were considered financial investments, while renewable energy, green roofs, and water conservation technologies were considered environmental sustainability initiatives. [ABSTRACT FROM AUTHOR]

• Method was established to model building stock composition in adopted technologies. • Stock model can estimate the baseline development of building stock. • Building stock energy models can be constructed to use building stock data. • The Japanese technology deployment trend can significantly reduce CO 2 emissions. • Efficiency measures help avoid the increase in electricity demand by electrification. A significant improvement in the building stock energy efficiency is imperative to mitigate climate change. Building stock energy models (BSEMs) that employ reference building models are useful for mitigation analysis. However, most existing BSEMs developed for commercial building stock focus on limited building systems and energy conservation measures, and technology deployments are suggested based on simple vintage-driven scenarios. These approaches are insufficient, particularly for regions where improvements to building insulation performances can have a modest impact. This study aims to establish a BSEM framework to overcome this issue and to validate the framework via its application to the Japanese commercial building stock. The framework develops statistical models for estimating the selection probabilities of system alternatives and utilizes them to disaggregate building stocks. A reference building model is developed for each stock segment. The results show that this approach facilitates the use of multiple technological options considering various factors that affect technological deployments, and it also helps estimate the baseline development of building stocks. Furthermore, the developed model well represents the observed distributions in energy use intensity and estimates the aggregated energy consumption of building stocks with a reasonable accuracy. The baseline development was estimated to reduce the CO 2 emission by 18% by 2030 from 2013. Efficiency measures can help avoid the increase in electricity demand caused by electrifying the heat source of heating, ventilating, and air-conditioning and water heating systems. The framework could help extend the scope of BSEM application because BSEM development is not information intensive. [ABSTRACT FROM AUTHOR]