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The optimization of energy, environmental and economic (3E) outcomes is the principal approach to identifying retrofit solutions for a sustainable built environment. By applying this approach and defining a set energy performance target, this study proposes a makeshift decision framework that integrates a data mining procedure (agglomerative hierarchical clustering (AHC)) into the decision-making process to provide a simplified 3E assessment of building retrofits on a macro-scale. The framework comprises of three model layers: (1) a building stock aggregation model, (2) an individualistic 3E model that provides the sensitivity analysis for (3) a life cycle cost-environmental assessment model. The framework is demonstrated and validated with a case study aimed at achieving the set energy targets for low-rise office buildings (LOB) in Shanghai. The model defines 4 prototypical buildings for the existing LOB blocks, which are used for the individual evaluation of 12 commonly applied retrofit measures. Subsequently, a simplified LCC-environmental assessment was performed to evaluate the 3E prospects of 2048 possible retrofit combinations. The results uniquely identify retrofit solutions to attain the set energy performance targets and optimal building performance. Furthermore, the decision criteria for different investment scenarios are discussed. Overall, this study provides building investors an innovative framework for a facile and holistic assessment of a broader range of retrofit alternatives based on set performance targets. • A bottom-up approach to develop building retrofit solutions on a macro scale is proposed. • A decision-making approach that incorporates agglomerative hierarchical clustering is discussed. • Decision criteria/indicators based on set performance targets is established. • The proposed approach is applied to low-rise office buildings located in Shanghai. • Minimum LCC varies within RMB 2–17 million depending on the building features. [ABSTRACT FROM AUTHOR]

Given that energy-efficiency policies focus on meso- or macro-scale interventions, it is imperative to establish a macro-scale evaluation approach for building retrofits to support policymaking in building energy conservation, management and sustainability. This study applies the generic idea of optimising the energy, economic and environmental outputs to propose a facile framework for evaluating the prospects of building retrofits on a macro-scale. Here, an extensive optimisation approach integrating life cycle cost evaluation and an environmental assessment is formulated, involving coordinated on-site survey, modelling and data analytics. The model framework is corroborated by a case study analysis focused on identifying the optimal retrofit solution for low-rise office buildings in Shanghai. Simulation results show that modifications in occupancy regime, improvements in natural ventilation, heating and cooling systems, cool roofs insulation and installation of renewable energy systems (such as geothermal and solar/photovoltaics) are the basic retrofit measures for a macro-scale intervention to attain maximum life-cycle benefits. Individually, an estimated investment cost for each retrofit project varied within RMB 1 – 5 million with a payback period < 13 years, depending on the building characteristics. Overall, an investment estimated at RMB 1.7 billion (with a payback period of 6 years) is required to achieve ~ 80% energy reduction with a carbon dioxide savings of ~ 243 Gg-CO 2 /yr. In summary, this study provides a guidance framework for stakeholders to evaluate investments on retrofit projects, including existing and prospective ones. [ABSTRACT FROM AUTHOR]

The enormous building energy consumption in Shanghai necessitates the identification of standard buildings to offer guidance for the adequate design of retrofitting strategies in order to promote a sustainable built and architectural environment. In this regard, this study develops a methodological approach to establish prototypical buildings using performance index system (PIS) founded on an on-site survey. Emphasis is focused on low-rise office buildings in Shanghai. A total of 10 office parks containing 136 single low-rise office buildings in Min Hang District were systemically selected for survey and data collection. The proposed PIS includes building orientation, number of floors, window/wall ratio, heat and cold source type, plan form, and construction year. Using cluster and correlation analysis, the surveyed buildings are classified based on the impact of each PIS on the annual building energy use intensity. Based on this approach, the most influencing indexes are construction year, the number of floors, window-wall ratio and building orientation. This result refines the surveyed building samples to four prototypical buildings as representative standards for low-rise office buildings. Subsequently, typical buildings representing each of the prototypical buildings were defined. The stipulated approach provides a systematic framework for building classification, characteristic-based evaluation of building energy performance and identification of key performance index for building retrofit purposes. Image, graphical abstract [ABSTRACT FROM AUTHOR]