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Improving the environmental performance of built environments has received wider interest in the recent past. Asset management is an under-utilized strategy to enhance and maintain desired environmental performance of a building at low life cycle costs (LCC). Current asset management decision-making methods are not fully capable of incorporating future technological changes and complex time sensitive environmental demands. There is a knowledge gap in incorporating factors such as environmental performance, risk and time sensitivity to foster dynamic asset management decision making. This paper presents a multi-period asset management method for public buildings to minimize LCC and attain climate action goals. A scenario planning approach is used to incorporate future changes in technologies, costs and organizational policies. Risk based prioritization is used to compare and select maintenance alternatives in a given period. Value at risk is used to identify the lowest risk capital planning strategy for an organization. The proposed approach was demonstrated using an operating aquatic centre building in British Columbia, Canada. Three possible future scenario were considered. Proposed method was used to develop multi-period maintenance plans for future scenarios. Based on the analysis, Great Transitions scenario is expected to achieve the least financial risk. The proposed approach enable capital asset planning by complying with changing climate action targets in a stochastic environment. [ABSTRACT FROM AUTHOR]

The use of the minimum energy to maintain the indoor thermal comfort of the large-space public building is always a challenging task due to the complex outdoor environment and indoor requirements. The lack of monitoring data and effective approaches limits the understanding of building thermal and energetic performance. This paper thus proposes a hybrid machine learning model based on factor generators and an optimization approach to address this research topic, aiming to provide the essential guide for future retrofit and design of large-space public buildings. The four machine learning (ML)-based factor generators are compared using the one-year monitoring data of building facility and indoor thermal management, where the high-performance multilayer perceptron neural networks (MLPNN) model is chosen as the data-driven method to generate the input data as the parent or intermediate populations in the GA optimization algorithm. Such a hybrid machine learning model can solve the multi-objective functions of thermal comfort and carbon emissions. The optimization results demonstrate that this model can achieve a maximum 29% improvement for thermal comfort and a reduction of 386.9 kg CO 2 (11.06%) for carbon emissions in comparisons with the human-based management. Moreover, such hybrid machine learning model exhibits tolerance for moderate deficit in one objective. Therefore, the optimal thermal comfort and carbon emissions of large-space public buildings are achieved and thus contributing to the carbon neutrality in the building sector. • The multi-objective problem of thermal comfort and carbon emission is addressed. • The MLPNN models perform well according to the prediction evaluation criteria. • A 29% thermal comfort improvement and the 386.9 kg CO 2 reduction are achieved. • The new model reduces the wasteful energy from human-based decision-making. • The hybrid machine learning model can tolerate the moderate deficit. [ABSTRACT FROM AUTHOR]

Given users' increasing demand for thermal comfort in large-scale public buildings, energy consumption of the refrigeration and air-conditioning system continually increases. There is a strong need for implementing Refrigeration and Air-Conditioning System Retrofit (RACSR) in order to achieve better energy performance and carbon emission reductions. Despite the importance of adopting RACSR, it remains uncertain whether various RACSR schemes demonstrate cost effectiveness to attract investors. This paper establishes a methodological framework to carry out an economic evaluation of RACSR projects. By conducting a case study of the Beijing Capital International Airport Terminals retrofit project, this research finds that: (1) Both the Conventional Electrical Air-Conditioning System (CEACS) and the Ice Storage Air-Conditioning System (ISACS) retrofit schemes exhibit favorable environmental performance and cost effectiveness; (2) Although the CEACS scheme achieves higher energy savings, its economic performance is inferior than that of the ISACS scheme; (3) Upfront capital investment is the most sensitive factor affecting the economic viability; (4) The energy saving effects of retrofit schemes seem not to be obviously affected by climate demarcation, but the economic performance of the ISACS increases from cold to warm climate zones; (5) The current economic subsidy policies are too favorable, thus once the government adjusts the subsidy level according to the theoretical environmental benefit, it will largely affect the interests of investors; (6) If the ISACS scheme is adopted, the peak electricity demand will be reduced and electricity usage in off-peak period will increase, which would benefit the stability and reliability of wider electricity networks. This paper offers a useful template to conduct an economic evaluation of RACSR schemes through comprehensive consideration of life-cycle costs and benefits. It offers important recommendations for policy makers and building owners to develop incentive policies and management interventions to encourage the implementation of RACSR. • CEACS scheme can save more energy while ISACS scheme is more cost effective. • The most sensitive factor is upfront capital investment, followed by the subsidy level. • The ISACS has optimum economic performance in Hot summer and Warm Winter Zone. • ISACS can contribute to shifting on-peak electricity demand to off-peak period. [ABSTRACT FROM AUTHOR]

Energy Consumption monitoring technology (ECMT) can effectively reduce the energy consumption of public buildings (PB) in the operational phase, but it is not used widely in China. The main reason for this is that the existing policies have a poor effect on diffusing ECMT; thus, studying the impact of different policy instruments on ECMT in PB is of great significance to the widespread use of ECMT and its role in decreasing the energy consumption of PB. This paper systematically reviews the policy related to ECMT in China from 1990 to 2018, and, on this basis, divides the promotion stage of China's ECMT into three stages: the concept stage, the demonstration stage and the gradual promotion stage. A questionnaire survey and semi-structured interview of 313 stakeholders (ECMT suppliers, government officials, and scholars) was used to study the influence of command-and-control instruments (CAC), economic incentive instruments (EI) and exhortatory instruments (EXI) on the diffusion of ECMT in PB. The empirical results show that CAC and EXI contribute to the diffusion of ECMT in PB, whereas EI do not. In more detail, laws and regulations, energy consumption quotes, education and information programs, and energy audits and disclosure also have a significant impact on diffusing ECMT, whereas technical standards, subsidies for adopting ECMT, energy performance contracting, and carbon trading do not. Finally, relevant policies and suggestions are put forward based on the results. [ABSTRACT FROM AUTHOR]