Japan Real Estate Institute

As cyber-physical service systems, comfort, safety, customer experience, cost, and the requirement that all are provided simultaneously-thus requiring a smart solution-are thus the fundamental properties of smart malls and hotels. BAS, or Building Automation System, with Building Energy Management Systems being a special case with emphasis on energy, have thus evolved from mere supervisory control to data acquisition, supervisory control, analytics, and, most importantly, a governing model. This systematic study will aggregate all available evidence from 2020 to 2025 on the benefits of Centralized Building Management System as a model for improving operation efficiency in Smart malls and hotels. Our study synthesized all relevant evidence in five key areas described by the individual authors, including energy and carbon, fault detection/diagnostics - maintenance productivity, indoor environmental quality, enterprise integration, cybersecurity, and assurance, by adopting PRISMA 2020 guidelines. Coherently in all ascertained evidence, there seems to be a coherent trend that the key to improved operational efficiency is totally encapsulated in the phrase "analytics to action" since even as there is "interoperable connectivity of all devices allowing high-fidelity visibility," there is room for a "continuous commissioning based on FDD/AFDD, occupancy-based control, and optimization, which must translate into 'governed actions' through CMMS/PMS with measurement & verification." Lately, studies have found Building Energy Management Systems with the aid of digital twin technology to be efficient, as well as Analytic Function Detection Diagnosis with the aid of agent/ontology technology to improve interpretable diagnosis reports, although there are gaps in evidence, with some authors citing the benefits seen by others by virtual modeling or simulation. We present our reference architecture as well as our KPIs, with a matching research requirement that will focus on credible study designs, as well as the use of semantic modeling as well as security by design, by utilizing protocol extension technologies like BACnet SC.

Purpose Load shedding and grid instability continue to pose a significant challenge for commercial buildings in West Africa. Various artificial intelligence–Heating, Ventilation and Air Conditioning (AI–HVAC) system algorithms show promise in improving energy efficiency across different models. However, its performance under load-shedding conditions in shopping malls remains understudied. This study assesses the resilience of AI-controlled systems under load-shedding conditions in shopping malls in Ghana. Design/methodology/approach A mixed quasi-experimental design was employed, integrating sensor data with simulation models and conducting interviews with HVAC professionals in Ghana’s shopping malls. AI-based control strategies, such as Model Predictive Control (MPC), Data-enabled Predictive Control (DeePC) and Reinforcement Learning (RL), were tested across the malls to assess their response times, thermal comfort and energy consumption. Findings The study found that RL achieved the lowest response latency, the highest energy consumption, the highest thermal comfort and the highest uptime (95.4%) compared with MPC and DeePC. Furthermore, DeePC established moderate performance across the indicators, while MPC reported the lowest values. Qualitative results indicate limited awareness of AI-based systems but a strong willingness to adopt them, given their expected benefits. Practical implications The RL–HVAC systems offer a viable solution for enhancing energy resilience during load shedding in shopping malls. Although beneficial, this requires shopping management readiness and professional training. Originality/value This critical performance of RL, along with other AI-based algorithms, in AI-controlled HVAC systems under unstable power in urban areas represents a new direction compared with MPC and DeePC functionalities in energy simulations in cold-climate regions.

Abstract Coresidence in the parental home is known to depend on young adult characteristics and market conditions, but there is more limited knowledge on whether or how parental characteristics matter. We model the coresidence outcome as a multigenerational joint optimization decision and use Panel Study of Income Dynamics data to examine the association of parental housing and wealth with young adult coresidence. The findings show that parental financial capacity is negatively associated with the likelihood of coresidence. Housing capacity, captured by the size and ownership status of the parental home, is positively associated with the likelihood of coresidence. We observe a stronger association of parental wealth and housing capacity with coresidence in less affordable markets and over time.

Due to the complexity of the vineyard ecosystem, a growing number of eco-certified inputs has been integrated into a framework of sustainable agricultural practices. This research is aimed at developing a hybrid framework that can be used to predict the eco-label readiness of a certain vineyard and provide guidance about the ecological indicators that should be prioritized in it as well as to find ways to increase the marketing effectiveness. Secondly, the sample of vineyards (n = 50) was divided into three groups—early, mid-season and late ripening varieties—for the analysis of a significant difference observed between and within groups, in terms of the effects of sensor-calibrated inputs on eco-performance indicators. In the second phase, the structural analysis focused on the relationship between eco-performance indicators and its outcomes (eco-label readiness, both direct and indirect effects). Then, the regression analysis and structural equation modeling showed that out of all examined eco-performance results, the timing of the chlorophyll decline, the behaviour of the sugar accumulation under controlled application, and the stability of the eco-readiness over time contributed to the total variance of eco-performance. Yet, the results confirm our assumption that an improvement in sensor calibration and nutrient scheduling is also necessary, particularly in the eco-label certification process of vineyards. The methodology can be used to make informed decisions both about the operational strategies for increasing the eco-marketing performance and the selection of inputs that should be applied in the vineyard.

Purpose This study aims to investigate the catalytic effects of urban regeneration (UR) on urban sustainable development and the complex mechanisms underlying the catalytic processes from a configurational perspective. Design/methodology/approach Rooted in the urban catalyst theory, the research develops a three-dimensional analytical framework that quantitatively evaluates UR projects in Chongqing. The study employs fuzzy-set qualitative comparative analysis (fsQCA) and necessary condition analysis (NCA) to identify the configurations of factors and necessary conditions leading to specific catalytic performance of projects. Moreover, the importance-performance analysis (IPA) method is utilized to prioritize projects with different configurational types. Findings Thirteen configurations are associated with high catalytic performance and seven with negative outcomes. Commercialization emerges as a necessary condition for achieving high catalytic performance. The empirical results demonstrate that multiple combinations of project-specific and district-specific conditions can achieve equivalent levels of catalytic performance, while pathways leading to opposite outcomes are asymmetric. Practical implications The findings guide policymakers in designing strategic interventions that maximize catalytic effects in resource-constrained contexts by identifying optimal combinations of factors. Originality/value Existing studies on UR catalysts have been limited to single-dimensional evaluations and case-specific analyses. The research fills research gaps by pioneering the quantification of Urban Catalyst theory concepts and transforming them into measurable dimensions. By revealing the asymmetric relationship between success and failure pathways in UR, the study helps to explore the inherent complexity of urban catalysts and establishes a new analytical paradigm for evaluating complex urban interventions.