Living wall systems have been widely recognized as one of the promising approaches for building applications due to their aesthetic value and ecological benefits. Compared with outdoor living wall systems, indoor living wall systems (ILWS) play a more vital role in indoor air quality. The aim of this study is to investigate the effects of ILWS on indoor air quality. In an office building, two parallel corridors were selected as comparative groups. A 10.6 m² ILWS was installed on the sidewall of the west corridor while the east corridor was empty. Some important parameters, including indoor air temperature, relative humidity, concentrations of carbon dioxide (CO₂), and particulate matter (PM) were obtained based on the actual environment monitoring. According to the statistical analysis of the data, there were significant differences in the concentrations of CO₂ and PMs in the corridors with and without ILWS, which indicated that CO₂ and PM_2.5 removal rate ranged from 12% to 17% and 8% to 14%, respectively. The temperature difference is quite small (0.13 °C on average), while relative humidity slightly increased by 3.1–6.4% with the presence of the ILWS.

Vertical farming is a new branch of urban agriculture using indoor vertical space and soil-less cultivation technology to obtain agricultural products. Despite its many advantages over traditional farming, it still faces some challenges and obstacles, including high energy consumption and costs, as well as uncertainty and a lack of social acceptance. This study aims to investigate the influence of public acceptance on micro-vertical farming based on the deconstructed theory of planned behavior model. This model is adopted for statistical analysis to reveal the factors and their weights in influencing people’s behavioral intentions. The results indicate that the overall mean of the public’s behavioral intentions to use vertical farming is 3.9, which is above neutral (M = 3.00) but less than positive (M = 4.00). Differences in age, education level, and the living area of the public have significantly impacted behavioral intentions. Meanwhile, the statistical results support the hypotheses concerning the behavioral attitudes, subjective norms, and perceived behavioral control of the model, and also demonstrate that their decomposed belief structures considerably influence the public’s behavioral intentions to use vertical farming. Notably, perceived usefulness is the most critical driving factor in planting using vertical farming. The findings of this study contribute to better predictions of the effects of different elements of behavioral intention on vertical farming at the urban scale, which may provide a basis for decision making in the development of sustainable urban agriculture.

Road infrastructure is a vital constituent element in the transportation network; however, roadway surface ice and snow accumulation leads to huge traffic accidents in winter. Geothermal roadway energy systems (GRES) and solar roadway energy systems (SRES) can increase or decrease roadway surface temperature for the de-icing and removal of snow in winter, or mitigation of heat in summer. Technology performance and economic evaluation of the GRES and SRES are reviewed in this paper based on numerical and economic models, and experimental analyses. Three crucial aspects of the technology performance assessment, i.e., roadway surface temperature, energy consumption and key factors, are explored in different regions and countries. Economic evaluation approaches for net present values and payback periods of the GRES and SRES are investigated. The recommendations and potential future developments on the two technologies are deliberated; it is demonstrated that the GRES and SRES could increase roadway surface temperature by around 5 °C in winter and decrease it by about 6 °C in summer, with the payback periods of 4 to 8 years and 2.3 to 5 years, respectively.

A Living wall system (LWS) is a biophilic design element that has been introduced into indoor environments in recent years. Previous studies have demonstrated that the LWS improves indoor visual comfort and air quality. However, studies on its psychological effects on occupants are still scarce. In this paper, the psychological effects were investigated by recording and analyzing the parameters of occupants’ physiological responses including an electroencephalogram (EEG) and heart rate variability (HRV). A cross-over experiment was conducted among 43 participants under three different desk work environments based on various materials involving an LWS, a fake LWS, and a bare white wall. The results conclude that LWSs effectively reduce the accumulation speed of negative psychological states caused by desk work to about 1/3, compared with a regular office environment without an LWS. However, occupants tend to be less excited and focused after resting in the LWS environment. Therefore, it is recommended to place LWSs in the rest area rather than close to the desk area in an office building, considering the balance between mental health and work efficiency.

As a key AI technology, Machine learning (ML) has witnessed growing adoption in landscape architecture through advanced algorithms and computational techniques. Despite this progress, a critical gap persists in systematically analyzing ML’s transformative impacts and emerging opportunities through an application-driven lens. This study integrates bibliometric analysis with a systematic literature review to synthesize methodological advancements and domain-specific applications. After systematically reviewing the applications of machine learning in the field of landscape architecture, five categories were identified: simulation and prediction, layout generation, image post-processing, management and evaluation, and text analysis. Furthermore, this paper proposes strategic implementation frameworks for ML integration while establishing methodological benchmarks for intelligent design systems.

This study implements techno-economic evaluations of different photovoltaic (PV) systems in the East Midlands of the UK. Three application case studies, including an office building, a domestic building, and a poultry shed, are achieved. The building electricity consumption is obtained according to hourly automatic meter readings, and the PV electricity production is predicted based on the Engineering Equation Solver 8.4 software. Meanwhile, the 25-years’ complete economic profitability investigations of the three PV systems are conducted on the basis of the Monte Carlo method; the sensitivity analyses of payback period and net present value are also carried out by using the @RISK 8 software. Furthermore, the payback period and yearly savings are investigated and compared between the Smart Export Guarantee (SEG) and feed-in tariff (FiT) schemes. Technical investigation outcomes conclude that the three PV systems are able to satisfy electrical energy requirements in summer, and the additional electricity could be exported to the grid in this period. In winter, however, the systems have less electricity output resulting in power shortage and input from the grid. Economic study results exhibit that the net present values of the office building, domestic building, and poultry shed are £9108.4, £1717.91, and £7275.86, respectively, corresponding to the payback periods of 6.15 years, 9.12 years, and 9.41 years. This implies that there is an acceptable payback period (<10 years) for the PV system installation; meanwhile, the FiT scheme has the shorter payback period compared with the SGE scheme.