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[Display omitted] • Carbon neutrality feasibility of buildings was reviewed. • Carbon neutrality of buildings is feasible, but complexity varies. • Multi-building carbon neutrality case studies are limited. • Climate type affects neutrality; high-rise research is deficient. • Long-term lifecycle impacts of carbon measures remain unclear. Achieving carbon–neutral building stock by 2050 contributes to coping with the detrimental impacts of global warming since buildings account for almost 37% of final energy-related CO 2 emissions. This paper reviews the publications on carbon neutrality (CN) feasibility at both single and multi-building complex scales. Publications are retrieved from the Scopus database, and the snowball method is used to find the most relevant studies. The paper addresses the primary question: "Is it possible to reach the life cycle carbon neutrality of buildings?". Various information such as building life cycle carbon assessment, building characteristics (usage type and number of stories), climate type, mitigation measures, and simulation results are extracted, classified, and analyzed. Technically, reaching CN is feasible, but it is challenging given the need to implement multiple mitigation measures simultaneously, especially in the regions with low emissions intensity of the electricity grid, and may not always be economically feasible. A lack of successful case studies for large multi-building complexes, such as cities, is evident in the literature. It can be attributed to the limited availability of input data for carbon assessments as well as the complexity of simulations. Knowledge gaps in the literature and suggestions for future works are also discussed in detail. Due to the small number of studies on the topic, conclusive paths to reach the CN of buildings, specific to different climates and types of buildings, remain unclear, and thus, further research is necessary. [ABSTRACT FROM AUTHOR]

• The most important desiccant wheel parameters are considered in the artificial neural network model. • The general trained ANN model predicts DW behavior for arbitrary input parameters. • The volume of the training database is determined by a comprehensive sensitivity analysis. • Various DW ANN models are trained to find the most accurate. • The obtained ANN model predictions are validated with experimental measurements. The design and operational parameters of a solid desiccant wheel (DW), as the heart of a desiccant system, significantly affect the performance of systems in which it is applied. A general and fast model to predict the DW operation is needed to allow investigation of desiccant systems under various working conditions. This paper presents a comprehensive study of the performance prediction of a DW using an artificial neural network (ANN) technique. Comprehensive design parameters, namely rotational speed, channel length, hydraulic diameter, process/regeneration section area ratio, desiccant layer thickness, and the purge angle, are applied as inputs to the ANN model. Moreover, the inlet process and regeneration air temperatures, humidity ratios, and velocities are considered as inputs to confirm that the model outcomes are applicable when various DW parameters are taken into account. Here, the network outputs are the process, the purge, and the regeneration air temperatures and humidity ratios at the DW outlet. This generality in the DW ANN model that contains most of the attributed parameters as inputs and predicts various outputs has not been provided in previous works. A data bank for training the network is generated through transient equations for a wide variation range of DW associated parameters. Several ANN structures are trained in MATLAB to study their prediction accuracy and eventually to find the optimum. The best architecture, which has four hidden layers and 50 neurons, yields a mean relative percentage and a mean square error of 0.54% and 5.9 × 10−5, respectively. This structure can successfully predict DW behavior. Comparing the ANN model predictions and experimental data indicates that the selected structure permits reasonably accurate predictions of the performance of a DW. [ABSTRACT FROM AUTHOR]