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The objective of this study is to examine the potential product yields and energy recovery of maize cobs (MC) through carbon dioxide-assisted torrefaction using thermogravimetric analysis (TGA). The CO2-assisted torrefaction of MC was performed from 240 °C to 300 °C (Δ 30 °C) for the residence time of 30 minutes based on the selected non-isothermal/isothermal heating program of the TGA. Furthermore, the physicochemical, microstructure and mineral characteristics of MC were examined. The results showed that the CO2-torrefaction of MC resulted in a mass loss (ML) ranging from 18.45% to 55.17%, which resulted in the mass yield (MY) ranging from 81.55% to 44.83%. The HHV of the solid product was in the range from 22.55 MJ/kg to 26 MJ/kg, which indicates the CO2-torrefaction process enhanced the energy content of MC by 40% – 60%. In conclusion, the findings showed that the CO2 torrefaction is a practical, sustainable, and cost-effective approach for the valorization of MC into a clean solid biofuel for enhanced energy recovery.

This research offers valuable perspectives on the nexus between urbanization and the smart environment within the Najran city, Saudi Arabian context. Najran City, with its diverse districts and evolving urban landscape, is at the forefront of adopting advanced technologies and participatory governance models to create a resilient, environmentally conscious, and sustainable urban environment, likewise positioning itself as a model of smart urban development but is also fostering an innovation-driven community equipped to address the difficulties and exploit the prospects given by the 21st century. The study systematically analyzes the multifaceted factors of smart city variables in Saudi Arabia and their direct impact on urban sustainability through extensive quantitative investigations. This study reveals the positive correlations between smart city attributes, on the attainment of urban sustainability. In order words, the smart city’s attributes such as IoT (Internet of Things) devices, smart infrastructure, and data-driven solutions are linked to the creation of a resilient urban environment in the face of urbanization and sustainability challenges. On the other hand, the study noted that the integration of smart technologies might lead to unintended consequences in urban planning. For example, an overemphasis on technology may overshadow human-centric approaches, which might potentially affect the community well-being and social cohesion. Study’s insights provide policymakers, urban planners, and players with an in-depth knowledge of the pathways to harness smart city attributes for sustainable Najran urban development. The implications of this research extend beyond the borders of Saudi Arabia, serving as a valuable reference for regions worldwide grappling with similar urbanization and sustainability challenges in the 21st century. By harnessing the potential of smart cities, Saudi Arabia and other nations can pave the way for greener, more resilient urban futures.

The research landscape on energy efficiency and affordable housing research (EE-AHR) was critically examined through bibliometric analysis and a literature review of Scopus-indexed publications from 2001 to 2023. Results showed that the total publications and total citations on AH are 630 documents and 4,347 citations. Document type distribution revealed articles (75.7%), conference proceedings (19.8%), and reviews (4.4%). The observably high EE-AHR metrics are attributed to stakeholders’ productivity, funding, and collaborations among other various authors, affiliations, and countries strategically involved in EE-AHR. The most prolific authors are Gurran, N. and Milligan, V., whereas UNSW Sydney is the most productive affiliation. Likewise, the most productive country is the United States, whereas China’s National Natural Science Foundation is EE-AHR’s most active funder. Social network analysis revealed that collaboration, particularly between affiliations, was critical to productivity in the EE-AHR landscape. Keywords co-occurrence identified Energy, Efficiency, Affordable Housing, Housing, and Housing Policy as the 3 top keywords. In contrast, Housing Sector Dynamics, Sustainable Housing Development, and Urban Development Planning are thematic research areas on AHR. Systemic literature review revealed that EE-AHR is multidisciplinary and impactful, particularly as it seeks to build secure, resilient, inclusive, and sustainable human settlements and cities as per the UN SDGs Goal 11. As such, it has significant potential for future research, collaboration, and growth. Nonetheless, critical research gaps (e.g., examining globalized application techniques, social outcomes, and organizational frameworks) in EE-AHR must be tackled comprehensively. Future research will also need to explore technology applications, innovative materials, social integration, environmental sustainability, and net-zero carbon goals applicable to AH.

The construction industry is witnessing a transformative shift towards sustainable and intelligent housing solutions driven by advancements in 3D printing, Artificial Intelligence (AI), and the Internet of Things (IoT). Several architectural and construction firms have adopted innovative technologies to make construction easier, sustainable, efficient, cheap, fast, low generation of waste etc. This explorative review critically examines the integration of these technologies in the construction of eco-friendly intelligent homes. Drawing on a comprehensive analysis of literature spanning from 2010 to 2024, the review explores the synergistic potential and challenges associated with amalgamating 3D printing, AI, and IoT in construction processes. The increase need of smart homes equipped with sensors that can sense and regulate temperature, prevent or control fire, sense gas leakage, motion detectors and alarms for security and other application is in high demand. These types of smart homes can only be achieved by integrating different technologies together which include 3D printing (3DP), AI and Internet of Things (IoT). Despite the growing research in the field of automated construction, there are few articles that attempt to integrate these technologies together for futuristic smart homes and potential of smart cities. This study is aim at providing up-to-date advancement in technological innovation within the construction sector with regards to applications of 3DP, IoT, and AI. Key findings highlight how 3D printing enables rapid prototyping and customization of building components, AI enhances energy efficiency and occupant comfort through predictive analytics and automation, while IoT facilitates real-time monitoring and control of building systems. Furthermore, the review discusses the environmental benefits, cost-effectiveness, and societal implications of adopting such integrated approaches. However, challenges such as regulatory barriers, technological limitations, and the need for skilled labor are identified as critical barriers to widespread implementation. Future research directions are proposed to address these challenges and further optimize the integration of 3D printing, AI, and IoT for the construction of sustainable intelligent homes. In this review article, the need for 3DP in construction, advantage and disadvantage of 3DP, (AI) and IoT and the application of these technologies in addressing challenges regarding 3DP and promoting sustainability in the construction industries were comprehensively explored.

This study employs diverse machine learning models, including classic artificial neural network (ANN), hybrid ANN models, and the imperialist competitive algorithm and emotional artificial neural network (EANN), to predict crucial parameters such as fresh water production and vapor temperatures. Evaluation metrics reveal the integrated ANN-ICA model outperforms the classic ANN, achieving a remarkable 20% reduction in mean squared error (MSE). The emotional artificial neural network (EANN) demonstrates superior accuracy, attaining an impressive 99% coefficient of determination (R2) in predicting freshwater production and vapor temperatures. The comprehensive comparative analysis extends to environmental assessments, displaying the solar desalination system's compatibility with renewable energy sources. Results highlight the potential for the proposed system to conserve water resources and reduce environmental impact, with a substantial decrease in total dissolved solids (TDS) from over 6,000 ppm to below 50 ppm. The findings underscore the efficacy of machine learning models in optimizing solar-driven desalination systems, providing valuable insights into their capabilities for addressing water scarcity challenges and contributing to the global shift toward sustainable and environmentally friendly water production methods. HIGHLIGHTS The study employs diverse machine learning models to predict crucial parameters such as fresh water production and vapor temperatures.; Machine learning models including classic artificial neural network (ANN), hybrid ANN models, and the imperialist competitive algorithm (ANN-ICA) and emotional artificial neural network (EANN); The EANN model emerges as the most accurate in predicting freshwater production and vapor temperatures.;

The promise of a healthier, more comfortable, and more productive way of life has fueled a rapid technological transition, and a regenerative built environment has emerged as the tagline to denote the recent sustainable development. In the built environment, the regenerative paradigm has emerged as a transformative approach that goes beyond mere sustainability, aiming to restore, renew, and enhance the ecosystems affected by human activities. However, the concept of the regenerative paradigm and its potential to foster sustainable development has been understudied in recent time. Therefore, this paper explores how we can transform the built environment in the face of the present impacts of climate change using a new regenerative paradigm concept. The objectives of the study are: (i) to explore the predictors of climate change, (ii) to determine the negative impacts of environmental issues on inhabitants’ health, and (iii) to explore adaptive climate change strategies for Nigeria’s regenerative built environment. The study sample consisted of 235 stratified respondents’ opinions from within the built environment in southwestern Nigeria collected via a self-administered questionnaire. The collected quantitative data was analysed using SPSS (version 22) logistic regression analysis. The major results of the analysis revealed: (i) the ten most important predictors of climate change indicators, (ii) the existence of negative consequences of the impacts of climate change on inhabitants’ health in southwestern regions of Nigeria, and (iii) a significant (p ≤ 0.05) in all regenerative factors: planting native species has the highest β coefficient of 0.499, followed by the biophilic approach (0.494), the establishment of a city’s tree canopy (0.467), the creation of a green functional green space (0.436), the use of smart landscaping techniques (0.388), and the development of a healthy watershed (0.314). This indicates that to have a regenerative built environment it is essential to create a functional green space, plant native species, establish a city’s tree canopy, create a healthy watershed, and render a biophilic approach. The study’s recommendations include urgent action to integrate climate change interventions into the decision-making processes, initiatives, and development plans of the Nigerian government. This integration should prioritize sustainable practices within the built environment, considering the regenerative paradigm’s potential to address climate change impact effectively.

The investigation collected 50 random water samples from wells and bore holes in the five wards. In the meantime, the Water Quality Index (WQI) in this region was assessed using a novel machine learning model. In this sphere of science, the Emotional Artificial Neural Network (EANN) was used as an innovative technique. The training dataset comprised 80% of the available data, while the remaining 20% was used to assess the performance of the network. The laboratory analysis revealed that the levels of magnesium (0.581 mg/L), mercury (0.0143 mg/L), iron (0.82 mg/L), lead (0.69 mg/L), calcium (2.03 mg/L), and total dissolved solid (105 mg/L) in the water sample were quite high and exceeded the maximum permissible limits established by the National Standard Water Quality (NSWQ) and Water Quality Association (WQA). Except for magnesium, mercury, iron, and lead, all physicochemical parameters are below the utmost permissible limit. Results showed that hydrogeological effects and anthropogenic activities, such as waste management and land use, impact groundwater pollution in the Chikun Local Government Area of Kaduna State up to 60 m deep. The results of the EANN showed that R2 index and normalized root mean square error (RMSENormalized) values for the training and test stages are 0.89 and 0.18, and 0.83 and 0.23, respectively. HIGHLIGHTS Groundwater quality is examined in the Chikun Local Government Area of Kaduna State and how it can be used for water supply to improve water management.; Machine learning outperformed the WQI in water quality estimation.; The EANN satisfactory performance when applied to studies characterized by limited data availability.;

Using the soil and water assessment tool (SWAT), runoff in pervious and impervious urban areas was simulated in this study. In the meantime, as a novel application of machine learning, the emotional artificial neural network (EANN) model was employed to enhance the SWAT obtained for this study. As a result of the EANN model's capabilities in rainfall–runoff phenomena, the SWAT-EANN couple model has been used to assess urban flooding. The pervious, impervious, and water body areas of the study area were classified and mapped to estimate the cover change over three epochs. Land use map, precipitation data, temperature (minimum and maximum) data, wind speed, relative humidity, soil map, solar radiation, and digital elevation model were used as inputs for modelling rainfall–runoff of the study area in the ArcGIS environment. The accuracy assessment of this study was excellent (root-mean-square error 1 mm of precipitation). It also revealed that (a) a land use map illustrating changes in impervious, pervious surface, and water body for 1998, 2008, and 2018; (b) runoff modelling using a historical pattern of rainfall–runoff changes (1998–2018); and (c) descriptive statistical analysis of the runoff results of the research. This research will aid in urban planning, administration, and development. Specifically, it will prevent flooding and environmental problems. HIGHLIGHT This study complimented others on exploring rainfall–runoff modelling of pervious and impervious areas in Cross River State, Nigeria. In this study, the run-up modelling using the soil and water assessment tool model, and geographical information system/remote sensing systems were utilized to extract details of the land cover and to analyse the rainfall–runoff using logistic regression and other descriptive statistical analysis.;

The surging demand for electricity, fueled by environmental concerns, economic considerations, and the integration of distributed energy resources, underscores the need for innovative approaches to smart home energy management. This research introduces a novel optimization algorithm that leverages electric vehicles (EVs) as integral components, addressing the intricate dynamics of household load management. The study’s significance lies in optimizing energy consumption, reducing costs, and enhancing power grid reliability. Three distinct modes of smart home load management are investigated, ranging from no household load management to load outages, with a focus on the time-of-use (ToU) tariff impact, inclining block rate (IBR) pricing, and the combined effect of ToU and IBR on load management outcomes. The algorithm, a multi-objective approach, minimizes the peak demand and optimizes cost factors, resulting in a 7.9% reduction in integrated payment costs. Notably, EVs play a pivotal role in load planning, showcasing a 16.4% reduction in peak loads and a 7.9% decrease in payment expenses. Numerical results affirm the algorithm’s adaptability, even under load interruptions, preventing excessive increases in paid costs. Incorporating dynamic pricing structures like inclining block rates alongside the time of use reveals a 7.9% reduction in payment costs and a 16.4% decrease in peak loads. In conclusion, this research provides a robust optimization framework for smart home energy management, demonstrating economic benefits, peak load reduction potential, and enhanced reliability through strategic EV integration and dynamic pricing.

This study was carried out to investigate the impact of building insulation, a method of reducing energy consumption, on the amount of energy consumed in a building, as well as its impact on cooling and heating loads and carbon emission. A residential structure was designed in Revit, and DesignBuilder determined the cooling and heating loads, as well as the energy consumption. Under three distinct climate conditions, the impact of the environment on the energy-carbon emission nexus of residential buildings was assessed. The cold mountain climate of Koick, Slovakia; the arid desert climate of Ha’il, Saudi Arabia; and the tropical monsoon climate of Borneo, Indonesia were chosen. During the design stage, the Particle Swarm Optimization (PSO) method was used to minimize the energy consumption cost (ECC) and CO2 emissions. Over the course of 24 h, the cooling and heating loads decreased by 2.51 kW and 1.9 kW, respectively. When the two modes in Ha’il were combined, the heating load was reduced to 850 kWh and the cooling load was reduced to 650 kWh, according to the results. In Borneo, the heating load was reduced by 200 kWh, while in Koick, it was reduced by 2,000 kWh. The cooling load was reduced by 550 and 50 kWh in Borneo and Koick, respectively. This system appears to perform better in arid and hot climates in terms of both heating and cooling loads. However, energy losses in the arid and hot climate (Ha’il) are greater than in other climates. This could be due to temperature and humidity differences between the inside and outside. According to the findings of the PSO evolutionary algorithm optimization, the building can be constructed to reduce ECC by 19% by taking into account input characteristics such as Wind-to-Wall Ratio (WWR), wall, glazes, and weather conditions. This research provides useful insights into the practical application of optimization methods for reducing CO2 emissions, paving the way for more sustainable and eco-conscious architectural practices.

It is impossible to overstate the role that smart cities and the building resilience strategy play in the movement toward environmental sustainability, particularly in industrialized and developing nations. There has been a rise in the use of efficient systems to enhance built environment control and accomplish infrastructure development projects, and for this to be successful, countries around the globe, including Nigeria, need robust smart cities and buildings. Few researchers have looked at how smart cities and building projects might improve the sustainability practices of Nigeria's built environment in the context of environmental issues. The major research aim of this study is to investigate the role of Information and Communication Technology (ICT) in advancing urban sustainability in the context of Lagos, Nigeria, amidst the city's rapid population growth and the implementation of smart city projects. The study's research questions include the following: First, How can information and communication technology (ICT) be leveraged to support the development of urban sustainability? Secondly, what is the impact of ICT on stakeholders' involvement and participation in urban sustainability? Thirdly, how can stakeholders' involvement and participation impact urban sustainability? Structural equation modeling using partial least squares (SmartPLS 3.0 Edition) as an analysis tool was used to assess the suggested model and the empirical study results in support of all the hypothesized associations. Results revealed that Information and Communication Technology (ICT) is positively associated with smart urban sustainability. Also, a positive and significant influence of ICT on consolidating stakeholder involvement and participation is paramount. Lastly, smart city and building initiatives have the potential to significantly improve urban sustainability. The implication of the study enables the possibility to optimize the impact of an ICT-based urban environment, thereby creating sustainable and resilient communities that meet the needs and priorities of all members of society.

Heating, ventilation, and air conditioning (HVAC) system performance research has received much attention in recent years. Many researchers suggest a set of appropriate fuzzy inputs that can be used to design fuzzy rules-based smart thermostats or controllers that can respond to demand-controlled ventilation, which in turn optimizes HVAC energy usage and provides satisfactory indoor temperatures. Previous research has focused on limited input parameters, such as indoor occupancy status, ambient temperature, and humidity constraints, which cannot efficiently and precisely manage thermal comfort. Hence, this study proposes a novel fuzzy controller with additional input parameters to keep indoor thermal comfort consistent with the corresponding number of occupants. The process employs an automatic fuzzy rule generation method to simplify the task of generating rules in the fuzzy inference system (FIS) using Mamdani FIS. A design-builder is used for modeling the HVAC systems. Local weather data were used to conduct simulations via EnergyPlus. The thermal comfort analysis using the Fanger model for three different scenarios shows that the proposed FIS controller can successfully respond to the indoor comfort variation in all possible scenarios and ensure a satisfactory comfort level. The proposed method demonstrates up to 50% energy savings if occupants do not worry about comfort.

There are strong indications that the built environment has had a great influence on the course of the COVID-19 pandemic and the post-disaster recovery. The COVID-19 pandemic has adversely affected both human and global development, while efforts to combat this menace call for an integrated human social capital index. This research seeks to enhance understanding of how the built environment can be enhanced through resilience against the backdrop of the COVID-19 pandemic. This study aims to investigate the impact of a resilient built environment on increasing resilience in the aftermath of the COVID-19 pandemic in Nigeria. The quantitative studies test the impact of four built environment resilience indices (built environment capital, disaster management indices, awareness of the COVID-19 pandemic, and built environment adaptive strategies) on human social capital and COVID-19 pandemic indices. This study reveals the role of human social capital in achieving a resilient built environment in the wake of the COVID-19 pandemic in Nigeria. Built environment capital, disaster management indices, and awareness of COVID-19 also indirectly affect the COVID-19 pandemic indices through human social capital. This study’s implications are useful for post-COVID-19 recovery, which is important for future planning of the built environment in Nigeria.

The aim of this research is to assess sustainable green building awareness in Kano State, in a case study of the Gwale local government area. This research makes use of both primary and secondary data to address these offered solutions. Descriptive and quantitative analysis using the BREAM and LEAD evaluation standards was used to analyze the case study and 251 questionnaires were distributed. To ensure a fair trial of each of the 251 building samples, they were chosen at random from various parts of the Gwale Yan-Alawa ward. A case study of a selected green building was chosen and analyzed. The logical comparison with Saudi Arabia was made. It is concluded that the Nigerian government at the national level should put more effort into encouraging green building construction through public awareness programs and incentives and subsidizing the green system.

Purpose: The aim of this study is to establish key performance indicators (KPIs) that can be deployed in the evaluation and promotion of safety performance of building developer`s in Nigeria Design/methodology/approach: A thorough review of the literature was performed to generally identify sets of KPIs used to evaluate the safety performances of building developer`s during construction. Interviews and focus group discussions were conducted with eleven (11) subject matter experts and professionals to ensure inclusion, validation and clarity of the indicators and to further provide agreement/disagreement, and importance ratings for the identified KPIs. The KPIs was grouped into appropriate categories, keeping out redundant KPIs, and ensuring KPIs are clear and measurable. The Relative Importance Index (RII) and Mean values were computed. Findings: Results from the study consist of 137 KPIs, grouped into 9 categories including: Planning, design and procurement, communication on & maintenance of effective safety behaviour, construction safety policy, construction safety personnel, management effort and support, safety training and enlightenment, administration of safety processes, investigation and reporting of accidents, and rewards and sanctions for project stakeholders. These KPIs were observed to be at different levels of importance from the respondents. The following KPIs were considered based on their extreme importance judging from the respective RII values: communicating safety requirements to designer (98.18%), safety performance set as part of contractor selection criterion (96.36%), availability and accessibility of the relevant insurance policies (98.18%) and appropriate issuance of motivational directives by the top management (100%). Originality/value: Many studies have been done in the past where KPIs as it relates to construction safety were identified. However, the specificity of these KPIs to countries other than Nigeria requires similar research be conducted to identify building developer safety performance KPIs for the Nigerian construction industry .