Diverse Climate Research Articles

A Study of Cultural Diversity and Ethical Climate towards Employee Selection in Higher Education Institutions

This study investigates the critical role of employee selection processes in organisational success, focusing on the impact of cultural diversity (CD) and ethical climate (EC) within Higher Education Institutions (HEIs) in Northern Malaysia. Employing a quantitative methodology and a descriptive research design, data were collected from academic staff through a structured questionnaire. The analysis using SPSS software reveals strong positive correlations among employee selection, CD, and EC. The Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s Test of Sphericity confirm the data's suitability for factor analysis, while Cronbach’s alpha coefficients indicate robust internal consistency. The findings underscore the importance of aligning employee selection processes with organisational strategies and values to attract competent employees. Additionally, effective management of CD enhances financial performance, competitive advantage, and profitability. An EC significantly promotes ethical behaviour, job satisfaction, and organisational performance. The study highlights practical implications for integrating diversity and ethical considerations into selection strategies, providing valuable insights for human resource management and organisational behaviour. The research emphasises the need for a holistic approach to talent acquisition by demonstrating how selection practices influence organisational outcomes. This approach fosters inclusivity and ethical leadership, ultimately contributing to long-term organisational success. These findings comprehensively understand employee selection, CD, and EC dynamics in enhancing institutional effectiveness and achieving strategic goals in HEI settings.

Investigation of the impact of diverse climate conditions on the cultivation suitability of Cinnamomum cassia using the MaxEnt model, HPLC and chemometric methods in China

Cinnamomum cassia Presl. is a subtropical plant that is used for food and medicine. Climate change has changed the suitable habitats of medicinal plants, which might have repercussions for the efficacy of herbal remedies. In this study, the potential distribution in each period of Cinnamomum cassia was predicted and the quality in different suitable habitats was evaluated. According to the results, (1) precipitation, temperature, and soil are the primary environmental variables influencing C. cassia distribution. (2) The high-suitable habitats of current climate scenarios were predominantly located in the southern regions (Guangdong and Guangxi etc.) of China, with an area of 706,129.08 km2. Under future climate scenarios, suitable habitats will increasingly move northward, with a greater concentration south of the Yangtze River, particularly in the 2090s SSP585 scenario, the total area of newly extended suitable habitat reaches 312,963.53 km2. (3) HPLC and FTIR, combined with chemometrics, can be effective methods for identifying different suitable habitats of C. cassia. The content of trans-cinnamaldehyde (0.85%) is significantly higher in the high suitability habitat compared to the medium-low suitability habitat (0.30%). Our findings can offer valuable guidance for the identification of suitable C. cassia cultivation areas in China, as well as for the evaluation of C. cassia resource quality and the rational use of resources in different suitable habitats.

Implications of CMIP6 Models‐Based Climate Biases and Runoff Sensitivity on Runoff Projection Uncertainties Over Central India

ABSTRACTAccurate runoff projections are vital for developing climate adaptation strategies, yet significant uncertainties persist. The commonly employed approaches to constrain these uncertainties rely on the stationarity of climate biases and runoff sensitivity, which may not hold for climate‐sensitive regions (e.g., semi‐arid regions). This study investigates the validity of the stationarity assumption across 29 CMIP6 models, encompassing diverse climate biases (Dry Warm, Wet Warm, Dry Cold, and Wet Cold), utilising a semi‐arid region in central India as a testbed. The implications of this assumption on runoff projection uncertainties were comprehensively assessed across the runoff modelling chain for three time periods (the 2030s, 2060s and 2090s) based on the Soil and Water Assessment Tool (SWAT) simulations. The results highlight the non‐stationary nature of climate biases and runoff sensitivity under future scenarios, challenging the widespread applicability of common uncertainty‐constraining approaches. Moreover, the impact of non‐stationarity on runoff projection uncertainty was found to be strongly influenced by the choice of GCMs, preprocessing methods and climate change scenarios. In the 2030s, GCMs dominate runoff uncertainty, with dry models exhibiting ~10%–15% higher uncertainty compared to warm models, which is further amplified when interacting with warm biases. However, from the mid‐century onwards, the bias‐adjustment approaches and climate change scenarios significantly shape runoff projection uncertainties under non‐stationary conditions. These findings emphasise the potential of climate bias and runoff sensitivity‐based GCM selection for reducing runoff uncertainty in near‐future assessment (2030s). For mid‐term and long‐term runoff projections, addressing diverse climate biases through bias‐adjustment approaches is more viable. This study offers critical insights to prioritise the development of a non‐stationarity‐based approach for reliable runoff projections in climate‐sensitive regions.

Unlocking the Potential of Teff for Sustainable, Gluten-Free Diets and Unravelling Its Production Challenges to Address Global Food and Nutrition Security: A Review

Sustainable diets, as defined by the Food and Agriculture Organisation, aim to be nutritionally adequate, safe, and healthy, while optimising natural and human resources. Teff (Eragrostis tef), a gluten-free grain primarily grown in Ethiopia, has emerged as a key contender in this context. Widely regarded as a “supergrain”, teff offers an outstanding nutrition profile, making it an excellent choice for people with gluten-related disorders. Rich with protein, essential amino acids, polyunsaturated fats, and fibre, and abundant in minerals like calcium and iron, teff rivals other popular grains like quinoa and durum wheat in promoting human health. Beyond its nutritional benefits, teff is a hardy crop that thrives in diverse climates, tolerating both drought and waterlogged conditions. Due to its resilience and rich nutrient content, teff holds the potential to address nine of the 17 United Nations’ Sustainable Development Goals (SDGs), including SDG 1 (no poverty), SDG 2 (zero hunger), and SDG 3 (good health and wellbeing), which are tied to improving food and nutrition security. However, teff production in Ethiopia faces significant issues. Traditional farming practices, insufficient storage infrastructure, and food safety challenges, including adulteration, hinder teff’s full potential. This review explores teff’s dual role as a nutritious, sustainable food source and outlines the key challenges in its production to conclude on what needs to be done for its adoption as a golden crop to address global food and nutrition security.

State of continental discharge estimation and modelling: challenges and opportunities for Africa

ABSTRACT Africa’s diverse climates and sparse hydro-meteorological networks create significant challenges in accurately estimating river discharge. Discharge data are crucial for managing water resources and predicting extremes. Our review assesses the data gap, existing methods, and technologies for river discharge estimation in Africa. Limited gauging networks on rivers, including in 63 transboundary basins, hinder accurate discharge modelling, affecting resource management and disaster response. Despite the potential of remote sensing, Geographic Information System (GIS), satellite imagery, and machine learning, their large-scale application for river discharge monitoring in Africa is limited. We propose the use of a monitoring system involving local communities in data collection and decision making, supported by global data centres, enhanced regional data sharing, and strengthened transboundary cooperation. For example, incorporating water data products, including discharge data, in data cubes, such as Digital Earth Africa, could improve monitoring. Strategic investments in hydro-meteorological instrumentation are crucial for strengthening climate resilience.

The Application of Rain Gardens in Urban Environments: A Bibliometric Review

The increasing challenges of urbanization and climate change have driven the need for innovative stormwater management solutions. Rain gardens, as a nature-based solution (NBS), have emerged as a critical component in urban water management, particularly in enhancing hydrological regulation, water quality, and ecosystem services. This bibliometric review examines the application of rain gardens in urban environments, focusing on their roles in stormwater management, pollutant removal, and ecological enhancement. Data from 728 academic papers published between 2000 and 2023 were analyzed using the Web of Science (WoS) Core Collection, employing bibliometric tools such as the “Bibliometrix” R package and CiteSpace. The analysis highlights the increasing global interest in rain gardens, particularly since 2015, with China and the United States leading research efforts. Key findings reveal that rain gardens significantly reduce runoff, improve water quality, and contribute to urban biodiversity. In addition, their integration into public spaces offers landscape esthetics and social benefits, enhancing the quality of life in urban areas. However, challenges remain in optimizing their design for diverse climates and long-term performance. The study underscores the need for further research on plant–soil interactions, pollutant removal mechanisms, and the broader ecological and social contributions of rain gardens. This review provides insights into the evolution of rain garden research and identifies future directions for advancing sustainable urban stormwater management.

A robust method for mapping soybean by phenological aligning of Sentinel-2 time series

Soybean is an important crop for food and animal feed. Production and area both continue to increase and expand into new areas and countries. Spatially explicit information on soybean cultivation is essential to crop monitoring, production estimation, and national accounting systems. However, its cultivation in diverse climate conditions, landscapes, and agricultural systems poses challenges to accurately map soybean across different regions and years. We propose an innovative soybean mapping method combining phenological alignment with machine learning (named here RF-DTW), which can be applied to diverse geographies and years by aligning phenological shifts and using distinctive features from Sentinel-2 time-series. The method first uses the dynamic time warping (DTW) algorithm to align the growing season between pixels across different sites. Then, based on the harmonized time-series, a set of distinctive features was identified and used to build random forest (RF) models to classify soybean across ten globally distributed sites and multiple years. Results show that the green chlorophyll vegetation index (GCVI), greenness and water content composite index (GWCCI), normalized difference senescent vegetation index (NDSVI), red edge position (REP), and short-wave infrared bands are important inputs for distinguishing soybean from other crops. Spectral-phenological features, particularly the curve slope metrics of GCVI and GWCCI during the peak to late growing season, rank as the most important features for mapping soybean. RF-DTW demonstrates good generalizability across ten study sites, achieving an overall accuracy (OA) of 0.92 and an F1-score of 0.84. F1-scores for eight out of ten sites ranged between 0.82 and 0.98, outperforming a benchmark method, although they were lower (F1-score < 0.60) for the two sites in Sub-Saharan Africa. Additionally, RF-DTW performs robustly when transferred to untrained regions and years, with most cases showing an F1-score higher than 0.70. Our proposed method, as a combination of phenological alignment and machine learning, can be used to map soybean accurately and efficiently across different regions and years, to provide crucial information for understanding the rapid dynamics of soybean cultivation and its global-scale impacts.

The Impact of Perceived Inclusion Climate for Leader Diversity (PICLD) on Organizational Justice and Its Effects on Employee Engagement, Emotional Wage, and Happiness at Work

In today's globalized world, workplace happiness is an increasingly important concern for many organizations, as it has a direct impact on employee productivity and well-being. An inclusive and equitable work environment plays a significant role in creating conditions that support employees' emotional and intellectual engagement, as well as providing emotional rewards that can increase their happiness. This research aims to analyze the impact of Perceived Inclusion Climate for Leader Diversity (PICLD), Organizational Justice, Employee Engagement, and Emotional Rewards on Workplace Happiness. Using a quantitative approach and survey method, data was collected from 248 employees working in various companies in Indonesia. The results showed that PICLD has a positive and significant influence on perceptions of workplace justice, which in turn increases employee engagement and emotional rewards. Employees who perceive fair treatment are more engaged and receive emotional rewards that improve their well-being. Employee engagement and emotional rewards are shown to have a positive impact on workplace happiness, so employees who feel emotionally rewarded tend to be more satisfied and happy. The implications of this research emphasize the importance of creating an inclusive, fair and supportive work environment to increase employee engagement, emotional well-being and happiness at work.

Proposal automatic water purging system for machinery in high humidity environments controlled by an ECU

This paper presents an advanced automatic water discharge system tailored for diesel-powered machinery, particularly for vehicles operating in diverse climates, including humid coastal regions. The system is designed to protect fuel circuits in tanks and diesel engines of boats, offering either automatic or semi-automatic protection against oxidation and corrosion. This enhancement extends the lifespan of diesel components and pipelines while simultaneously reducing maintenance costs and intervention times. Additionally, the system improves fuel purity, resulting in increased engine power and efficiency. The proposed solution was experimentally validated using an Arduino board coupled with a water-sensitive detector in diesel fuel, suitable for installation in diesel filters or tanks. Experimental findings demonstrated a remarkably swift separation time of 150 microseconds for diesel and water at a pressure of 1 bar, achieving an impressive efficiency of 99.84%. The system's rapid and effective separation capability highlights its practical value, particularly in scenarios where efficient water removal is critical to sustaining engine performance and longevity.

Key Chemical Soil Parameters for the Assembly of Rhizosphere Bacteria Associated with Avocado Cv Hass Grafted on Landrace Rootstocks

Avocado cultivation holds significant economic importance in many countries, ranking Colombia as the fifth largest global producer. Particularly, the Hass cultivar plays a pivotal role in Colombia’s avocado industry, especially in the Department of Antioquia, the primary export region. This cultivar is grown under diverse soil and climate conditions and exhibits considerable genetic polymorphism due to the hybridization of varieties of agronomic significance, leading to a diverse array of landrace rootstocks. However, the role of soil conditions and rootstock genotype in structuring rhizosphere bacterial communities is still lacking. In addressing this knowledge gap, we investigated the influence of two soil conditions on the structure of rhizosphere bacterial communities associated with two landrace genotypes of Persea americana cv. Hass, utilizing 16S rRNA sequencing. Notably, no significant differences related to genotypes were observed. This study reports that the rhizosphere bacterial microbiome remains consistent across avocado landrace rootstocks, while variations in key parameters such as phosphorus, pH, Mg, and Ca drive distinct rhizosphere effects. Our results reveal that despite the soils having similar management, increases in these crucial parameters can lead to bacterial communities with lower alpha diversity and a more complex co-occurrence network. In addition, we found substantial variations in beta diversity, bacterial composition, and metagenome predictions between the two farms, underscoring the role of soil variables in shaping the bacterial microbiome. These findings provide valuable insights into the factors influencing the bacterial communities that may play a role in the health and productivity of crops with agro-industrial potential, such as Hass avocado.

Allelic variants confer Arabidopsis adaptation to small regional environmental differences.

Natural populations of Arabidopsis thaliana provide powerful systems to study the adaptation of wild plant species. Previous research has predominantly focused on global populations or accessions collected from regions with diverse climates. However, little is known about the genetics underlying adaptation in regions with mild environmental clines. We have examined a diversity panel consisting of 192 A. thaliana accessions collected from the Netherlands, a region with limited climatic variation. Despite the relatively uniform climate, we identified evidence of local adaptation within this population. Notably, semidwarf accessions, due to mutation of the GIBBERELLIC ACID REQUIRING 5 (GA5) gene, occur at a relatively high frequency near the coast and these displayed enhanced tolerance to high wind velocities. Additionally, we evaluated the performance of the population under iron deficiency conditions and found that allelic variation in the FE SUPEROXIDE DISMUTASE 3 (FSD3) gene affects tolerance to low iron levels. Moreover, we explored patterns of local adaptation to environmental clines in temperature and precipitation, observing that allelic variation at LA RELATED PROTEIN 1C (LARP1c) likely affects drought tolerance. Not only is the genetic variation observed in a diversity panel of A. thaliana collected in a region with mild environmental clines comparable to that in collections sampled over larger geographic ranges but it is also sufficiently rich to elucidate the genetic and environmental factors underlying natural plant adaptation.

Detecting Drought-Related Temporal Effects on Global Net Primary Productivity

Drought has extensive, far-reaching, and long-lasting asymmetric effects on vegetation growth worldwide in the context of global warming. However, to date, few scholars have attempted the systematic quantification of the temporal effects of drought on global vegetation across various vegetation types and diverse climate zones. Addressing this gap, we quantitatively investigated the effects of drought on global vegetation growth under various scenarios, considering lagged and cumulative effects as well as combined effects in the 1982–2018 period. Our investigation was based on long-term net primary productivity (NPP) and two multiple-timescale drought indices: the standardised precipitation index (SPI) and the standardised precipitation and evapotranspiration index (SPEI). Our main findings were the following: (1) SPI and SPEI exhibited lagged effects on 52.08% and 37.05% of global vegetation, leading to average time lags of 2.48 months and 1.76 months, respectively. The cumulative effects of SPI and SPEI were observed in 80.01% and 72.16% of global vegetated areas, respectively, being associated with relatively longer cumulative timescales of 5.60 months and 5.16 months, respectively. (2) Compared to the scenario excluding temporal effects, there were increases in the explanatory powers of SPI and SPEI for variations in vegetation NPP based on the lagged, cumulative, and combined effects of drought: SPI increased by 0.82%, 6.65%, and 6.92%, respectively, whereas SPEI increased by 0.67%, 5.73%, and 6.07%, respectively. The cumulative effects of drought on global vegetation NPP were stronger than the lagged effects in approximately two-thirds (64.95% and 63.52% for SPI and SPEI, respectively) of global vegetated areas. (3) The effects of drought on vegetation NPP varied according to climate zones and vegetation types. Interestingly, vegetation in arid zones was the most sensitive and resilient to drought, as indicated by its rapid response to drought and the longest cumulative timescales. The vegetation NPP in tropical and temperate zones exhibited a relatively stronger response to drought than that in cold and polar zones. The strongest correlation of vegetation NPP with drought occurred in shrubland areas, followed by grassland, cropland, forest, and tundra areas. Moreover, for each vegetation type, the correlations between vegetation NPP and drought differed significantly among most climate zones. (4) The vegetation NPP in warming-induced drought regions displayed a higher correlation to drought than that in non-warming-induced drought regions, with shorter lagged and longer cumulative timescales. Our findings highlight the heterogeneity of the lagged, cumulative, and combined effects of drought across various climate zones and vegetation types; this could enhance our understanding of the coupling relationship between drought and global vegetation.

The Impact of Agricultural Inputs and Environmental Factors on Potato Yields and Traits

Potato, a component of global food security and economic stability, is cultivated extensively worldwide due to its adaptability to diverse climates and soil types. Ongoing research and technological advancements, including the use of unconventional products destined to fight the most harmful pathogens, are essential for enhancing productivity and resilience in potato farming. The current study aimed to identify the most appropriate fertilizers and phytosanitary treatments in order to attain optimal potato yields, dry matter, and starch contents, under climate-specific environments. The experiment was conducted in the northwest of Romania in 2023. The research was organized as a trifactorial experiment with the factors variety, fertilization, and phytosanitary treatment. XLSTAT (2022.2.1v.) was utilized for data processing. Mineral fertilization combined with conventional phytosanitary treatment led to the highest average yields in both potato varieties. Also, a combination of organic fertilization and treatment with 4% A. cepa extracts led to notable results, which suggests the possibility of successfully using these inputs in organic agriculture. The study shows the importance of selecting appropriate agricultural inputs to optimize potato yields, achieve specific levels of dry matter and starch content in a specific area, and promote sustainability.

Climate-Responsive Architectural Design: Analyzing and Adapting to Diverse Climates in U.S. Cities

The energy consumption challenge within the built environment is growing in urgency, underscoring an imperative for climate-responsive design solutions. This paper presents an in-depth analysis of the climatic characteristics of 12 U.S. cities, with a parametric platform, categorized under different Köppen climate classifications, with the aim of informing effective passive design strategies. Upon meticulously assessing the efficacy of various passive strategies tailored to distinct climate zones, this paper distills actionable guidance for deploying architectural methods that embody climate-responsive design principles. Theoretical models are developed to correlate climatic variables with specific passive design interventions. The research contributes to the theoretical discourse on passive design by advocation for a data-driven, climate-responsive design guidance that aligns with contemporary sustainability goals.

Optimizing passive energy savings in rural self-built houses: Integrating phase change materials across China's climate zones

The energy consumption of rural residential buildings in China is increasing, and the integration of phase change materials (PCMs) has emerged as an effective strategy to mitigate building energy use and carbon emissions. Given China's diverse climate zones and the varying construction forms of rural self-built houses (RSHs), the efficacy of PCM applications varies significantly across regions. This study presents a comprehensive investigation into passive energy savings using PCMs in RSHs across various climate zones. We investigate the energy saving potential and thermal comfort effects of installing PCMs with varying phase change temperatures and thicknesses at different laying positions in representative buildings from each climate zone. The results indicate that the optimal phase change temperatures in different regions closely align with the outdoor average temperatures during the application season. When the optimal phase change temperatures and appropriate thicknesses of PCM are applied, cost-effective energy savings are consistently achieved in building roofing across regions, with the most significant effects observed in the Severe Cold region, where the uncomfortable degree hours reduction rate (φUDH) exceeds 90 %.

Propagation pathways from meteorological to agricultural drought in different climatic basins in iran.

The propagation of meteorological drought (MD) to agricultural drought (AD) is influenced by various factors, particularly the climate type. This research examined the characteristics of drought propagation, encompassing propagation rates, lag time, and response time, from MD to AD within the context of Iran's diverse climate conditions. This was accomplished using three crucial meteorological and agricultural drought indices, namely the Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Precipitation Index (SPI), and Standardized Soil Moisture Index. The research data included in-situ and ERA5 datasets from 30 basins (catchments) across Iran in the 1979-2021 period. Based on reports from the Global Drought Observatory, the three most prominent MD events were identified in 1999-2002, 2008-2009, and 2017-2019. The correlation coefficients between MD and AD indices across various timescales, in climates ranging from hyper-arid to humid, exhibited a decline from 0.75 to 0.44. The response time, varying between 2.42 to 6.63months, was determined by the strong correlation between SPI (or SPEI) and SSI1 (or SSI2) within the studied basins. Furthermore, the lag time, which was affected by the onset of MD and AD events, fluctuated between 2 to 6months in hyper-arid and arid basins, revealing a 1-3month variation compared to humid basins. The findings on propagation rates highlighted heightened sensitivity or response from meteorological to agricultural drought in humid climates, as opposed to hyper-arid climates. In light of these outcomes, comprehending the transition from MD to AD holds substantial significance for predicting, issuing early warnings, and fortifying preparedness in managing drought risks.

DNA barcoding and cryptic diversity in fishes from the Ili River Valley in China, Xinjiang.

The Ili River Valley, located in the northwest of China, serves as a vital repository for fish genetic resources. Its extensive water network and diverse climate have given rise to a unique fish composition and endemic species. In this study, we collected the cytochrome c oxidase subunit I (COI) sequences from 660 fish specimens in the Ili River Valley. The effectiveness of DNA barcoding in identifying fish species in the area was assessed by examining genetic distances, constructing phylogenetic trees, and performing ABGD (Automatic Barcode Gap Discovery) analyses, among other methods. In total, 20 species were identified, including one unidentified species (Silurus sp.). Except for Silurus asotus and Hypophthalmichthys molitrix (only one sample), the maximum intraspecific genetic distance among the remaining species was smaller than the minimum interspecific distance, which proves that the species exhibit obvious barcode gaps. In the Neighbor-Joining trees, 20 species formed separate monophyletic branches. According to ABGD analysis, 660 sequences were categorized into 19 Operational Taxonomic Units, with Silurus sp. and S. asotus grouped into a single OTU. The Silurus in this study exhibits shared haplotypes and significant genetic divergence, suggesting the potential presence of cryptic species. Furthermore, the nucleotide diversity across all species fell below the threshold level, indicating that the local fish population is gradually declining. In conclusion, this study has demonstrated the effectiveness of DNA barcoding in identifying fish species in the Ili River Valley, providing valuable data to support the conservation of local fish resources.

Racial Diversity Influences Community Leadership Identity Development: Homogeneity as a Baseline for Measurement

This study uses multilevel regression modeling to explore how racial diversity and other campus climate variables influence the development of community leadership identity on college campuses. Pulling from a longitudinal sample of students ( n = 7,829) over a 4-year period, findings show that attending a college campus with more racially similar peers (i.e., racial homogeneity) played a positive indirect role in predicting community leadership goals. This study's findings contribute to the existing literature on the influence of race and diversity on leadership development and provide insights that shape the development of community leaders.

Factors influencing spatiotemporal cooling potential of blue–green infrastructure across diverse local climate zones—Case study of Wroclaw, Poland

Factors influencing spatiotemporal cooling potential of blue–green infrastructure across diverse local climate zones—Case study of Wroclaw, Poland

Design and analysis of zero-energy and carbon buildings with renewable energy supply and recycled materials

Given the notable consumption of fossil fuels, buildings prioritize their energy utilization. This paper’s aim is to explore the primary strategies in attaining zero-energy and zero-carbon buildings. The investigation employs two specialized software programs: Design Builder, which analyzes energy usage, and PV Syst, which evaluates renewable energy requirements. The study is conducted in Fort Myers, USA, and Dubai, UAE, leveraging diverse regional and climate discrepancies, and real-world data on zero-energy and carbon constructions for comparison and validation. Within the Design Builder software, the research examines four strategies, with the first serving as the realistic baseline. By implementing the second strategy, utilizing internal shading devices such as Shade Roll, a reduction of 376 kWh in Fort Myers and 591 kWh in Dubai is achieved. The third strategy encompasses the deployment of blind-type internal shading systems, resulting in reductions of 212 kWh in Fort Myers and 348 kWh in Dubai. The fourth strategy involves integrating renewable energy via photovoltaic panels, injecting 323.5 kWh in Fort Myers and 2237 kWh in Dubai into the power grid for lighting purposes. Furthermore, 3200.5 kWh and 4722 kWh of energy are respectively injected into the grid for heating in Fort Myers and Dubai. Cooling, however, necessitates additional energy from the grid, amounting to 2243.94 kWh in Fort Myers and 9211.64 kWh in Dubai. In the pursuit of zero-carbon goals, the implementation of suitable low-carbon recycled materials, in place of primary building materials, reduces carbon dioxide emissions from 79 tons to 7.315 tons in Fort Myers and 7.038 tons in Dubai. By employing appropriate materials, the zero-carbon objective is attained.