The intersection of sustainability demands and digital advancements has led to the rapid emergence of smart-green housing as a paradigm-shifting approach to urban planning and development, contributing directly to UN Sustainable Development Goals for sustainable cities (SDG 11) and clean energy (SDG 7). Nevertheless, the current literature remains limited in its ability to explain the relationship between technological efficiency and its financial implications. This paper aims to conduct a comprehensive literature review of global trends in smart-green housing investment using the PRISMA 2020 guidelines, identifying key technological, management, and institutional variables that affect asset performance. The framework maps how Technological Inputs (Mtech ) pass through a lifecycle operational channel (nops), moderated by Institutional Frameworks (& empty;inst), to determine Risk-Adjusted Value (VSG) while accounting for value leaks via the 'Performance-Branding Gap'. The review demonstrates a significant gap between environmental certifications and risk-adjusted financial metrics, also known as the performance-branding divide. To bridge this gap, this study will propose the Smart-Green Asset Valuation (SGAV) model and introduce a new equation for valuing smart-green housing based on technology maturity, adjusted by institutional governance and management effectiveness throughout its life cycle. The article presents a theoretical understanding of the financial viability of smart-green buildings using the introduced discounted valuation model, providing a data-driven, dynamic approach that facilitates asset value adjustment for property owners, shifting from mere green marketing to true financial asset valuation.
The purpose of the study is to provide theoretical and methodological substantiation of managerial and economic approaches to ensuring the competitiveness of agribusiness on an innovation-investment basis in conditions of significant risks and to provide tools for the practical implementation of the proposed approaches. It is indicated that the main way to ensure competitiveness is the adaptability of Ukrainian agribusiness not only to the challenges and conditions of coevolution, digital agriculture, and Economy 4.0, but also to direct threats caused by war and economic crisis. It is noted that these threats create uncertainty regarding the main factors influencing the competitiveness of Ukrainian agribusiness and the implementation of management actions to ensure the innovation and investment processes. Therefore, the main obstacle to investment in the innovation process is the uncertainty of risks, which, in particular, prevents an accurate assessment of the necessary costs. An approach to the mathematical formalization of operations with an uncertainty factor has been developed. Using the example of risks to the grain industry due to the increase in diesel fuel prices in 2026, the nature of the influence of risk uncertainty on the outcome is illustrated. Comparing the honey and dairy production industries, it is indicated that the obstacle to ensuring a competitive level is not only such an important function of the management and economic mechanism as marketing activities, but also a complex of technological, organizational, analytical, forecasting, financial, accounting, and other innovations. It is indicated that, in principle, the sustainable development of agricultural production through innovation and investment requires integrating institutional, general, and internal economic and social mechanisms, along with the introduction of dynamic management of the economic base, which is identified as a system-forming factor. This substantiated the interpretation of the scientific category of "management" as a complex-structured phenomenon, which includes not only a set of different levels of management, but also an array of connections between them. The competitiveness of agricultural sector products is not determined only by their price parameters; the purpose of applying a management and economic mechanism may be to achieve higher-level competitive advantages: achieving complementarity of the activities of an agricultural enterprise, that is, interaction with other market structures to form a holistic system or ensure increased functionality of this system. Belonging to the specified system can stabilize the activities of an agricultural enterprise and increase its competitiveness, which is important for such weak elements of innovative activity in the agricultural sector as small and medium-sized enterprises.
The world is moving toward more sustainable construction, which means we need to find eco-friendly substitutes for traditional materials. Steel reinforcement in concrete makes it very strong and long-lasting, but it poses significant environmental and economic problems. Bamboo is a cheap, eco-friendly choice because it grows quickly and can be used repeatedly. However, concerns about how well it holds up structurally have kept it from being used more widely. An examination of the strength and deformation properties of plain and bamboo-reinforced concrete beams was conducted. All beams that feature bamboo strips as reinforcement in the stress zone have been cast, cured in normal conditions for 28, 56, and 90 days, and then tested to failure with three-point loading. The grade of concrete for investigation was M25, with a proportion of cement, sand, and coarse aggregate of 1:1.34:2.5. Plain concrete beam (PCB) and bamboo-reinforced concrete beam (BRCB) with six other specifications and orientations were tested for flexural strength and deformation. It was observed that the split tensile strength follows a nearly linear relationship with compressive strength. This shows that bamboo reinforcement not only increases load capacity, but in some cases, BRCB6 also provides better energy absorption and ductility. The deflection at first crack was more than 100 percent as compared to PCB for BRCB6. It is also observed that the bitumen-coated BRCB shows higher flexural strength than the non-coated BRCB and PCB. BRCB6 exhibits the highest flexural strength compared to other beam designations on all test days.
The aggravation of climate challenges and the need to green fiscal policy in the context of European integration necessitate increasing the efficiency of tax and budgetary instruments. The purpose of the study is to analyze the practice of EU member states in using fiscal instruments in the environmental sphere and to substantiate the directions of their adaptation in Ukraine. The methodological basis comprises a systematic approach, comparative and structural-functional analysis, and statistical methods. It has been established that taxes in EU countries perform a dual function – fiscal and regulatory – ensuring the internalization of external effects, the formation of price incentives, and the implementation of the "double dividend" effect due to the dominance of energy and carbon components. In Ukraine, fiscal and regulatory potential is limited by low rates, a narrow base, and insufficient integration with climate policy. At the same time, budget expenditures in the EU act as a catalyst for structural transformation, providing financing for decarbonization, innovation, and adaptation through climate-oriented budgeting. In Ukraine, they are characterized by insignificant scales and fragmentation, which reduce their effectiveness. The need for a comprehensive transformation of Ukraine's fiscal policy is proven, involving strengthening environmental taxation, introducing carbon pricing, and institutionalizing climate-oriented budget expenditures to ensure a coordinated impact on achieving climate resilience.
International road freight transport depends strongly on the availability and retention of professional drivers, whose total remuneration is affected not only by wages but also by travel allowances. In Slovakia, meal allowances for foreign business trips are set by fixed rates, while accommodation reimbursement is linked mainly to documented costs. This fact creates uncertainty when international road freight drivers spend their rest periods abroad without providing proof of accommodation. This paper designs and evaluates a flat-rate accommodation allowance model for Slovak international road freight transport. The research applies an analytical, model-based approach to compare Slovak with Polish, German, French, Spanish, and Italian scenarios, using a one-week model route from Prague to Athens and back. The results show that accommodation-related compensation differs considerably between the compared systems. The designed model offers a transparent, administratively simple, and sustainability-oriented mechanism for compensating accommodation-related costs without replacing documented reimbursement for accommodation. The proposed model is also interpreted within the broader framework of sustainable development, linking economic efficiency, social stability of driver remuneration, and indirect environmental relevance in international road freight transport.
The National Botanical Garden, Mirpur (Bangladesh), was established in 1961 with a wide landscape of 214 acres. The garden is located in one of the fastest-developing communities of Mirpur 1, at the center of the capital, Dhaka. The garden has a large herbarium with 100,000 samples, including rare collections of mammals (70 species), birds (190 species), and various plants and trees (1042 species), as well as six water bodies. Despite such a wonderful collection, the garden attracts only about 100,000 visitors annually. The main visitors are local senior citizens, some students, and a couple of tourists, according to witnesses. However, reports say the ecotourism market valuation was USD 235.54 billion in the year 2023. It is expected to reach around 665.20 billion USD within this decade (till 2030). Additionally, global market reports say that garden tourism alone accounts for USD 5.02 billion of the ecotourism industry. Besides these, scholars have reported that the current generations (Millennials and Generation Z) have serious concerns and an attraction to natural sustainability, as well as sensitivity to animal lives worldwide. Considering the potential of the botanical garden in Dhaka, the authors found that a gap in public relations and marketing is a major barrier to the garden reaching its full capacity as a tourist attraction. This paper is based on a systematic literature review (SLR) and participant observation to analyze the current situation and develop a marketing model specific to the national botanical garden in Mirpur.
The traditional soil stabilization techniques normally utilize cement and lime, which are known to lead to high carbon emissions and environmental degradation. This has led to increased interest in ecologically friendly alternatives, such as bio-based additives. Nevertheless, the shear strength behavior of bio-stabilized soils cannot be easily predicted from differences in soil properties and treatment conditions. ML models were developed using experimental data: Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost). Input variables: additive content (0-12%), dry density, moisture content (OMC +/- 2%), and curing period (0-56 days). Output: peak shear strength (kPa). XGBoost achieved the highest prediction accuracy (R2 = 0.96, RMSE = 3.2 kPa on test data). Model predictions validated with direct comparison to experimental values. This paper explores the shear strength performance of soils stabilized with bio-based additives using a combination of experimental and machine learning (ML) methodologies. Direct shear tests on soil samples with varying percentages of bio-based additives were conducted under controlled laboratory conditions of moisture and curing. ML models were then created using the experimental data, including Random Forests and Artificial Neural Networks, to predict shear strength as a function of important input variables, such as additive content, dry density, and moisture content. Findings show that bio-based stabilization greatly reduces shear strength, and the best additive ranges yield the greatest benefits. The ML models demonstrated high predictive performance, with a strong correlation between measured and predicted values. The results outline opportunities to combine experimental testing with ML tools to optimize sustainable soil stabilization. This design will help achieve sustainable geotechnical construction by reducing the use of conventional chemical stabilizers and encouraging the use of renewable resources.
Natural healing resources (NHR) are presented as ecosystem resources that have a dual nature of ecosystem services and economic significance for the country, and the result of their sustainable use should provide a reserve for the provision of rehabilitation and health services to the population. Currently, international scientists' achievements in accounting for natural capital include the System of Ecological-Economic Accounting (SEEA) and the Gross Ecosystem Product (GEP), which have become widely adopted. In contrast, the economic and social functions of healing natural assets that are not included in the system of ecological and economic accounts are not reflected at all. This situation creates institutional collapse within the sectoral economy, which is focused on the development of resort areas, and within the social contribution-rehabilitation, and health. The study is devoted to developing a hybrid methodological framework for capitalizing on natural healing assets by adapting the GEP approach to healing ecosystems. The hybrid methodology proposed by the authors involves conducting an assessment across three areas: ecological, medical-biological, and financial-economic (based on replacement cost, willingness to pay, and market approaches consistent with the principles of ecological-economic accounting).
The increasing need to use sustainable pavement materials has prompted the pursuit of alternatives to Petroleum bitumen made of bio-based materials. This work explores the environmental and mechanical behavior of bio-oil-modified bitumen within the framework of a hybrid experimental and machine-learning approach. Bio-binder was waste cooking oil used to partially replace conventional bitumen (ranging from 0 to 20 percent). The experimental analysis aimed to assess the binder's high-temperature deformation resistance using the rutting parameter (G*/sin delta). Three machine learning models were created with the use of key input variables such as bio-oil content, mixing temperature, aging condition, viscosity, and density to improve the predictive power and decrease the amount of effort expended on the experiment, including Artificial Neural Network (ANN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). Statistical measures such as R2, RMSE, MAE, and MAPE were used to assess model performance. XGBoost proved to be the most accurate, with higher accuracy than ANN and Random Forest. The environmental benefits have been estimated by quantifying the binder-related CO2 reduction when using bio-oil instead of oil. A Pareto optimization model was used to determine the best trade-off between environmental sustainability and mechanical performance. The findings suggest that a replacement level of bio-oil of 10-15% offers the most promising compromise, as it retains acceptable rutting resistance whilst achieving a considerable carbon reduction. In general, the suggested hybrid experimental-machine learning method can be considered an effective instrument for optimizing sustainable asphalt binder formulations and offers the possibility of the continuous incorporation of environmentally friendly materials into pavement engineering. The novelty of this study lies in integrating experimental characterization, machine learning prediction, and Pareto-based environmental optimization within a single framework for bio-oil-modified bitumen. Unlike previous studies that focused separately on mechanical performance and predictive modeling, the present work simultaneously evaluates rutting resistance, CO2 reduction potential, and predictive accuracy using ANN, Random Forest, and XGBoost models to develop a sustainable and optimized asphalt binder formulation.
The construction sector is a huge contributor to carbon emissions in the world, mostly because of the heavy applications of Ordinary Portland Cement (OPC). Geopolymer concrete with low carbon content has become a viable alternative to concrete; nevertheless, accurate prediction and evaluation of its CO2 emissions have not been fully achieved, especially through combined experimental and data analysis. The literature on this study focuses mainly on mechanical performance, and there is limited understanding of environmental emission modeling. To measure and forecast CO2 emissions from low-carbon geopolymer concrete, this paper develops an experimental and machine-learning system. An array of geopolymer blends comprising fly ash and ground granulated blast furnace slag (GGBS) was developed at different binder proportions, alkaline activator ratios, and curing conditions. The amount of CO2 was determined using a cradle-to-gate evaluation method (kg CO2/m(3)). Data were subsequently trained on and tested on machine learning models, such as Artificial Neural Networks (ANN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) using experimental data. The R & sup2;, RMSE, and MAE measures were used to assess model performance. Findings show that the XGBoost model has the best prediction accuracy (R & sup2; > 0.95), indicating good generalization ability. The highest CO2 emission reduction was 45-65 percent for geopolymer mixtures compared to traditional OPC concrete. The construct can offer a trustworthy decision support system in geopolymer concrete design that is environmentally friendly and further sustainability in construction methods with predictive modeling of emissions.
The article examines innovative strategic management approaches in Ukraine's agricultural sector amid fullscale military aggression, macroeconomic instability, and accelerated European integration. Employing statistical, comparative, and systematic analytical methods, the study evaluates the strategic effectiveness of key management instruments-digital transformation tools, EU harmonization mechanisms, FAO-supported recovery initiatives, and the National Agricultural Development Strategy until 2030-in maintaining and restoring the productive and export potential of the agri-food complex. The empirical analysis reveals a profound structural disruption: the agricultural sector's share of GDP contracted sharply from 12% (2021) to 4% (2022), while grain production declined from a historic peak of 86.5 million tonnes (2020) to 56 million tonnes (2024), reflecting cumulative losses attributable to territorial occupation, infrastructure destruction, extensive land contamination, and acute labour shortages. The study demonstrates that systematic deployment of precision agriculture technologies-including unmanned aerial vehicles, IoT-integrated soil and crop monitoring, satellite remote sensing, and unified digital management portals-has yielded measurable gains in resource efficiency and operational cost reduction, strengthening sectoral resilience under wartime constraints. Strategic priorities for sustainable recovery are identified as follows: modernization of agro-industrial infrastructure, regulatory harmonization with EU food safety and phytosanitary standards, targeted support for small and medium agricultural enterprises, stimulation of innovation activity reflected in the European Innovation Scoreboard index growth from 0.21 (2020) to 0.28 (2023), and diversification of export market access. The critical stabilizing role of international institutional support is substantiated-FAO-coordinated initiatives secured storage capacity for 4.07 million tonnes of grain and delivered essential agricultural inputs to over 45,000 farming households. It is concluded that strategic management innovation constitutes a necessary yet insufficient condition for comprehensive sector recovery; effective rehabilitation demands integrated policy frameworks combining digital transformation, physical infrastructure restoration, institutional capacity building, regulatory reform, and sustained international cooperation.
The growing urgency of environmental challenges and the need for sustainable economic growth have spurred significant interest in green innovation. This research explores how reported green innovation practices are associated with firms' perceived competitiveness and sustainability-oriented outcomes. The interplay between environmental sustainability and economic performance is critical in today's market, where consumers and stakeholders demand more responsible business practices. The primary aim of this article is to examine the relationship between green innovation and firms' perceived competitiveness and to identify pathways through which these innovations can contribute to sustainable development. The article aims to offer fresh insights into the mechanisms that drive this relationship by examining case studies and theoretical frameworks. This study uses a comparative analysis to examine firms that have successfully implemented green innovations alongside those that have not. Data were collected through surveys distributed to a diverse range of companies, supplemented by in-depth interviews with industry experts. Additionally, a systematic literature review was conducted to synthesize existing research on the impact of green innovation on competitiveness. The results indicate that firms reporting green innovation activities also report higher perceived competitiveness and stronger sustainability-oriented outcomes. However, given the study's cross-sectional design, these findings should be interpreted as evidence of association rather than direct causality. Future longitudinal research is needed to verify whether these associations remain stable over time and whether green innovation contributes to sustainable competitive advantages. Key findings highlight the importance of regulatory frameworks, consumer preferences, and technological advancements in fostering green innovation. This article adds value by providing a comprehensive framework for understanding the dynamics between green innovation and competitiveness and by offering practical recommendations for policymakers and business leaders aiming to achieve sustainable development goals.
This research explores the use of data-driven modelling approaches to predict the stability and flow behaviour of bituminous concrete mixtures incorporating polyethylene terephthalate (PET) waste as a modifier. A total of 35 unique mix combinations were produced via the dry-mixing technique, varying both PET and bitumen contents, and tested in accordance with MoRTH and ASTM D1559 protocols. Three predictive frameworks, Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), and Random Forest (RF), were developed using a 66:34 traintest data split. Model performance was assessed through the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). For stability estimation, RF achieved an R2 of 0.9886 on the training set and 0.7902 on the test set, while ANN delivered 0.9916 and 0.9459, respectively. MLR provided a reliable yet lower R2 of 0.8683 for the training data. Similar performance patterns were noted for flow value predictions. Although RF demonstrated superior accuracy during training, ANN showed better generalization on test data. MLR remained consistent and interpretable across both datasets. The findings demonstrate the effectiveness of machine learning in streamlining laboratory processes and enhancing the design of PET-modified asphalt mixes, contributing to environmentally sustainable pavement engineering.
This article presents an analysis of indoor air quality, overall comfort, and air humidity preferences in educational buildings, considering the subjective perceptions of occupants in the rooms. The results of measurements of microclimate parameters (air temperature, relative humidity) were compared with the occupants' subjective perceptions. The analyses of indoor environmental parameters focused on assessing differences in subjective assessment with regard to gender. The results show that, typically, more men than women considered their state (overall comfort) to be positive, and men were more likely to report slightly higher overall comfort than women at the same air temperature values. Generally speaking, women seemed to consider air quality poorer, while the relationship between overall comfort and indoor air quality was the same for women and men. Additionally, women seemed to rate the air humidity as drier.
*corresponding author's e-mail: biega@agh.edu.pl Abstract: This article reviews conventional methods for the disposal (neutralization) of waste pesticides belonging to the POPs group and demonstrates their effectiveness. Organochlorine pesticides from this group were characterized, with particular emphasis on their impact on human health and the environment. An unconventional disposal method was also presented, highlighting its advantages. While discussing the types of explosives used in mining, the possibility of modifying them for this purpose was noted. Using a selected organochlorine pesticide as an example, the effectiveness of its decomposition by the detonation method was demonstrated. The potential application of this method for the disposal of organochlorine pesticide waste under industrial limestone mining conditions was also discussed.
One of the key problems related to water pollution is the presence of odorous nitrogen compounds, especially ammonia, which often lacks a clearly defined odour threshold value. Odour intensity does not always correlate directly with gas concentration, and this complexity has encouraged broader scientific cooperation, such as the COST Action CA18225 & quot;Taste and Odor in early diagnosis of source and drinking Water Problems & quot; (2019-2023), which focuses on taste and odour issues in water. The work aimed to determine the dependencies between odour concentration and the temperature and concentration of an aqueous ammonia solution. A method was developed to measure odour concentration in water contaminated with ammonia at various levels. Results show that increasing solution temperature significantly raises odour concentration in the air: from 616 OUE/m3 at 20 degrees C to 1052 OUE/m3 at 30 degrees C and 1758 OUE/m3 at 40 degrees C. The odour threshold also rises with temperature, doubling between 20 degrees C and 30 degrees C. odour emission flow enables odour in ambient air.
This paper examines the need for high-speed rail (HSR) in Romania by analyzing its economic, social, and environmental benefits. HSR has become an important part of modern transportation in Europe, and its introduction in Romania would not only improve domestic travel but also strengthen the country's connection to the wider European transport network. A well-developed HSR system would enhance cross-border mobility, support trade, attract investment, and contribute to a more efficient and sustainable transport system. To identify the best route alternatives, this study uses the National Transport Model, applying different scenarios based on traffic flows and demographic factors. The analysis also considers the natural terrain to ensure that proposed routes follow the most suitable geographical conditions. Beyond the clear advantage of reducing travel times between major cities, HSR could play a key role in regional development by reducing economic differences and encouraging more balanced growth across Romania. By aligning with European transport policies, the development of high-speed rail in Romania would be a major step toward modernizing infrastructure and improving the country's role in Europe's transport network. The objective of this study is to integrate Romania's National Transport Model with a land suitability analysis to identify the most suitable high-speed rail corridors and to evaluate their transport demand potential.
The purpose of the study is to provide a theoretical and methodological justification for approaches to assessing the environmental and economic efficiency of various types of economic activity based on comparing costs for environmental protection measures (waste management and soil and water protection) with the actual volume of environmental services provided. The study is based on the application of the Data Envelopment Analysis (DEA) method as a nonparametric assessment tool. A graphical method was used to construct lines of technical efficiency and inefficiency, and the boundary lines. The analytical tools were supplemented by the calculation of coverage coefficients, as well as the author's development of two complementary indices: the index of the need to increase the volume of services (INIVS) and the imbalance coefficient (IF), based on which a matrix of the spatial distribution of industries was constructed. The study was conducted based on statistical data from Ukraine for 2023. A deep asymmetry of environmental and economic efficiency was revealed in the context of types of economic activity. It is proven that the agricultural sector (agriculture, forestry, and fisheries) and the extractive industry are in a state of critical inefficiency: the volume of their environmental protection costs exceeds the volume of implemented environmental services by a factor of 1,000. Instead, the processing industry, electricity supply, and water supply serve as benchmarks for efficiency. It has been established that the low efficiency of the agricultural sector is due not so much to a lack of funding as to a critically low level of actual implementation of environmental services in response to the costs incurred. The paper first proposes a complex matrix model for classifying types of economic activity by the level of environmental and economic efficiency and the nature of structural imbalance (INIVS & times; IF). The method for analyzing the operating environment is specifically adapted for assessing environmental costs (where the production factors are the costs of waste management and soil/water protection, and the result is the volume of environmental services). The proposed approach forms a universal toolkit for strategic management of environmental activities at the macro and meso levels. The distribution matrix allows state bodies and top management to identify "bottlenecks", justify targeted mechanisms for transferring positive experience from reference industries to problem ones, and also optimize the distribution of investments in the green economy (in particular, in the field of rational land use).
Low-density polyethylene (LDPE) is considered a widespread environmental pollutant that threatens ecosystems due to its non-biodegradable nature. However, different types of bacteria could help with the biodegradation process of synthetic LDPE. This study aimed to investigate the capacity of local bacteria to degrade LDPE in landfill soil and to evaluate their degradation efficiency. Fifty bacterial isolates were obtained through enrichment in a mineral salt medium supplemented with LDPE as the sole carbon source. Initial screening was performed using the clear-zone method with polyethylene glycol (PEG), and 7 isolates showed initial degrading activity. Following characterization, Staphylococcus haemolyticus and Acinetobacter baumannii were found to possess the highest potential degrading capacity. After 60 days of incubation, the degrading capacity was assessed by measuring the weight loss of LDPE sheets. S. haemolyticus recorded a 20% potential weight loss, while A. baumannii complex achieved the highest weight loss at 27.5%. The control sample showed no significant weight change. To confirm chemical changes in the polymer, GC-MS analysis of the degradation products was performed. The bacterial-treated samples showed a range of organic compounds, including fatty acid derivatives, aromatic acids, and esters, while no peaks were recorded in the control sample. These results indicate oxidation and gradual breakdown of the polyethylene hydrocarbon chains, leading to the formation of low-molecular-weight compounds that can enter bacterial metabolic pathways. The correlation between weight loss and the appearance of chemical degradation products also reflects partial biodegradation of the plastic. This study highlights the environmental importance of landfill sites as natural sources for isolating bacteria that can adapt to and break down plastics, thereby opening prospects for using these microorganisms to develop bioremediation technologies to reduce the accumulation of plastic waste in the environment.
*corresponding mariselvamak@gmail.com Abstract: Here, we propose a quantum circuit-based model to predict rainfall using nonlinear meteorological variables, grounded in quantum computational principles. The qubits are encoded with temperature, pressure, and humidity using the Ry(8) gate, and the CNOT gate is used for entangling the qubits to gain inter-variable correlations. RZ(8) and RX(8) gates are also used to enhance the circuit's ability to represent complex weather patterns. The model is validated using meteorological data consisting of Temperature, Pressure, and Humidity measurements collected between August 1 and August 31, 2025. The model achieved a theoretical fidelity of 87.99% in predicting rainfall conditions. Using the SPINQ Gemini quantum platform, the model is implemented experimentally and achieved a quantum state purity of 100% to score a practical fidelity of 82.62%, indicating a high probability of rainfall with a density matrix of (|0) = 0.04, |1) = 0.96). The consistency between theoretical and practical outcomes is shown by rainfall accuracy of 87.99% and no-rainfall accuracy of 12.01%. These results show the potential of data-driven prediction in the future and of rainfall prediction using quantum circuits.
