rss_2.0Sciendo RSS Feed for Production Engineering Archiveshttps://sciendo.com/journal/PEAhttps://www.sciendo.comhttps://sciendo-parsed.s3.eu-central-1.amazonaws.com/64726f2a215d2f6c89dc7c3f/cover-image.jpghttps://sciendo.com/journal/PEA140216https://sciendo.com/article/10.30657/pea.2024.30.43<abstract> <title style='display:none'>Abstract</title> <p>Despite the abundance of researches on innovation and green innovation, there remains a necessity to further research in this field. This is particularly crucial in regions like Central and Eastern Europe, including Poland. This publication is a part of research on business innovation utilizing public funds. The paper aims to pinpoint directions for further empirical research on innovation within enterprises funded publicly. Empirical research was conducted using a database of 95 projects, all of which were included in the lists of projects selected for funding under the Opolskie Voivodeship Regional Operational Programme 2014-2020 (Enterprise investments in innovation).</p> <p>The vast majority of projects involve products/services/technologies that are innovative not only regionally and nationally, but also globally. The innovation of the solutions applied was assessed as high. On the other hand, the green innovation of applied solutions was assessed as average. This opens up an interesting field of research into the barriers to green innovation. The data shows that none of the projects implemented by SMEs was implemented as a partnership (SME Cooperation). In the case of large enterprises, 76% of projects were implemented in cooperation with SMEs and/or NGOs and/or research institutions. Interesting line of research could be the evaluation of the barriers for cooperation between SMEs when implementing a green innovation project.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.30657/pea.2024.30.432024-11-21T00:00:00.000+00:00https://sciendo.com/article/10.30657/pea.2024.30.46<abstract> <title style='display:none'>Abstract</title> <p>This study presents a numerical analysis of natural convection heat transfer within inclined square cavities featuring sinusoidal heating elements. The analysis, conducted using a finite volume approach implemented in ANSYS 16.0, aims to estimate flow and heat regimes under steady-state conditions. Grid-independent analyses were performed to ensure numerical accuracy. The vertical walls of the enclosure were maintained at a cold temperature, while the other two walls were perfectly insulated. Key parameters investigated include Rayleigh numbers (10⁴, 10⁵, 10⁶), corrugation numbers (3, 5, and 7), amplitude values (0.1, 0.3 and 0.5), and enclosure inclination angles (δ = 0°, 15°, 30°, 45°, 60°, 75°). The sinusoidal element’s diameter to enclosure length ratio was set at 0.4, and fluid properties were assumed constant with a Prandtl number of 7.0. Results were illustrated using isothermal and flow lines, with heat transfer discussed in terms of local and average Nusselt numbers. Findings indicate that at Ra = 10⁶, local Nusselt numbers exhibited a sinusoidal distribution influenced by corrugation and amplitude, with a 50% increase in local Nusselt number as amplitude increased from 0.1 to 0.5. Average Nusselt number enhancements were observed with higher corrugation numbers and wave amplitudes, while the number and size of eddies were sensitive to Rayleigh numbers. Enclosure inclination significantly affected the formation of vortices, particularly at angles of 60° and 75°.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.30657/pea.2024.30.462024-11-21T00:00:00.000+00:00https://sciendo.com/article/10.30657/pea.2024.30.49<abstract> <title style='display:none'>Abstract</title> <p>In this study, a film was prepared from polyvinyl alcohol (PVA) and zinc oxide (ZnO) nanoparticles (nano-ZnO) via the casting method. Nanoparticles were added to PVA biopolymer to create reinforced biocomposite films with different loading contents (2, 4, and 6 wt.%), and they were tested by performing the following assays: the FTIR test, the antibacterial, soil burial test, DSC, AFM, and SEM. The results showed an improvement of the membranes in the antibacterial properties for both Escherichia coli (E. coli, Gram-negative) and Staphylococcus aureus (S. aureus, Gram-positive) when nano-ZnO was added. The biodegradation through weight loss was observed for all samples, and the results showed that the weight loss increased with the increase in ZnO nanoparticle content from 2% to 6% wt. The DSC results showed that the addition of ZnO led to an increase in Tg, and increasing the degree of glass transition led to an increase in the degradation rate. In the FTIR results, only physical interference was observed; no chemical interference was evident. The AFM results showed some agglomerations of nano-ZnO in the PVA matrix led to an increase in the surface roughness of the PVA/nano-ZnO film.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.30657/pea.2024.30.492024-11-21T00:00:00.000+00:00https://sciendo.com/article/10.30657/pea.2024.30.42<abstract> <title style='display:none'>Abstract</title> <p>Economic analysis of the field of housing construction indicates a certain specificity of its functioning in Ukraine, which is primarily related to the lack of opportunities for developers to invest their own resources in this construction and the need to attract financing at the early stages of the construction of residential real estate objects. The results of the empirical analysis of the dynamics of housing construction financing indicate that the main source of this financing is public funds (from 55% to more than 73% of the total volume of housing construction investments, depending on the year of their implementation). The insufficient level of quantitative and qualitative provision of housing for Ukrainian citizens provokes a constant demand for residential real estate objects, which in turn stimulates the development of housing construction. An analysis of the dynamics of residential real estate commissioning volumes and the amount of capital investments in residential buildings indicates a steady growth of these indicators over the past 10 years, with the exception of the crisis years of 2014 and 2020, in which there was a general decline in the national economy (2014 ) or even the global (2020) economy, caused by extraordinary circumstances (the Revolution of Dignity and the coronavirus epidemic). However, in subsequent years after these crises, the amount of capital investment in residential construction continued to grow.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.30657/pea.2024.30.422024-11-21T00:00:00.000+00:00https://sciendo.com/article/10.30657/pea.2024.30.41<abstract> <title style='display:none'>Abstract</title> <p>The coffee sector stands as a cornerstone of Colombia’s economy, ranking third in the nation’s export portfolio. Despite the Colombian coffee esteemed global reputation, it has yet to fully exploit its potential for diversification into differentiated products. Present agro-industrial paradigms emphasize trade and sustainable, efficient agricultural practices, underscoring the imperative for innovation across production, marketing, and distribution channels. This study aims to pinpoint the pivotal innovation factors within coffee farm production processes. To this end, a sample of 66 coffee farms was selected through simple random sampling. Drawing from the 2018 Oslo model, innovation types associated with sustainable specialty coffee certifications were delineated. Within this framework, seven fundamental factors emerged for investigation: economic, social, environmental, production, knowledge, technology, and change management. Through cluster analysis, it became evident that economic, environmental, knowledge, technological, and change management factors are indispensable for fortifying the coffee industry.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.30657/pea.2024.30.412024-11-21T00:00:00.000+00:00https://sciendo.com/article/10.30657/pea.2024.30.40<abstract> <title style='display:none'>Abstract</title> <p>The current state of technological development shows that most inventions arise in response to specific circumstances, such as new business risks, changes in legislation, or crisis events. Reactions to new social trends, business models, problematic processes, or competing goods and services are also some of the main drivers of technological innovation. But before these technologies are implemented, there should be adequate regulations in place to prevent them from negatively impacting businesses or society. Two main practical research objectives are the subject of this study. To analyze 100 leading technology companies to identify the main trends and issues in the field of Technology innovation management, as well as their links to economics and society. And to identify the most important technological innovation trends and the corresponding macro-factors more suitable for realistic innovation assessment. As a result of this study, the innovation progress of certain nations (China, the U.S., Japan, and the EU) was measured to a certain extent. The research focus is to depict the key macro-factors that can characterize more complex technological innovation development and certain regional differences. The important intention of this study is also to raise awareness of technological innovation and cooperation in different countries. This research study was carried out from 2022 to 2024.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.30657/pea.2024.30.402024-11-21T00:00:00.000+00:00https://sciendo.com/article/10.30657/pea.2024.30.47<abstract> <title style='display:none'>Abstract</title> <p>The purpose of this study is to outline the current state of research and development in Industry 4.0 by highlighting key topics, cooperative networks, and emerging trends that characterize this ground-breaking stage of manufacturing and technology. The article answers the question what are the key keywords, countries, collaboration networks and most frequently occurring terms in Industry 4.0 research, and what conclusions can be drawn from the bibliometric analysis regarding their frequency, strength of connections and mutual relationships. With a focus on the incorporation of digital technology into manufacturing processes, the article aims to provide a detailed overview of the international initiatives driving the fourth industrial revolution. This study uses bibliometric analysis to look at 4,981 scientific papers from 2020 to 2024 that are available in the Scopus database. The text of these articles is carefully examined, with an emphasis on titles, abstracts, and keywords, in order to map out the network of co-authorships and the frequency of certain terminologies. The VOSviewer program was used to provide a network visualization, offering a pictorial depiction of the connections of coauthoring nations and the phrases that are shared across the corpus of work. The results show a strong and complex web of global partnerships, suggesting a broad dedication to pushing the boundaries of Industry 4.0. Five significant co-authorship clusters were found, demonstrating the prominent significance that certain nations have played in various Industry 4.0 research domains. Key phrases like “digital transformation,” “smart manufacturing,” “machine learning,” and “internet of things” were used a lot, highlighting the importance of digitization and smart manufacturing technology. This publication offers a comprehensive statistical and visual study of the worldwide research dynamics in Industry 4.0, making it a unique contribution to the body of knowledge. Understanding the complex nature of the fourth industrial revolution is made easier with its mapping of cooperative networks and thematic goals as well as its emphasis on the discourse’s essential place for sustainability.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.30657/pea.2024.30.472024-11-21T00:00:00.000+00:00https://sciendo.com/article/10.30657/pea.2024.30.52<abstract> <title style='display:none'>Abstract</title> <p>This study presents an investigation into the tribological, corrosion, and tribocorrosion properties of AISI 316Ti (austenitic) and AISI 430 (ferritic) stainless steels. The comparative analysis focuses on microstructural characterization, hardness, and a series of tribological, electrochemical, and tribocorrosion tests conducted in 0.9% NaCl using a specialized linear tribometer to reveal the quality of the studied materials in tribocorrosion applications. Friction tests were performed under both dry and corrosive conditions, while tribocorrosion tests were conducted under open circuit potential (OCP) conditions in 0.9% NaCl, with the electrode potential of the test specimen monitored during friction. To evaluate the electrochemical behavior of the materials, potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) were conducted using a 0.9% NaCl solution. The measured corrosion potential (Ecorr) suggests that AISI 430 is thermodynamically more stable than AISI 316Ti; however, AISI 316Ti demonstrated higher polarization resistance (RP) values compared to AISI 430. The findings indicate that material qualities significantly influence the coefficient of friction (CoF). Additionally, a notable antifriction effect of 0.9% NaCl was observed during tribological testing, resulting in a lower CoF compared to dry friction conditions. A cathodic shift in OCP during tribocorrosion testing was also observed in both materials, indicating an increase in corrosion vulnerability when the passive layer is degraded.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.30657/pea.2024.30.522024-11-21T00:00:00.000+00:00https://sciendo.com/article/10.30657/pea.2024.30.48<abstract> <title style='display:none'>Abstract</title> <p>The article discusses the application of computer simulation in the optimization of production processes, particularly in the context of analyzing scenarios related to the addition of new production lines. The conducted research and simulations have shown that computer simulation is a key tool for precise modeling and analysis of various options, allowing for better understanding and optimization of production activities. The article presents the theoretical foundations of simulation along with practical examples of its application, focusing on assessing the impact of different production line configurations on the overall system’s efficiency. The analysis of benefits includes shortening the production cycle time, increasing flexibility, and improving operational efficiency. The challenges associated with implementing computer simulation, such as the need for specialized knowledge and the necessity for continuous updates of simulation models, are also discussed. Based on the research and analyses conducted, the article demonstrates that computer simulation is an effective tool supporting strategic and operational decision-making in production management, particularly in the context of expanding production infrastructure.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.30657/pea.2024.30.482024-11-21T00:00:00.000+00:00https://sciendo.com/article/10.30657/pea.2024.30.44<abstract> <title style='display:none'>Abstract</title> <p>The article presents the results of adapting the digital image correlation method for the possibility of diagnosing reinforced concrete structures. Reinforced concrete (RC) bending elements are the most widely used in construction practice, which determines the importance of reliable estimation of their stress-strain state. The purpose of this study includes reliable theoretical and experimental investigation of the strength and deformability parameters of the RC beam. The experimental study was conducted using digital image correlation and sub-micron contactless gauges. Experimental data was verified with the calculation of the stress-strain state of the RC beam according to DBN V.2.6-98:2009 and Eurocode 2 and the finite-element modelling (FEM). As a result, the values of deflections, concrete and rebar strains were obtained and presented as corresponding diagrams. The results of all the methods are within the same ranges. Also, the form and character of corresponding diagrams are very similar. The indicated deviations were within acceptable limits. It was noted that the theoretical calculation generally provides lower strain values, which is a satisfactory result, as it indicates the bearing capacity reserves provided by the current regulations. The propagation of cracks was monitored during the experiment and the measured cracks opening was compared with theoretical assumptions. Theoretical values are higher than experimental, which shows certain conservativity of valid normative regulations. The experimental and theoretical results were in good correspondence, which confirms their reliability. It was concluded, that the proposed in the study complex theoretic-experimental approach provides essential information about the strength and deformability of the structure.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.30657/pea.2024.30.442024-11-21T00:00:00.000+00:00https://sciendo.com/article/10.30657/pea.2024.30.45<abstract> <title style='display:none'>Abstract</title> <p>Vacuum freeze-dried fruit processes consisting of heating and holding are modelled as a mixed batch scheduling with the objective of minimizing the makespan. The jobs differ from each other in job family, size, weight and ready time. The batch processing time is determined by the longest job and the total weight of the jobs in the batch. A mixed-integer linear programming model is developed and tested with small-scale examples. Typical batch scheduling strategies are analysed and a machine-based dynamic programming strategy is proposed. The machine-based dynamic scheduling strategy is applied to design improved genetic and particle swarm optimization algorithms, which demonstrate the effectiveness of this strategy. The worst-case ratio of the algorithms using machine dynamic programming strategy are proved. Numerical experiments show that the heuristic algorithm, genetic algorithm, and particle swarm optimization algorithm based on machine dynamic scheduling strategy outperform related algorithms using greedy and job-based dynamic scheduling strategies.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.30657/pea.2024.30.452024-11-21T00:00:00.000+00:00https://sciendo.com/article/10.30657/pea.2024.30.51<abstract> <title style='display:none'>Abstract</title> <p>Environmental issues have become an important consideration to be included in business operations. One of the main environmental problems in the wood industry is the high production of wood waste and increasing scarcity and cost of raw materials. For this reason, companies need to utilize wood waste to reduce material costs and, at the same time, reduce the impact of waste on the environment. Converting wood waste into products that can be sold will increase its economic value. This research aims to identify the types of waste from a furniture company and reduce waste by designing various products made from wood waste. Wood chips are wood waste that have the potential to be reused. Waste wood chips from the materials station can be used to create bedside table products. The bedside table was chosen because of its high selling price, and the company could make it with its existing resources. Apart from that, the company still needs to expand its variety of bedside tables. The bedside table was designed using the TRIZEE method. TRIZEE is a method that combines eco-efficiency with 40 TRIZ principles, which can reduce environmental impacts in alignment with company goals. The design process resulted in 4 bedside table variations. Production capacity is estimated to produce 56 bedside tables per month. If scrap waste is successfully used as bedside table material. Apart from saving raw materials, the company will be able to reduce wood waste and gain greater profits from waste utilization.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.30657/pea.2024.30.512024-11-21T00:00:00.000+00:00https://sciendo.com/article/10.30657/pea.2024.30.50<abstract> <title style='display:none'>Abstract</title> <p>This study explores the integration of sustainability in decision-making processes within a steel manufacturing company in Poland. As global clients increasingly demand sustainable practices, companies must adapt their operations to meet these expectations. We applied Multi-Criteria Decision Analysis (MCDA) methods, specifically TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) and VIKOR (in Serbian: VlseKriterijuska Optimizacija I Komoromisno Resenje), along with three weighting techniques, namely Entropy, Standard Deviation, and CRITIC (Criteria Importance Through Inter-criteria Correlation), to evaluate the sustainability of four products. Nine criteria were considered, including material type, corrosion protection, surface treatment, and various manufacturing processes. Our findings reveal that the MCDA framework effectively ranks products from most to least sustainable, highlighting the importance of raw material efficiency and waste management. This research demonstrates the practical application of MCDA methods in assessing sustainability within the steel industry, providing a basis for future studies to extend this framework to other manufacturing sectors and regions. Overall, this approach supports informed decision-making, aligning with broader sustainability goals while satisfying the demands of business partners and clients.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.30657/pea.2024.30.502024-11-21T00:00:00.000+00:00https://sciendo.com/article/10.30657/pea.2024.30.30<abstract> <title style='display:none'>Abstract</title> <p>This study focuses on the delivery routing problem faced by a transport company located in Phuket, Thailand. The goal of this study is to find a daily optimum route in order to minimize the total transportation cost, which comprises fixed costs associated with vehicle rental and variable costs calculated based on factors of travel distance, fuel prices, and fuel consumption. The complexity of this problem is compounded by the fact that customer demand often exceeds a vehicle capacity, in terms of weight and volume. In addition, delivery must be made within specific time windows. To tackle this issue, the delivery routing problem is classified as a multi-trip capacitated vehicle routing problem with time window (MTCVRPTW). Since the problem is NP-hard, an application of metaheuristic is more practical to determine the delivery routing of the company within a reasonable computing time. In this study, Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithm are applied to solve MTCVRPTW. The numerical results show that DE provides better solution quality compared to those obtained from PSO and company current practices.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.30657/pea.2024.30.302024-09-07T00:00:00.000+00:00https://sciendo.com/article/10.30657/pea.2024.30.36<abstract> <title style='display:none'>Abstract</title> <p>The paper presents the results of contact strength and tribological property tests of spare parts made of a popular resin using a 3D DLP printing technology. Two printer models by the same manufacturer were used in the study. The post-processing technique, which shapes the final functional properties, was diversified. Surface performance properties were compared, i.e. Shore hardness, indentation hardness, Martens hardness, elastic modulus, and parameters related to surface creep and relaxation. Tribo-logical durability in rotary motion and tribological wear in reciprocating linear motion were also evaluated using micro- and nanotribometers. This was followed by surface analyses of the friction track of the analysed materials using microscopic methods: a scanning electron microscope, a WLI interferometric microscope, and an optical microscope. The results were statistically processed and the relationship between the parameters determined in the indentation test was determined.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.30657/pea.2024.30.362024-09-07T00:00:00.000+00:00https://sciendo.com/article/10.30657/pea.2024.30.38<abstract> <title style='display:none'>Abstract</title> <p>The article addresses a critical and timely issue: improving safety in underground coal mining. The primary objective of the paper was to develop a research methodology based on modelling studies to identify and assess the state of methane hazards during mining operations. To achieve this, structural modelling of the physical and chemical phenomena occurring in mining regions was conducted using Computational Fluid Dynamics. The core research was performed using the finite volume method on a real longwall exploitation site ventilated by a Y-system. This approach enabled the determination of methane and oxygen concentration distributions in the mining region and goafs, treated as a porous and permeable medium. Based on these findings, potential fire and/or methane explosion hazard zones were identified in the goaf. The model test results underwent a validation process, comparing them with actual measurements. The determined errors were within an acceptable range, confirming the accuracy of the developed model of the mining region and the phenomena within it. Furthermore, the model was used to predict the locations of zones at risk of fire and/or methane explosion in the goafs, particularly in areas with potentially increased gas emissions. The results clearly demonstrate the significant potential of using model studies to diagnose and forecast methane hazards in underground mining operations. Identifying these potential danger zones allows for the implementation of preventive measures to reduce the likelihood of dangerous incidents.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.30657/pea.2024.30.382024-09-07T00:00:00.000+00:00https://sciendo.com/article/10.30657/pea.2024.30.37<abstract> <title style='display:none'>Abstract</title> <p>Demand-Driven Material Requirements Planning (DDMRP) is an emerging inventory management approach that has garnered significant attention from academia and industry. Numerous recent studies have highlighted the advantages of DDMRP compared to traditional methods such as material requirement planning (MRP), Theory of constraint (TOC), and Kanban. However, the performance of DDMRP relies on several parameters that affect its effectiveness. Parameterization models and the optimization of control variables have significantly contributed to the field of inventory management and have proven to be effective and practical in addressing challenges by providing a structured approach to handling complex variables and constraints. This paper introduces an innovative parameterization model that leverages deep reinforcement learning (DRL) to parameterize a DDMRP system in the face of uncertain demand. The main objective is to dynamically determine the optimal values for the variability and lead time factors within the DDMRP framework, to maximize customer service levels and optimize inventory efficiency. The results of this study emphasize the effectiveness of DRL as an automated decision-making approach for controlling DDMRP parameters. Additionally, the findings highlight the potential for enhancing the performance of the DDMRP approach, particularly in terms of on-time delivery (OTD) and average on-hand inventory (AOHI) by adjusting the variability and lead-time factors.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.30657/pea.2024.30.372024-09-07T00:00:00.000+00:00https://sciendo.com/article/10.30657/pea.2024.30.33<abstract> <title style='display:none'>Abstract</title> <p>The use of HR analytics has been on the rise in recent years, with organizations increasingly recognizing its potential to improve HR processes, increase employee productivity and engagement, and reduce costs. The research presented in this paper extends the knowledge base, especially the characteristics of the degree of implementation of HR analysis into working systems of human resources management utilized within businesses operating within the Slovakian context, emphasizing their role in bolstering and enhancing competitiveness within the European economic arena. Although there was a clear interest in the use of predictive analytics in Slovak companies, there was still a lot of room for improvement and adoption of this approach in HR practice. The authors' findings also suggest that companies in Slovakia are increasingly aware of the value of data-driven decision-making in HR and are willing to invest in these technologies to gain a competitive advantage. The objective of this study is to ascertain contemporary human resource management instruments utilized within businesses operating within the Slovakian context. The authors assumed that the perceived importance of the data approach in HR among Slovak companies is strong, and companies are open to learning more about this approach. A sample of 841 respondents was collected throughout 2020, sample included enterprises from the Slovak Republic. The interviews were conducted via phone in November 2022. The interview respondents are 7 HR representatives. The authors' findings suggest that companies in Slovakia are increasingly aware of the value of data-driven decision-making in HR and are willing to invest in these technologies to gain a competitive advantage.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.30657/pea.2024.30.332024-09-07T00:00:00.000+00:00https://sciendo.com/article/10.30657/pea.2024.30.31<abstract> <title style='display:none'>Abstract</title> <p>In recent decades, cataclysmic events, deterioration of air and water quality, and loss of biodiversity have forced us to look for ways to save nature. One of the ways to solve the problems is to ensure rational environmental management, which is possible by establishing an effective balance between consumption and compensation by creating an effective financial mechanism. The purpose of the study is to assess the efficiency of the current financial mechanism for environmental management in Ukraine and to determine the prospects for its improvement. The study uses analysis, synthesis, specification, systematization, and generalization. The graphical method was used to assess environmental taxes, and mathematical modelling was used to analyze the dependence of emissions on direct costs and capital investments in air protection and climate change. Environmental taxes in Ukraine are an ineffective instrument of the financial mechanism of environmental management. Their share in the structure of domestic GDP is lower than the share in the EU. The author suggests ways to improve them: to replace the CO2 tax with an energy tax; to cancel the tax-free limit of 500.000 tons of CO2 emissions per year; to change the structure of tax distribution; to introduce tax rebates. The correlation and regression analysis of the dependence of air pollutant emissions on current expenditures and capital investments in air protection and climate change issues showed the existence of a feedback loop. Investment support for environmental management should be provided from various sources in the following areas: national, local and international finances - primarily for the restoration of air, water and contaminated areas; own funds and international investments - for the modernization and greening of production.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.30657/pea.2024.30.312024-09-07T00:00:00.000+00:00https://sciendo.com/article/10.30657/pea.2024.30.32<abstract> <title style='display:none'>Abstract</title> <p>Poland has experienced a remarkable growth in renewable energy adoption, notably in photovoltaic (PV) solar systems. The majority of installations are prosumer PV micro-installations, exceeding predicted capacity targets outlined in the Energy Policy of Poland until 2040. Despite the significant growth in installed PV capacity, there is still a lack of comprehensive research focusing on fast assessment of energy generation capacity for solar PV micro-systems. This study aims to address this gap by providing a comprehensive analysis of energy production potential across different configurations and locations in Poland. Using geocoding techniques, solar irradiation data from PVGIS database and pvlib-python library, a methodology was developed to rapidly estimate energy generation from 1 kWp solar PV systems . Results reveal spatial disparities in energy yield from solar PV micro installations in Poland, influenced by factors such as geographical location and panel orientation and inclination. Recognizing that the presented energy indicators provide valuable initial parameters for determining solar PV system power output, this data can serve as a critical reference point for stakeholders, assisting them in estimating potential energy generation capacities in different regions of Poland.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.30657/pea.2024.30.322024-09-07T00:00:00.000+00:00en-us-1