The article proposes to use machine learning as one of the areas of artificial intelligence to forecast the volume of biogas production from household organic waste. The use of five regression algorithms (Linear Regression, Ridge Regression, Lasso Regression, Random Forest Regression, and Gradient Boosting Regression) to create an effective model for forecasting the volume of biogas production from household organic waste is considered. Based on the comparison of these algorithms by MSE and MAE indicators, the quality of training and their accuracy during forecasting are evaluated. The proposed algorithm for creating a model for forecasting biogas production volumes from household organic waste involves the implementation of 10 main and 3 auxiliary steps. Their advantage is that they aid in the performance of component data analysis, which is carried out based on the method of reducing the dimensionality of the data set, increasing interpretability, and minimizing the risk of data loss. An analysis of 2433 data is was carried out, which characterizes the formation of biogas from food (FW) and yard waste (YW) according to four features. Data preparation is performed using the Jupyter Notebook environment in Python. We select five machine learning algorithms to substantiate an effective model for forecasting volumes of biogas production from household organic waste. On the basis of the conducted research, the main advantages and disadvantages of the used algorithms for building forecasting models of biogas production volumes from household organic waste are determined. It is found that two models, “Random Forest Regressor” and “Gradient Boosting Regressor”, show the best accuracy indicators. The other three models (Linear Regression, Ridge Regression, Lasso Regression) are inferior in accuracy and were not considered further. To determine the accuracy of the “Random Forest Regressor” and “Gradient Boosting Regressor” models, we choose the MSE and MAE indicators. The Random Forest Regressor model is found to be a more accurate model compared to the Gradient Boosting Regressor. This is confirmed by the fact that the MSE of the “Random Forest Regressor” model on the training data set is 7.14 times smaller than that of the “Gradient Boosting Regressor” model. At the same time, MAE is 2.67 times smaller in the “Random Forest Regressor” model than in the “Gradient Boosting Regressor” model. The MSE and MAE of both models are worse on the test data set, which indicates overtraining tendencies. The Gradient Boosting Regressor model has worse MSE and MAE than the Random Forest Regressor model on both the training and test data sets. It is established that the model based on the “Random Forest Regressor” algorithm is the most effective for forecasting the volume of biogas production from household organic waste. It provides MAE = 0.088 on test data and the smallest absolute errors in predictions. Further systematic improvement of the “Random Forest Regressor” model for forecasting biogas production volumes from household organic waste based on new data will ensure its accuracy and maintain competitive advantages.

Though often overlooked due to its scale, small sacral Christian architecture has a significant importance in cultural landscapes in Europe and beyond. It represents a shared international cultural heritage and is significant in its diversity, distribution and abundance across cultural landscapes. The tradition of the artistic depiction of the cross in Christianity dates back to the 4 ^th century AD. The first monuments in the form of crosses were placed in open landscapes in Scotland in the 7 ^th century. The most important period for the spread of small sacral architecture of Catholic origin in eastern Europe was during the Baroque, thus most of the preserved small sacral monuments date back to the late 17 ^th ,18 ^th and 19 ^th centuries. They are often accompanied by monumental single trees or a compositionally organised group of trees and create a sacred composition of nature and culture. They have become important landmarks, indicators of place and landscape features of spatial organization, representing a significant historical legacy and cultural heritage for future generations. This article elaborates on the origin, historical development and landscape values of small sacral Christian architecture, as well as their relation to separate natural monuments or natural features that create part of the sacral composition, such as memorial trees growing around them. This article introduces the topic of sacral architecture and its contribution to the character and identity of European cultural landscapes.

Based on the analysis conducted on the state of theory and practice, the expediency of assessing the relationships between the functional indicators of bioenergy production systems using the organic waste of residential areas is substantiated in the projects of the European Green Deal. It is based on the use of existing results published in scientific works, as well as on the use of methods of system analysis and mathematical modeling. The proposed approach avoids limitations associated with the one-sidedness of sources or subjectivity of data and also ensures complete consideration of various factors affecting the functional indicators of the bioenergy production system from the organic waste of residential areas. Four types of organic waste generated within the territory of residential areas are considered. In our work, we used passive experimental methods to collect data on the functional characteristics of bioenergy production systems, mathematical statistics methods to process and interpret trends in the functional characteristics of bioenergy production systems using municipal organic waste, and mathematical modeling methods to develop mathematical models that reflect the patterns of change in the functional characteristics of bioenergy production systems. The results indicate the presence of dependencies with close correlations. The resulting dependencies can be used to optimize processes and increase the efficiency of bioenergy production. It was found that: (1) yard waste has the highest volume of the total volume of solid organic substances but has a low yield of biogas and low share of methane production; (2) food waste has the highest yield of biogas and, accordingly, the highest share of methane production; (3) mixed organic waste has the lowest volume of the total volume of solid organic substances and the lowest content of volatile organic substances. The amount of electricity and thermal energy production varies by type of organic waste, with mixed organic waste having a higher average amount of electricity production compared to other types of waste. It was established that the production volume of the solid fraction (biofertilizer) is also different for different types of organic waste. Less solid fraction is produced from food waste than from yard waste. The obtained research results are of practical importance for the development of sustainable bioenergy production from organic waste in residential areas during the implementation of the European Green Deal projects. They provide further research on the development of effective models for determining the rational configuration of bioenergy production systems using organic waste for given characteristics of residential areas.

Eco-spatial indices are commonly used tools to improve the quality of the environment in cities. Initially modelled on the Berlin BAF, indices have evolved to address current challenges, particularly climate change adaptation. The Ratio of Biologically Vital Areas (RBVA), introduced in Poland in the mid-1990s, is an early planning tool for implementing Nature-based Solutions (NbSs) at the site level. This research aimed to assess the effectiveness of the RBVA in Poland compared to its counterparts in Oslo and Malmö. The study employed a serious simulation game developed under the Norwegian-funded CoAdapt project, testing six development scenarios, varied in terms of applied NbSs, for a typical multi-family housing estate. The adaptive potential of the tested scenarios was assessed based on the values of five environmental parameters calculated in the game; that is, air temperature, oxygen production, CO₂ sequestration, rainwater harvesting, and biodiversity. The findings revealed that the RBVA, in its current form, has limited effectiveness in supporting climate adaptation. Its two-dimensional nature makes it less effective than the more comprehensive Green Factors used in Oslo and Malmö. The research presented in the article proves that better-constructed indices result in the efficiency of applied NbSs and consequently better adaptation to climate change.

The purpose of this work is to substantiate the approach to assessing the state of organic waste generation by households of a given community, which is based on passive production observations and intellectual analysis of statistical data, which ensures consideration of the factors and features of organic waste generation, as well as the development of qualitative models for forecasting their receipt. To achieve the goal, the following tasks were solved: the analysis of the state of organic waste generation by households in the EU countries was performed; an approach to assessing the state of organic waste generation by households of a given community is proposed; based on the use of the proposed approach, and models for assessing the state of organic waste generation of households in a given community were substantiated. The hypothesis of the study is to substantiate and use an approach to assessing the generation of organic waste by households in individual communities, based on the method of association learning and search for association rules, which will identify factors that have a significant impact on the volume of organic waste generated by households, the consideration of which will improve the accuracy of forecasting models and improve the quality of management of the processes of collection and processing of this waste in communities. The research methodology used allows for the use of data mining, probability theory, mathematical statistics, machine learning technology, and the Associative Rule Learning (ARL) method. Based on the use of a reasonable algorithm, they identify key trends and relationships between the factors of organic waste generation in communities in different countries, which is the basis for creating accurate models for predicting the volume of collection and processing of this waste in communities. The study found that the largest number of households produced organic waste per capita in the range of 0.14–0.25 kg/person. At the same time, most households have from two to four residents and are located on the adjoining territory from 350 m² to 680 m². Based on the method of learning associative rules, it was found that there are no close correlations between individual factors that determine the daily volume of organic waste generation by households per capita. The highest correlation coefficient between the type of housing and the income level of household residents is 0.13. The number of residents and the occupied area of the adjacent territory have the greatest impact on the daily volume of organic waste generated by households per capita. The substantiated associative rules of relationships, as well as the diagrams of relationships between factors, have helped to identify those factors that have the greatest impact on the volume of organic waste generation. They are the basis for creating accurate models for predicting the volume of collection and planning the processing of this waste in communities. Based on the proposed approach, Python 3.9 software was developed. It makes it possible to quickly carry out calculations and perform a quantitative assessment of the state of organic waste generation by households of a given community according to the specified rules of association between the volumes of organic waste generation and their factors. The results of the study are the basis for the further development of models for accurate forecasting of the collection and planning of the processing of organic waste from households in communities.

Transport areas in urban environments typically cover 10–20% of a city’s area. Due to their hierarchical structure and network layout, they present a unique opportunity to integrate Nature-based Solutions (NbSs) within cities strategically. In Poland, however, the current use of NbSs in streetscapes tends to be sporadic, localized, and often resulting from grassroots initiatives. This study aimed to assess how much the provision of ecosystem services (ESs) in cities depends on and can be enhanced by NbSs. To explore this, simulations were conducted using six NbSs scenarios, selected based on an analysis of solutions specifically designed for streets and their characteristics. This research focused on a densely built and populated district of Warsaw. The findings revealed that applying NbSs can significantly reduce stormwater runoff, increase carbon sequestration, and improve air quality. The level of ES provision depends on the solutions used, with the introduction of woody vegetation, particularly tall shrubs and trees, proving most effective. The results show that the scenario-based approach allows for flexible streetscape design, enabling the application of individually selected NbSs. Moreover, the approach helps to select optimal elements that enhance the provision of ES crucial to adapting cities to climate change.