This paper deals with problems of the development and security of distributed information systems. It explores the challenges of risk modeling in such systems and suggests a risk-modeling approach that is responsive to the requirements of complex, distributed, and large-scale systems. This article provides aggregate information on various risk assessment methodologies; such as quantitative, qualitative, and hybrid methods; a comparison of their advantages and disadvantages; as well as an analysis of the possibility of application in distributed information systems. It also presents research on a comprehensive, dynamic, and multilevel approach to cyber risk assessment and modeling in distributed information systems based on security metrics and techniques for their calculation, which provides sufficient accuracy and reliability of risk assessment and demonstrates an ability to solve problems of intelligent classification and risk assessment modeling for large arrays of distributed data. The paper considers the main issues and recommendations for using risk assessment techniques based on the suggested approach.

Manufacturing and the further utilization of biogas is an efficient way of complementing or replacing primary energy resources. Ukraine has followed the global tendency to substitute fossil energy sources with renewable ones, which particularly includes biogas. Considering the experience of European countries, as well as the potential and needs of Ukraine, the necessity of biogas plant building implies three main factors: obtaining the energy sources, selling agricultural products and by-products of biogas production, and managing the environmental issues surrounding biogas generation. The growth dynamics of biogas plants and their productivity were analyzed for the period of 2012–2019. In doing so, the capacity and potential capabilities of biogas production in Ukraine by means of biological waste and agricultural raw materials were assessed. The article focuses on the importance of resolving the issues of sanitary and technogenic protection in biogas plant building and biogas manufacturing. It also points out the reasons that prevent Ukraine from going forward with biogas projects, as well as the main factors that affect biogas plant viability and efficiency. Based on the statistical data of previous years and regression analysis, the authors calculated the production output and biogas transformation. Herein, the trend model and Excel software were applied.

This article is devoted to assessing and substantiating the threats for countries/exporters of agricultural products to the EU under conditions of the European Green Deal. The revealed comparative advantages index (RCA), comparison method, correlation and regression analysis, and taxonomic method have been applied. According to the RCA index the main causes for the relatively significant volume of agri-food exports by some countries to the EU have been identified; using the comparison method it was found that among the leading countries by agricultural products export to the EU, many states do not meet the European Green Deal target criteria for agriculture. Correlation and regression analysis has revealed that among the chosen factors only the volume of fertilisers use per cropland has direct and strong influence on CO2eq emissions; by a taxonomic method the threats value for the leading agri-food exporters to the EU has been calculated. The major agri-food exporters to the EU under conditions of the European Green Deal targets till 2030 have a high threat regarding reduction of their supply to the Member States in the case of a possible Carbon Border Adjustment Mechanism or the introduction of other import restriction mechanisms in future. The results of the study can be used by the government and other executive bodies of the analysed countries to make adequate and rapid decisions to avoid the threats of possible agri-food exports reduction to the EU under the further European Green Deal implementation.

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.

The article analyzed the state of the global problem of ecological safety of the EU and argued the need to create a methodology for planning technologically integrated projects of the “European Green Deal” (TIP “EGD”) that accounts for the risks to the stakeholders, i.e., the state, project executors, resource suppliers, project managers, and clients. Each of these has an individual set of values that determines the project risk for them. Herein is proposed a taxonomy of three ranks of TIP “EGD” involving agricultural waste, determined by their characteristics, products, and requirements for the stakeholders. The authors point out the need to create tools for quantitative risk assessment for the stakeholders of TIP “EGD” involving agricultural waste and distinguish four groups of risk components with regard to the value of such projects. A model of value risk formation is presented that addresses the risk management of each of these stakeholder values. The need to develop tools (models, methods, and algorithms) for quantitative risk assessment of the values of each type of project is discussed. Regularities in the formation of stakeholder values, which were a foundation of the model of formation, are established.