This article attempts to model interdependencies between socio-economic, energy, and environmental factors with selected data characterizing the development of the hydrogen economy. The study applies Spearman’s correlation and a linear regression model to estimate the influence of gross domestic product, population, final energy consumption, renewable energy, and CO₂ emission on chosen hydrogen indicators—production, patents, energy technology research, development, and demonstration budgets. The study was conducted in nine countries selected for their actions towards a hydrogen economy based on analyses of national strategies, policies, research and development programs, and roadmaps. The results confirm the statistically significant impact of the chosen indicators, which are the drivers for the development of the hydrogen economy from 2008 to 2018. Moreover, the empirical results show that different characteristics in each country contribute to the development of the hydrogen economy vision.

In Poland, various solar collector systems are used; among them, the most popular are flat plate collectors (FPCs) and evacuated tube collectors (ETCs). The work presents two installations located at a distance of 80 km apart, working in similar external conditions. One of them contains 120 flat plate collectors and works for the preparation of hot water in a swimming pool building; the second one consists of 32 evacuated tube collectors with a heat pipe and supports the preparation of domestic hot water for a multi-family house. During the comparison of the two quite large solar installations, it was confirmed that the use of evacuated tube solar collectors shows a much better solar energy productivity than flat plate collectors for the absorber area. Higher heat solar gains (by 7.9%) were also observed in the case of the gross collector area. The advantages of evacuated tube collectors are observed mainly during colder periods, which allows for a steadier thermal energy production.

This study examines the potential of the soft computing technique—namely, Gaussian process regression (GPR), to predict the ultimate bearing capacity (UBC) of cohesionless soils beneath shallow foundations. The inputs of the model are width of footing (B), depth of footing (D), footing geometry (L/B), unit weight of sand (γ), and internal friction angle (ϕ). The results of the present model were compared with those obtained by two theoretical approaches reported in the literature. The statistical evaluation of results shows that the presently applied paradigm is better than the theoretical approaches and is competing well for the prediction of UBC (q_u). This study shows that the developed GPR is a robust model for the q_u prediction of shallow foundations on cohesionless soil. Sensitivity analysis was also carried out to determine the effect of each input parameter.

The several government subsidies available in Poland contributed to an increased interest in PV installations. Installed PV capacity increased from 100 MW in 2016 up to 2682.7 MW in July 2020. In 2019 alone, 104,000 microinstallations (up to 50 kWp) were installed in Poland. The paper determines the energy gain and the associated reduction of CO₂ emissions for two types of solar installation located in Poland. The monofacial solar modules with a power of 5.04 kWp (located in Leki) and bifacial solar modules with a power of 6.1 kWp (located in Bydgoszcz). Both installations use mono-crystalline Si-based 1st generation PV cells. With comparable insolation, a bifacial installation produces approx. 10% (for high insolation) to 28% (for low insolation) more energy than a monofacial PV installation. Avoided annual CO₂ emission in relation to the installation capacity ranges from 0.58 to 0.64 Mg/kWp for monofacial and from 0.68 to 0.74 Mg/kWp for bifacial and is on average approx. 16% higher for bifacial installations. Cost-benefit analyses were made. For different electricity prices, the NPV for monofacial and bifacial was determined.

Supervised machine learning and its algorithms are a developing trend in the prediction of rockfill material (RFM) mechanical properties. This study investigates supervised learning algorithms—support vector machine (SVM), random forest (RF), AdaBoost, and k-nearest neighbor (KNN) for the prediction of the RFM shear strength. A total of 165 RFM case studies with 13 key material properties for rockfill characterization have been applied to construct and validate the models. The performance of the SVM, RF, AdaBoost, and KNN models are assessed using statistical parameters, including the coefficient of determination (R²), Nash–Sutcliffe efficiency (NSE) coefficient, root mean square error (RMSE), and ratio of the RMSE to the standard deviation of measured data (RSR). The applications for the abovementioned models for predicting the shear strength of RFM are compared and discussed. The analysis of the R² together with NSE, RMSE, and RSR for the RFM shear strength data set demonstrates that the SVM achieved a better prediction performance with (R² = 0.9655, NSE = 0.9639, RMSE = 0.1135, and RSR = 0.1899) succeeded by the RF model with (R² = 0.9545, NSE = 0.9542, RMSE = 0.1279, and RSR = 0.2140), the AdaBoost model with (R² = 0.9390, NSE = 0.9388, RMSE = 0.1478, and RSR = 0.2474), and the KNN with (R² = 0.6233, NSE = 0.6180, RMSE = 0.3693, and RSR = 0.6181). Furthermore, the sensitivity analysis result shows that normal stress was the key parameter affecting the shear strength of RFM.