The use of renewable energy sources for electricity generation in the Western Balkan countries is analyzed in this review paper. Since those countries are part of EU or intend to be, data for Western Balkan are also compared with data for EU-28. The first part of the paper presents a brief overview of main promotion mechanism for electricity generation from renewable energy sources. As a dominant support policy, the feed-in tariff is more elaborated as an incentive measure and a detailed overview of the amount of tariffs and quotas for dominant technologies in the Western Balkan countries is presented. Furthermore, the current state of installed capacities and annual productions of three particular renewable electricity technologies (small hydro power, wind power, and solar photovoltaic) are analyzed in detailes. Based on presented data, there is a discussion and consideration of the impact of incentive measures on the electricity market and power production from renewable sources.

Co-digestion implementation in wastewater treatment plants reduces waste output and enhances biogas yield. In this regard, the objective of this research was to determine the optimal ratio of biodegradable waste and sewage sludge for co-digestion process in a city plant in central Serbia. The increase in biogas production was investigated through batch tests while synergistic effects were evaluated by chemical oxygen demand (COD) balance. Analyses were performed in four volume basis ratios (3/1, 1/1, 1/3, 1/0) of primary sludge and food waste with added low food waste: 3.375%, 4.675%, and 5.35%, respectively. The best proportion was found to be 1/3 with the highest biogas production (618.7 ml/g VS added) and the organic removal of 52.8% COD elimination. COD reductions further supported the synergistic impact; specifically, an additional 7.1%, 12.8%, and 17% of COD were converted into biogas during the co-digestions 1, 2, and 3, respectively. The rise in co-substrate concentrations was accompanied by a uniform hydrolysis rate constant, the absence of any lag phase, and a greater biogas generation rate. The amount of biogas produced within co-digestions proves to be equivalent to the volume of biogas acquired from individual substrates and the mass of added COD in co-digestions but inversely proportional to the mass of COD utilized in mono-digestion. Further on, specific biogas productions and COD values in digesters show linear dependence. Finally, the study points out that COD method can be used for developing relatively accurate model for biogas potential estimation in wastewater treatment plants.

Co-digestion implementation in wastewater treatment plants enhances biogas yield, so this research investigated the optimal ratio of biodegradable waste and sewage sludge. The increase in biogas production was investigated through batch tests using basic BMP equipment, while synergistic effects were evaluated by chemical oxygen demand (COD) balance. Analyses were performed in four volume basis ratios (3/1, 1/1, 1/3, 1/0) of primary sludge and food waste with added low food waste: 3.375%, 4.675%, and 5.35%, respectively. The best proportion was found to be 1/3 with the maximum biogas production (618.7 mL/g VS added) and the organic removal of 52.8% COD elimination. The highest enhancement rate was observed among co-digs 3/1 and 1/1 (105.72 mL/g VS). A positive correlation between biogas yield and COD removal is noticed while microbial flux required an optimal pH, value of 8 significantly decreased daily production rate. COD reductions further supported the synergistic impact; specifically, an additional 7.1%, 12.8%, and 17% of COD were converted into biogas during the co-digestions 1, 2, and 3, respectively. Three mathematical models were applied to estimate the kinetic parameters and check the accuracy of the experiment. The first-order model with a hydrolysis rate of 0.23–0.27 indicated rapidly biodegradable co-/substrates, modified Gompertz confirmed immediate commencement of co-digs through zero lag phase, while the Cone model had the best fit of over 99% for all trials. Finally, the study points out that the COD method based on linear dependence can be used for developing relatively accurate model for biogas potential estimation in anaerobic digestors. Supplementary Information The online version contains supplementary material available at 10.1007/s12155-023-10620-8.

Imposed measures for the protection of public health and prevention of the uncontrolled spread of COVID-19 disease affected before established community traveling patterns. One of the questions scientific community consequently wonders consider the potentials of the occurred circumstances to leave permanent change to community traveling behavior. With that regard, this study examines changes in students’ traveling habits in a medium-sized university city in the Balkans. The research was based on 486 students’ responses amassed by Google questionnaires, before and during the COVID-19 pandemic. The study includes data interpretation and data analysis. Among six introduced classification models, random forest proved as the most suitable to accurately fit the students’ demographic details to students’ attitude towards future traveling habits (as changed or not changed permanently). Additionally, the model was optimized and further analyzed on variable influence and variable partial dependence. Data interpretation indicates higher reliance on personal automobiles as a cause for a 15% increase of the families owning two of the vehicles. Because of that, the share of the students who walked to the University during the pandemic declined by approximately 20%, while those using public transportation stayed relatively unchanged (decrease by around 3%). On the other side, the best of six classification models was used to determine the factors causing a permanent change in students’ attitudes towards inner-city transportation. Derived classification models deduce where to expect potentially permanent changes in traveling patterns – distances between 2 and 4 km from the University, and among which students – those who are visiting lectures less frequently than others.

Imposed measures for the protection of public health and prevention of the uncontrolled spread of COVID-19 disease affected before established community traveling patterns. One of the questions scientific community consequently wonders consider the potentials of the occurred circumstances to leave permanent change to community traveling behavior. With that regard, this study examines changes in students’ traveling habits in a medium-sized university city in the Balkans. The research was based on 486 students’ responses amassed by Google questionnaires, before and during the COVID-19 pandemic. The study includes data interpretation and data analysis. Among six introduced classification models, random forest proved as the most suitable to accurately fit the students’ demographic details to students’ attitude towards future traveling habits (as changed or not changed permanently). Additionally, the model was optimized and further analyzed on variable influence and variable partial dependence. Data interpretation indicates higher reliance on personal automobiles as a cause for a 15% increase of the families owning two of the vehicles. Because of that, the share of the students who walked to the University during the pandemic declined by approximately 20%, while those using public transportation stayed relatively unchanged (decrease by around 3%). On the other side, the best of six classification models was used to determine the factors causing a permanent change in students’ attitudes towards inner-city transportation. Derived classification models deduce where to expect potentially permanent changes in traveling patterns – distances between 2 and 4 km from the University, and among which students – those who are visiting lectures less frequently than others.