Croatia produced 21,366 tonnes of dry matter (DM) sewage sludge (SS) in 2016, a quantity expected to surpass 100,000 tonnes DM by 2024. Annual production rates for future wastewater treatment plants (WWTP) in Croatia are estimated at 5.8–7.3 Nm³/people equivalent (PE) for biogas and 20–25 kg_DM/PE of sewage sludge. Biogas can be converted into 12–16 kWh_el/PE of electricity and 19–24 kWh_th/PE of heat, which is sufficient for 30–40% of electrical and 80–100% of thermal autonomy. The WWTP autonomy can be increased using energy recovery from sewage sludge incineration by 60% for electricity and 100% of thermal energy (10–13 kWh_el/PE and 30–38 kWh_th/PE). However, energy for sewage sludge drying exceeds energy recovery, unless solar drying is performed. The annual solar drying potential is estimated between 450–750 kg_DM/m² of solar drying surface. The lower heating value of dried sewage sludge is 2–3 kWh/kg_DM and this energy can be used for assisting sludge drying or for energy generation and supply to WWTPs. Sewage sludge can be considered a renewable energy source and its incineration generates substantially lower greenhouse gases emissions than energy generation from fossil fuels. For the same amount of energy, sewage sludge emits 58% fewer emissions than natural gas and 80% less than hard coal and fuel oil. Moreover, this paper analysed the feasibility of sludge disposal practices by analysing three scenarios (landfilling, co-incineration, and mono-incineration). The analysis revealed that the most cost-effective sewage sludge disposal method is landfilling for 60% and co-incineration for 40% of the observed WWTPs in Croatia. The lowest CO₂ emissions are obtained with landfilling and mono-incineration in 53% and 38% of the cases, respectively.

Making organizations aware of their carbon footprint (CF) and proposing measures to reduce it are important segments of climate change mitigation. As a part of this process, an enhanced Bilan Carbone model with incorporated country-specific greenhouse gas (GHG) emission factors was applied for CF calculations of three organizations (Agency, Faculty, and Institute). The model, fully in line with international CF calculation standards, can be applied to calculate the CF of any organization on the global level. The paper provides a comparative assessment of CFs of considered organizations and preconditions for a reliable comparison. The calculated CFs values for 2017 were 513.4 t CO₂ e for the Agency, 4254.7 t CO₂ e for the Faculty, and 477.0 t CO₂ e for the Institute. Comparing specific CF, the Faculty had the highest value per employee (9.4 t CO₂ e/employee) and the lowest value per heated area (131 kg CO₂ e/m²), followed by the Institute (5.4 t CO₂ e/employee and 222 kg CO₂ e/m²) and the Agency (4.5 t CO₂ e/employee and 294 kg CO₂ e/m²). Using the enhanced Bilan Carbone model, adapted to national conditions, could lead to the harmonization of the organizations’ CF calculation and enable the identification of significant emission sources. This will facilitate the definition of GHG reduction targets and the identification of mitigation measures for achieving the targets, as presented in the example of the Institute.

Making cities smart represents a major potential for sustainable development, where both the quality of life and the economy improve. Implementing new and efficient solutions in a smart city involves a large spectrum of uncertainties related to the size and project complexity. Characterization and assessment of the variables uncertainty in planning methodology seem necessary to reach the best decision about the best approach to achieve favorable realization outcomes for planned projects. By including uncertainties in the planning, assessment makes it possible to calculate result uncertainties for all expectations, and project cost-effectiveness. In this way, planning can be improved, if the most important parameters of result uncertainties are identified, better defined, and controlled. This study describes a parameter uncertainty characterization methodology applied on the cost-benefit analysis of smart city development with a case study, focused on smart metering infrastructure. Parameter uncertainty characterization is performed based on its variable nature (epistemic and aleatory), time-dependency, and the available information. Cost-benefit analysis results are given as both point value and as uncertainties. Uncertainty is considered for 25 variables of investment and operating costs, and benefits estimation. The presented methodology in smart city planning provides a way to better identify the critical parameters for achieving the defined objectives.

Results of the research on how different generation plants can satisfy heat demands and at the same time minimise operation costs, carbon dioxide (CO2) emission and consumption of primary energy are presented in the paper. In that sense...