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Abstract This paper provides a forecast of electricity consumption in Cyprus up to the year 2030, based on econometric analysis of energy use as a function of macroeconomic variables, prices and weather conditions. If past trends continue electricity use is expected to triple in the coming 20–25 years, with the residential and commercial sectors increasing their already high shares in total consumption. Besides this reference scenario it was attempted to assess the impact of climate change on electricity use. According to official projections, the average temperature in the Eastern Mediterranean is expected to rise by about 1 °C by the year 2030. Using our econometrically estimated model, we calculated that electricity consumption in Cyprus may be about 2.9% higher in 2030 than in the reference scenario. This might lead to a welfare loss of 15 million Euros in 2020 and 45 million Euros in 2030; for the entire period 2008–2030 the present value of costs may exceed 200 million Euros (all expressed in constant Euros of 2007). Moreover, we assessed the additional peak electricity load requirements in the future because of climate change: extra load may amount to 65–75 Megawatts (MW) in the year 2020 and 85–95 MW in 2030.

Technical and economic developments in battery and fast-charging technologies could soon make fuel cell electric vehicles, which run on hydrogen, superfluous in road transport

Diesel engine exhausts from a common rail 3.0 L F1C diesel engine were analyzed at two different load conditions of the WLTC testing cycle downstream of both the diesel particulate filter (DPF) and selective catalytic reactor (SCR) to verify their effect on the characteristics of carbon particulate matter. An array of chemical, physical and spectroscopic techniques (gas chromatography coupled with mass spectrometry (GC-MS), mobility analyzer, UV-Visible absorption and fluorescence spectroscopy) was applied for characterizing polycyclic aromatic hydrocarbons (PAH), heavy aromatic compounds and soot, constituting the particulate matter (PM) sampled from the exhaust. The engine was operated in half load (HL) (188 Nm, representing the more common condition for engine in urban traffic) and full load (FL) (452 Nm, representing the best performance of the engine operation) conditions, at the same engine speed (2000 rpm). Soot formation was enhanced in HL condition, with respect to FL, but, just because of the much lower soot amount, the after-treatment systems in this last condition resulted to be less efficient in the soot abatement. Indeed, the abatement through DPF was about 40% lower in the FL condition with respect to HL condition, and any significant further concentration decrease was found after SCR, in both conditions. By contrast, PAH concentration after DPF abatement was found to be higher in the HL with respect to FL condition. A further PAH concentration decrease of about 30% was found after the SCR in the HL condition whereas in FL the reduction was only about 5-6%. Also the heavy aromatic compounds having molecular weight above the GC-MS detection limit (300 u), were mitigated by SCR. Therefore, SCR did not cause a further soot reduction, whereas it was effective in largely reducing PAH and heavy aromatics emissions, especially in the lower temperature condition featuring the half-load condition, when combustion efficiency is worse. Moreover, SCR system reduced the emission of small particles probably due to an enhanced agglomeration of particles, with beneficial effect on the harmfulness to human health.

Since 2015, the intended climate actions of the Paris Agreement signatories have been reported as nationally determined contributions (NDC). These climate actions are fully aligned with the 13th Sustainable Development Goal (SDG) which calls for urgent action to combat climate change. The same, however, cannot be said for their relation to the other 16 SDGs of the 2030 Agenda for Sustainable Development, since climate action can either enhance or compromise the prospects for SDG implementation. In light of this challenge, this paper proposes a simple method for quantifying the synergies and trade-offs between national climate actions and the SDGs. The method, referred to as Q-SCAN, makes use of a seven-step scale and the SDG Climate Action Nexus tool. The effectiveness of the method has been demonstrated on a case study of North Macedonia, a non-Annex I, Western Balkan country with a coal-intensive energy system. Based on the experience in the preparation of the country’s enhanced NDC, the paper elaborates how the method can be used to contribute to the alignment of the national climate actions with the SDGs and how it can be used to improve stakeholder engagement.

A priority of the industrial applications of microalgae is the reduction of production costs while maximizing algae biomass productivity. The purpose of this study was to carry out a comprehensive evaluation of the effects of pH control on the production of Nannochloropsis gaditana in tubular photobioreactors under external conditions while considering the environmental, biological, and operational parameters of the process. Experiments were carried out in 3.0 m3 tubular photobioreactors under outdoor conditions. The pH values evaluated were 6.0, 7.0, 8.0, 9.0, and 10.0, which were controlled by injecting pure CO2 on-demand. The results have shown that the ideal pH for microalgal growth was 8.0, with higher values of biomass productivity (Pb) (0.16 g L-1 d-1), and CO2 use efficiency ([Formula: see text]) (74.6% w w-1); [Formula: see text]/biomass value obtained at this pH (2.42 [Formula: see text] gbiomass-1) was close to the theoretical value, indicating an adequate CO2 supply. At this pH, the system was more stable and required a lower number of CO2 injections than the other treatments. At pH 6.0, there was a decrease in the Pb and [Formula: see text]; cultures at pH 10.0 exhibited a lower Pb and photosynthetic efficiency as well. These results imply that controlling the pH at an optimum value allows higher CO2 conversions in biomass to be achieved and contributes to the reduction in costs of the microalgae production process.

The number of natural disasters is growing. A considerable number of them derives directly from climate changes and generates a deep impact on cultural heritages. As a result, the need for solutions able to cope with uncertain weather conditions and increased natural disasters is getting urgent. STORM is a European Research and Innovation Action co-funded in the H2020 framework that is aimed at creating intelligent tools which will gather data from libraries, sensors and crowd-sensing techniques in order to enable cultural heritage stakeholders (e.g., the organisations which have to manage the sites) to implement the most appropriate actions in the prevention, response and recovery phases of emergencies which could impact on cultural heritages. More specifically, the project aims at addressing those risks deriving from climate change, which in the near future is going to worsen most of the present hazards, as flash floods, heat waves and forest fires. As such, the approach adopted by the project is particularly fit for the purpose of being integrated with smart city systems which are increasingly growing in number and variety. Even if safety issues are not considered central in the overall concept of smart city, it’s plain that the huge quantity of data that can be gathered and processed in any future STORM-like urban cultural heritage compound should feed into the available smart city systems. On the other way round, the mass of data produced or processed by smart city systems have the potential to impact dramatically on emergency management and prevention activities to be implemented by cultural heritage stakeholders. The paper will illustrate the approach that the STORM project is adopting to mitigate the impact of climate changes on cultural heritages and the mutual benefits which could derive from an integration of the STORM outcomes with smart cities systems through the use of standard emergency data exchange protocols and an integrated framework aimed at improving existing processes related to the three identified areas: Prevention, Response and Policy

Lack of consensus on an international agreement for reducing Greenhouse Gas Emissions (GHG) emissions eventually leads to asymmetric climate policies which not only increase the cost of reducing emissions but also decrease the effectiveness of the climate policy, through carbon leakage. We calculate the carbon leakage rate when EU undertakes a unilateral climate policy and we assess the importance of the competitiveness channel on carbon leakage. Our analysis is global and mirrors energy and climate policies and commitments that are currently announced at country level. The effectiveness of possible measures to mitigate carbon leakage is also evaluated and the results emphasize on the importance of the size of the group of countries participating in the GHG mitigation effort. The analysis is based on the results obtained using the GEM-E3 model, a global multi-sector and multi-country computable general equilibrium model. It is found that total carbon leakage is around 28%, over the 2015–2050 period, when the EU acts alone with moderate Armington trade substitution elasticity values; leakage rates are found to increase when assuming higher trade elasticities. The size and composition, in terms of GHG and energy intensities, of the group of regions undertaking emission reductions matter for carbon leakage. The paper finds that the leakage is significantly reduced when China joins the mitigation effort. If the USA joins the EU effort, the leakage rate drops only to 25% and if alternatively China joins the EU the leakage rate drops to 3% over the 2015–2050 period. This is attributed to both the market size of China and to the energy intensity features of its production. Chemicals and metals are industries prone to higher leakage rates.

Water supply systems (WSS) frequently present high-energy consumption values, which correspond to the major expenses of these systems. Energy costs are a function of its real consumption and of the variability of the daily energy tariff. This paper presents a model of optimization for the energy efficiency in a water supply system. The system is equipped with a pump station and presents excess of available energy in the gravity branch. First, a water turbine is introduced in the system in order to use this excess of hydraulic available energy. Then, an optimization method to define the pump operation planning along the 24 h of simulation, as well as the analysis of the economic benefits resulting from the profit of wind energy to supply the water pumping, while satisfying the system constraints and population demands, is implemented, in order to minimize the global operational costs. The model, developed in MATLAB, uses linear programming and provides the planning strategy to take in each time step, which will influence the following hours. The simulation period considered is one day, sub-divided in hourly time steps. The rules obtained as output of the optimization procedures are subsequently introduced in a hydraulic simulator (e.g. EPANET), in order to verify the system behaviour along the simulation period. The results are compared with the normal operating mode (i.e. without optimization algorithm) and show that energy cost's savings are achieved dependently of the initial reservoir levels or volume. The insertion of the water turbine also generates significant economical benefits for the water supply system.

Assessment of the global burden of disease is based on epidemiological cohort studies that connect premature mortality to a wide range of causes, including the long-term health impacts of ozone and fine particulate matter with a diameter smaller than 2.5 micrometres (PM2.5). It has proved difficult to quantify premature mortality related to air pollution, notably in regions where air quality is not monitored, and also because the toxicity of particles from various sources may vary. Here we use a global atmospheric chemistry model to investigate the link between premature mortality and seven emission source categories in urban and rural environments. In accord with the global burden of disease for 2010 (ref. 5), we calculate that outdoor air pollution, mostly by PM2.5, leads to 3.3 (95 per cent confidence interval 1.61-4.81) million premature deaths per year worldwide, predominantly in Asia. We primarily assume that all particles are equally toxic, but also include a sensitivity study that accounts for differential toxicity. We find that emissions from residential energy use such as heating and cooking, prevalent in India and China, have the largest impact on premature mortality globally, being even more dominant if carbonaceous particles are assumed to be most toxic. Whereas in much of the USA and in a few other countries emissions from traffic and power generation are important, in eastern USA, Europe, Russia and East Asia agricultural emissions make the largest relative contribution to PM2.5, with the estimate of overall health impact depending on assumptions regarding particle toxicity. Model projections based on a business-as-usual emission scenario indicate that the contribution of outdoor air pollution to premature mortality could double by 2050.