It is well known that the use of photovoltaic (PV) systems helps to preserve the environment, produce lower levels of greenhouse gases (GHGs), and reduce global warming, however, whether it is economically profitable for customers or not is highly debatable. This paper aims to address this issue. To be comprehensive, three different types of buildings are considered as case studies. Then, these three buildings are modeled in EnergyPlus to determine the rate of energy consumption. Afterward, comparisons of various solar system sizes based on economic parameters such as the internal rate of return, the net present value, payback period and profitability indexing for various-sized PV systems are carried out. The results show that by the demand charge tariffs, using PV systems has no economic justification. It has been shown that even with neglecting further costs of the PV system like maintenance, by demand charge tariffs, it is not economically beneficial for customers to use the PV systems. Profitability index of all three buildings with various PV power systems is between 0.2 to 0.8, which are by no means is desirable. Moreover, it was found that bigger solar systems are less cost-effective in the presence of demand charges.

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The development of electric vehicles (EVs) is happening around the world with different goals. Many researchers have worked on various aspects of EVs from technological and supporting policy issues to the development of required infrastructures. However, arguing the proper time to realize the spreading of EVs in each region is neglected. For this purpose, the performance of two contextual factors in each region on the growth of EVs is investigated. Low carbon electricity generation and greenhouse gases emissions are the selected parameters, which are explored in the context of nine European countries, besides Luxembourg, to find their impacts on the issue. These countries have the highest shares of EVs in their energy systems. The achieved results are applied to the Luxembourg case to evaluate how different contextual factors may have hindered the growth of EVs here. In the next step, an analogy between the spreading EVs in Luxembourg and leapfrogging different technologies in the world is made to build a theory of the development of EVs. The theory defines the spreading EVs in Luxembourg as a leapfrogging energy technology to adopt new technology. It is concluded that the development of EVs has a normal priority in Luxembourg.

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Building energy assessment is essential to accomplish the sustainable energy targets of new and present buildings. Retrofitting of the existing buildings by assessing them through energy models is the most prominent method. Studies revealed that there is still blank information about the building stocks, and these affect the valuation of building energy efficiency policies. Literature also recommends that the existing energy models are too complex and unreliable to predict the energy use. Reliability of such energy models would improve through a better alignment of the input parameters and the model assumptions. The authors hypothesized that the reliability of models would be improved through identification of the most relevant energy use parameters for the building stocks in different regions and models. One of the most commonly accepted methods for detecting the most dominant input parameters is sensitivity analysis, though its shortcomings include the need for a large number of data samples and long computing time. In this research, the Energy, Carbon, and Cost Assessment for Buildings Stocks (ECCABS) model is adopted to identify the most important parameters of the presented model. The research team uses the model that has been validated by studies conducted for the UK building stock. Moreover, by assessing the feasibility study with the stepwise regression to identify the significant input parameters have been discussed. Results show that stepwise regression method could produce the same results compared to sensitivity analysis. This paper also indicates that stepwise regression is considerably faster and less computationally intensive compared to common sensitivity analysis methods.