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In present times, sustainability is to be demanded to attain high economic growth in longer run. Thus, countries are obliged to scrutinize the factors which balance the economic growth and prove to be efficient indicator of environment. This study, knowing the significance of such indicators, the study formulates a framework in which a role of sustainable energy system, creativity, and green finance on the environmental efficiency and sustainable economic development intends to be examined in the context of high carbon emission economies such as Iran, Canada, China, Indonesia, India, Japan, Saudi Arabia, United States, Russia, and South Korea. In order to tackle the abovementioned issue, the study used the data extracted from world development indicators (WDI) covering the period from 2010 to 2020. The study employed Random fixed model to test the proposed hypotheses. Obtained results expose that sustainable energy systems, creativity and green finance share negative association with carbon emissions, hence, revealing this point that the indicators are useful to increase sustainable economic development and environmental efficiency. Thereby, in order to achieve sustainable economic growth, governments are liable to implement policies with a long-term approach. The outcomes are also helpful for the upcoming researchers and regulators while formulating the policies about environmental efficiency.

The article will contain a scientific analysis, showing thematic links between publications and consist of searching the data in the Scopus database. The timeframe of the searched publications will be 2010–2022. The parameters should also be narrowed down by selecting the following indexes: Science Citation Index Expanded (SCI-E), Social Science Citation Index (SSCI) and Emerging Sources Citation Index (ESCI), which was intended to eliminate abstracts of books and conference materials, leaving only reviewed works with the highest level of relevance for furthering knowledge. An additional limit of five minimum citations will be introduced. The analysis included 342 articles. Texts cited at least 100 times were highlighted. The research showed that authors focus piecemeal on selected aspects or problems, so an attempt was made to show thematic connections of keywords, illustrating the complexity of the transformations underway. The discussion identifies the most active authors and countries, especially exploring the topic of a transition to a sustainable energy system for smart cities. The authors have employed scientometric analysis to provide an objective and data-driven exploration of the transformation of sustainable energy systems for smart cities. This approach offers valuable insights into the research landscape, trends, and relationships within the field, which can guide future scientific research and contribute to a deeper understanding of the subject matter. As an additional element of this conclusion, the authors proposed supplementing the scientometric analysis with the foresight methodology. The authors’ research approach is distinguished by the following stages: problem formulation, data collection, choice of scientometric methodology, analysis of advantages and limitations of scientometrics, clustering analysis, data analysis, and presentation of results. Our systematic literature review systematizes the existing literature on the sustainable energy systems for smart cities, isolates main research interests, identifies future research avenues, and provides several important hints for researchers.

The fast deployment of renewable energy in Italy by 2030, according to the National Energy and Climate Plan, will lead to renewable electricity surplus mainly in Southern Italy where adequate energy storage is not currently available. In this paper, a Power to Green Hydrogen followed by a methanation process is proposed to accelerate decarbonisation and the integration of higher shares of variable renewable energies in Southern Italy. Green hydrogen can be injected directly into the natural gas grid or used to produce biomethane that can be injected afterwards into the natural gas grid. The main results of our assessment are: 1) 819 – 1,638 Mm3 green hydrogen that could be produced by water electrolysis using the renewable electricity surplus; 2) 160 Mm3 biomethane produced through biogas upgrading and 3) 14 – 117 Mm3 additional biomethane produced through methanation by using biogenic carbon dioxide from biogas upgrading. The approach would enable a) green hydrogen production cost competitive with grey hydrogen (44 €/MWh) only when the electrolysis system uses electricity for free and its load factor is above 1,800 h/y and 5,000 h/y for the optimistic and conservative case, respectively; b) biomethane production cost via biogas upgrading (about 58 €2020/MWh); and c) biomethane production cost via methanation competitive versus natural gas price (30 €/MWh) only when using electricity for free and operates at full load for over 5,400 h/y in the optimistic case.

The increased share of variable renewable energy sources such as wind and solar power poses constraints on the stability of the grid and the security of supply due to the imbalance between electricity production and demand. Chemical storage or power-to-X technologies can provide the flexibility that is needed to overcome this issue. To quantify the needs of such storage systems, energy system optimization models (ESOMs) are used, guiding policy makers in nationwide energy planning. The key input parameters for such models are the capacity and efficiency values of the conversion devices. Gas turbines, reciprocating engines, fuel cells and Rankine engines are often mentioned here as cogeneration technologies. Their performance parameters will however need to be revised when switching from fossil to renewable fuels. This study therefore investigates the possibility of using size-based scaling laws to predict the efficiency and power values of one type of conversion technology: the reciprocating engine. The most straightforward scaling laws are the ones based on the fundamental engine performance parameters and are constructed by fitting an arithmetic function for a large set of representative engine data. Their accuracy was tested with a case study, consisting of thirty large-bore, spark-ignited gas engines. Two alternative methods were also investigated: scaling laws based on the Willans line method and scaling laws based on the similarity theory. Their use is deemed impractical for the current research problem.

The article will contain a scientific analysis, showing thematic links between publications and consist of searching the data in the Scopus database. The timeframe of the searched publications will be 2010–2022. The parameters should also be narrowed down by selecting the following indexes: Science Citation Index Expanded (SCI-E), Social Science Citation Index (SSCI) and Emerging Sources Citation Index (ESCI), which was intended to eliminate abstracts of books and conference materials, leaving only reviewed works with the highest level of relevance for furthering knowledge. An additional limit of five minimum citations will be introduced. The analysis included 342 articles. Texts cited at least 100 times were highlighted. The research showed that authors focus piecemeal on selected aspects or problems, so an attempt was made to show thematic connections of keywords, illustrating the complexity of the transformations underway. The discussion identifies the most active authors and countries, especially exploring the topic of a transition to a sustainable energy system for smart cities. The authors have employed scientometric analysis to provide an objective and data-driven exploration of the transformation of sustainable energy systems for smart cities. This approach offers valuable insights into the research landscape, trends, and relationships within the field, which can guide future scientific research and contribute to a deeper understanding of the subject matter. As an additional element of this conclusion, the authors proposed supplementing the scientometric analysis with the foresight methodology. The authors' research approach is distinguished by the following stages: problem formulation, data collection, choice of scientometric methodology, analysis of advantages and limitations of scientometrics, clustering analysis, data analysis, and presentation of results. Our systematic literature review systematizes the existing literature on the sustainable energy systems for smart cities, isolates main research interests, identifies future research avenues, and provides several important hints for researchers. [ABSTRACT FROM AUTHOR]

The power supply and demand have been studied, modelled, analysed, and foresighted as one of the most important energy carriers. The Business as usual (BAU) scenario was compared based on continuing the status quo with seven other proposed possible scenarios up to the horizon 2050. Possible solutions such as demand management, increased productivity, upgrading power plant technology, and development of energy resources attempt to reduce electricity demand, as well as improving and promoting the share of renewable energies in power generation and reducing emissions. The cost–benefit technique has also been used to analyse the economic and environmental benefits. As a results, a Khuzestan electricity policy scenario that has a well-coordinated and cost-effective solution for both supply and demand. It is preferred scenario for flexibility and stability of grid with a $3,549 million profit with 35% renewable energy share.

Along the roadmap to a Sustainable Real Estate-Scape, energy retrofit campaigns on wide city compartments represent a pivotal task, where the importance of the collaboration between the public and private sectors is crucial. Energy retrofit programs on building assets are subject to multiple uncertainty factors (e.g., climate, energy-economy forecasts, etc.) that act as a primary barrier to investment in this field. This paper aims to discuss risk management techniques to understand better how to deal with this kind of uncertainty. The research specifically addresses the techniques of sensitivity analysis and Monte Carlo simulation, focusing first on the phase of variables selection and their probability definition, including climatic, environmental, energy, economic, financial, and stochastic parameters. In this article, it is suggested to include correlation coefficients in the input variables of risk analysis, preferring the two-dimension Monte Carlo simulation to its simple version, since the results are more reliable when separating aleatory from epistemic uncertainty; thus, the results are more reliable. Moreover, it is seen how a sensitivity analysis based on percentile variations of the inputs provides a more accurate representation of risk if compared to the most common sensitivity analysis based on percentage deviations of the inputs. Conducting a sensitivity analysis using percentile variations gives realistic and reliable results, reflecting the tailored definition of uncertainty around the inputs on the basis of specific market analyses or historical series.