Sustainable and Environmental Development of Energy Economy in Smart Regions of Russia

Introduction

The energy economy is undergoing a digital transformation, which contributes to its sustainable and environmental development. This process has been studied in sufficient detail at the level of the national economy in the existing literature (Ram et al., 2022; Zhao et al., 2022). The problem is that the regional level includes specific characteristics of territories that become less noticeable and are not taken into account at the macroeconomics level (Cortese et al., 2022; Li, 2022; Xu, 2022; Zaidan et al., 2022). This is especially true for countries with a large territory.

In this regard, Russia can serve as an example, since it not only has a territory with a diverse geography, but is also a major energy power. Its exports in the field of energy have been studied in sufficient detail and have been the subject of active scientific discussion in recent years (Popkova et al., 2021; Popkova, 2022). At the same time, Russia’s internal energy policy related to the energy supply and energy consumption in the Russian regional economy has been studied and developed to a lesser extent, which is a research gap.

The purpose of this article is to study the experience of sustainable and environmental development of the energy economy in the regions of Russia and the role of “smart” technologies in this process. The originality of this article lies in the fact that it reveals a new–mesoeconomic perspective of studying the sustainable and environmental development of the energy economy based on “smart” technologies, and also highlights the best practices of “smart” regions of Russia.

Sustainable and Environmental Development of the Energy Economy Based on “Smart” Technologies: Literature Review

The research in this paper is based on the Theory of sustainable and environmental (“green”) development of the energy economy, which foundations are set in the works of Ajiboye et al. (2022), Ali et al. (2022), Mahmoud et al. (2022), Matsunaga et al. (2022), Qu et al. (2022) and Sharma et al. (2022). In the existing literature, the issues of sustainable and environmental development of the energy economy based on smart technologies are thoroughly elaborated. The scientific concept of the energy economy of a smart region is formed in the works of Bellocchi et al. (2020), Dominković et al. (2018), Guo et al. (2022), Kleineidam et al. (2016), Li (2022) and Ortiz Cebolla and Navas (2019).

In the works of Chen et al. (2022), Elnour et al. (2022), Kabeyi and Olanrewaju (2022), Kamruzzaman and Alruwaili (2022), Nasir et al. (2022), it is noted that “smart” technologies contribute to the sustainable and environmental development of the energy economy. Environmental benefits are also highlighted, which mainly include combating climate change (SDG 13) and protecting ecosystems (SDGs 14-15).

However, uncertainty remains regarding the social and economic benefits. Studies by Ahmad et al. (2022), Rezaei et al. (2022) indicate that responsible corporate management of “smart” technologies allows them to be introduced as environmental (“green”) innovations in the activities of energy companies, creating benefits for the environment. The examination of the characteristics of the region as an economic system in which the sustainable and environmentally friendly development of the energy economy on the basis of “smart” eco-innovations occurs is a gap in the literature. To fill the identified gap, a comprehensive quantitative and qualitative study is conducted in this article.

The Role of “Smart” Technologies in the Sustainable and Environmental Development of the Energy Economy in the Regions of Russia

In this paper, energy efficiency is considered in three aspects: 1) reduction of energy intensity; 2) decarbonization; 3) environmental friendliness of industry as the most energy-intensive sphere of economy. This allows for the most correct research of energy efficiency as the indicator of the sustainable and environmental development of the energy economy.

In a quantitative aspect, this article presents and analyzes the rate of use of “smart” technologies in organizations (based on statistics from the Ministry of Finance of Russia, Rosstat, HSE, 2022), which allows identifying “smart” regions of Russia. Regional statistics of Rosstat (2022) characterizing the introduction of “green” energy innovations in Russia are also being studied. The study is conducted on the example of the top 10 regions of Russia, which are the leaders of the environmental rating of the NGO “Green Patrol” (2022) for the winter of 2021–2022. Statistical characteristics of the sample regions are presented in Table 1.

TABLE 1

TABLE 1. Environmental profile of the “smart” regions of Russia from the standpoint of the energy economy according to data for the winter of 2021–2022.

Using the data from Table 1, regression analysis for one year—2021—is performed; it reflects the most up-to-date statistics. The data are not studied in dynamics in this paper, for statistics on the previous period (2020) largely reflects the impact of the COVID-19 pandemic and crisis (the study of which goes beyond this paper), and so its consideration might have distorted the treatment of the results of this research.

Using the methods of regression and correlation analysis, it is established that “smart” technologies play an important role in the sustainable and environmental development of the energy economy in the regions of Russia. Firstly, it has been revealed that “smart” technologies contribute to improving the energy efficiency of the regional economy of Russia. This conclusion is based on the determined regularity of the reduction of energy efficiency in the course of dissemination of smart technologies (demonstrated by the example of online sales). Though the proof is given by the example of ten regions in this paper’s sample, the conclusion would be correct for all regions of Russia, because the sample is representative of all of them (it includes regions from all federal districts). This is evidenced by the obtained regression model (Eq. 1):

$\mathrm{enp}=\mathrm{102}\mathrm{.41}-\mathrm{6}\mathrm{.07erps}+\mathrm{3}\mathrm{.95ecmc}+\mathrm{0}\mathrm{.41rfid}(1)$

Eq. 1 indicates that with an increase in the share of organizations engaged in electronic sales by 1%, the share of organizations implementing innovations that reduce the energy intensity of production increases by 3.95%. And with an increase in the share of organizations using RFID technologies by 1%, the share of organizations implementing innovations that reduce the energy intensity of production increases by 0.41%. The correlation of energy efficiency with “smart” technologies was 66.27% (high).

Secondly, it was revealed that “smart” technologies contribute to the decarbonization of the regional economy of Russia. This is evidenced by the obtained regression model (Eq. 2):

$\mathrm{crp}=\mathrm{86}\mathrm{.37}-\mathrm{3}\mathrm{.49erps}-\mathrm{1}\mathrm{.86ecmc}+\mathrm{4}\mathrm{.57rfid}(2)$

Eq. 2 shows that with an increase in the share of organizations using RFID technologies by 1%, the share of organizations implementing innovations that reduce carbon dioxide (CO₂) emissions into the atmosphere increases by 4.57%. The correlation of decarbonization with “smart” technologies was 72.87% (high). Decarbonization here is treated as a component of energy efficiency in the aspect of an increase in the environmental friendliness of the energy economy.

Thirdly, it is determined that “green” energy innovations contribute to the growth of environmental friendliness of industry in the regions of Russia. This is evidenced by the obtained regression model (Eq. 3):

$\mathrm{PEI}=\mathrm{64}\mathrm{.43}+\mathrm{0}\mathrm{.004enp}+\mathrm{0}\mathrm{.02crb}(3)$

Eq. 3 indicates that with an increase in the share of organizations implementing innovations that reduce the energy intensity of production by 1%, the industrial and environmental index increases by 0.004%. And with an increase in the share of organizations implementing innovations that reduce the energy intensity of production, reducing carbon dioxide (CO₂) emissions into the atmosphere by 1%, the industrial and environmental index increases by 0.02%. The correlation of the environmental friendliness of industry with the sustainability of the energy economy was 26.77% (moderate).

The reliability of all three obtained regression models (Equations 1–3) is confirmed by high values of correlation coefficients, which prove the close connection between the studied indicators. Based on the obtained models, it was revealed that with an increase in the share of organizations engaged in electronic sales by 87.72% (from 11.14% in 2021 to 20.91%), as well as an increase in the share of organizations using RFID technologies by 250.59% (from 6.67% in 2021 to 23.38%), the following advantages will be achieved for the sustainability of the energy economy in the regions of Russia:

˗ Increase in the share of organizations implementing innovations that reduce the energy intensity of production by 83.42% (from 54.52% in 2021 to 100%);

˗ Increase in the share of organizations implementing innovations that reduce carbon dioxide (CO₂) emissions into the atmosphere by 139.23% (from 41.80% in 2021 to 100%).

Together, this will ensure an increase in the environmental friendliness of industry in the regions of Russia by 2.50% (from 65.70 points in 2021 to 67.34 points).

Case experience of sustainable and environmental development of the energy economy in the “smart” regions of Russia based on EnergyTech and Smart Grid.

In the qualitative aspect, the practical experience of sustainable and environmental development of the energy sector in the “smart” regions of Russia is studied, while special attention is paid to the best practices of Moscow. Successful examples of improving the energy efficiency of the regional economy of Russia involve the use of the following “smart” technologies.

1) Internet of Things (IoT). Moscow United Energy Company (MUEC), in partnership with the mobile operator MTS, has been implementing a project for monitoring energy consumption for more than 10 years (since 2009), within which a unified automated system for monitoring and accounting for the transmission of thermal energy and hot water has been created. The intelligent energy efficiency monitoring system is based on digital sensors (UC)—47 thousand metering devices in municipal houses and social facilities equipped with MTS SIM cards, which continuously transmit data to the server of the Central Electricity Metering System of the MUEC. The consumption indices of thermal energy in residential buildings in Moscow are taken remotely via the Internet (RBC, 2022).

2) Ubiquitous Computing (UC). JSC “Rusnano” is implementing a project for the development of innovative energy storage systems for testing on the railway network in partnership with JSC “Russian Railways” and LLC “Rusenergosbyt”. A pilot sample of an energy storage device for testing on the railway has been designed and is being tested. At the same time, PJSC “Rosseti” pays considerable attention to the development of accumulation systems, regulatory systems, distributed generation systems, as well as the development of electric transport (Russian Union of Industrialists and Entrepreneurs, 2022).

3) Blockchain. In a number of “smart” regions of Russia, an experiment is being conducted to test the technology of distributed ledger (blockchain) based on the collection and processing of data on energy consumption in retail electricity markets on the terms of public-private partnership. The experiment is aimed at improving the reliability and quality of power supply to consumers through the introduction of new technologies and optimization of the activities of network organizations (CNews, 2022).

4) Artificial Intelligence (AI). JSC “IDGC Holding” has created a smart distribution grid based on AI. “Smart” electricity metering devices have been introduced, network management centers have been created, and the observability of substations in the Smart Grid network has been increased. The primary task in the “intellectualization” of the distribution network is smart accounting. A unified information landscape of the accounting system using of open-source, flexible multifunctional components (in particular, of metering devices) operating on the “plug and play” principle is being created (Systems and Technologies, 2022).

The described technologies are used in smart regions of Russia. Thus, in “smart Ulyanovsk” (Ulyanovsk Region, Povolzhye of Russia), there is one system of consumption records, which allows reducing expenses for energy consumption, and a modernised system of street lighting helps control the state of networks (Com News, 2022). Moscow’s (Moscow Region, Central Russia) strategy “Smart city 2030” already provides the following (Moscow City Government, 2022):

− Increase in energy efficiency, environmental friendliness and effectiveness of real estate objects management;

− Implementation of the principles of “green construction” through using energy-effective technologies and reduction of waste and emissions during the construction and exploitation of buildings;

− Information modelling will be used during the planning and design of energy effective buildings and city transport infrastructure, reconstruction and capital repair;

− Construction of the pilot road infrastructure with the use of technologies of solar generation of energy, which allows providing electric energy to the objects of road infrastructure (lighting, control systems) and charging electric vehicles.

− Use of photoelectrical covering for electric energy production through collecting solar power.

In the Sverdlovsk Region (North of Russia), a regional information system of energy-saving is functioning successfully; it consolidates data on the municipal systems of the region, which allows forming the ranking of municipalities’ energy efficiency, performing heat and hydraulic calculations online and creating an interactive regional scheme of heat and water supply (All Events, 2022).

Discussion

The contribution of the article to the literature consists in clarifying the features of Smart Grid and EnergyTech in the sustainable development of the energy economy in “smart” regions and increasing their environmental friendliness. Unlike Chen et al. (2022), Elnour et al. (2022), Kabeyi and Olanrewaju (2022), Kamruzzaman and Alruwaili (2022), Nasir et al. (2022), it has been proven that “smart” technologies contribute much more to the sustainable and environmental development of the energy economy (than previously supposed in the literature), providing not only environmental but also socio-economic benefits. Owing to “green” energy, the “smart” region becomes a favorable socio-economic environment for responsible production and consumption (implementation of SDG 12), as well as sustainable human settlement (implementation of SDG 11). The scientific value of the conclusion is that it has expanded the relationship of SDG 7 with other SDGs.

Unlike Ahmad et al. (2022), Li et al. (2022), Rezaei et al. (2022), it is justified that the management of “smart” technologies for sustainable and environmentally friendly development of the energy economy should be carried out at the regional level. Public management in the “smart” region harmoniously complements the corporate management of energy companies, and public-private partnership also demonstrates high efficiency. The scientific value of the conclusion is that he expanded the idea of the boundaries of EnergyTech and Smart Grid management, transferring them from the micro level to the meso level.

Conclusion

Thus, the results of the study on the example of the “smart” regions of Russia demonstrated that the EnergyTech and Smart Grid markets have not only technological but also territorial boundaries. The theoretical significance of the results obtained in the course of the study is in the fact that they revealed the features of the region as an economic system in which there is a sustainable and environmentally friendly development of the energy economy on the basis of “smart” eco-innovations. If “smart” technologies provide advantages at the level of energy companies, this does not mean advantages on the scale of the region’s economy.

For example, in the “smart” regions of Russia, RFID has proven to be the most universal technology for sustainable and environmentally friendly development of the energy economy. The advantages of electronic sales have proven to be reduced, and ERP systems have been contradictory. This requires more flexible management of “smart” technologies in the energy sector at the regional level compared to the level of energy companies. The practical significance of the results obtained is that the applied case experience of the “smart” regions of Russia, considered in the article, can be useful for the rest of the Russian regions and regions of other countries of the world in terms of sustainable and environmental development of the energy economy.

Research limitations are connected to the fact that despite this paper’s substantiating the expedience and providing successful examples from Russia’s leading experience, it does not cover the perspectives of the full-scale transition to a smart regional economy. As the performed case study showed, even in such progressive digital economy with the developed oil and energy complex, the sustainable and environmental development of the energy economy has been achieved only in separate smart regions. Future studied should form the scientific and methodological framework for the systemic sustainable and environmental development of the energy economy in smart regions at the scale of the regional economy on the whole.

Author Contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

Ahmad, T., Madonski, R., Zhang, D., Huang, C., and Mujeeb, A. (2022). Data-driven Probabilistic Machine Learning in Sustainable Smart Energy/smart Energy Systems: Key Developments, Challenges, and Future Research Opportunities in the Context of Smart Grid Paradigm. Renew. Sustain. Energy Rev. 160, 112128. doi:10.1016/j.rser.2022.112128

CrossRef Full Text | Google Scholar

Ajiboye, A. A., Popoola, S. I., Adewuyi, O. B., Atayero, A. A., and Adebisi, B. (2022). Data-driven Optimal Planning for Hybrid Renewable Energy System Management in Smart Campus: A Case Study. Sustain. Energy Technol. Assessments 52, 102189. doi:10.1016/j.seta.2022.102189

CrossRef Full Text | Google Scholar

Ali, S., Ullah, K., Hafeez, G., Khan, I., Albogamy, F. R., and Haider, S. I. (2022). Solving Day-Ahead Scheduling Problem with Multi-Objective Energy Optimization for Demand Side Management in Smart Grid. Eng. Sci. Technol. Int. J. 36, 101135. doi:10.1016/j.jestch.2022.101135

CrossRef Full Text | Google Scholar

All Events (2022). Smart City & Region: Digital Technologies on a Path to “Smart Country”. Ekaterinburg. Available at: https://all-events.ru/events/smart-city-region-tsifrovye-tekhnologii-na-puti-k-umnoy-strane-ekaterinburg/(Access date: 21.05.2022).

Google Scholar

Bellocchi, S., De Iulio, R., Guidi, G., Manno, M., Nastasi, B., Noussan, M., et al. (2020). Analysis of Smart Energy System Approach in Local Alpine Regions - A Case Study in Northern Italy. Energy 202, 117748. doi:10.1016/j.energy.2020.117748

CrossRef Full Text | Google Scholar

Chen, J., Su, F., Jain, V., Salman, A., Tabash, M. I., Haddad, A. M., et al. (2022). Does Renewable Energy Matter to Achieve Sustainable Development Goals? the Impact of Renewable Energy Strategies on Sustainable Economic Growth. Front. Energy Res. 10, 829252. doi:10.3389/fenrg.2022.829252

CrossRef Full Text | Google Scholar

CNews (2022). Smart Energetics” with Blockchain Computing in Russia. Available at: https://www.cnews.ru/news/top/2019-03-20_vlasti_stroyat_v_rossiyu_umnuyu_energetiku_s_raschetami (Access date: 01.05.2022).

Google Scholar

Com News (2022). How Smart Our Smart Cities Are. Available at: https://www.comnews.ru/digital-economy/content/113663/2018-06-25/naskolko-umny-nashi-umnye-goroda (Access date: 21.05.2022).

Google Scholar

Cortese, T. T. P., Almeida, J. F. S. d., Batista, G. Q., Storopoli, J. E., Liu, A., and Yigitcanlar, T. (2022). Understanding Sustainable Energy in the Context of Smart Cities: A Prisma Review. Energies 15 (7), 2382. doi:10.3390/en15072382

CrossRef Full Text | Google Scholar

Dominković, D. F., Dobravec, V., Jiang, Y., Nielsen, P. S., and Krajačić, G. (2018). Modelling Smart Energy Systems in Tropical Regions. Energy 155, 592–609. doi:10.1016/j.energy.2018.05.007

CrossRef Full Text | Google Scholar

Elnour, M., Fadli, F., Himeur, Y., Petri, I., Rezgui, Y., Meskin, N., et al. (2022). Performance and Energy Optimization of Building Automation and Management Systems: Towards Smart Sustainable Carbon-Neutral Sports Facilities. Renew. Sustain. Energy Rev. 162, 112401. doi:10.1016/j.rser.2022.112401

CrossRef Full Text | Google Scholar

Green Patrol (2022). National Environmental Rating of Russia by Region – Deputy 2021-2022. Available at: https://greenpatrol.ru/ru/stranica-dlya-obshchego-reytinga/ekologicheskiy-reyting-subektov-rf?tid=449&order=field_prom&sort=desc (Access date: 01.05.2022).

Google Scholar

Guo, M., Xia, M., and Chen, Q. (2022). A Review of Regional Energy Internet in Smart City from the Perspective of Energy Community. Energy Rep. 8, 161–182. doi:10.1016/j.egyr.2021.11.286

CrossRef Full Text | Google Scholar

Kabeyi, M. J. B., and Olanrewaju, O. A. (2022). Sustainable Energy Transition for Renewable and Low Carbon Grid Electricity Generation and Supply. Front. Energy Res. 9, 743114. doi:10.3389/fenrg.2021.743114

CrossRef Full Text | Google Scholar

Kamruzzaman, M. M., and Alruwaili, O. (2022). Energy Efficient Sustainable Wireless Body Area Network Design Using Network Optimization with Smart Grid and Renewable Energy Systems. Energy Rep. 8, 3780–3788. doi:10.1016/j.egyr.2022.03.006

CrossRef Full Text | Google Scholar

Kleineidam, G., Krasser, M., and Reischböck, M. (2016). The Cellular Approach: Smart Energy Region Wunsiedel. Testbed for Smart Grid, Smart Metering and Smart Home Solutions. Electr. Eng. 98, 335–340. doi:10.1007/s00202-016-0417-y

CrossRef Full Text | Google Scholar

Li, B. (2022). Effective Energy Utilization through Economic Development for Sustainable Management in Smart Cities. Energy Rep. 8, 4975–4987. doi:10.1016/j.egyr.2022.02.303

CrossRef Full Text | Google Scholar

Li, Q., Liu, Z., Xiao, Y., Zhao, P., Zhao, Y., Yang, T., et al. (2022). An Intelligent Optimization Method for Preliminary Design of Lead-Bismuth Reactor Core Based on Kriging Surrogate Model. Front. Energy Res. 10, 849229. doi:10.3389/fenrg.2022.849229

CrossRef Full Text | Google Scholar

Mahmoud, F. S., Diab, A. A. Z., Ali, Z. M., El-Sayed, A.-H. M., Alquthami, T., Ahmed, M., et al. (2022). Optimal Sizing of Smart Hybrid Renewable Energy System Using Different Optimization Algorithms. Energy Rep. 8, 4935–4956. doi:10.1016/j.egyr.2022.03.197

CrossRef Full Text | Google Scholar

Matsunaga, F., Zytkowski, V., Valle, P., and Deschamps, F. (2022). Optimization of Energy Efficiency in Smart Manufacturing through the Application of Cyber-Physical Systems and Industry 4.0 Technologies. J. Energy Resour. Technol. Trans. ASME 144 (10), 102104. doi:10.1115/1.4053868

CrossRef Full Text | Google Scholar

Ministry of Finance of Russia, Rosstat, HSE (2022). Indicators of the digital economy 2021: a statistical collection. The main indicators of the development of the digital economy in the subjects of the Russian Federation. Available at: https://publications.hse.ru/pubs/share/direct/491154070.pdf (Access date: 01.05.2022).

Google Scholar

Moscow City Government (2022). Strategy” Moscow – “smart city – 2030”. Available at: https://www.mos.ru/upload/alerts/files/3_Tekststrategii.pdf (Access date: 21.05.2022).

Google Scholar

Nasir, M. H., Wen, J., Nassani, A. A., Haffar, M., Igharo, A. E., Musibau, H. O., et al. (2022). Energy Security and Energy Poverty in Emerging Economies: A Step Towards Sustainable Energy Efficiency. Front. Energy Res. 10, 834614. doi:10.3389/fenrg.2022.834614

CrossRef Full Text | Google Scholar

Ortiz Cebolla, R., and Navas, C. (2019). Supporting hydrogen technologies deployment in EU regions and Member States: The Smart Specialisation Platform on Energy (S3PEnergy). Int. J. Hydrogen Energy 44, 19067–19079. doi:10.1016/j.ijhydene.2018.05.041

CrossRef Full Text | Google Scholar

Popkova, E., Bogoviz, A. V., and Sergi, B. S. (20212021). Towards digital society management and 'capitalism 4.0' in contemporary Russia. Humanit Soc. Sci. Commun. 8 (1), 77. doi:10.1057/s41599-021-00743-8

CrossRef Full Text | Google Scholar

Popkova, E. G. (2022). International trade in the era of neo-globalization: disintegration vs digital partnership. Res. Econ. Anthropol. 42, 7–13. doi:10.1108/S0190-128120220000042001

CrossRef Full Text | Google Scholar

Qu, D., Li, J., and Yong, M. (2022). Real-time pricing for smart grid considering energy complementarity of a microgrid interacting with the large grid. Int. J. Electr. Power & Energy Syst. 141, 108217. doi:10.1016/j.ijepes.2022.108217

CrossRef Full Text | Google Scholar

Ram, M., Bogdanov, D., Aghahosseini, A., Gulagi, A., Oyewo, A. S., Mensah, T. N. O., et al. (2022). Global energy transition to 100% renewables by 2050: Not fiction, but much needed impetus for developing economies to leapfrog into a sustainable future. Energy 246, 123419. doi:10.1016/j.energy.2022.123419

CrossRef Full Text | Google Scholar

RBC (2022). Smart technologies in energetics based on the IoT World Summit Russia. Available at: https://rt.rbc.ru/tatarstan/freenews/5b9f49e69a794786292f71bb (Access date: 01.05.2022).

Google Scholar

Rezaei, N., Tarimoradi, H., and Deihimi, M. (2022). A coordinated management scheme for power quality and load consumption improvement in smart grids based on sustainable energy exchange based model. Sustain. Energy Technol. Assessments 51, 101903. doi:10.1016/j.seta.2021.101903

CrossRef Full Text | Google Scholar

Rosstat (2022). Regions of Russia. Socio-economic indicators - 2020. Available at: https://gks.ru/bgd/regl/b20_14p/Main.htm (Access date: 01.05.2022).

Google Scholar

Russian Union of Industrialists and Entrepreneurs (2022). Session "Digitalization of Modern Energy: from local solutions to industry transformation. Available at: https://rspp.ru/events/news/sostoyalas-sessiya-tsifrovizatsiya-sovremennoy-energetiki-ot-lokalnykh-resheniy-k-transformatsiii-ot/(Access date: 01.05.2022).

Google Scholar

Sharma, P., Reddy Salkuti, S., and Kim, S.-C. (2022). Advancements in energy storage technologies for smart grid development. Ijece 12 (4), 3421–3429. doi:10.11591/ijece.v12i4.pp3421-3429

CrossRef Full Text | Google Scholar

Systems and Technologies (2022). Smart Grid smart grids are the future of Russian energy. Available at: http://www.sicon.ru/about/articles/?base=&news=16 (Access date: 01.05.2022).

Google Scholar

Xu, C. (2022). Designing and planning of energy efficient sustainable cities and societies: A smart energy. Trans. Emerg. Tel Tech. 33 (2), e4460. doi:10.1002/ett.4460

CrossRef Full Text | Google Scholar

Zaidan, E., Ghofrani, A., Abulibdeh, A., and Jafari, M. (2022). Accelerating the Change to Smart Societies- a Strategic Knowledge-Based Framework for Smart Energy Transition of Urban Communities. Front. Energy Res. 10, 852092. doi:10.3389/fenrg.2022.852092

CrossRef Full Text | Google Scholar

Zhao, J., Sinha, A., Inuwa, N., Wang, Y., Murshed, M., and Abbasi, K. R. (2022). Does structural transformation in economy impact inequality in renewable energy productivity? Implications for sustainable development. Renew. Energy 189, 853–864. doi:10.1016/j.renene.2022.03.050

CrossRef Full Text | Google Scholar