Impacts of Digital Technologies for the Provision of Energy Market Services on the Safety of Residents and Consumers
Abstract
:1. Introduction
2. Literature Review
3. Digitalization and Residential Emission Reduction
4. Impacts of Digital Technologies on Energy Consumption
5. Materials and Methods
6. Empirical Model: Smart Home Technologies and Market Services
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Heffron, R.; Körner, M.-F.; Wagner, J.; Weibelzahl, M.; Fridgen, G. Industrial demand-side flexibility: A key element of a just energy transition and industrial development. Appl. Energy 2020, 269, 115026. [Google Scholar] [CrossRef]
- Strielkowski, W.; Dvořák, M.; Rovný, P.; Tarkhanova, E.; Baburina, N. 5G Wireless Networks in the Future Renewable Energy Systems. Front. Energy Res. 2021, 9, 714803. [Google Scholar] [CrossRef]
- Sarker, E.; Halder, P.; Seyedmahmoudian, M.; Jamei, E.; Horan, B.; Mekhilef, S.; Stojcevski, A. Progress on the demand side management in smart grid and optimization approaches. Int. J. Energy Res. 2021, 45, 36–64. [Google Scholar] [CrossRef]
- Sovacool, B.K.; Del Rio, D.D.F. Smart home technologies in Europe: A critical review of concepts, benefits, risks and policies. Renew. Sustain. Energy Rev. 2020, 120, 109663. [Google Scholar] [CrossRef]
- Rausser, G.; Strielkowski, W.; Štreimikienė, D. Smart meters and household electricity consumption: A case study in Ireland. Energy Environ. 2018, 29, 131–146. [Google Scholar] [CrossRef] [Green Version]
- IEA. Energy Efficiency. Available online: https://www.iea.org/reports/energy-efficiency-2018 (accessed on 28 January 2022).
- Bañales, S. The enabling impact of digital technologies on distributed energy resources integration. J. Renew. Sustain. Energy 2020, 12, 045301. [Google Scholar] [CrossRef]
- Siano, P.; De Marco, G.; Rolan, A.; Loia, V. A Survey and Evaluation of the Potentials of Distributed Ledger Technology for Peer-to-Peer Transactive Energy Exchanges in Local Energy Markets. IEEE Syst. J. 2019, 13, 3454–3466. [Google Scholar] [CrossRef]
- Heidarinejad, M.; Dalgo, D.A.; Mattise, N.W.; Srebric, J. Personalized cooling as an energy efficiency technology for city energy footprint reduction. J. Clean. Prod. 2018, 171, 491–505. [Google Scholar] [CrossRef]
- Strielkowski, W.; Veinbender, T.; Tvaronavičienė, M.; Lace, N. Economic efficiency and energy security of smart cities. Econ. Res. Ekon. Istraživanja 2020, 33, 788–803. [Google Scholar] [CrossRef]
- Kohlhepp, P.; Harb, H.; Wolisz, H.; Waczowicz, S.; Müller, D.; Hagenmeyer, V. Large-scale grid integration of residential thermal energy storages as demand-side flexibility resource: A review of international field studies. Renew. Sustain. Energy Rev. 2019, 101, 527–547. [Google Scholar] [CrossRef]
- Moşteanu, N.R. Challenges for organizational structure and design as a result of digitalization and cybersecurity. J. Bus. Retail Manag. Res. 2020, 11, 278–286. [Google Scholar] [CrossRef]
- IEA. Digitalization and Energy. 2017. Available online: https://www.iea.org/reports/digitalisation-and-energy (accessed on 29 January 2022).
- Foroudi, P.; Gupta, S.; Sivarajah, U.; Broderick, A. Investigating the effects of smart technology on customer dynamics and customer experience. Comput. Hum. Behav. 2018, 80, 271–282. [Google Scholar] [CrossRef]
- Herrero, S.T.; Nicholls, L.; Strengers, Y. Smart home technologies in everyday life: Do they address key energy challenges in households? Curr. Opin. Environ. Sustain. 2018, 31, 65–70. [Google Scholar] [CrossRef]
- Khan, N.; Baloch, M.A.; Saud, S.; Fatima, T. The effect of ICT on CO2 emissions in emerging economies: Does the level of income matters? Environ. Sci. Pollut. Res. 2018, 25, 22850–22860. [Google Scholar] [CrossRef]
- Borowski, P.F.; Patuk, I.; Bandala, E.R. Innovative Industrial Use of Bamboo as Key “Green” Material. Sustainability 2022, 14, 1955. [Google Scholar] [CrossRef]
- Manandhar, R.; Kim, J.-H.; Kim, J.-T. Environmental, social and economic sustainability of bamboo and bamboo-based construction materials in buildings. J. Asian Arch. Build. Eng. 2019, 18, 49–59. [Google Scholar] [CrossRef]
- Chaowana, K.; Wisadsatorn, S.; Chaowana, P. Bamboo as a Sustainable Building Material—Culm Characteristics and Properties. Sustainability 2021, 13, 7376. [Google Scholar] [CrossRef]
- Strielkowski, W. Social and Economic Implications for the Smart Grids of the Future. Econ. Sociol. 2017, 10, 310–318. [Google Scholar] [CrossRef] [Green Version]
- Prieto González, L.; Fensel, A.; Gómez Berbís, J.M.; Popa, A.; de Amescua Seco, A. A Survey on Energy Efficiency in Smart Homes and Smart Grids. Energies 2021, 14, 7273. [Google Scholar] [CrossRef]
- Inderwildi, O.; Zhang, C.; Wang, X.; Kraft, M. The impact of intelligent cyber-physical systems on the decarbonization of energy. Energy Environ. Sci. 2020, 13, 744–771. [Google Scholar] [CrossRef]
- Monteiro, V.; Afonso, J.A.; Ferreira, J.C.; Afonso, J.L. Vehicle Electrification: New Challenges and Opportunities for Smart Grids. Energies 2019, 12, 118. [Google Scholar] [CrossRef] [Green Version]
- Strielkowski, W. Social Impacts of Smart Grids: The Future of Smart Grids and Energy Market Design, 1st ed.; Elsevier: London, UK, 2019. [Google Scholar]
- Kakran, S.; Chanana, S. Smart operations of smart grids integrated with distributed generation: A review. Renew. Sustain. Energy Rev. 2018, 81, 524–535. [Google Scholar] [CrossRef]
- Ghorbanian, M.; Dolatabadi, S.H.; Masjedi, M.; Siano, P. Communication in Smart Grids: A Comprehensive Review on the Existing and Future Communication and Information Infrastructures. IEEE Syst. J. 2019, 13, 4001–4014. [Google Scholar] [CrossRef]
- Kim, S.C.; Ray, P.; Reddy, S.S. Features of Smart Grid Technologies: An Overview. ECTI Trans. Electr. Eng. Electron. Commun. 2019, 17, 169–180. [Google Scholar] [CrossRef] [Green Version]
- Vaka, M.; Walvekar, R.; Rasheed, A.K.; Khalid, M. A review on Malaysia’s solar energy pathway towards carbon-neutral Malaysia beyond COVID’19 pandemic. J. Clean. Prod. 2020, 273, 122834. [Google Scholar] [CrossRef] [PubMed]
- Zhou, X.; Zhou, D.; Wang, Q.; Su, B. How information and communication technology drives carbon emissions: A sector-level analysis for China. Energy Econ. 2019, 81, 380–392. [Google Scholar] [CrossRef]
- Williams, L.; Sovacool, B.K.; Foxon, T.J. The energy use implications of 5G: Reviewing whole network operational energy, embodied energy, and indirect effects. Renew. Sustain. Energy Rev. 2022, 157, 112033. [Google Scholar] [CrossRef]
- Strielkowski, W.; Streimikiene, D.; Fomina, A.; Semenova, E. Internet of Energy (IoE) and High-Renewables Electricity System Market Design. Energies 2019, 12, 4790. [Google Scholar] [CrossRef] [Green Version]
- Powroźnik, P.; Szcześniak, P.; Piotrowski, K. Elastic Energy Management Algorithm Using IoT Technology for Devices with Smart Appliance Functionality for Applications in Smart-Grid. Energies 2022, 15, 109. [Google Scholar] [CrossRef]
- Li, W.; Logenthiran, T.; Phan, V.-T.; Woo, W.L. A Novel Smart Energy Theft System (SETS) for IoT-Based Smart Home. IEEE Internet Things J. 2019, 6, 5531–5539. [Google Scholar] [CrossRef]
- Park, C.; Heo, W. Review of the changing electricity industry value chain in the ICT convergence era. J. Clean. Prod. 2020, 258, 120743. [Google Scholar] [CrossRef]
- Lange, S.; Pohl, J.; Santarius, T. Digitalization and energy consumption. Does ICT reduce energy demand? Ecol. Econ. 2020, 176, 106760. [Google Scholar] [CrossRef]
- Masanet, E.; Shehabi, A.; Lei, N.; Smith, S.; Koomey, J. Recalibrating global data center energy-use estimates. Science 2020, 367, 984–986. [Google Scholar] [CrossRef] [PubMed]
- Malmodin, J.; Lundén, D. The Energy and Carbon Footprint of the Global ICT and E&M Sectors 2010–2015. Sustainability 2018, 10, 3027. [Google Scholar] [CrossRef] [Green Version]
- E-redes, Projeto InovGrid. Available online: https://www.e-redes.pt/en/power-grids-future/intelligent-networks/project (accessed on 4 February 2022).
- Abubakr, M.; Abbas, A.T.; Tomaz, I.; Soliman, M.S.; Luqman, M.; Hegab, H. Sustainable and Smart Manufacturing: An Integrated Approach. Sustainability 2020, 12, 2280. [Google Scholar] [CrossRef] [Green Version]
- U.S. Department of Energy. Electricity Industry Primer. Available online: https://www.energy.gov/sites/prod/files/2015/12/f28/united-states-electricity-industry-primer.pdf (accessed on 4 February 2022).
- Millward-Hopkins, J.; Steinberger, J.K.; Rao, N.D.; Oswald, Y. Providing decent living with minimum energy: A global scenario. Glob. Environ. Chang. 2020, 65, 102168. [Google Scholar] [CrossRef]
- Castán Broto, V.; Kirshner, J. Energy access is needed to maintain health during pandemics. Nat. Energy 2020, 5, 419–421. [Google Scholar] [CrossRef]
- Ahmad, T.; Zhang, D. Using the internet of things in smart energy systems and networks. Sustain. Cities Soc. 2021, 68, 102783. [Google Scholar] [CrossRef]
- Sabory, N.R.; Senjyu, T.; Danish, M.S.S.; Ahmadi, M.; Zaheb, H.; Halim, M. A Framework for Integration of Smart and Sustainable Energy Systems in Urban Planning Processes of Low-Income Developing Countries: Afghanistan Case. Sustainability 2021, 13, 8428. [Google Scholar] [CrossRef]
- Asaithambi, S.P.R.; Venkatraman, S.; Venkatraman, R. Big Data and Personalisation for Non-Intrusive Smart Home Automation. Big Data Cogn. Comput. 2021, 5, 6. [Google Scholar] [CrossRef]
- Stolojescu-Crisan, C.; Crisan, C.; Butunoi, B.-P. An IoT-Based Smart Home Automation System. Sensors 2021, 21, 3784. [Google Scholar] [CrossRef]
- Aliero, M.S.; Qureshi, K.N.; Pasha, M.F.; Jeon, G. Smart Home Energy Management Systems in Internet of Things networks for green cities demands and services. Environ. Technol. Innov. 2021, 22, 101443. [Google Scholar] [CrossRef]
- Wang, J.; Spicher, N.; Warnecke, J.M.; Haghi, M.; Schwartze, J.; Deserno, T.M. Unobtrusive Health Monitoring in Private Spaces: The Smart Home. Sensors 2021, 21, 864. [Google Scholar] [CrossRef] [PubMed]
- Isnen, M.; Kurniawan, S.; Garcia-Palacios, E. A-SEM: An adaptive smart energy management testbed for shiftable loads optimisation in the smart home. Measurement 2020, 152, 107285. [Google Scholar] [CrossRef]
- Gram-Hanssen, K.; Darby, S.J. “Home is where the smart is?” Evaluating smart home research and approaches against the concept of home. Energy Res. Soc. Sci. 2018, 37, 94–101. [Google Scholar] [CrossRef]
- Bibri, S.E.; Krogstie, J. Environmentally data-driven smart sustainable cities: Applied innovative solutions for energy efficiency, pollution reduction, and urban metabolism. Energy Inform. 2020, 3, 1–59. [Google Scholar] [CrossRef]
- Jain, M.; Siva, V.; Hoppe, T.; Bressers, H. Assessing governance of low energy green building innovation in the building sector: Insights from Singapore and Delhi. Energy Policy 2020, 145, 111752. [Google Scholar] [CrossRef]
- Statista. Smart Home. 2022. Available online: https://www.statista.com/outlook/dmo/smart-home/worldwide (accessed on 15 February 2022).
- Lobaccaro, G.; Carlucci, S.; Löfström, E. A Review of Systems and Technologies for Smart Homes and Smart Grids. Energies 2016, 9, 348. [Google Scholar] [CrossRef] [Green Version]
- Blind, K.; Böhm, M.; Grzegorzewska, P.; Katz, A.; Muto, S.; Pätsch, S.; Schubert, T. The Impact of Open Source Software and Hardware on Technological Independence, Competitiveness and Innovation in the EU Economy; Final Study Report; European Union: Brussels, Belgium, 2021; Available online: https://www.ospi.es/export/sites/ospi/documents/documentos/CNECT_OpenSourceStudy_EN_28_6_2021_LMBhSihnCeC7JEDsHXkK1JlZ0_79021_compressed.pdf (accessed on 17 February 2022).
- Reyes-Campos, J.; Alor-Hernández, G.; Machorro-Cano, I.; Olmedo-Aguirre, J.O.; Sánchez-Cervantes, J.L.; Rodríguez-Mazahua, L. Discovery of Resident Behavior Patterns Using Machine Learning Techniques and IoT Paradigm. Mathematics 2021, 9, 219. [Google Scholar] [CrossRef]
- Iacoviello, M.; Pavan, M. Housing and debt over the life cycle and over the business cycle. J. Monet. Econ. 2021, 60, 221–238. [Google Scholar] [CrossRef] [Green Version]
- Goldstein, B.; Gounaridis, D.; Newell, J.P. The carbon footprint of household energy use in the United States. Proc. Natl. Acad. Sci. USA 2020, 117, 19122–19130. [Google Scholar] [CrossRef] [PubMed]
- Perez, A.J.; Zeadally, S.; Cochran, J. A review and an empirical analysis of privacy policy and notices for consumer Internet of things. Secur. Priv. 2018, 1, e15. [Google Scholar] [CrossRef] [Green Version]
- Sun, Q.; Yu, W.; Kochurov, N.; Hao, Q.; Hu, F. A Multi-Agent-Based Intelligent Sensor and Actuator Network Design for Smart House and Home Automation. J. Sens. Actuator Netw. 2013, 2, 557–588. [Google Scholar] [CrossRef]
- Darby, S. The Effectiveness of Feedback on Energy Consumption. A Review for DEFRA of the Literature on Metering, Billing and Direct Displays; Environmental Change Institute; University of Oxford: Oxford, UK, 2006; Available online: https://www.eci.ox.ac.uk/research/energy/downloads/smart-metering-report.pdf (accessed on 17 February 2022).
- Machorro-Cano, I.; Alor-Hernández, G.; Paredes-Valverde, M.A.; Rodríguez-Mazahua, L.; Sánchez-Cervantes, J.L.; Olmedo-Aguirre, J.O. HEMS-IoT: A Big Data and Machine Learning-Based Smart Home System for Energy Saving. Energies 2020, 13, 1097. [Google Scholar] [CrossRef] [Green Version]
- O’brolcháin, F.; Gordijn, B. Privacy challenges in smart homes for people with dementia and people with intellectual disabilities. Ethic Inf. Technol. 2019, 21, 253–265. [Google Scholar] [CrossRef]
- Pal, D.; Funilkul, S.; Charoenkitkarn, N.; Kanthamanon, P. Internet-of-Things and Smart Homes for Elderly Healthcare: An End User Perspective. IEEE Access 2018, 6, 10483–10496. [Google Scholar] [CrossRef]
- Nicholls, L.; Strengers, Y. Robotic vacuum cleaners save energy? Raising cleanliness conventions and energy demand in Australian households with smart home technologies. Energy Res. Soc. Sci. 2019, 50, 73–81. [Google Scholar] [CrossRef]
- Nelson, S.; Allwood, J.M. Technology or behaviour? Balanced disruption in the race to net zero emissions. Energy Res. Soc. Sci. 2021, 78, 102124. [Google Scholar] [CrossRef]
- European Commission. National Energy Efficiency Plan 2020. Available online: https://ec.europa.eu/energy/sites/default/files/si_neeap_2017_en.pdf (accessed on 20 January 2022).
- McIlwaine, N.; Foley, A.M.; Morrow, D.J.; Al Kez, D.; Zhang, C.; Lu, X.; Best, R.J. A state-of-the-art techno-economic review of distributed and embedded energy storage for energy systems. Energy 2021, 229, 120461. [Google Scholar] [CrossRef]
- El-Azab, R. Smart homes: Potentials and challenges. Clean Energy 2021, 5, 302–315. [Google Scholar] [CrossRef]
- Kim, H.; Choi, H.; Kang, H.; An, J.; Yeom, S.; Hong, T. A systematic review of the smart energy conservation system: From smart homes to sustainable smart cities. Renew. Sustain. Energy Rev. 2021, 140, 110755. [Google Scholar] [CrossRef]
- Strengers, Y.; Nicholls, L. Convenience and energy consumption in the smart home of the future: Industry visions from Australia and beyond. Energy Res. Soc. Sci. 2017, 32, 86–93. [Google Scholar] [CrossRef]
- Neshenko, N.; Bou-Harb, E.; Crichigno, J.; Kaddoum, G.; Ghani, N. Demystifying IoT Security: An Exhaustive Survey on IoT Vulnerabilities and a First Empirical Look on Internet-Scale IoT Exploitations. IEEE Commun. Surv. Tutor. 2019, 21, 2702–2733. [Google Scholar] [CrossRef]
- ACEEE. Energy Impacts of Smart Home Technologies. Available online: https://www.aceee.org/research-report/a1801 (accessed on 21 January 2022).
- Aldossari, M.Q.; Sidorova, A. Consumer Acceptance of Internet of Things (IoT): Smart Home Context. J. Comput. Inf. Syst. 2020, 60, 507–517. [Google Scholar] [CrossRef]
- Marikyan, D.; Papagiannidis, S.; Alamanos, E. A systematic review of the smart home literature: A user perspective. Technol. Forecast. Soc. Chang. 2019, 138, 139–154. [Google Scholar] [CrossRef]
- Cheng, P.; Mugge, R.; De Bont, C. “Smart home system is like a mother”: The potential and risks of product metaphors to influence consumers’ comprehension of really new products (RNPs). Int. J. Des. 2019, 13, 1–19. [Google Scholar] [CrossRef]
- Murshed, M. An empirical analysis of the non-linear impacts of ICT-trade openness on renewable energy transition, energy efficiency, clean cooking fuel access and environmental sustainability in South Asia. Environ. Sci. Pollut. Res. 2020, 27, 36254–36281. [Google Scholar] [CrossRef]
- Santiago, I.; Moreno-Munoz, A.; Quintero-Jiménez, P.; Garcia-Torres, F.; Gonzalez-Redondo, M.J. Electricity demand during pandemic times: The case of the COVID-19 in Spain. Energy Policy 2021, 148, 111964. [Google Scholar] [CrossRef]
- Strielkowski, W.; Firsova, I.; Lukashenko, I.; Raudeliūnienė, J.; Tvaronavičienė, M. Effective Management of Energy Consumption during the COVID-19 Pandemic: The Role of ICT Solutions. Energies 2021, 14, 893. [Google Scholar] [CrossRef]
- Chen, C.-F.; Nelson, H.; Xu, X.; Bonilla, G.; Jones, N. Beyond technology adoption: Examining home energy management systems, energy burdens and climate change perceptions during COVID-19 pandemic. Renew. Sustain. Energy Rev. 2021, 145, 111066. [Google Scholar] [CrossRef]
- Borowski, P.F. Innovative Processes in Managing an Enterprise from the Energy and Food Sector in the Era of Industry 4.0. Processes 2021, 9, 381. [Google Scholar] [CrossRef]
- Honarmand, M.E.; Hosseinnezhad, V.; Hayes, B.; Shafie-Khah, M.; Siano, P. An Overview of Demand Response: From its Origins to the Smart Energy Community. IEEE Access 2021, 9, 96851–96876. [Google Scholar] [CrossRef]
- Rezaei, M.; Dampage, U.; Das, B.K.; Nasif, O.; Borowski, P.F.; Mohamed, M.A. Investigating the Impact of Economic Uncertainty on Optimal Sizing of Grid-Independent Hybrid Renewable Energy Systems. Processes 2021, 9, 1468. [Google Scholar] [CrossRef]
- Pawlak, J.; Imani, A.F.; Sivakumar, A. How do household activities drive electricity demand? Applying activity-based modelling in the context of the United Kingdom. Energy Res. Soc. Sci. 2021, 82, 102318. [Google Scholar] [CrossRef]
- Borowski, P.F. New Technologies and Innovative Solutions in the Development Strategies of Energy Enterprises. HighTech Innov. J. 2020, 1, 39–58. [Google Scholar] [CrossRef]
- Caccavale, M. The Impact of The Digital Revolution on The Smart Home Industry. Available online: https://www.forbes.com/sites/forbesagencycouncil/2018/09/24/the-impact-of-the-digital-revolution-on-the-smart-home-industry/?sh=6625d9f73c76 (accessed on 1 February 2022).
- Zdnet. 80 Million US Households Intend to Purchase a New Type of Smart Home Device. 2020. Available online: https://www.zdnet.com/article/80-million-us-households-intend-to-purchase-a-new-type-of-smart-home-device (accessed on 26 January 2022).
- Korneeva, E.; Olinder, N.; Strielkowski, W. Consumer Attitudes to the Smart Home Technologies and the Internet of Things (IoT). Energies 2021, 14, 7913. [Google Scholar] [CrossRef]
- Shouran, Z.; Ashari, A.; Priyambodo, T. Internet of Things (IoT) of Smart Home: Privacy and Security. Int. J. Comput. Appl. 2019, 182, 3–8. [Google Scholar] [CrossRef]
- Yang, H.; Lee, H.; Zo, H. User acceptance of smart home services: An extension of the theory of planned behavior. Ind. Manag. Data Syst. 2017, 117, 68–89. [Google Scholar] [CrossRef]
Country of Origin | |||||
---|---|---|---|---|---|
Smart Homes Attitudes | Russian Federation | Czech Republic | Germany | Poland | Slovakia |
Positive attitude | 44% | 58% | 75% | 62% | 53% |
Negative attitude | 29% | 12% | 14% | 18% | 14% |
Do not know | 27% | 30% | 11% | 20% | 33% |
Familiarity with the Smart Home Appliances | |||||
Smartphone | 95% | 90% | 96% | 95% | 89% |
Smart TV | 32% | 54% | 58% | 55% | 50% |
Voice assistants (e.g., Alexa) | 25% | 45% | 51% | 48% | 40% |
Smart lights | 13% | 34% | 46% | 41% | 29% |
Motion detectors | 10% | 30% | 38% | 34% | 23% |
Smart cameras | 48% | 21% | 26% | 24% | 17% |
Smart thermostat | 11% | 13% | 24% | 16% | 12% |
Smart window blinds | 4% | 11% | 19% | 17% | 10% |
N | 152 | 86 | 133 | 102 | 50 |
Stage 1 | Stage 2 | Stage 3 | Stage 4 | |||||
---|---|---|---|---|---|---|---|---|
Variable | B(SE) | Beta | B(SE) | B(SE) | B(SE) | Beta | B(SE) | Beta |
Gender | −0.054 (0.095) | −0.034 | 0.041 (0.094) | 0.032 | 0.043 (0.091) | 0.028 | −0.036 (0.085) | 0.013 |
Age | 0.009 (0.006) * | 0.223 * | 0.011 (0.004) ** | 0.237 ** | 0.012 (0.004) ** | 0.247 ** | 0.009 (0.004) ** | 0.121 ** |
Income | 0.063 (0.047) | 0.071 | 0.054 (0.046) | 0.069 | 0.043 (0.044) | 0.054 | 0.063 (0.042) | 0.069 |
Education | −0.007 (0.206) | −0.003 | −0.035 (0.204) | −0.021 | −0.032 (0.101) | −0.011 | 0.079 (0.101) | 0.030 |
Home size | 0.319 (0.098) | 0.229 | 0.322 (0.095) ** | 0.223 * | 0.268 (0.09) * | −0.096 * | 0.208 (0.086) | 0.068 |
Openness to SH | 0.427 (0.089) ** | 0.287 | 0.229 (0.089) | 0.077 | 0.101 (0.086) | 0.066 | ||
SH devices | 0.057 (0.059) | 0.054 | 0.046 (0.056) | 0.043 | 0.031 (0.053) | 0.021 | ||
Trust in SH | 0.076 (0.031) ** | 0.256 ** | 0.049 (0.031) * | 0.096 * | 0.035 (0.029) | 0.065 | ||
Familiarity with SH | −0.102 (0.093) | −0.058 | −0.092 (0.088) | −0.052 | ||||
Personal security | 0.069 (0.043) | 0.085 | 0.040 (0.041) | 0.062 | ||||
Positive attitude to SH | 0.544 (0.071) ** | 0.455 ** | 0.297 (0.075) ** | 0.258 ** | ||||
Negative attitude to SH | 0.024 (0.064) | 0.023 | 0.036 (0.050) | 0.032 | ||||
Tariffs flexibility | 0.520 (0.062) ** | 0.503 ** | ||||||
Utilities proofs | −0.053 (0.043) | −0.064 | ||||||
Sharing personal data | −0.022 (0.054) | −0.025 | ||||||
Constant | 3.979 (0.428) ** | 2.092 (0.556) * | 0.45 (0.656) | 0.327 (0.630) | ||||
R2 = 0.058 ** | R2 = 0.069 *** | R2 = 0.206 *** | R2 = 0.205 *** |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Strielkowski, W.; Kovaleva, O.; Efimtseva, T. Impacts of Digital Technologies for the Provision of Energy Market Services on the Safety of Residents and Consumers. Sustainability 2022, 14, 2934. https://doi.org/10.3390/su14052934
Strielkowski W, Kovaleva O, Efimtseva T. Impacts of Digital Technologies for the Provision of Energy Market Services on the Safety of Residents and Consumers. Sustainability. 2022; 14(5):2934. https://doi.org/10.3390/su14052934
Chicago/Turabian StyleStrielkowski, Wadim, Olga Kovaleva, and Tatiana Efimtseva. 2022. "Impacts of Digital Technologies for the Provision of Energy Market Services on the Safety of Residents and Consumers" Sustainability 14, no. 5: 2934. https://doi.org/10.3390/su14052934