Analysis of the Social Aspect of Smart Cities Development for the Example of Smart Sustainable Buildings
Abstract
:1. Introduction
2. Research Methodology
- Indication of the role of society in the development of smart cities based on critical analysis of the literature;
- Characterization of smart buildings, basic components of smart cities’ technical infrastructure, and their development trends. Based on literature about the research subject and analyzed survey results published in domestic and foreign literature;
- Development of the SWOT matrix to assess the potential of smart sustainable buildings;
- Analysis of factors contained in the SWOT matrix in the social context of smart sustainable buildings;
3. Characteristics of the Smart City and Its Social Dimension
- An active population implementing knowledge-intensive activities or a cluster of such activities;
- Effectively operating entities, institutions, and procedures in the field of knowledge creation, enabling its acquisition, adaptation and development;
- Developed broadband infrastructure, digital spaces, e-services and online knowledge management tools;
- Innovative potential.
- Development of human capital: strengthening the position of the inhabitant (conscious, educated, and participatory), strengthening intellectual capital and generating knowledge;
- Development of social capital: sustainable social development and digital inclusion;
- Behavioral change: a sense of causality and meaning, i.e., the feeling that all inhabitants are co-owners and responsible for their city;
- Social dimension: implementing technology that responds to the needs, skills, and interests of users, respecting their diversity and individuality.
4. Smart Building—An Element of Infrastructure of the Smart City
5. Assessment of the Relevance of Determinants for Smart Sustainable Building Development
5.1. Identification of Factors for Assessing Smart Sustainable Buildings
5.2. Cause-and-Effect Analysis of Smart Sustainable Buildings and Smart Cities
5.2.1. Research Methodology
- Determining a set of influence factors in the proposed study based on the SWOT matrix (Figure 1);
- Development of a direct influence graph. A scale with a parameter value of N = 3 (where: 0—no influence, 1—weak influence, 2—influence, 3—strong influence) was used to assess the “strength” of influence for each factor. The values of the direct influence relations within each pair of factors were determined based on the evaluations of the expert group (Figure 1);
- Based on the relationships determined within the graph, a matrix of direct mutual influence of factors on each other was created;
- Determination of the normalized direct influence matrix . The normalizing number () is taken as the largest of the sum of the rows or columns of the matrix :
- It is also possible to develop an indirect impact matrix :
- Determination of the total influence matrix T:
- Based on the above matrices, the determination of the indices of position and relationship, respectively, express in turn:—tells about the role of a given factor in the process of determining the structure of links between objects, while—expresses the total influence of a given factor on the others.When these values are plotted on a graphical representation, observation is clear as to which factors have the greatest influence on the others. It is also possible to determine the causes and effects of the actions taken (Figure 2)
- Finally, the net impact value is also determined, which tells the factor with the greatest impact on the others considering both the cause-and-effect nature (Table 2):
5.2.2. Study Results and Its Analysis
6. Discussion
7. Conclusions
- Increasing legal provisions conducive to the development of smart building;
- Assistance to public institutions in financing and crediting green building;
- Educating and shaping public awareness;
- Facilitating and supporting society’s creative and innovative activities;
- Support city authorities and urban infrastructure service companies in implementing strategies to optimize the simultaneous implementation of intelligent power supply systems, intelligent logistics, and transport systems, and intelligent systems for rationalizing the use of city and even regional resources.
- The inseparable characteristics of green (sustainable) buildings and intelligent buildings. A building constructed and used in an environmentally friendly manner must be equipped with integrated control systems for its equipment, and is an intelligent sustainable building;
- The social aspect of intelligent sustainable building and its conscious, active beneficiary.
8. Directions for Further Research
Author Contributions
Funding
Conflicts of Interest
References
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STRENGTHS | Weight | WEAKNESSES | Weight |
1.1. Smart building creates a sense of community (applies to public buildings) 1 | 1.87 | 2.1. Higher costs of building and equipping the building | 4.23 |
1.2. Increases worker productivity of those who work inside them 1 | 2.83 | 2.2. Higher costs of renting office space | 4.06 |
1.3. Intelligent construction activates the national economy 1 | 2.94 | 2.3. Management of more complex supply chain | 3.4 |
1.4. Encourages sustainable business practice 1 | 2.98 | 2.4 Increased project complexity | 3.57 |
1.5. Improves the company’s attractiveness in terms of attracting and retaining employees—improving competitiveness 1 | 3.09 | 2.5. Capacity of the local industry to carry out such works | 3.02 |
1.6. Protection of natural resources: reduction of energy consumption during the operation of buildings, reduction of water consumption, reduction of greenhouse gas emissions | 4.14 | 2.6. Increased project risks of numerous kinds (financial, regulatory, safety, design issues, …) | 3.19 |
1.7. Lower operating costs in use and long-term operation | 4.18 | ||
1.8. Increasing indoor air quality and improving the wellbeing of residents 1 | 4.04 | ||
1.9. Possibility to equip smart sustainable buildings (BSS) with technologies and systems to integrate BSS with infrastructure (e.g., technical, social) of smart cities. | 3.62 | ||
OPPORTUNITIES | Weight | THREATS | Weight |
3.1. Increased interest of green/smart buildings | 3.73 | 4.1. Financial possibilities of poorer countries with a weaker economy | 3.4 |
3.2. Increased demand for healthy air 1 | 4.08 | 4.2. Belief that intelligent buildings apply only to buildings with large cubature 1 | 2.34 |
3.3. Increased demand for remote working and other smart city facilities 1 | 3.42 | 4.3. The lack of legal, fiscal incentives taxations, no support from politicians | 3.64 |
3.4. Increase in legal regulations, norms, recommendations, guidelines encouraging smart buildings | 3.19 | 4.4. Low demand for green/smart buildings | 3.02 |
3.5. Right thing to do 1 | 3.15 | 4.5. Lack of specialists in green building 1 | 3.36 |
3.6. Development of automation and building technologies and IT achievements | 3.86 | 4.6. No easily accessible offer | 3.51 |
3.7. Improving the competitiveness of a company located in an intelligent building | 2.94 | 4.7. Lack of knowledge and awareness of current and potential benefits 1 | 3.53 |
3.8 Development of technologies and systems integrating smart building complexes and smart city infrastructures | 3.34 |
Criterion | Netto | ||||
---|---|---|---|---|---|
1.1 | 0.3556 | 0.1067 | 0.4622 | 0.2489 | 0.7111 |
1.2 | 0.2000 | 0.3511 | 0.5511 | −0.1511 | 0.4000 |
1.3 | 0.3200 | 0.0000 | 0.3200 | 0.3200 | 0.6400 |
1.4 | 0.4089 | 0.6444 | 1.0533 | −0.2356 | 0.8178 |
1.5 | 0.0622 | 0.5822 | 0.6444 | −0.5200 | 0.1244 |
1.6 | 0.3911 | 0.3156 | 0.7067 | 0.0756 | 0.7822 |
1.7 | 0.2533 | 0.3422 | 0.5956 | −0.0889 | 0.5067 |
1.8 | 0.4222 | 0.4578 | 0.8800 | −0.0356 | 0.8444 |
1.9 | 0.4000 | 0.1200 | 0.5200 | 0.2800 | 0.8000 |
2.1 | 0.5822 | 0.3111 | 0.8933 | 0.2711 | 1.1644 |
2.2 | 0.0933 | 0.2444 | 0.3378 | −0.1511 | 0.1867 |
2.3 | 0.1156 | 0.2133 | 0.3289 | −0.0978 | 0.2311 |
2.4 | 0.1867 | 0.4311 | 0.6178 | −0.2444 | 0.3733 |
2.5 | 0.0533 | 0.1511 | 0.2044 | −0.0978 | 0.1067 |
2.6 | 0.0533 | 0.6356 | 0.6889 | −0.5822 | 0.1067 |
3.1 | 0.1644 | 0.0000 | 0.1644 | 0.1644 | 0.3289 |
3.2 | 0.0178 | 0.0000 | 0.0178 | 0.0178 | 0.0356 |
3.3 | 0.1067 | 0.0000 | 0.1067 | 0.1067 | 0.2133 |
3.4 | 0.0889 | 0.0000 | 0.0889 | 0.0889 | 0.1778 |
3.5 | 0.0533 | −0.0089 | 0.0444 | 0.0622 | 0.1067 |
3.6 | 0.2089 | 0.0578 | 0.2667 | 0.1511 | 0.4178 |
3.7 | 0.1511 | 0.0844 | 0.2356 | 0.0667 | 0.3022 |
3.8 | 0.0356 | 0.1244 | 0.1600 | −0.0889 | 0.0711 |
4.1 | 0.0622 | 0.0356 | 0.0978 | 0.0267 | 0.1244 |
4.2 | 0.0533 | 0.0356 | 0.0889 | 0.0178 | 0.1067 |
4.3 | 0.0622 | 0.0000 | 0.0622 | 0.0622 | 0.1244 |
4.4 | 0.0000 | 0.0356 | 0.0356 | −0.0356 | 0.0000 |
4.5 | 0.1244 | 0.0000 | 0.1244 | 0.1244 | 0.2489 |
4.6 | 0.1200 | 0.0000 | 0.1200 | 0.1200 | 0.2400 |
4.7 | 0.1244 | 0.0000 | 0.1244 | 0.1244 | 0.2489 |
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Radziejowska, A.; Sobotka, B. Analysis of the Social Aspect of Smart Cities Development for the Example of Smart Sustainable Buildings. Energies 2021, 14, 4330. https://doi.org/10.3390/en14144330
Radziejowska A, Sobotka B. Analysis of the Social Aspect of Smart Cities Development for the Example of Smart Sustainable Buildings. Energies. 2021; 14(14):4330. https://doi.org/10.3390/en14144330
Chicago/Turabian StyleRadziejowska, Aleksandra, and Bartosz Sobotka. 2021. "Analysis of the Social Aspect of Smart Cities Development for the Example of Smart Sustainable Buildings" Energies 14, no. 14: 4330. https://doi.org/10.3390/en14144330