2. Literature Review
3. Research Method
4. Research Materials
5. Research Results
- Smart city is an important research direction, which is confirmed by the growing publication number.
- Multi-criteria decision support methods can be an effective and relatively simple tool for analysis and assessment of cities, useful for development strategy design.
- MCDM techniques are one of the important tools in solving decision-making problems in the context of urban smartness, especially urban efficiency, sustainability performance, environmental efficiency and low-carbon ecological city evaluation.
- TOPSIS, AHP and DEA are the most popular MCDM techniques in terms of the smart city.
- The result of the conducted multi-criteria analysis using the TOPSIS technique was a ranking of the smart cities based on the urban smartness. This method can recommend cities that are worthy of being smart cities. Krakow (Alternative CR) has the highest value in the feasibility of being a smart city-based city that is equal to 0.82156. Krakow is the best of the assessed cities for location enterprises and projects. Konin was ranked last.
- The proposed model can be used to analyze the potential of cities in the field of contemporary urban development, such as compact cities and sustainable cities.
- It was limitations in the implementation phase with the availability of necessary statistical data. Furthermore, multi-criteria analysis using TOPSIS makes to take into account local conditions because of the possibility of defining the importance of criteria and the preference function.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
|Web of Science||Scopus||Elsevier||Web of Science||Scopus||Elsevier|
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|AHP||Analytic Hierarchy Process||It represents an approach to quantifying the weights of criteria.|
|TOPSIS||Technique for Order of Preference by Similarity to Ideal Solution||It compares alternatives to the positive and the negative ideal solution.|
|SAW||Simple Additive Weight||It is based on the weighted average. An assessment score is calculated for each alternative by multiplying the scaled value given to the alternative of that attribute with the weights of relative importance directly assigned by decision maker followed by summing of the products for all criteria.|
|ANP||Analytic Network Process||It structures a decision problem as a network criteria and alternatives.|
|DEA||Data Envelopment Analysis||It is used to measure productive efficiency of decision-making units.|
|DEMATEL||Decision Making Trial and Evaluation Laboratory||It is used to verify the independence between variables and try to improve by offering a specific chart to reflect interrelationships between variables.|
|VIKOR||Vlsekrzterijumska Optimizacija i Kompromisno Resenje||It ranks alternatives and determines the best (compromise) solution that is the closest to the ideal.|
|ELECTRE||Elimination and Choice Translating Reality||It is used to discard some alternatives to the problem, which are unacceptable.|
|PROMETHEE||Preference Ranking Organization Method for Enrichment Evaluation||It provides a framework for structuring a decision problem, identifying synergies, highlighting the main alternatives and the structured reasoning behind it.|
|MACBETH||Measuring Attractiveness by a Categorical Based Evaluation TecHnique||It needs qualitative assessment about the difference of attractiveness between 2 elements in order to generate numerical scores for the options in each criterion.|
|MULTIMOORA||Multi-Objective Optimization on the basis of Ratio Analysis plus full multiplicative form||It requires a matrix of responses of the alternative to the objectives. A ration system is developed in which each response of an alternative of an objective is compared to a denominator, which is the representative for all alternatives concerning that objective.|
|Authors||Kind of Methods||Aims||Objects||Main Indicators|
|Rana, Luthra, Mangla, Islam, Roderick, Dwivedi, 2019 ||Fuzzy AHP, sensitivity analysis||To prioritize of barriers to recognize the most important barrier category and ranking of specific barriers within the categories to the development of smart cities||India’s cities||31 barriers, such as lack of cooperation and coordination between city’s operational networks, high IT infrastructure and intelligence deficit, lack of involvement of citizens, lacking technological knowledge among the planners, lacking ecological view in behavior and cultural issues|
|Luo, Chen, Sun, Zhu, Zeng, Chen, 2020 ||TOPSIS||To measure of the centrality together with the factors influencing centrality using data for the population flow||Cities in the Yangtze River Economic Belt||17 indicators, such as total permanent residential population, GDP, added value of secondary and tertiary industries, total fixed assets investment, total retail sales of consumer goods, actual use of foreign investment, R&D expenditure and tourism income|
|Zhu, Li, Feng, 2019 ||hybrid AHP-TOPSIS method||To explore the potential links between urban smartness and resilience||187 Chinese cities||21 indicators, such as principal arterial, tertiary industry, population natural growth, persons covered of unemployment insurance and green coverage|
|Gokhan, Ceren, 2020 ||ANP, TOPSIS||To evaluate the dimensions of smart cities||44 cities around the world||47 criteria, such as innovation index score, research and development score and entrepreneurship index score|
|Shi, Tsai, Lin, Zhang, 2018 ||AHP||To evaluate the intelligent development level and compare from the perspective of model accuracy and time cost||151 Chinese cities||16 indicators, such as information service industry practitioners, government online service level, open data service level, urban innovation and entrepreneurship level|
|Ma, Zhang, Lu, Xue, 2020 ||DEA||To assess the impact of sustainable development pilot zones on the environmental efficiency||187 prefecture-level Chinese cities||Input: wastewater discharge, industrial sulfur dioxide emission, industrial smoke and dust emissions and unutilized rate of general industrial solid waste. Output: output of the secondary industry.|
|Feizi, Joo, Kwigizile, Oh, 2020 ||TOPSIS||To assess transportation performance measures and smart growth of cities||46 cities in the U.S.||4 groups of criteria: network performance, traffic safety, environmental impact and physical activity|
|Stanković, Džunić, Džunić, Marinković, 2017 ||Multi-criteria analysis, combining the AHP and TOPSIS||To analyze of social, economic and environmental aspects of urban life and to provide the ranking cities according to smart performance||23 Central and Eastern European cities||26 qualitative indicators divided into 5 thematic categories: infrastructure, liveability and housing conditions, environment, employment and finance, governance, urban safety, trust and social cohesion as well as 2 indictors referring to citizens’ perceptions of the quality of life in the city (satisfaction with cities and aspects of urban life)|
|Pang, Fang, 2016 ||TOPSIS||To investigate the dynamic of smart low-carbon development||52 Chinese cities||52 indicators from 6 categories (science and technology, resource and environment, economy and industry, facilities and functions, critical capital, institution and culture), e.g., number of national key laboratories, energy intensity, GPD city, internet penetration rate, number of R&D personnel and urbanization level|
|Su et al., 2013 ||Set pair analysis, information entropy weight||To assess of urban low-carbon development level||12 Chinese cities||Economic development and social progress (GDP per capita, GDP growth rate, proportion of tertiary industry to GDP, urbanization rate, R&D as a percentage of GDP), energy structure and usage efficiency (proportion of non-coal energy, carbon productivity, elasticity coefficient of energy consumption), living consumption (angel’s coefficient, number of public transportations vehicles), development surrounding (public green areas per capita, forest coverage, coverage rate of green area in built-up area, proportion of investment for environmental protection to GDP)|
|Lombardi et al., 2017 ||MACBETH, “Playing Cards”||To analyze and test approaches into ranking of the evaluation criteria||2 projects: District Information Modeling and Management for Energy Reduction, Zero Energy Buildings in Smart Urban District||Economic (investment costs, payback period), environmental (reduction of the CO2 emissions) and technical (reduction of the energy requirement, resilience of the energy systems)|
|Moutinho et al., 2018 ||DEA||To assess urban performance in term of eco-efficiency||24 German and 14 French cities||Input: energy consumption, population density, labor productivity, resource productivity, patents per inhabitant. Output: GDP, CO2 emissions|
|Song et al., 2016 ||Energy Synthesis, Slacks-Based Measure DEA||To measure the urban metabolic evolution index||31 major Chinese cities||Renewable energy, indigenous renewable energy, locally non-renewable energy, imported energy, exported energy and waste energy|
|Liu et al., 2020 ||DEA||To measure urban green total factor productivity (GTFP) with a difference-in-difference (DID) approach||283 prefecture-level Chinese cities (96 pilot, 187 non-pilot cities)||Input variables: capital stock, number of employees, energy consumption. Output variables: GDP, CO2 emissions.Control variables: innovation index|
|Wang et al., 2020 ||Slacks-Based Measure DEA based on non-expected output||To explore the spatiotemporal evolution of urban carbon emission performance||283 cities in China||Input: fixed-asset investment, inventory assets, number of employees, energy consumption, urban electricity consumption. Expected output: GDP. Non-expected output: urban CO2 emissions.|
|Geng, Zhang, 2020 ||TOPSIS||To establish a correlation model and a comprehensive evaluation system between environment and urbanization||13 cities in Hunan province of China||Environment subsystem: resource elements (sown area, water consumption), ecological elements (park green land, green area coverage rate), ecological pressure (sewage discharged, energy consumption) and ecological response (gas utilization rate, sewage treatment rate). Urbanization subsystem: population (population growth rate, urbanization rate), economic (GDP, investment in fixed assets, output value of the tertiary industries), spatial (area of paved roads, population density) and social (retail sales of consumer goods, number of vehicles, number of general educations, number of health institutions).|
|Fang, Pang, Liu, 2016 ||TOPSIS||To creatively take a quantitative study on a smart low-carbon city’s dynamic mechanism||64 Chinese cities||59 major indicators in 6 categories: science and technology, resource and environment, economy and industry, facilities and functions, critical capital and institution and culture|
|Porro, Pardo-Bosch, Agell, Sanchez 2020 ||Integrated AHP and fuzzy linguistic TOPSIS||To design a framework oriented to public managers based on the assessment of criteria and sub-criteria the strategic location decision made by enterprises||Energy sector enterprises of European cities||27 sub-criteria in 6 criteria: characteristics of the city’s host country or region, structural factors, government and its policies, socioeconomic context, environmental conditions and market condition for energy firms|
|Carli, Dotoli, Pellegrino, 2018 ||Sensitive analysis for AHP||To analyze the sustainable development of energy, water and environmental systems, through a set of objective performance indicators||4 Italian metropolitan areas: Bari, Bitonto, Mola, Molfetta||35 indicators from 7 dimensions: energy consumption and climate (e.g., energy consumption per capita); penetration of energy and CO2 saving measures; renewable energy potential and utilization (e.g., renewable energy in electricity production); water and environmental quality; CO2 emissions and industrial profile (e.g., CO2 emissions of buildings); city planning and social welfare (e.g., GDP per capita); R&D, innovation and sustainability policy (e.g., patents in clean technologies)|
|Tariq, Faumatu, Hussein, Shahid, Muttil, 2020 ||AHP||To compare and identify the smartness of cities in multi-dimensions||Australian major cities||90 indicators in 26 factors and six components (economy, governance, environment, livability, mobility, people)|
|Name||Important Cities||Domains of Indicators|
|Smart City Index ||Singapore, Helsinki (Finland), Zurich (Switzerland)||affordable housing, fulling employment, unemployment, health services, basic amenities, school education, air pollution, road congestion, green spaces, public transport, recycling, security, citizen engagement, social mobility, corruption|
|Global Smart City Performance ||Singapore, London (UK), New York (USA)||mobility, healthcare, public safety and productivity|
|Global Cities Ranking ||London (UK), New York (USA), Paris (France)||business activity, human capital, information exchange, cultural experience and political engagement|
|Ranking of Cities in Motion ||New York (USA), London (UK), Paris (France)||economy, human capital, technology, environment, international outreach, social cohesion, mobility and transport, governance, urban planning, public management|
|Global Power City Index ||London (UK), New York (USA), Tokyo (Japan)||economy, R& D, cultural interaction, liveability, environment, accessibility|
|Ranking of World Cities ||New York (USA), London (UK), Singapore||economic strength, physical capital, financial maturity, institutional character, human capital, environmental and natural hazards, global appeal|
|Innovation Cities Global Index ||London (UK), New York (USA), Tokyo (Japan)||cultural assets, human infrastructure, networked markets|
|Economy||%||X1—registered unemployment rate|
|number||X2—entities entered in the REGON register per 10,000 residents|
|%||X3—share of newly registered creative sector entities in the number of newly registered entities|
|number||X4—patents per 1,000,000 residents|
|PLN||X5—city income per capita|
|Environment||ton/year||X6—annual average concentration of NO2|
|m3||X7—total consumption of water per capita|
|%||X8—share of recycled waste|
|Transport||person||X9—fatalities in road accidents per 100,000 inhabitants|
|number||X10—number of passengers cars per 1000 inhabitants|
|km||X11—bicycle paths per 10,000 inhabitants|
|Social capital||%||X12—net enrolment rate (middle schools)|
|person||X13—graduates of universities|
|person||X14—number of inhabitants per 1 library outlet (including library points)|
|Quality of life||person||X15—number of residents per 1 hospital bed|
|m2||X16—average size of flat per 1 inhabitant|
|%||X17—share of parks, lawns and green areas in total area|
|%||X18—detectability of perpetrators of identified crimes|
|Management||%||X19—turnout in the local government election in 2018|
|%||X20—participation of women in the city council|
|%||X21—share of Local Spatial Developments Plans; planning support|
|Voivodship||Cities||Population [Person]||City Land Area [km2]||Population Density [person/km2]||Own Income per Capita [PLN]|
|Alternative||dolnoslaskie (JG, LG, WR and WL)|
kujawsko-pomorskie (BD, GR, TR and WL)
lubelskie (BP, CL, LB and ZM)
lubuskie (GW and ZG)
lodzkie (LD, PT and SK)
malopolskie (KR, NS and TR)
mazowieckie (OS, PL, RD, SD and WA)
podkarpackie (KS, PR, RZ and TA)
podlaskie (BL, LM and SU)
pomorskie (SL, SP, GD and G)
sląskie (BB, BY, CH, CZ, DG, GL, JZ, JA, KT, MY, PS, RS, RB, SS, SO, SW, TY, ZB and ZR)
warmińsko-mazurskie (EL and OL)
wielkopolskie (KL, KN, LE and PZ)
zachodniopomorskie (KS, SZ and SW)
|Calculation of TOPSIS method||Results, ranking and clustering|
|Criteria||economy: X1 X2 X3 X4 X5|
environment: X6 X7 X8
transport: X9 X10 X11
social capital: X12 X13 X14
quality of life: X15 X16 X17 X18
management: X19 X19 X21
|Quality||Average Value||Standard Deviation||Variation Coefficient||Best Value|
|X1||↓||1350.53||2217.10||164.17||10,044.00 RB||10.00 SP|
|X2||↑||461.62||2357.95||510.81||1760.90 KN||39.10 ZM|
|X3||↑||11.56||18.01||155.84||98.46 JZ||0.00 SP|
|X4||↑||5.15||2.16||41.97||11.40 CZ||2.00 PZ|
|X5||↑||1246.18||358.41||28.76||2429.65 SP||655.52 JZ|
|X6||↓||7.31||1.34||18.40||9.71 RZ||4.14 SW|
|X7||↓||10.98||11.91||108.38||67.50 GL||0.00 SW|
|X8||↑||3392.16||1017.73||30.00||7236.69 WA||2143.67 BP|
|X9||↓||3.72||2.28||61.33||11.16 LM||0.00 SW|
|X10||↓||519.20||77.10||14.85||732.42 SP||237.38 WL|
|X11||↑||3.47||1.90||54.87||8.80 SU||0.10 MY|
|X12||↑||97.14||9.73||10.02||120.78 SZ||74.73 BY|
|X13||↑||1504.42||1577.38||104.85||6406.40 RZ||0.00 TY|
|X14||↑||9070.94||3782.82||41.70||24,898.00 PT||2468.00 LE|
|X15||↑||142.01||60.72||42.75||351.41 SW||61.06 PR|
|X16||↓||27.42||2.71||9.89||36.40 WR||23.20 EL|
|X17||↑||3.75||2.87||76.60||21.20 CH||0.50 SW|
|X18||↑||71.90||9.09||12.64||89.80 NS||46.00 WR|
|X19||↑||40.97||6.49||15.85||51.34 SP||0.00 ZG|
|X20||↑||26.27||8.41||32.01||50.00 WA||12.00 BY|
|X21||↑||52.27||26.55||50.79||101.10 CH||13.00 RD|
|Dimensions||The Best Cities||The Worst Cities|
|Economy||GL, PZ, LB||BP, WL, PR|
|Environment||JZ, BP, EL||RB, KO, OS|
|Transport||SW, KS, GR||GL, DG, LO|
|Social capital||RZ, KT, LB||EL, BY, PT|
|Quality of life||CH, SS, BD||GN, SW, MY|
|Management||ZR, JZ, ST||BY, ZB, ZG|
|I||Excellent||KR, WR, JZ|
|II||Good||0.758 < 0.802||KT, RD, BP, EL, LB, GL, CZ, PZ, KL, KS, GD, NS, TR, RZ, CH, BL, PT, ZB, LD, OL, OP, RS, SS, BD, GN, SZ, KS, SP, CL, SD, PS, WL, SU, WA|
|III||Medium||DG, ST, BB, KL, SK, GR, SW, GW, ZM, SO, PR, LE|
|IV||Low||<||SL, LG, ZG, LO, TY, TA, ZR, JG, BY, WL, JA, MY, PL, TR, RB, OS, KN|
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