Research ArticleApplication of Financial Cloud in the SustainableDevelopment of Smart Cities
Yangchun Cao Guangyu Zhang and Chunyao Ou
School of Management Guangdong University of Technology Guangzhou 510520 China
Correspondence should be addressed to Guangyu Zhang 1111908005mail2gduteducn
Received 4 September 2020 Revised 27 September 2020 Accepted 30 September 2020 Published 13 October 2020
Academic Editor Wei Wang
Copyright copy 2020 YangchunCao et al+is is an open access article distributed under the Creative CommonsAttribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited
+is article first investigates the status of financial cloud development in smart cities and studies the sustainable development ofsmart cities Secondly it investigates the construction of the evaluation model using the power of the financial cloud throughexploratory factor analysis selecting the principal component analysis method to extract the factors for screening and di-mensionality reduction of the indicators and making hypotheses We used confirmatory factor analysis to establish the structuralequation +e measurement model analyzes and validates the assumptions of the previous stage and finally determines thesustainable development evaluation index system +en starting from the scoring coefficient matrix of the exploratory factoranalysis of the final model the variance contribution rate of each common factor is weighted to construct a comprehensiveevaluation model to calculate the comprehensive evaluation score For the comprehensive evaluation score of the sustainabledevelopment level of the smart city SPSS software performs cluster analysis performs regional clustering and determines the levelof urban development Finally by comparing the sustainable development levels of smart cities the related causes of the gaps areanalyzed Key factors affecting the sustainable development of smart cities are identified and corresponding countermeasuresare proposed
1 Introduction
In recent years the concept of smart city has been vigorouslypromoted in the direction and concept of urban construc-tion in various countries in the world and it has set off awave [1ndash3] Many places in life have applications of smarttechnologies and they are becoming more and more in-telligent in medicine transportation electricity food cur-rency retail infrastructure and cities which also makes theearth continue to move toward the field of intelligence [4]ldquoSmart Earthrdquo has driven the enthusiasm of countries tocreate intelligent cities Carrying out smart city constructionhas become a consensus on the development of advancedcities and economic society with informatization levels andhas become an important method and approach for manycities to achieve urban cross-domain development improveurban public service levels and urban operating efficiencyand develop strategic emerging industries [5] Many man-agers and city decision-makers in various countries around
the world regard the construction of smart cities as the key tosustainable development of resources and the environmentand scientific urban management Various banks have triedto build smart outlets [6] Although the official names orspecific forms of smart bank outlets are slightly different thedevelopment direction of ldquobrain outlet smart transforma-tionrdquo has become the consensus of the banking channeltransformation It plays a huge role in urban construction[7]
+e emerging thing of financial technology is especiallyimportant to the financial industry so researchers from allsides pay high attention to it International organizationssovereign countries and research scholars have conductedin-depth research on the meaning of financial technologybut they have not yet formed a unified opinion on thedefinition of financial technology [8] Among them Inter-national Financial Stability Council defines fintech as the useof technological means to promote financial business in-novation forming business models technology applications
HindawiComplexityVolume 2020 Article ID 8882253 11 pageshttpsdoiorg10115520208882253
processes and products that have a significant impact onfinancial markets institutions and financial services [9] +edefinition of financial technology is not limited to theperspective of science and technology but also pays moreattention to the innovation of financial enterprises broughtabout by the application of technology [10] Masera et alpointed out that global fintech research can help promotethe efficiency of financial banking business services reducetransaction costs and form economies of scale [11] Panoriet al pointed out that in data-driven credit borrowing isgradually no longer a unique field of banks [12] Xie et alhave a significant impact on the development of the bankingindustry and financial supervision models and have a sig-nificant effect in the construction planning of ecologicalsmart cities [13]
Since the development of the smart city it has entered a newnormal state from concept to implementation application closeto peoplersquos livelihood open urban public resource data andgovernment-enterprise cooperation to activate urban energy[14] +e soft power of environmental improvement has beenenhanced Kong et al pointed out that a networked city isequivalent to a smart city so themost obvious sign of this smartcity is the Internet of +ings [15] Eckhoff andWagner pointedout that to build a smart city it is necessary to increase theutilization rate of informatization-related technologies coor-dinate various functional departments improve and arrangeexisting resources optimize services strengthen greening builda harmonious society and promote sustainable urban devel-opment [16] Enterprises and the masses create an excellentworking living and entertainment atmosphere +e estab-lishment of this environment is reflected in aspects of urbantransportation system command center resource managementsystem safety in public places and environmental protection[17] Professor Xu andGeng vividly pointed out that smart citiesare the sum of digital cities and the Internet of +ings [18] It isemphasized that the construction of smart cities is based onmodern information and communication technology [19]Humanwisdom and attitude will make intelligent objects betterunderstand people and complement each otherrsquos wisdom thusbuilding this service relationship in economic and social ac-tivities [20]
To sum up the evaluation system for sustainabledevelopment of smart cities can allow researchers tounderstand the degree of smartness and sustainable de-velopment of the city horizontally and vertically Dif-ferent countries or cities have different evaluation indexsystems With the rapid development of Internet financeemerging online personal finance products have grownrapidly which has a strong impact on the entire tradi-tional financial industry Internet finance has changed theliving environment of commercial banks and changedpeoplersquos payment and financial management habits Withthe increasing popularity of Internet finance conceptssuch as big data and precision marketing are well knownand valued Based on the financial cloud this articleconducts research on the sustainable development ofsmart cities and fintech and banking channel manage-ment innovation are gradually integrated and developed
2 Smart City Construction under theFinancial Cloud
21 Smart Finance Cloud Financial cloud is an industry cloudapplication scenario It uses cloud computing technology todeploy financial products information and services to the cloudnetwork to improve the overall efficiency of financial institu-tions improve processes reduce operating costs and providecustomers with more convenience Financial business servicesand financial information service [21] +e goal of smart cityconstruction is to optimize existing resources through analysismonitoring integration and intelligent response through theuse of next-generation information technology to make cityoperations more efficient safer and greener From the per-spective of the overall technical framework of a smart city it canbe divided into four layers from the bottom up the perceptionbase layer the network middle layer the platform layer and thesmart application layer [22] +e basic layer of perception thatis the smart city needs to obtain information by sensing thecity compare it to the human sensory organ and place varioussensing devices (mobile phones sensor nodes personal com-puters PDA radio frequency tags and monitoring probes) inthe city Among them the chemical or physical changes of themonitored objects are converted into information and passed tothe next layer +ese sensing devices can not only monitor thecity in real time but also provide information feedback to themwhich has the performance of two-way conduction To enablethe perception layer to perceive the city the perceptionequipment needs to be constantly updated and technicallyimproved more accurately and comprehensively +e smartfinancial cloud panorama is shown in Figure 1
+e middle layer of the network is the ldquomeridianrdquo of thesmart city +rough the wireless sensor network cloud com-puting network wireless LAN telecommunications networkP2P network Internet Internet of +ings 3G 4G and othernetworks the information obtained from the perception layerwill be carried out Process and pass to the next layer thenetwork layer has extensive connectivity realize the intercon-nection of information resources between cities or countries andform a channel for information transmission between cities+eplatform layer uses cloud computing and other technologies toprocess the collected information then reflects the changes of thevarious elements of the city in time and feeds back the analysisresults to the city decision-makers+e smart application layer isa smart city that provides services for citizens through theanalysis and processing of information such as audio videocluster scheduling and data collection such as mobile wallet(scanning type) smart home smart transportation street lightcontrol (control type) logistics information and environmentalpollution (monitoring type) Connect massive data on citymanagement and make smart city management automatedhumanized and intelligent +e application layer can be dividedinto a terminal device layer and an application program layer+e terminal device layer that is the human-machine interfacemainly implements human-computer interaction Applicationprogram layer mainly performs data processing on all aspects ofsociety and economy including agriculture banking medicinelogistics industry electricity home life and others
2 Complexity
22 Smart City Financial CloudModel +e construction of asmart city requires the interaction and interaction of the in-dustrial ecological chain information chain innovation chainfinancial chain cultural chain and service chain to form acomplete smart city operation system that integrates humancapital infrastructure construction and capital [23] Resourceelements are put into this system and smart government smarteconomy and smart life have become the products of thissystem +e specific logical relationship is shown in Figure 2
Enterprises and governments differ in the wisdom of citymanagement In the course of time the government no longervalues the advantages of intelligent construction of publicservices but pays more attention to the potential and devel-opment of ICT in addition to the effectiveness of city man-agement after the use of its products
In smart city operation and management DMU is thedecision-making unit and there are n number of participantsthat need to participate in the evaluation +e inputs andoutputs of these DMUs are m and s respectively +e inputvector of the jth DMU is xj (x1j xmj)Tgt0(j 1 n) and the output vector is yj (y1j
ysj)Tgt 0(j 1 n) +e weight coefficients of the outputand input indicators of these decision-making units areu (u1 us)T v (v1 vm)T +e efficient evaluationindex corresponding to each DMU is
Kj 1113936
sr1 uryrj
1113936ni1 vixij
(1)
In the evaluation process it is necessary to select appropriateindex values When the input values of some indicators areincreased all output values will be increased when the values of
other indicators are increased some output values will be re-duced +e output index project of the smart city is mainlydivided into four parts smart government smart life smarthumanities and smart economic development +e requiredinput indicators are mainly divided into three parts capital andhuman capital investment and infrastructure construction In-put indicators are divided into positive and negative We defineinput indicators that have a positive impact on citizensrsquo lives aspositive input indicators while indicators that have a negativeimpact on citizensrsquo lives are defined as passive input indicators
+e construction of the Malmquist productivity indexwith reference to t and t+ 1 period has made the Malmquistindex widely used +e specific formula is as follows
W xt y
t x
t+1 y
t+11113872 1113873
Dct
xt+1
yt+1
1113872 1113873
Dct
xt y
t1113872 1113873
timesDc
tx
t+1 y
t+11113872 1113873
Dct+1
xt y
t1113872 1113873
⎡⎢⎣ ⎤⎥⎦
W xt y
t x
t+1 y
t+11113872 1113873
Dct
xt+1
yt+1
1113872 1113873
Dct
xt y
t1113872 1113873
timesDct xt+1 yt+1( 1113857
Dct xt yt( 11138571113890
timesDct xt+1 yt+1( 1113857
Dct+1 xt yt( 11138571113891
12
(2)
+is paper quantitatively analyzes the coupling andcoordination relationship of the industrial-city integrationof smart cities through the evaluation of the coupling co-ordination degree After fully referring to the relevant re-search a scientifically constructed coupling and coordinatedevaluation model of smart city construction industrial
Intelligent identificationartificial intelligencebig data
Cloud ComputingCloud StorageCloud Network
Disaster recovery
Multioperation
O amp M
Data isolation
Network resources
Security resources
Server resources
Storage resources
Virt
ualiz
atio
n m
anag
emen
tFinancial data
centerSmart city
Financial cloud center
Internet resources
PAAS IAAS
Big dataSAAS
Financial cooperation
Supply chain financial
Big data risk control
Corporate services
IntelligentInternet
Figure 1 Smart financial cloud
Complexity 3
development and urban ecological protection are built +especific formula is shown in
T U1 times U2 times U3( 1113857
U1 + U2 + U3( 1113857 times U1 + U2 + U3( 11138571113896 1113897
13
(3)
C represents the degree of coupling between the smartcity construction system industrial development systemand ecological protection system +e level of developmentof a system is not proportional to the degree of couplingwithin the system and there may be a low degree of couplingwithin the system of high-level development and vice versaOn this basis we can revise the coupling model and measurethe true coordination between the three systems to build acoordination model as follows
R (CT)12
(4)
Indicators have a positive effect and a negative effect onthe system +e unit of each indicator also has a certaindifference Each indicator needs to be processed beforecalculation After determining the positive and negativedirections of the index use the following formula for dataprocessing
vij xij minus min xij1113872 1113873
max xij1113872 1113873 minus min xij1113872 1113873
vij min xij1113872 1113873 minus xij
max xij1113872 1113873 minus min xij1113872 1113873
(5)
Weight refers to the contribution rate of an indicator tothe entire indicator system Only after determining theweight can the system be reasonably measured In this paperthe weight of the entropy method is used to determine theweight +e formula is as follows
Hi123 1113944m
j1λijuij (6)
All the subsystems of the smart city ecosystem need tobuild a comprehensive information platform to realize theinteraction and link of information between the smart cityand the industry +e industrial development goals of smartcities are different from other cities +e pillar industries ofcities should focus on the cultivation of smart industries+rough resource integration and high-tech support forsmart cities as well as the creation of a market environmentthe industryrsquos supporting cooperation will be fully utilizedfor urban construction +e ldquoInnovation + Entrepreneur-shiprdquo dual-innovation platform relies on the joint action ofsmart city innovation elements innovation awareness andinnovation capabilities Innovation is the vitality of smartcities Entrepreneurship injects fresh blood into smart citiesand cities create incisiveness for ldquodouble innovationrdquo socialenvironment +e informatization industry is the leader ofcities towards wisdom emphasizing that informatizationtechnology informatization industry and informatizationservices not only promote the development of smart citiesbut also promote the development of smart cities and co-ordinated development of industries and cities +e coor-dinated integration of ecological chain resources furtherbuilds a more complete ldquoecological circlerdquo of smart cityindustry-city integration to achieve full utilization of cityfunctions and industrial development and maximize sharingof information technology (Figure 3)
23 Sustainability Evaluations +e scope of constructioncan divide the construction of smart cities Individual keybreakthroughs can also be carried out in an all-roundmanner Within the scope of the ability of city constructionthe two can also be considered at the same time From the
Figure 2 Schematic diagram of smart city financial investment
4 Complexity
beginning of Chinarsquos proposal for smart city construction tothe present day a total of three batches of smart city pilotlists have been released Increased cities are taking smartcities as their construction goals and the selected smart citythemes are not the same +e first batch of cities partici-pating in the construction Shenzhen and Nanjing weremainly led by innovation and the pilot cities that partici-pated in the construction afterwards mainly determined thedevelopment priorities according to the characteristics of theconstruction main body and future needs so as to integratethe cityrsquos established development strategic goals andachieve smart cities +e construction of the building isorganically combined +e sustainable development modelof smart city can be divided into four categories
(1) Innovative at this stage in the army of Chinarsquos smartcity construction most cities regard innovation as animportant driving force Most of these cities alreadyhave a good development foundation and strongstrength and regard the development of ldquosmartrdquoindustries as a strategy to enhance cities which arethe key factors of status and competitive strength
(2) +e urban development of smart industries in theprocess of urban development such cities havegradually cultivated and formed characteristic in-dustries with their own advantages and formed a
complete upstream and downstream ecological en-vironment in related fields+erefore these cities aremore focused on maintaining and displaying theirown advantages and intelligent industries For thedevelopment of Kunshan for example the authorsin [23] proposed focusing on accelerating the de-velopment of the Internet of +ings electronictechnology and other industries and promoting theconstruction of smart cities
(3) Developing the wisdom and livelihood such citiesfocus on the development of intelligent managementand livelihood services to promote+e wisdom of the city avoids the ldquobig city diseaserdquo insmall and medium-sized cities +e top priority ofthe development of smart cities is people-orientedand its connotation is that all ldquopeoplerdquo of peoplersquoslivelihood are the core serving the ldquopeoplerdquo in thecity as much as possible so as to realize a green safeand efficient intelligent life
(4) Development of information technology infra-structure the construction of smart cities in thiscategory focuses on information technology andinformation infrastructure For example with thehelp of advanced information technology a modernand integrated administrative decision-making
Smart city industry
ecosystem
Resource utilization Business choice
Material and financial
integration
Scientific and technological development
Smart city operations
Collaborative integration
Collaborative integration
Collaborative integration
Collaborative integration
Improve development
Promote diversity
Promote interaction
Information Industry
Financial industry Culture industry
Innovation industry
Pillar industry
Figure 3 Smart city industrial ecosystem
Complexity 5
auxiliary system has been established Operation inmajor enterprises and national administrative de-partments has proved that similar systems can ef-fectively improve managersrsquo work efficiency anddecision-making ability
+e goal of constructing an evaluation index system forsmart city sustainable development is to reflect the actualstatus of smart city sustainable development When selectingindicators pay attention to the representativeness andquantification of indicators and build a comprehensiveindicator system Quantitative analysis of targets is donethrough quantifying indicators +e specific evaluation in-dex system is shown in Table 1
3 Results Analysis
31 Model Analyses +e reliability test is to check thestability and consistency of the measurement results +isstudy uses SPSS software for internal reliability testingand reflects its reliability through Cronbachrsquos X coeffi-cient Assuming the reliability of the evaluation indexsystem and each dimension as shown in Table 2 weconsidered the overall internal consistency of the eval-uation index system Cronbachrsquos coefficient is 0386greater than 05 and Cronbachrsquos coefficients of the threecommon factors are all greater than 05 so the indexsystemmeets certain reliability requirements +e specificindicators are shown in Table 2 Starting from the scoringcoefficient matrix of exploratory factor analysis of themodel a comprehensive evaluation model is constructedwith the variance contribution rate of each commonfactor as shown in Figure 4+e score coefficient matrix ofeach common factor and the variance contribution rate ofeach factor are shown in Figure 4
According to the coupling and coordination modelsystematically calculate the coupling and coordination be-tween smart city construction industrial development andecological protection and finally determine the evaluationlevel +e results are shown in Figure 5 In terms of couplingdegree the 12 smart cities are in low-level coupling andhigh-level phases respectively In terms of coordinationdegree except for Shenzhen Beijing and Shanghai whichhave reached moderate coordination the remaining citiesare in low coordination +e overall development index ofindividual cities is low and the evaluation index of theindustrial development system is higher than that of theurban construction system indicating that industrial de-velopment has failed to drive the development of infra-structure construction in smart cities +e proportion of thesecondary industry in the industrial structure of some citiesis still higher than that of the tertiary industry which leads toa higher comprehensive evaluation index of the industrialdevelopment system than the urban construction system+is is because the proportion of emerging industries is verysmall and cannot become the pillars or leading enterprises ofthe cities +e endogenous power is insufficient and theconstruction of smart cities fails to effectively promote thedevelopment of the industry
32 Sustainability Analyses +e study used SPSS software forcase clustering and cluster analysis of the comprehensive rankingof sustainable development of smart cities in provincial ad-ministrative regions+e results are shown in Figure 6 From theresults of the above five-year factor equivalence and compre-hensive evaluation score ranking it can be intuitively found thatthe factor score and comprehensive rankings of category A-typecities are all in the top It can be concluded that such areas are atthe forefront of the country in the construction of smart citiesand a reference for the smart construction of other cities can beprovided +erefore these areas are positioned as leading areasas the ldquoleadersrdquo for the sustainable development of smart cityconstruction +e analysis of C-type cities shows that the de-velopment of cities is not stable enough and there is no con-tinuous growth It is necessary to adjust their smart cityconstruction models and other policies and find and deal withthe problems in time +e economic level of such cities lagsbehind other provincial administrative regions+e constructionof smart cities will inevitably be restricted and affectedHoweversuch regions have a strong willingness to progress and developDevelopment requires strong national support and the for-mulation of relevant policies It can be seen that such provincialadministrative regions should be positioned as catch-up areas asa ldquocatch-up armyrdquo for the sustainable development of smart cityconstruction
In the financial industry funds and securities of capitalsuppliers are continuously transmitted in the process offinancial operation through a series of investment and fi-nancing activities of financial entities Various financialentities are formed according to the relationship betweeninvestment and financing With the development of theeconomy an intricate financial ecological network has beenformed +e more the cooperation and the closer the co-operation in the financial industry the higher the value of itsproducts the richer the variety the more developed themarket and the stronger the financing ability +e morecomplex its industry is the more stable it will be +econstruction and development of smart cities should inte-grate ldquoInternet +rdquo into the financial industry integrate andutilize the development resources of the Internet financialindustry truly realize the financial innovation driven byinformation technology and use the Internet to reduceeconomic search costs and transaction costs macroscopi-cally focusing on externalities and the network effect makesuse of the Internet to promote the positive development ofmanagement mechanisms technological innovation andcorporate culture in the industry Improve the financialentities required for each development link in the financialindustry so as to provide a stable chain of funds for urbandevelopment and industrial innovation in the process ofcomplication strengthen communication and cooperationamong various financial entities and realize the financialand information industryrsquos complementary advantages andwin-win cooperation +e cityrsquos intelligent construction alsocreates a good financial environment for industrial devel-opment During the development of the industry throughthe innovation of financial institutions financial servicesfinancial mechanisms financial structures and the expan-sion of financing channels optimize the industrial
6 Complexity
upgrading model improve the services of urban financialorganizations and form a financial center with a reasonablelayoutmdashindustry as a financial entity+e characteristics andcapital advantages of Internet companies have technologicaland innovation advantages As a huge platform for thedevelopment of the financial industry cities have pipelineadvantages and many mobile terminals and ultimatelyachieve the integration of industry and city Financial ITcostanalysis is shown in Figure 7
33 Empirical Analyses +e financial cloud has the char-acteristics of huge investment in the early stage of businessdevelopment and the short-term and short-term incomemay not be fully guaranteed Many companies rely onventure capital funds or through the stock market to obtainfunds to ensure the companyrsquos sustainable developmentFigure 8 is the net profit compiled according to the smartcityrsquos financial reports in the past years We have seen anegative net profit since 2011 Now with the support offinancial cloud technology you do not have to worry aboutchanges in business cycles or customer groups that causeproblems with working capital You can calmly face changesin various market environments
To evaluate an object a certain decision-making unitneeds to be selected and certain criteria need to be adoptedin this selection +e specific selection principle shouldensure that the selected decision-making unit can achieve
the same when it is in the same external environment Tasksachieve the same input and output +ere are three ways toselect DMU in the evaluation process of smart citiesnamely vertical and horizontal comparison and verticaland horizontal comprehensive comparison +is articleuses a longitudinal comparison to select the DMU Basedon the obtained data this paper selects the comprehensiveevaluation of the urban construction in the past three yearsand analyzes the results of the smart city construction inShanghai in the past three years based on scientific resultsBased on the above analysis it can be found that the smartcity construction has achieved certain results through inputand output Pay attention to the human cost infrastructureand capital input and the corresponding output of smartgovernment smart economy smart life and smart humanliteracy is efficient With continuous investment in majorfactors the effectiveness of the governmentrsquos DMU fluc-tuates around 1 which also proves that the principle ofeffectiveness can be met in the selection of input and outputindicators In further analysis of factor productivity we cansee that all major factors are effective and the application ofnew technologies and innovations in production andmanagement has achieved good results +is is also furthershowing that if the government major financial institu-tions and other stakeholders attach importance to theinvestment in various elements they can achieve goodresults to a large extent +e regression test results areshown in Figure 9
Table 1 Evaluation index system
Smart city construction
Infrastructure
A1 investment in fixed assets of the whole society Million dollarsA2 urban road area Square kilometersA3 drainage pipe length KilometersA4 passenger traffic of the whole society Ten thousandsA5 total volume of post and telecommunications services Million dollars
UrbanizationA6 urbanization rate A7 urban population density A8 per capita disposable income of urban residents Yuan
Composite indexindustrial development
Development scale
B1 the amount of investment in fixed assets of informationtechnology and Internet companies Million dollars
B2 number of information technology and software companies PcB3 information technology software practitioners People
Industrial structure B4 tertiary industry output value as a percentage of GDP B5 the proportion of output value of secondary industry to GDP Item
Technology supportcapability
B6 patent authorization PcB7 number of national key laboratories Million dollarsB8 RampD internal expenditure PeopleB9 number of RampD researchers
Composite index ecologicalprotectioncomprehensive index
Urban ecologyC1 green coverage rate in built-up area Square meter
C2 park green area per capita Ten thousandtons
Environmentalprotection
C3 industrial solid waste generation Ten thousandtons
C4 comprehensive utilization of industrial solid waste Million dollars
Voice networkIT service deskEnd-user computingData center
0
10
20
30
40
50
60
70
80
90
100
2016 2017 2018 20192015Year
Figure 7 Distribution of IT costs
ndash300
ndash250
ndash200
ndash150
ndash100
ndash50
0
50
100
One
mill
ion
US
dolla
rs
2012 2013 2014 2015 2016 2017 2018 20192011Year
Figure 8 City net profit over the years
Complexity 9
4 Conclusion
+is article first discusses the relationship between smartcities and finance and existing models based on existingrelevant theories and literature By comparing and an-alyzing the status of smart cities and financial clouds athome and abroad this paper applies financial cloud in-telligence to build smart cities It has laid the foundationfor empirical analysis and model innovation +eoreticalresearch of financial technology and smart city is inte-grated with each other to study and analyze the health andperfect sustainable road of smart city +e problems in thesustainable development of smart cities should be solvedfrom the aspects of economy human problems envi-ronmental science urban infrastructure and ecologicalenvironment Explore the innovation of the smart citymodel based on the ldquofinancial cloudrdquo perspective Inorder to achieve the sustainable development goals ofsmart cities explore the dynamic mechanism of sus-tainable development of smart cities through the analysisof the structure and elements of smart cities and improvethe construction of financial ecological chain in order toachieve the goal of model innovation Finally this paperproposes a way to realize the integration of smart city andfinancial cloud which has certain guiding significance forthe sustainable development of the smart city
Data Availability
+e data used to support the findings of this study areavailable from the corresponding author upon request
Conflicts of Interest
+e authors declare that they have no conflicts of interestregarding the publication of this paper
Acknowledgments
+is work was supported by the General Project of theNational Natural Science Foundation of China (Nos71874037 and 71673062) and Natural Science Foundation ofGuangdong Province (No 2018A030313397)
References
[1] C Kakderi N Komninos and P Tsarchopoulos ldquoSmart citiesand cloud computing lessons from the STORM CLOUDSexperimentrdquo Journal of Smart Cities vol 1 no 2 pp 4ndash132019
[2] P Tsarchopoulos N Komninos and C Kakderi ldquoAcceler-ating the uptake of smart city applications through cloudcomputing World Academy of Science Engineering andTechnologyrdquo International Journal of Social BehavioralEducational Economic Business and Industrial Engineeringvol 11 no 1 pp 129ndash138 2017
[3] N Komninos C Kakderi A Panori and P TsarchopoulosldquoSmart city planning from an evolutionary perspectiverdquoJournal of Urban Technology vol 26 no 2 pp 3ndash20 2019
[4] N P Rana S Luthra S K Mangla R Islam S Roderick andY K Dwivedi ldquoBarriers to the development of smart cities inIndian contextrdquo Information Systems Frontiers vol 21 no 3pp 503ndash525 2019
[5] G Trencher and A Karvonen ldquoStretching ldquosmartrdquo advancinghealth and well-being through the smart city agendardquo LocalEnvironment vol 24 no 7 pp 610ndash627 2019
[6] S P Mohanty U Choppali and E Kougianos ldquoEverythingyou wanted to know about smart cities the internet of thingsis the backbonerdquo IEEE Consumer Electronics Magazine vol 5no 3 pp 60ndash70 2016
[7] Y Mehmood F Ahmad I Yaqoob A Adnane M Imranand S Guizani ldquoInternet-of-things-based smart cities recentadvances and challengesrdquo IEEE Communications Magazinevol 55 no 9 pp 16ndash24 2017
[8] M Angelidou ldquo+e role of smart city characteristics in theplans of fifteen citiesrdquo Journal of Urban Technology vol 24no 4 pp 3ndash28 2017
[9] M Sookhak H Tang Y He et al ldquoSecurity and privacy ofsmart cities a survey research issues and challengesrdquo IEEECommunications Surveys amp Tutorials vol 21 no 2pp 1718ndash1743 2018
[10] S Berke ldquo+e sustainable development of data-driven smartcities citizen-centered urban governance and networkeddigital technologiesrdquo Geopolitics History and InternationalRelations vol 11 no 2 pp 122ndash127 2019
[11] M Masera E F Bompard F Profumo and N HadjsaidldquoSmart (electricity) grids for smart cities assessing roles andsocietal impactsrdquo Proceedings of the IEEE vol 106 no 4pp 613ndash625 2018
[12] A Panori A Gonzalez-Quel M Tavares et al ldquoMigration ofapplications to the Cloud a user-driven approachrdquo Journal ofSmart Cities vol 2 no 1 pp 16ndash27 2019
[13] J Xie H Tang T Huang et al ldquoA survey of blockchaintechnology applied to smart cities research issues and chal-lengesrdquo IEEE Communications Surveys amp Tutorials vol 21no 3 pp 2794ndash2830 2019
[14] E F Z Santana A P Chaves M A Gerosa et al ldquoSoftwareplatforms for smart cities concepts requirements challengesand a unified reference architecturerdquo ACM Computing Sur-veys vol 50 no 6 pp 1ndash37 2017
Unstandardized regression coefficientUnstandardized standard errorStandard coefficientt
SigToleranceVIF
0
1
2
3
4
5
Valu
es
X2 X3 X4X1Variable
Figure 9 Regression coefficient test and significance level
10 Complexity
[15] X Kong X Liu B Jedari M Li L Wan and F Xia ldquoMobilecrowdsourcing in smart cities technologies applications andfuture challengesrdquo IEEE Internet of 2ings Journal vol 6no 5 pp 8095ndash8113 2019
[16] D Eckhoff and I Wagner ldquoPrivacy in the smart cit-ymdashapplications technologies challenges and solutionsrdquoIEEE Communications Surveys amp Tutorials vol 20 no 1pp 489ndash516 2017
[17] A-M Valdez M Cook P-A Langendahl H Roby andS Potter ldquoPrototyping sustainable mobility practices user-generated data in the smart cityrdquo Technology Analysis ampStrategic Management vol 30 no 2 pp 144ndash157 2018
[18] H Xu and X Geng ldquoPeople-centric service intelligence forsmart citiesrdquo Smart Cities vol 2 no 2 pp 135ndash152 2019
[19] J Arthi ldquoBottom billion strategy-smart Villagesrdquo AsianJournal of Multidimensional Research (AJMR) vol 7 no 1pp 63ndash66 2018
[20] A Gharaibeh M A Salahuddin S J Hussini et al ldquoSmartcities a survey on data management security and enablingtechnologiesrdquo IEEE Communications Surveys amp Tutorialsvol 19 no 4 pp 2456ndash2501 2017
[21] N F Alwan andM K AL-Nuaimi ldquo+e role and important ofinternet of things in building sustainable cityrdquo Engineeringand Technology Journal vol 36 no 1 pp 22ndash29 2018
[22] M Aamir S Masroor Z A Ali and B T Ting ldquoSustainableframework for smart transportation system a case study ofkarachirdquo Wireless Personal Communications vol 106 no 1pp 27ndash40 2019
[23] S P Caird and S H Hallett ldquoTowards evaluation design forsmart city developmentrdquo Journal of Urban Design vol 24no 2 pp 188ndash209 2019
Complexity 11
processes and products that have a significant impact onfinancial markets institutions and financial services [9] +edefinition of financial technology is not limited to theperspective of science and technology but also pays moreattention to the innovation of financial enterprises broughtabout by the application of technology [10] Masera et alpointed out that global fintech research can help promotethe efficiency of financial banking business services reducetransaction costs and form economies of scale [11] Panoriet al pointed out that in data-driven credit borrowing isgradually no longer a unique field of banks [12] Xie et alhave a significant impact on the development of the bankingindustry and financial supervision models and have a sig-nificant effect in the construction planning of ecologicalsmart cities [13]
Since the development of the smart city it has entered a newnormal state from concept to implementation application closeto peoplersquos livelihood open urban public resource data andgovernment-enterprise cooperation to activate urban energy[14] +e soft power of environmental improvement has beenenhanced Kong et al pointed out that a networked city isequivalent to a smart city so themost obvious sign of this smartcity is the Internet of +ings [15] Eckhoff andWagner pointedout that to build a smart city it is necessary to increase theutilization rate of informatization-related technologies coor-dinate various functional departments improve and arrangeexisting resources optimize services strengthen greening builda harmonious society and promote sustainable urban devel-opment [16] Enterprises and the masses create an excellentworking living and entertainment atmosphere +e estab-lishment of this environment is reflected in aspects of urbantransportation system command center resource managementsystem safety in public places and environmental protection[17] Professor Xu andGeng vividly pointed out that smart citiesare the sum of digital cities and the Internet of +ings [18] It isemphasized that the construction of smart cities is based onmodern information and communication technology [19]Humanwisdom and attitude will make intelligent objects betterunderstand people and complement each otherrsquos wisdom thusbuilding this service relationship in economic and social ac-tivities [20]
To sum up the evaluation system for sustainabledevelopment of smart cities can allow researchers tounderstand the degree of smartness and sustainable de-velopment of the city horizontally and vertically Dif-ferent countries or cities have different evaluation indexsystems With the rapid development of Internet financeemerging online personal finance products have grownrapidly which has a strong impact on the entire tradi-tional financial industry Internet finance has changed theliving environment of commercial banks and changedpeoplersquos payment and financial management habits Withthe increasing popularity of Internet finance conceptssuch as big data and precision marketing are well knownand valued Based on the financial cloud this articleconducts research on the sustainable development ofsmart cities and fintech and banking channel manage-ment innovation are gradually integrated and developed
2 Smart City Construction under theFinancial Cloud
21 Smart Finance Cloud Financial cloud is an industry cloudapplication scenario It uses cloud computing technology todeploy financial products information and services to the cloudnetwork to improve the overall efficiency of financial institu-tions improve processes reduce operating costs and providecustomers with more convenience Financial business servicesand financial information service [21] +e goal of smart cityconstruction is to optimize existing resources through analysismonitoring integration and intelligent response through theuse of next-generation information technology to make cityoperations more efficient safer and greener From the per-spective of the overall technical framework of a smart city it canbe divided into four layers from the bottom up the perceptionbase layer the network middle layer the platform layer and thesmart application layer [22] +e basic layer of perception thatis the smart city needs to obtain information by sensing thecity compare it to the human sensory organ and place varioussensing devices (mobile phones sensor nodes personal com-puters PDA radio frequency tags and monitoring probes) inthe city Among them the chemical or physical changes of themonitored objects are converted into information and passed tothe next layer +ese sensing devices can not only monitor thecity in real time but also provide information feedback to themwhich has the performance of two-way conduction To enablethe perception layer to perceive the city the perceptionequipment needs to be constantly updated and technicallyimproved more accurately and comprehensively +e smartfinancial cloud panorama is shown in Figure 1
+e middle layer of the network is the ldquomeridianrdquo of thesmart city +rough the wireless sensor network cloud com-puting network wireless LAN telecommunications networkP2P network Internet Internet of +ings 3G 4G and othernetworks the information obtained from the perception layerwill be carried out Process and pass to the next layer thenetwork layer has extensive connectivity realize the intercon-nection of information resources between cities or countries andform a channel for information transmission between cities+eplatform layer uses cloud computing and other technologies toprocess the collected information then reflects the changes of thevarious elements of the city in time and feeds back the analysisresults to the city decision-makers+e smart application layer isa smart city that provides services for citizens through theanalysis and processing of information such as audio videocluster scheduling and data collection such as mobile wallet(scanning type) smart home smart transportation street lightcontrol (control type) logistics information and environmentalpollution (monitoring type) Connect massive data on citymanagement and make smart city management automatedhumanized and intelligent +e application layer can be dividedinto a terminal device layer and an application program layer+e terminal device layer that is the human-machine interfacemainly implements human-computer interaction Applicationprogram layer mainly performs data processing on all aspects ofsociety and economy including agriculture banking medicinelogistics industry electricity home life and others
2 Complexity
22 Smart City Financial CloudModel +e construction of asmart city requires the interaction and interaction of the in-dustrial ecological chain information chain innovation chainfinancial chain cultural chain and service chain to form acomplete smart city operation system that integrates humancapital infrastructure construction and capital [23] Resourceelements are put into this system and smart government smarteconomy and smart life have become the products of thissystem +e specific logical relationship is shown in Figure 2
Enterprises and governments differ in the wisdom of citymanagement In the course of time the government no longervalues the advantages of intelligent construction of publicservices but pays more attention to the potential and devel-opment of ICT in addition to the effectiveness of city man-agement after the use of its products
In smart city operation and management DMU is thedecision-making unit and there are n number of participantsthat need to participate in the evaluation +e inputs andoutputs of these DMUs are m and s respectively +e inputvector of the jth DMU is xj (x1j xmj)Tgt0(j 1 n) and the output vector is yj (y1j
ysj)Tgt 0(j 1 n) +e weight coefficients of the outputand input indicators of these decision-making units areu (u1 us)T v (v1 vm)T +e efficient evaluationindex corresponding to each DMU is
Kj 1113936
sr1 uryrj
1113936ni1 vixij
(1)
In the evaluation process it is necessary to select appropriateindex values When the input values of some indicators areincreased all output values will be increased when the values of
other indicators are increased some output values will be re-duced +e output index project of the smart city is mainlydivided into four parts smart government smart life smarthumanities and smart economic development +e requiredinput indicators are mainly divided into three parts capital andhuman capital investment and infrastructure construction In-put indicators are divided into positive and negative We defineinput indicators that have a positive impact on citizensrsquo lives aspositive input indicators while indicators that have a negativeimpact on citizensrsquo lives are defined as passive input indicators
+e construction of the Malmquist productivity indexwith reference to t and t+ 1 period has made the Malmquistindex widely used +e specific formula is as follows
W xt y
t x
t+1 y
t+11113872 1113873
Dct
xt+1
yt+1
1113872 1113873
Dct
xt y
t1113872 1113873
timesDc
tx
t+1 y
t+11113872 1113873
Dct+1
xt y
t1113872 1113873
⎡⎢⎣ ⎤⎥⎦
W xt y
t x
t+1 y
t+11113872 1113873
Dct
xt+1
yt+1
1113872 1113873
Dct
xt y
t1113872 1113873
timesDct xt+1 yt+1( 1113857
Dct xt yt( 11138571113890
timesDct xt+1 yt+1( 1113857
Dct+1 xt yt( 11138571113891
12
(2)
+is paper quantitatively analyzes the coupling andcoordination relationship of the industrial-city integrationof smart cities through the evaluation of the coupling co-ordination degree After fully referring to the relevant re-search a scientifically constructed coupling and coordinatedevaluation model of smart city construction industrial
Intelligent identificationartificial intelligencebig data
Cloud ComputingCloud StorageCloud Network
Disaster recovery
Multioperation
O amp M
Data isolation
Network resources
Security resources
Server resources
Storage resources
Virt
ualiz
atio
n m
anag
emen
tFinancial data
centerSmart city
Financial cloud center
Internet resources
PAAS IAAS
Big dataSAAS
Financial cooperation
Supply chain financial
Big data risk control
Corporate services
IntelligentInternet
Figure 1 Smart financial cloud
Complexity 3
development and urban ecological protection are built +especific formula is shown in
T U1 times U2 times U3( 1113857
U1 + U2 + U3( 1113857 times U1 + U2 + U3( 11138571113896 1113897
13
(3)
C represents the degree of coupling between the smartcity construction system industrial development systemand ecological protection system +e level of developmentof a system is not proportional to the degree of couplingwithin the system and there may be a low degree of couplingwithin the system of high-level development and vice versaOn this basis we can revise the coupling model and measurethe true coordination between the three systems to build acoordination model as follows
R (CT)12
(4)
Indicators have a positive effect and a negative effect onthe system +e unit of each indicator also has a certaindifference Each indicator needs to be processed beforecalculation After determining the positive and negativedirections of the index use the following formula for dataprocessing
vij xij minus min xij1113872 1113873
max xij1113872 1113873 minus min xij1113872 1113873
vij min xij1113872 1113873 minus xij
max xij1113872 1113873 minus min xij1113872 1113873
(5)
Weight refers to the contribution rate of an indicator tothe entire indicator system Only after determining theweight can the system be reasonably measured In this paperthe weight of the entropy method is used to determine theweight +e formula is as follows
Hi123 1113944m
j1λijuij (6)
All the subsystems of the smart city ecosystem need tobuild a comprehensive information platform to realize theinteraction and link of information between the smart cityand the industry +e industrial development goals of smartcities are different from other cities +e pillar industries ofcities should focus on the cultivation of smart industries+rough resource integration and high-tech support forsmart cities as well as the creation of a market environmentthe industryrsquos supporting cooperation will be fully utilizedfor urban construction +e ldquoInnovation + Entrepreneur-shiprdquo dual-innovation platform relies on the joint action ofsmart city innovation elements innovation awareness andinnovation capabilities Innovation is the vitality of smartcities Entrepreneurship injects fresh blood into smart citiesand cities create incisiveness for ldquodouble innovationrdquo socialenvironment +e informatization industry is the leader ofcities towards wisdom emphasizing that informatizationtechnology informatization industry and informatizationservices not only promote the development of smart citiesbut also promote the development of smart cities and co-ordinated development of industries and cities +e coor-dinated integration of ecological chain resources furtherbuilds a more complete ldquoecological circlerdquo of smart cityindustry-city integration to achieve full utilization of cityfunctions and industrial development and maximize sharingof information technology (Figure 3)
23 Sustainability Evaluations +e scope of constructioncan divide the construction of smart cities Individual keybreakthroughs can also be carried out in an all-roundmanner Within the scope of the ability of city constructionthe two can also be considered at the same time From the
Figure 2 Schematic diagram of smart city financial investment
4 Complexity
beginning of Chinarsquos proposal for smart city construction tothe present day a total of three batches of smart city pilotlists have been released Increased cities are taking smartcities as their construction goals and the selected smart citythemes are not the same +e first batch of cities partici-pating in the construction Shenzhen and Nanjing weremainly led by innovation and the pilot cities that partici-pated in the construction afterwards mainly determined thedevelopment priorities according to the characteristics of theconstruction main body and future needs so as to integratethe cityrsquos established development strategic goals andachieve smart cities +e construction of the building isorganically combined +e sustainable development modelof smart city can be divided into four categories
(1) Innovative at this stage in the army of Chinarsquos smartcity construction most cities regard innovation as animportant driving force Most of these cities alreadyhave a good development foundation and strongstrength and regard the development of ldquosmartrdquoindustries as a strategy to enhance cities which arethe key factors of status and competitive strength
(2) +e urban development of smart industries in theprocess of urban development such cities havegradually cultivated and formed characteristic in-dustries with their own advantages and formed a
complete upstream and downstream ecological en-vironment in related fields+erefore these cities aremore focused on maintaining and displaying theirown advantages and intelligent industries For thedevelopment of Kunshan for example the authorsin [23] proposed focusing on accelerating the de-velopment of the Internet of +ings electronictechnology and other industries and promoting theconstruction of smart cities
(3) Developing the wisdom and livelihood such citiesfocus on the development of intelligent managementand livelihood services to promote+e wisdom of the city avoids the ldquobig city diseaserdquo insmall and medium-sized cities +e top priority ofthe development of smart cities is people-orientedand its connotation is that all ldquopeoplerdquo of peoplersquoslivelihood are the core serving the ldquopeoplerdquo in thecity as much as possible so as to realize a green safeand efficient intelligent life
(4) Development of information technology infra-structure the construction of smart cities in thiscategory focuses on information technology andinformation infrastructure For example with thehelp of advanced information technology a modernand integrated administrative decision-making
Smart city industry
ecosystem
Resource utilization Business choice
Material and financial
integration
Scientific and technological development
Smart city operations
Collaborative integration
Collaborative integration
Collaborative integration
Collaborative integration
Improve development
Promote diversity
Promote interaction
Information Industry
Financial industry Culture industry
Innovation industry
Pillar industry
Figure 3 Smart city industrial ecosystem
Complexity 5
auxiliary system has been established Operation inmajor enterprises and national administrative de-partments has proved that similar systems can ef-fectively improve managersrsquo work efficiency anddecision-making ability
+e goal of constructing an evaluation index system forsmart city sustainable development is to reflect the actualstatus of smart city sustainable development When selectingindicators pay attention to the representativeness andquantification of indicators and build a comprehensiveindicator system Quantitative analysis of targets is donethrough quantifying indicators +e specific evaluation in-dex system is shown in Table 1
3 Results Analysis
31 Model Analyses +e reliability test is to check thestability and consistency of the measurement results +isstudy uses SPSS software for internal reliability testingand reflects its reliability through Cronbachrsquos X coeffi-cient Assuming the reliability of the evaluation indexsystem and each dimension as shown in Table 2 weconsidered the overall internal consistency of the eval-uation index system Cronbachrsquos coefficient is 0386greater than 05 and Cronbachrsquos coefficients of the threecommon factors are all greater than 05 so the indexsystemmeets certain reliability requirements +e specificindicators are shown in Table 2 Starting from the scoringcoefficient matrix of exploratory factor analysis of themodel a comprehensive evaluation model is constructedwith the variance contribution rate of each commonfactor as shown in Figure 4+e score coefficient matrix ofeach common factor and the variance contribution rate ofeach factor are shown in Figure 4
According to the coupling and coordination modelsystematically calculate the coupling and coordination be-tween smart city construction industrial development andecological protection and finally determine the evaluationlevel +e results are shown in Figure 5 In terms of couplingdegree the 12 smart cities are in low-level coupling andhigh-level phases respectively In terms of coordinationdegree except for Shenzhen Beijing and Shanghai whichhave reached moderate coordination the remaining citiesare in low coordination +e overall development index ofindividual cities is low and the evaluation index of theindustrial development system is higher than that of theurban construction system indicating that industrial de-velopment has failed to drive the development of infra-structure construction in smart cities +e proportion of thesecondary industry in the industrial structure of some citiesis still higher than that of the tertiary industry which leads toa higher comprehensive evaluation index of the industrialdevelopment system than the urban construction system+is is because the proportion of emerging industries is verysmall and cannot become the pillars or leading enterprises ofthe cities +e endogenous power is insufficient and theconstruction of smart cities fails to effectively promote thedevelopment of the industry
32 Sustainability Analyses +e study used SPSS software forcase clustering and cluster analysis of the comprehensive rankingof sustainable development of smart cities in provincial ad-ministrative regions+e results are shown in Figure 6 From theresults of the above five-year factor equivalence and compre-hensive evaluation score ranking it can be intuitively found thatthe factor score and comprehensive rankings of category A-typecities are all in the top It can be concluded that such areas are atthe forefront of the country in the construction of smart citiesand a reference for the smart construction of other cities can beprovided +erefore these areas are positioned as leading areasas the ldquoleadersrdquo for the sustainable development of smart cityconstruction +e analysis of C-type cities shows that the de-velopment of cities is not stable enough and there is no con-tinuous growth It is necessary to adjust their smart cityconstruction models and other policies and find and deal withthe problems in time +e economic level of such cities lagsbehind other provincial administrative regions+e constructionof smart cities will inevitably be restricted and affectedHoweversuch regions have a strong willingness to progress and developDevelopment requires strong national support and the for-mulation of relevant policies It can be seen that such provincialadministrative regions should be positioned as catch-up areas asa ldquocatch-up armyrdquo for the sustainable development of smart cityconstruction
In the financial industry funds and securities of capitalsuppliers are continuously transmitted in the process offinancial operation through a series of investment and fi-nancing activities of financial entities Various financialentities are formed according to the relationship betweeninvestment and financing With the development of theeconomy an intricate financial ecological network has beenformed +e more the cooperation and the closer the co-operation in the financial industry the higher the value of itsproducts the richer the variety the more developed themarket and the stronger the financing ability +e morecomplex its industry is the more stable it will be +econstruction and development of smart cities should inte-grate ldquoInternet +rdquo into the financial industry integrate andutilize the development resources of the Internet financialindustry truly realize the financial innovation driven byinformation technology and use the Internet to reduceeconomic search costs and transaction costs macroscopi-cally focusing on externalities and the network effect makesuse of the Internet to promote the positive development ofmanagement mechanisms technological innovation andcorporate culture in the industry Improve the financialentities required for each development link in the financialindustry so as to provide a stable chain of funds for urbandevelopment and industrial innovation in the process ofcomplication strengthen communication and cooperationamong various financial entities and realize the financialand information industryrsquos complementary advantages andwin-win cooperation +e cityrsquos intelligent construction alsocreates a good financial environment for industrial devel-opment During the development of the industry throughthe innovation of financial institutions financial servicesfinancial mechanisms financial structures and the expan-sion of financing channels optimize the industrial
6 Complexity
upgrading model improve the services of urban financialorganizations and form a financial center with a reasonablelayoutmdashindustry as a financial entity+e characteristics andcapital advantages of Internet companies have technologicaland innovation advantages As a huge platform for thedevelopment of the financial industry cities have pipelineadvantages and many mobile terminals and ultimatelyachieve the integration of industry and city Financial ITcostanalysis is shown in Figure 7
33 Empirical Analyses +e financial cloud has the char-acteristics of huge investment in the early stage of businessdevelopment and the short-term and short-term incomemay not be fully guaranteed Many companies rely onventure capital funds or through the stock market to obtainfunds to ensure the companyrsquos sustainable developmentFigure 8 is the net profit compiled according to the smartcityrsquos financial reports in the past years We have seen anegative net profit since 2011 Now with the support offinancial cloud technology you do not have to worry aboutchanges in business cycles or customer groups that causeproblems with working capital You can calmly face changesin various market environments
To evaluate an object a certain decision-making unitneeds to be selected and certain criteria need to be adoptedin this selection +e specific selection principle shouldensure that the selected decision-making unit can achieve
the same when it is in the same external environment Tasksachieve the same input and output +ere are three ways toselect DMU in the evaluation process of smart citiesnamely vertical and horizontal comparison and verticaland horizontal comprehensive comparison +is articleuses a longitudinal comparison to select the DMU Basedon the obtained data this paper selects the comprehensiveevaluation of the urban construction in the past three yearsand analyzes the results of the smart city construction inShanghai in the past three years based on scientific resultsBased on the above analysis it can be found that the smartcity construction has achieved certain results through inputand output Pay attention to the human cost infrastructureand capital input and the corresponding output of smartgovernment smart economy smart life and smart humanliteracy is efficient With continuous investment in majorfactors the effectiveness of the governmentrsquos DMU fluc-tuates around 1 which also proves that the principle ofeffectiveness can be met in the selection of input and outputindicators In further analysis of factor productivity we cansee that all major factors are effective and the application ofnew technologies and innovations in production andmanagement has achieved good results +is is also furthershowing that if the government major financial institu-tions and other stakeholders attach importance to theinvestment in various elements they can achieve goodresults to a large extent +e regression test results areshown in Figure 9
Table 1 Evaluation index system
Smart city construction
Infrastructure
A1 investment in fixed assets of the whole society Million dollarsA2 urban road area Square kilometersA3 drainage pipe length KilometersA4 passenger traffic of the whole society Ten thousandsA5 total volume of post and telecommunications services Million dollars
UrbanizationA6 urbanization rate A7 urban population density A8 per capita disposable income of urban residents Yuan
Composite indexindustrial development
Development scale
B1 the amount of investment in fixed assets of informationtechnology and Internet companies Million dollars
B2 number of information technology and software companies PcB3 information technology software practitioners People
Industrial structure B4 tertiary industry output value as a percentage of GDP B5 the proportion of output value of secondary industry to GDP Item
Technology supportcapability
B6 patent authorization PcB7 number of national key laboratories Million dollarsB8 RampD internal expenditure PeopleB9 number of RampD researchers
Composite index ecologicalprotectioncomprehensive index
Urban ecologyC1 green coverage rate in built-up area Square meter
C2 park green area per capita Ten thousandtons
Environmentalprotection
C3 industrial solid waste generation Ten thousandtons
C4 comprehensive utilization of industrial solid waste Million dollars
Voice networkIT service deskEnd-user computingData center
0
10
20
30
40
50
60
70
80
90
100
2016 2017 2018 20192015Year
Figure 7 Distribution of IT costs
ndash300
ndash250
ndash200
ndash150
ndash100
ndash50
0
50
100
One
mill
ion
US
dolla
rs
2012 2013 2014 2015 2016 2017 2018 20192011Year
Figure 8 City net profit over the years
Complexity 9
4 Conclusion
+is article first discusses the relationship between smartcities and finance and existing models based on existingrelevant theories and literature By comparing and an-alyzing the status of smart cities and financial clouds athome and abroad this paper applies financial cloud in-telligence to build smart cities It has laid the foundationfor empirical analysis and model innovation +eoreticalresearch of financial technology and smart city is inte-grated with each other to study and analyze the health andperfect sustainable road of smart city +e problems in thesustainable development of smart cities should be solvedfrom the aspects of economy human problems envi-ronmental science urban infrastructure and ecologicalenvironment Explore the innovation of the smart citymodel based on the ldquofinancial cloudrdquo perspective Inorder to achieve the sustainable development goals ofsmart cities explore the dynamic mechanism of sus-tainable development of smart cities through the analysisof the structure and elements of smart cities and improvethe construction of financial ecological chain in order toachieve the goal of model innovation Finally this paperproposes a way to realize the integration of smart city andfinancial cloud which has certain guiding significance forthe sustainable development of the smart city
Data Availability
+e data used to support the findings of this study areavailable from the corresponding author upon request
Conflicts of Interest
+e authors declare that they have no conflicts of interestregarding the publication of this paper
Acknowledgments
+is work was supported by the General Project of theNational Natural Science Foundation of China (Nos71874037 and 71673062) and Natural Science Foundation ofGuangdong Province (No 2018A030313397)
References
[1] C Kakderi N Komninos and P Tsarchopoulos ldquoSmart citiesand cloud computing lessons from the STORM CLOUDSexperimentrdquo Journal of Smart Cities vol 1 no 2 pp 4ndash132019
[2] P Tsarchopoulos N Komninos and C Kakderi ldquoAcceler-ating the uptake of smart city applications through cloudcomputing World Academy of Science Engineering andTechnologyrdquo International Journal of Social BehavioralEducational Economic Business and Industrial Engineeringvol 11 no 1 pp 129ndash138 2017
[3] N Komninos C Kakderi A Panori and P TsarchopoulosldquoSmart city planning from an evolutionary perspectiverdquoJournal of Urban Technology vol 26 no 2 pp 3ndash20 2019
[4] N P Rana S Luthra S K Mangla R Islam S Roderick andY K Dwivedi ldquoBarriers to the development of smart cities inIndian contextrdquo Information Systems Frontiers vol 21 no 3pp 503ndash525 2019
[5] G Trencher and A Karvonen ldquoStretching ldquosmartrdquo advancinghealth and well-being through the smart city agendardquo LocalEnvironment vol 24 no 7 pp 610ndash627 2019
[6] S P Mohanty U Choppali and E Kougianos ldquoEverythingyou wanted to know about smart cities the internet of thingsis the backbonerdquo IEEE Consumer Electronics Magazine vol 5no 3 pp 60ndash70 2016
[7] Y Mehmood F Ahmad I Yaqoob A Adnane M Imranand S Guizani ldquoInternet-of-things-based smart cities recentadvances and challengesrdquo IEEE Communications Magazinevol 55 no 9 pp 16ndash24 2017
[8] M Angelidou ldquo+e role of smart city characteristics in theplans of fifteen citiesrdquo Journal of Urban Technology vol 24no 4 pp 3ndash28 2017
[9] M Sookhak H Tang Y He et al ldquoSecurity and privacy ofsmart cities a survey research issues and challengesrdquo IEEECommunications Surveys amp Tutorials vol 21 no 2pp 1718ndash1743 2018
[10] S Berke ldquo+e sustainable development of data-driven smartcities citizen-centered urban governance and networkeddigital technologiesrdquo Geopolitics History and InternationalRelations vol 11 no 2 pp 122ndash127 2019
[11] M Masera E F Bompard F Profumo and N HadjsaidldquoSmart (electricity) grids for smart cities assessing roles andsocietal impactsrdquo Proceedings of the IEEE vol 106 no 4pp 613ndash625 2018
[12] A Panori A Gonzalez-Quel M Tavares et al ldquoMigration ofapplications to the Cloud a user-driven approachrdquo Journal ofSmart Cities vol 2 no 1 pp 16ndash27 2019
[13] J Xie H Tang T Huang et al ldquoA survey of blockchaintechnology applied to smart cities research issues and chal-lengesrdquo IEEE Communications Surveys amp Tutorials vol 21no 3 pp 2794ndash2830 2019
[14] E F Z Santana A P Chaves M A Gerosa et al ldquoSoftwareplatforms for smart cities concepts requirements challengesand a unified reference architecturerdquo ACM Computing Sur-veys vol 50 no 6 pp 1ndash37 2017
Unstandardized regression coefficientUnstandardized standard errorStandard coefficientt
SigToleranceVIF
0
1
2
3
4
5
Valu
es
X2 X3 X4X1Variable
Figure 9 Regression coefficient test and significance level
10 Complexity
[15] X Kong X Liu B Jedari M Li L Wan and F Xia ldquoMobilecrowdsourcing in smart cities technologies applications andfuture challengesrdquo IEEE Internet of 2ings Journal vol 6no 5 pp 8095ndash8113 2019
[16] D Eckhoff and I Wagner ldquoPrivacy in the smart cit-ymdashapplications technologies challenges and solutionsrdquoIEEE Communications Surveys amp Tutorials vol 20 no 1pp 489ndash516 2017
[17] A-M Valdez M Cook P-A Langendahl H Roby andS Potter ldquoPrototyping sustainable mobility practices user-generated data in the smart cityrdquo Technology Analysis ampStrategic Management vol 30 no 2 pp 144ndash157 2018
[18] H Xu and X Geng ldquoPeople-centric service intelligence forsmart citiesrdquo Smart Cities vol 2 no 2 pp 135ndash152 2019
[19] J Arthi ldquoBottom billion strategy-smart Villagesrdquo AsianJournal of Multidimensional Research (AJMR) vol 7 no 1pp 63ndash66 2018
[20] A Gharaibeh M A Salahuddin S J Hussini et al ldquoSmartcities a survey on data management security and enablingtechnologiesrdquo IEEE Communications Surveys amp Tutorialsvol 19 no 4 pp 2456ndash2501 2017
[21] N F Alwan andM K AL-Nuaimi ldquo+e role and important ofinternet of things in building sustainable cityrdquo Engineeringand Technology Journal vol 36 no 1 pp 22ndash29 2018
[22] M Aamir S Masroor Z A Ali and B T Ting ldquoSustainableframework for smart transportation system a case study ofkarachirdquo Wireless Personal Communications vol 106 no 1pp 27ndash40 2019
[23] S P Caird and S H Hallett ldquoTowards evaluation design forsmart city developmentrdquo Journal of Urban Design vol 24no 2 pp 188ndash209 2019
Complexity 11
22 Smart City Financial CloudModel +e construction of asmart city requires the interaction and interaction of the in-dustrial ecological chain information chain innovation chainfinancial chain cultural chain and service chain to form acomplete smart city operation system that integrates humancapital infrastructure construction and capital [23] Resourceelements are put into this system and smart government smarteconomy and smart life have become the products of thissystem +e specific logical relationship is shown in Figure 2
Enterprises and governments differ in the wisdom of citymanagement In the course of time the government no longervalues the advantages of intelligent construction of publicservices but pays more attention to the potential and devel-opment of ICT in addition to the effectiveness of city man-agement after the use of its products
In smart city operation and management DMU is thedecision-making unit and there are n number of participantsthat need to participate in the evaluation +e inputs andoutputs of these DMUs are m and s respectively +e inputvector of the jth DMU is xj (x1j xmj)Tgt0(j 1 n) and the output vector is yj (y1j
ysj)Tgt 0(j 1 n) +e weight coefficients of the outputand input indicators of these decision-making units areu (u1 us)T v (v1 vm)T +e efficient evaluationindex corresponding to each DMU is
Kj 1113936
sr1 uryrj
1113936ni1 vixij
(1)
In the evaluation process it is necessary to select appropriateindex values When the input values of some indicators areincreased all output values will be increased when the values of
other indicators are increased some output values will be re-duced +e output index project of the smart city is mainlydivided into four parts smart government smart life smarthumanities and smart economic development +e requiredinput indicators are mainly divided into three parts capital andhuman capital investment and infrastructure construction In-put indicators are divided into positive and negative We defineinput indicators that have a positive impact on citizensrsquo lives aspositive input indicators while indicators that have a negativeimpact on citizensrsquo lives are defined as passive input indicators
+e construction of the Malmquist productivity indexwith reference to t and t+ 1 period has made the Malmquistindex widely used +e specific formula is as follows
W xt y
t x
t+1 y
t+11113872 1113873
Dct
xt+1
yt+1
1113872 1113873
Dct
xt y
t1113872 1113873
timesDc
tx
t+1 y
t+11113872 1113873
Dct+1
xt y
t1113872 1113873
⎡⎢⎣ ⎤⎥⎦
W xt y
t x
t+1 y
t+11113872 1113873
Dct
xt+1
yt+1
1113872 1113873
Dct
xt y
t1113872 1113873
timesDct xt+1 yt+1( 1113857
Dct xt yt( 11138571113890
timesDct xt+1 yt+1( 1113857
Dct+1 xt yt( 11138571113891
12
(2)
+is paper quantitatively analyzes the coupling andcoordination relationship of the industrial-city integrationof smart cities through the evaluation of the coupling co-ordination degree After fully referring to the relevant re-search a scientifically constructed coupling and coordinatedevaluation model of smart city construction industrial
Intelligent identificationartificial intelligencebig data
Cloud ComputingCloud StorageCloud Network
Disaster recovery
Multioperation
O amp M
Data isolation
Network resources
Security resources
Server resources
Storage resources
Virt
ualiz
atio
n m
anag
emen
tFinancial data
centerSmart city
Financial cloud center
Internet resources
PAAS IAAS
Big dataSAAS
Financial cooperation
Supply chain financial
Big data risk control
Corporate services
IntelligentInternet
Figure 1 Smart financial cloud
Complexity 3
development and urban ecological protection are built +especific formula is shown in
T U1 times U2 times U3( 1113857
U1 + U2 + U3( 1113857 times U1 + U2 + U3( 11138571113896 1113897
13
(3)
C represents the degree of coupling between the smartcity construction system industrial development systemand ecological protection system +e level of developmentof a system is not proportional to the degree of couplingwithin the system and there may be a low degree of couplingwithin the system of high-level development and vice versaOn this basis we can revise the coupling model and measurethe true coordination between the three systems to build acoordination model as follows
R (CT)12
(4)
Indicators have a positive effect and a negative effect onthe system +e unit of each indicator also has a certaindifference Each indicator needs to be processed beforecalculation After determining the positive and negativedirections of the index use the following formula for dataprocessing
vij xij minus min xij1113872 1113873
max xij1113872 1113873 minus min xij1113872 1113873
vij min xij1113872 1113873 minus xij
max xij1113872 1113873 minus min xij1113872 1113873
(5)
Weight refers to the contribution rate of an indicator tothe entire indicator system Only after determining theweight can the system be reasonably measured In this paperthe weight of the entropy method is used to determine theweight +e formula is as follows
Hi123 1113944m
j1λijuij (6)
All the subsystems of the smart city ecosystem need tobuild a comprehensive information platform to realize theinteraction and link of information between the smart cityand the industry +e industrial development goals of smartcities are different from other cities +e pillar industries ofcities should focus on the cultivation of smart industries+rough resource integration and high-tech support forsmart cities as well as the creation of a market environmentthe industryrsquos supporting cooperation will be fully utilizedfor urban construction +e ldquoInnovation + Entrepreneur-shiprdquo dual-innovation platform relies on the joint action ofsmart city innovation elements innovation awareness andinnovation capabilities Innovation is the vitality of smartcities Entrepreneurship injects fresh blood into smart citiesand cities create incisiveness for ldquodouble innovationrdquo socialenvironment +e informatization industry is the leader ofcities towards wisdom emphasizing that informatizationtechnology informatization industry and informatizationservices not only promote the development of smart citiesbut also promote the development of smart cities and co-ordinated development of industries and cities +e coor-dinated integration of ecological chain resources furtherbuilds a more complete ldquoecological circlerdquo of smart cityindustry-city integration to achieve full utilization of cityfunctions and industrial development and maximize sharingof information technology (Figure 3)
23 Sustainability Evaluations +e scope of constructioncan divide the construction of smart cities Individual keybreakthroughs can also be carried out in an all-roundmanner Within the scope of the ability of city constructionthe two can also be considered at the same time From the
Figure 2 Schematic diagram of smart city financial investment
4 Complexity
beginning of Chinarsquos proposal for smart city construction tothe present day a total of three batches of smart city pilotlists have been released Increased cities are taking smartcities as their construction goals and the selected smart citythemes are not the same +e first batch of cities partici-pating in the construction Shenzhen and Nanjing weremainly led by innovation and the pilot cities that partici-pated in the construction afterwards mainly determined thedevelopment priorities according to the characteristics of theconstruction main body and future needs so as to integratethe cityrsquos established development strategic goals andachieve smart cities +e construction of the building isorganically combined +e sustainable development modelof smart city can be divided into four categories
(1) Innovative at this stage in the army of Chinarsquos smartcity construction most cities regard innovation as animportant driving force Most of these cities alreadyhave a good development foundation and strongstrength and regard the development of ldquosmartrdquoindustries as a strategy to enhance cities which arethe key factors of status and competitive strength
(2) +e urban development of smart industries in theprocess of urban development such cities havegradually cultivated and formed characteristic in-dustries with their own advantages and formed a
complete upstream and downstream ecological en-vironment in related fields+erefore these cities aremore focused on maintaining and displaying theirown advantages and intelligent industries For thedevelopment of Kunshan for example the authorsin [23] proposed focusing on accelerating the de-velopment of the Internet of +ings electronictechnology and other industries and promoting theconstruction of smart cities
(3) Developing the wisdom and livelihood such citiesfocus on the development of intelligent managementand livelihood services to promote+e wisdom of the city avoids the ldquobig city diseaserdquo insmall and medium-sized cities +e top priority ofthe development of smart cities is people-orientedand its connotation is that all ldquopeoplerdquo of peoplersquoslivelihood are the core serving the ldquopeoplerdquo in thecity as much as possible so as to realize a green safeand efficient intelligent life
(4) Development of information technology infra-structure the construction of smart cities in thiscategory focuses on information technology andinformation infrastructure For example with thehelp of advanced information technology a modernand integrated administrative decision-making
Smart city industry
ecosystem
Resource utilization Business choice
Material and financial
integration
Scientific and technological development
Smart city operations
Collaborative integration
Collaborative integration
Collaborative integration
Collaborative integration
Improve development
Promote diversity
Promote interaction
Information Industry
Financial industry Culture industry
Innovation industry
Pillar industry
Figure 3 Smart city industrial ecosystem
Complexity 5
auxiliary system has been established Operation inmajor enterprises and national administrative de-partments has proved that similar systems can ef-fectively improve managersrsquo work efficiency anddecision-making ability
+e goal of constructing an evaluation index system forsmart city sustainable development is to reflect the actualstatus of smart city sustainable development When selectingindicators pay attention to the representativeness andquantification of indicators and build a comprehensiveindicator system Quantitative analysis of targets is donethrough quantifying indicators +e specific evaluation in-dex system is shown in Table 1
3 Results Analysis
31 Model Analyses +e reliability test is to check thestability and consistency of the measurement results +isstudy uses SPSS software for internal reliability testingand reflects its reliability through Cronbachrsquos X coeffi-cient Assuming the reliability of the evaluation indexsystem and each dimension as shown in Table 2 weconsidered the overall internal consistency of the eval-uation index system Cronbachrsquos coefficient is 0386greater than 05 and Cronbachrsquos coefficients of the threecommon factors are all greater than 05 so the indexsystemmeets certain reliability requirements +e specificindicators are shown in Table 2 Starting from the scoringcoefficient matrix of exploratory factor analysis of themodel a comprehensive evaluation model is constructedwith the variance contribution rate of each commonfactor as shown in Figure 4+e score coefficient matrix ofeach common factor and the variance contribution rate ofeach factor are shown in Figure 4
According to the coupling and coordination modelsystematically calculate the coupling and coordination be-tween smart city construction industrial development andecological protection and finally determine the evaluationlevel +e results are shown in Figure 5 In terms of couplingdegree the 12 smart cities are in low-level coupling andhigh-level phases respectively In terms of coordinationdegree except for Shenzhen Beijing and Shanghai whichhave reached moderate coordination the remaining citiesare in low coordination +e overall development index ofindividual cities is low and the evaluation index of theindustrial development system is higher than that of theurban construction system indicating that industrial de-velopment has failed to drive the development of infra-structure construction in smart cities +e proportion of thesecondary industry in the industrial structure of some citiesis still higher than that of the tertiary industry which leads toa higher comprehensive evaluation index of the industrialdevelopment system than the urban construction system+is is because the proportion of emerging industries is verysmall and cannot become the pillars or leading enterprises ofthe cities +e endogenous power is insufficient and theconstruction of smart cities fails to effectively promote thedevelopment of the industry
32 Sustainability Analyses +e study used SPSS software forcase clustering and cluster analysis of the comprehensive rankingof sustainable development of smart cities in provincial ad-ministrative regions+e results are shown in Figure 6 From theresults of the above five-year factor equivalence and compre-hensive evaluation score ranking it can be intuitively found thatthe factor score and comprehensive rankings of category A-typecities are all in the top It can be concluded that such areas are atthe forefront of the country in the construction of smart citiesand a reference for the smart construction of other cities can beprovided +erefore these areas are positioned as leading areasas the ldquoleadersrdquo for the sustainable development of smart cityconstruction +e analysis of C-type cities shows that the de-velopment of cities is not stable enough and there is no con-tinuous growth It is necessary to adjust their smart cityconstruction models and other policies and find and deal withthe problems in time +e economic level of such cities lagsbehind other provincial administrative regions+e constructionof smart cities will inevitably be restricted and affectedHoweversuch regions have a strong willingness to progress and developDevelopment requires strong national support and the for-mulation of relevant policies It can be seen that such provincialadministrative regions should be positioned as catch-up areas asa ldquocatch-up armyrdquo for the sustainable development of smart cityconstruction
In the financial industry funds and securities of capitalsuppliers are continuously transmitted in the process offinancial operation through a series of investment and fi-nancing activities of financial entities Various financialentities are formed according to the relationship betweeninvestment and financing With the development of theeconomy an intricate financial ecological network has beenformed +e more the cooperation and the closer the co-operation in the financial industry the higher the value of itsproducts the richer the variety the more developed themarket and the stronger the financing ability +e morecomplex its industry is the more stable it will be +econstruction and development of smart cities should inte-grate ldquoInternet +rdquo into the financial industry integrate andutilize the development resources of the Internet financialindustry truly realize the financial innovation driven byinformation technology and use the Internet to reduceeconomic search costs and transaction costs macroscopi-cally focusing on externalities and the network effect makesuse of the Internet to promote the positive development ofmanagement mechanisms technological innovation andcorporate culture in the industry Improve the financialentities required for each development link in the financialindustry so as to provide a stable chain of funds for urbandevelopment and industrial innovation in the process ofcomplication strengthen communication and cooperationamong various financial entities and realize the financialand information industryrsquos complementary advantages andwin-win cooperation +e cityrsquos intelligent construction alsocreates a good financial environment for industrial devel-opment During the development of the industry throughthe innovation of financial institutions financial servicesfinancial mechanisms financial structures and the expan-sion of financing channels optimize the industrial
6 Complexity
upgrading model improve the services of urban financialorganizations and form a financial center with a reasonablelayoutmdashindustry as a financial entity+e characteristics andcapital advantages of Internet companies have technologicaland innovation advantages As a huge platform for thedevelopment of the financial industry cities have pipelineadvantages and many mobile terminals and ultimatelyachieve the integration of industry and city Financial ITcostanalysis is shown in Figure 7
33 Empirical Analyses +e financial cloud has the char-acteristics of huge investment in the early stage of businessdevelopment and the short-term and short-term incomemay not be fully guaranteed Many companies rely onventure capital funds or through the stock market to obtainfunds to ensure the companyrsquos sustainable developmentFigure 8 is the net profit compiled according to the smartcityrsquos financial reports in the past years We have seen anegative net profit since 2011 Now with the support offinancial cloud technology you do not have to worry aboutchanges in business cycles or customer groups that causeproblems with working capital You can calmly face changesin various market environments
To evaluate an object a certain decision-making unitneeds to be selected and certain criteria need to be adoptedin this selection +e specific selection principle shouldensure that the selected decision-making unit can achieve
the same when it is in the same external environment Tasksachieve the same input and output +ere are three ways toselect DMU in the evaluation process of smart citiesnamely vertical and horizontal comparison and verticaland horizontal comprehensive comparison +is articleuses a longitudinal comparison to select the DMU Basedon the obtained data this paper selects the comprehensiveevaluation of the urban construction in the past three yearsand analyzes the results of the smart city construction inShanghai in the past three years based on scientific resultsBased on the above analysis it can be found that the smartcity construction has achieved certain results through inputand output Pay attention to the human cost infrastructureand capital input and the corresponding output of smartgovernment smart economy smart life and smart humanliteracy is efficient With continuous investment in majorfactors the effectiveness of the governmentrsquos DMU fluc-tuates around 1 which also proves that the principle ofeffectiveness can be met in the selection of input and outputindicators In further analysis of factor productivity we cansee that all major factors are effective and the application ofnew technologies and innovations in production andmanagement has achieved good results +is is also furthershowing that if the government major financial institu-tions and other stakeholders attach importance to theinvestment in various elements they can achieve goodresults to a large extent +e regression test results areshown in Figure 9
Table 1 Evaluation index system
Smart city construction
Infrastructure
A1 investment in fixed assets of the whole society Million dollarsA2 urban road area Square kilometersA3 drainage pipe length KilometersA4 passenger traffic of the whole society Ten thousandsA5 total volume of post and telecommunications services Million dollars
UrbanizationA6 urbanization rate A7 urban population density A8 per capita disposable income of urban residents Yuan
Composite indexindustrial development
Development scale
B1 the amount of investment in fixed assets of informationtechnology and Internet companies Million dollars
B2 number of information technology and software companies PcB3 information technology software practitioners People
Industrial structure B4 tertiary industry output value as a percentage of GDP B5 the proportion of output value of secondary industry to GDP Item
Technology supportcapability
B6 patent authorization PcB7 number of national key laboratories Million dollarsB8 RampD internal expenditure PeopleB9 number of RampD researchers
Composite index ecologicalprotectioncomprehensive index
Urban ecologyC1 green coverage rate in built-up area Square meter
C2 park green area per capita Ten thousandtons
Environmentalprotection
C3 industrial solid waste generation Ten thousandtons
C4 comprehensive utilization of industrial solid waste Million dollars
Voice networkIT service deskEnd-user computingData center
0
10
20
30
40
50
60
70
80
90
100
2016 2017 2018 20192015Year
Figure 7 Distribution of IT costs
ndash300
ndash250
ndash200
ndash150
ndash100
ndash50
0
50
100
One
mill
ion
US
dolla
rs
2012 2013 2014 2015 2016 2017 2018 20192011Year
Figure 8 City net profit over the years
Complexity 9
4 Conclusion
+is article first discusses the relationship between smartcities and finance and existing models based on existingrelevant theories and literature By comparing and an-alyzing the status of smart cities and financial clouds athome and abroad this paper applies financial cloud in-telligence to build smart cities It has laid the foundationfor empirical analysis and model innovation +eoreticalresearch of financial technology and smart city is inte-grated with each other to study and analyze the health andperfect sustainable road of smart city +e problems in thesustainable development of smart cities should be solvedfrom the aspects of economy human problems envi-ronmental science urban infrastructure and ecologicalenvironment Explore the innovation of the smart citymodel based on the ldquofinancial cloudrdquo perspective Inorder to achieve the sustainable development goals ofsmart cities explore the dynamic mechanism of sus-tainable development of smart cities through the analysisof the structure and elements of smart cities and improvethe construction of financial ecological chain in order toachieve the goal of model innovation Finally this paperproposes a way to realize the integration of smart city andfinancial cloud which has certain guiding significance forthe sustainable development of the smart city
Data Availability
+e data used to support the findings of this study areavailable from the corresponding author upon request
Conflicts of Interest
+e authors declare that they have no conflicts of interestregarding the publication of this paper
Acknowledgments
+is work was supported by the General Project of theNational Natural Science Foundation of China (Nos71874037 and 71673062) and Natural Science Foundation ofGuangdong Province (No 2018A030313397)
References
[1] C Kakderi N Komninos and P Tsarchopoulos ldquoSmart citiesand cloud computing lessons from the STORM CLOUDSexperimentrdquo Journal of Smart Cities vol 1 no 2 pp 4ndash132019
[2] P Tsarchopoulos N Komninos and C Kakderi ldquoAcceler-ating the uptake of smart city applications through cloudcomputing World Academy of Science Engineering andTechnologyrdquo International Journal of Social BehavioralEducational Economic Business and Industrial Engineeringvol 11 no 1 pp 129ndash138 2017
[3] N Komninos C Kakderi A Panori and P TsarchopoulosldquoSmart city planning from an evolutionary perspectiverdquoJournal of Urban Technology vol 26 no 2 pp 3ndash20 2019
[4] N P Rana S Luthra S K Mangla R Islam S Roderick andY K Dwivedi ldquoBarriers to the development of smart cities inIndian contextrdquo Information Systems Frontiers vol 21 no 3pp 503ndash525 2019
[5] G Trencher and A Karvonen ldquoStretching ldquosmartrdquo advancinghealth and well-being through the smart city agendardquo LocalEnvironment vol 24 no 7 pp 610ndash627 2019
[6] S P Mohanty U Choppali and E Kougianos ldquoEverythingyou wanted to know about smart cities the internet of thingsis the backbonerdquo IEEE Consumer Electronics Magazine vol 5no 3 pp 60ndash70 2016
[7] Y Mehmood F Ahmad I Yaqoob A Adnane M Imranand S Guizani ldquoInternet-of-things-based smart cities recentadvances and challengesrdquo IEEE Communications Magazinevol 55 no 9 pp 16ndash24 2017
[8] M Angelidou ldquo+e role of smart city characteristics in theplans of fifteen citiesrdquo Journal of Urban Technology vol 24no 4 pp 3ndash28 2017
[9] M Sookhak H Tang Y He et al ldquoSecurity and privacy ofsmart cities a survey research issues and challengesrdquo IEEECommunications Surveys amp Tutorials vol 21 no 2pp 1718ndash1743 2018
[10] S Berke ldquo+e sustainable development of data-driven smartcities citizen-centered urban governance and networkeddigital technologiesrdquo Geopolitics History and InternationalRelations vol 11 no 2 pp 122ndash127 2019
[11] M Masera E F Bompard F Profumo and N HadjsaidldquoSmart (electricity) grids for smart cities assessing roles andsocietal impactsrdquo Proceedings of the IEEE vol 106 no 4pp 613ndash625 2018
[12] A Panori A Gonzalez-Quel M Tavares et al ldquoMigration ofapplications to the Cloud a user-driven approachrdquo Journal ofSmart Cities vol 2 no 1 pp 16ndash27 2019
[13] J Xie H Tang T Huang et al ldquoA survey of blockchaintechnology applied to smart cities research issues and chal-lengesrdquo IEEE Communications Surveys amp Tutorials vol 21no 3 pp 2794ndash2830 2019
[14] E F Z Santana A P Chaves M A Gerosa et al ldquoSoftwareplatforms for smart cities concepts requirements challengesand a unified reference architecturerdquo ACM Computing Sur-veys vol 50 no 6 pp 1ndash37 2017
Unstandardized regression coefficientUnstandardized standard errorStandard coefficientt
SigToleranceVIF
0
1
2
3
4
5
Valu
es
X2 X3 X4X1Variable
Figure 9 Regression coefficient test and significance level
10 Complexity
[15] X Kong X Liu B Jedari M Li L Wan and F Xia ldquoMobilecrowdsourcing in smart cities technologies applications andfuture challengesrdquo IEEE Internet of 2ings Journal vol 6no 5 pp 8095ndash8113 2019
[16] D Eckhoff and I Wagner ldquoPrivacy in the smart cit-ymdashapplications technologies challenges and solutionsrdquoIEEE Communications Surveys amp Tutorials vol 20 no 1pp 489ndash516 2017
[17] A-M Valdez M Cook P-A Langendahl H Roby andS Potter ldquoPrototyping sustainable mobility practices user-generated data in the smart cityrdquo Technology Analysis ampStrategic Management vol 30 no 2 pp 144ndash157 2018
[18] H Xu and X Geng ldquoPeople-centric service intelligence forsmart citiesrdquo Smart Cities vol 2 no 2 pp 135ndash152 2019
[19] J Arthi ldquoBottom billion strategy-smart Villagesrdquo AsianJournal of Multidimensional Research (AJMR) vol 7 no 1pp 63ndash66 2018
[20] A Gharaibeh M A Salahuddin S J Hussini et al ldquoSmartcities a survey on data management security and enablingtechnologiesrdquo IEEE Communications Surveys amp Tutorialsvol 19 no 4 pp 2456ndash2501 2017
[21] N F Alwan andM K AL-Nuaimi ldquo+e role and important ofinternet of things in building sustainable cityrdquo Engineeringand Technology Journal vol 36 no 1 pp 22ndash29 2018
[22] M Aamir S Masroor Z A Ali and B T Ting ldquoSustainableframework for smart transportation system a case study ofkarachirdquo Wireless Personal Communications vol 106 no 1pp 27ndash40 2019
[23] S P Caird and S H Hallett ldquoTowards evaluation design forsmart city developmentrdquo Journal of Urban Design vol 24no 2 pp 188ndash209 2019
Complexity 11
development and urban ecological protection are built +especific formula is shown in
T U1 times U2 times U3( 1113857
U1 + U2 + U3( 1113857 times U1 + U2 + U3( 11138571113896 1113897
13
(3)
C represents the degree of coupling between the smartcity construction system industrial development systemand ecological protection system +e level of developmentof a system is not proportional to the degree of couplingwithin the system and there may be a low degree of couplingwithin the system of high-level development and vice versaOn this basis we can revise the coupling model and measurethe true coordination between the three systems to build acoordination model as follows
R (CT)12
(4)
Indicators have a positive effect and a negative effect onthe system +e unit of each indicator also has a certaindifference Each indicator needs to be processed beforecalculation After determining the positive and negativedirections of the index use the following formula for dataprocessing
vij xij minus min xij1113872 1113873
max xij1113872 1113873 minus min xij1113872 1113873
vij min xij1113872 1113873 minus xij
max xij1113872 1113873 minus min xij1113872 1113873
(5)
Weight refers to the contribution rate of an indicator tothe entire indicator system Only after determining theweight can the system be reasonably measured In this paperthe weight of the entropy method is used to determine theweight +e formula is as follows
Hi123 1113944m
j1λijuij (6)
All the subsystems of the smart city ecosystem need tobuild a comprehensive information platform to realize theinteraction and link of information between the smart cityand the industry +e industrial development goals of smartcities are different from other cities +e pillar industries ofcities should focus on the cultivation of smart industries+rough resource integration and high-tech support forsmart cities as well as the creation of a market environmentthe industryrsquos supporting cooperation will be fully utilizedfor urban construction +e ldquoInnovation + Entrepreneur-shiprdquo dual-innovation platform relies on the joint action ofsmart city innovation elements innovation awareness andinnovation capabilities Innovation is the vitality of smartcities Entrepreneurship injects fresh blood into smart citiesand cities create incisiveness for ldquodouble innovationrdquo socialenvironment +e informatization industry is the leader ofcities towards wisdom emphasizing that informatizationtechnology informatization industry and informatizationservices not only promote the development of smart citiesbut also promote the development of smart cities and co-ordinated development of industries and cities +e coor-dinated integration of ecological chain resources furtherbuilds a more complete ldquoecological circlerdquo of smart cityindustry-city integration to achieve full utilization of cityfunctions and industrial development and maximize sharingof information technology (Figure 3)
23 Sustainability Evaluations +e scope of constructioncan divide the construction of smart cities Individual keybreakthroughs can also be carried out in an all-roundmanner Within the scope of the ability of city constructionthe two can also be considered at the same time From the
Figure 2 Schematic diagram of smart city financial investment
4 Complexity
beginning of Chinarsquos proposal for smart city construction tothe present day a total of three batches of smart city pilotlists have been released Increased cities are taking smartcities as their construction goals and the selected smart citythemes are not the same +e first batch of cities partici-pating in the construction Shenzhen and Nanjing weremainly led by innovation and the pilot cities that partici-pated in the construction afterwards mainly determined thedevelopment priorities according to the characteristics of theconstruction main body and future needs so as to integratethe cityrsquos established development strategic goals andachieve smart cities +e construction of the building isorganically combined +e sustainable development modelof smart city can be divided into four categories
(1) Innovative at this stage in the army of Chinarsquos smartcity construction most cities regard innovation as animportant driving force Most of these cities alreadyhave a good development foundation and strongstrength and regard the development of ldquosmartrdquoindustries as a strategy to enhance cities which arethe key factors of status and competitive strength
(2) +e urban development of smart industries in theprocess of urban development such cities havegradually cultivated and formed characteristic in-dustries with their own advantages and formed a
complete upstream and downstream ecological en-vironment in related fields+erefore these cities aremore focused on maintaining and displaying theirown advantages and intelligent industries For thedevelopment of Kunshan for example the authorsin [23] proposed focusing on accelerating the de-velopment of the Internet of +ings electronictechnology and other industries and promoting theconstruction of smart cities
(3) Developing the wisdom and livelihood such citiesfocus on the development of intelligent managementand livelihood services to promote+e wisdom of the city avoids the ldquobig city diseaserdquo insmall and medium-sized cities +e top priority ofthe development of smart cities is people-orientedand its connotation is that all ldquopeoplerdquo of peoplersquoslivelihood are the core serving the ldquopeoplerdquo in thecity as much as possible so as to realize a green safeand efficient intelligent life
(4) Development of information technology infra-structure the construction of smart cities in thiscategory focuses on information technology andinformation infrastructure For example with thehelp of advanced information technology a modernand integrated administrative decision-making
Smart city industry
ecosystem
Resource utilization Business choice
Material and financial
integration
Scientific and technological development
Smart city operations
Collaborative integration
Collaborative integration
Collaborative integration
Collaborative integration
Improve development
Promote diversity
Promote interaction
Information Industry
Financial industry Culture industry
Innovation industry
Pillar industry
Figure 3 Smart city industrial ecosystem
Complexity 5
auxiliary system has been established Operation inmajor enterprises and national administrative de-partments has proved that similar systems can ef-fectively improve managersrsquo work efficiency anddecision-making ability
+e goal of constructing an evaluation index system forsmart city sustainable development is to reflect the actualstatus of smart city sustainable development When selectingindicators pay attention to the representativeness andquantification of indicators and build a comprehensiveindicator system Quantitative analysis of targets is donethrough quantifying indicators +e specific evaluation in-dex system is shown in Table 1
3 Results Analysis
31 Model Analyses +e reliability test is to check thestability and consistency of the measurement results +isstudy uses SPSS software for internal reliability testingand reflects its reliability through Cronbachrsquos X coeffi-cient Assuming the reliability of the evaluation indexsystem and each dimension as shown in Table 2 weconsidered the overall internal consistency of the eval-uation index system Cronbachrsquos coefficient is 0386greater than 05 and Cronbachrsquos coefficients of the threecommon factors are all greater than 05 so the indexsystemmeets certain reliability requirements +e specificindicators are shown in Table 2 Starting from the scoringcoefficient matrix of exploratory factor analysis of themodel a comprehensive evaluation model is constructedwith the variance contribution rate of each commonfactor as shown in Figure 4+e score coefficient matrix ofeach common factor and the variance contribution rate ofeach factor are shown in Figure 4
According to the coupling and coordination modelsystematically calculate the coupling and coordination be-tween smart city construction industrial development andecological protection and finally determine the evaluationlevel +e results are shown in Figure 5 In terms of couplingdegree the 12 smart cities are in low-level coupling andhigh-level phases respectively In terms of coordinationdegree except for Shenzhen Beijing and Shanghai whichhave reached moderate coordination the remaining citiesare in low coordination +e overall development index ofindividual cities is low and the evaluation index of theindustrial development system is higher than that of theurban construction system indicating that industrial de-velopment has failed to drive the development of infra-structure construction in smart cities +e proportion of thesecondary industry in the industrial structure of some citiesis still higher than that of the tertiary industry which leads toa higher comprehensive evaluation index of the industrialdevelopment system than the urban construction system+is is because the proportion of emerging industries is verysmall and cannot become the pillars or leading enterprises ofthe cities +e endogenous power is insufficient and theconstruction of smart cities fails to effectively promote thedevelopment of the industry
32 Sustainability Analyses +e study used SPSS software forcase clustering and cluster analysis of the comprehensive rankingof sustainable development of smart cities in provincial ad-ministrative regions+e results are shown in Figure 6 From theresults of the above five-year factor equivalence and compre-hensive evaluation score ranking it can be intuitively found thatthe factor score and comprehensive rankings of category A-typecities are all in the top It can be concluded that such areas are atthe forefront of the country in the construction of smart citiesand a reference for the smart construction of other cities can beprovided +erefore these areas are positioned as leading areasas the ldquoleadersrdquo for the sustainable development of smart cityconstruction +e analysis of C-type cities shows that the de-velopment of cities is not stable enough and there is no con-tinuous growth It is necessary to adjust their smart cityconstruction models and other policies and find and deal withthe problems in time +e economic level of such cities lagsbehind other provincial administrative regions+e constructionof smart cities will inevitably be restricted and affectedHoweversuch regions have a strong willingness to progress and developDevelopment requires strong national support and the for-mulation of relevant policies It can be seen that such provincialadministrative regions should be positioned as catch-up areas asa ldquocatch-up armyrdquo for the sustainable development of smart cityconstruction
In the financial industry funds and securities of capitalsuppliers are continuously transmitted in the process offinancial operation through a series of investment and fi-nancing activities of financial entities Various financialentities are formed according to the relationship betweeninvestment and financing With the development of theeconomy an intricate financial ecological network has beenformed +e more the cooperation and the closer the co-operation in the financial industry the higher the value of itsproducts the richer the variety the more developed themarket and the stronger the financing ability +e morecomplex its industry is the more stable it will be +econstruction and development of smart cities should inte-grate ldquoInternet +rdquo into the financial industry integrate andutilize the development resources of the Internet financialindustry truly realize the financial innovation driven byinformation technology and use the Internet to reduceeconomic search costs and transaction costs macroscopi-cally focusing on externalities and the network effect makesuse of the Internet to promote the positive development ofmanagement mechanisms technological innovation andcorporate culture in the industry Improve the financialentities required for each development link in the financialindustry so as to provide a stable chain of funds for urbandevelopment and industrial innovation in the process ofcomplication strengthen communication and cooperationamong various financial entities and realize the financialand information industryrsquos complementary advantages andwin-win cooperation +e cityrsquos intelligent construction alsocreates a good financial environment for industrial devel-opment During the development of the industry throughthe innovation of financial institutions financial servicesfinancial mechanisms financial structures and the expan-sion of financing channels optimize the industrial
6 Complexity
upgrading model improve the services of urban financialorganizations and form a financial center with a reasonablelayoutmdashindustry as a financial entity+e characteristics andcapital advantages of Internet companies have technologicaland innovation advantages As a huge platform for thedevelopment of the financial industry cities have pipelineadvantages and many mobile terminals and ultimatelyachieve the integration of industry and city Financial ITcostanalysis is shown in Figure 7
33 Empirical Analyses +e financial cloud has the char-acteristics of huge investment in the early stage of businessdevelopment and the short-term and short-term incomemay not be fully guaranteed Many companies rely onventure capital funds or through the stock market to obtainfunds to ensure the companyrsquos sustainable developmentFigure 8 is the net profit compiled according to the smartcityrsquos financial reports in the past years We have seen anegative net profit since 2011 Now with the support offinancial cloud technology you do not have to worry aboutchanges in business cycles or customer groups that causeproblems with working capital You can calmly face changesin various market environments
To evaluate an object a certain decision-making unitneeds to be selected and certain criteria need to be adoptedin this selection +e specific selection principle shouldensure that the selected decision-making unit can achieve
the same when it is in the same external environment Tasksachieve the same input and output +ere are three ways toselect DMU in the evaluation process of smart citiesnamely vertical and horizontal comparison and verticaland horizontal comprehensive comparison +is articleuses a longitudinal comparison to select the DMU Basedon the obtained data this paper selects the comprehensiveevaluation of the urban construction in the past three yearsand analyzes the results of the smart city construction inShanghai in the past three years based on scientific resultsBased on the above analysis it can be found that the smartcity construction has achieved certain results through inputand output Pay attention to the human cost infrastructureand capital input and the corresponding output of smartgovernment smart economy smart life and smart humanliteracy is efficient With continuous investment in majorfactors the effectiveness of the governmentrsquos DMU fluc-tuates around 1 which also proves that the principle ofeffectiveness can be met in the selection of input and outputindicators In further analysis of factor productivity we cansee that all major factors are effective and the application ofnew technologies and innovations in production andmanagement has achieved good results +is is also furthershowing that if the government major financial institu-tions and other stakeholders attach importance to theinvestment in various elements they can achieve goodresults to a large extent +e regression test results areshown in Figure 9
Table 1 Evaluation index system
Smart city construction
Infrastructure
A1 investment in fixed assets of the whole society Million dollarsA2 urban road area Square kilometersA3 drainage pipe length KilometersA4 passenger traffic of the whole society Ten thousandsA5 total volume of post and telecommunications services Million dollars
UrbanizationA6 urbanization rate A7 urban population density A8 per capita disposable income of urban residents Yuan
Composite indexindustrial development
Development scale
B1 the amount of investment in fixed assets of informationtechnology and Internet companies Million dollars
B2 number of information technology and software companies PcB3 information technology software practitioners People
Industrial structure B4 tertiary industry output value as a percentage of GDP B5 the proportion of output value of secondary industry to GDP Item
Technology supportcapability
B6 patent authorization PcB7 number of national key laboratories Million dollarsB8 RampD internal expenditure PeopleB9 number of RampD researchers
Composite index ecologicalprotectioncomprehensive index
Urban ecologyC1 green coverage rate in built-up area Square meter
C2 park green area per capita Ten thousandtons
Environmentalprotection
C3 industrial solid waste generation Ten thousandtons
C4 comprehensive utilization of industrial solid waste Million dollars
Voice networkIT service deskEnd-user computingData center
0
10
20
30
40
50
60
70
80
90
100
2016 2017 2018 20192015Year
Figure 7 Distribution of IT costs
ndash300
ndash250
ndash200
ndash150
ndash100
ndash50
0
50
100
One
mill
ion
US
dolla
rs
2012 2013 2014 2015 2016 2017 2018 20192011Year
Figure 8 City net profit over the years
Complexity 9
4 Conclusion
+is article first discusses the relationship between smartcities and finance and existing models based on existingrelevant theories and literature By comparing and an-alyzing the status of smart cities and financial clouds athome and abroad this paper applies financial cloud in-telligence to build smart cities It has laid the foundationfor empirical analysis and model innovation +eoreticalresearch of financial technology and smart city is inte-grated with each other to study and analyze the health andperfect sustainable road of smart city +e problems in thesustainable development of smart cities should be solvedfrom the aspects of economy human problems envi-ronmental science urban infrastructure and ecologicalenvironment Explore the innovation of the smart citymodel based on the ldquofinancial cloudrdquo perspective Inorder to achieve the sustainable development goals ofsmart cities explore the dynamic mechanism of sus-tainable development of smart cities through the analysisof the structure and elements of smart cities and improvethe construction of financial ecological chain in order toachieve the goal of model innovation Finally this paperproposes a way to realize the integration of smart city andfinancial cloud which has certain guiding significance forthe sustainable development of the smart city
Data Availability
+e data used to support the findings of this study areavailable from the corresponding author upon request
Conflicts of Interest
+e authors declare that they have no conflicts of interestregarding the publication of this paper
Acknowledgments
+is work was supported by the General Project of theNational Natural Science Foundation of China (Nos71874037 and 71673062) and Natural Science Foundation ofGuangdong Province (No 2018A030313397)
References
[1] C Kakderi N Komninos and P Tsarchopoulos ldquoSmart citiesand cloud computing lessons from the STORM CLOUDSexperimentrdquo Journal of Smart Cities vol 1 no 2 pp 4ndash132019
[2] P Tsarchopoulos N Komninos and C Kakderi ldquoAcceler-ating the uptake of smart city applications through cloudcomputing World Academy of Science Engineering andTechnologyrdquo International Journal of Social BehavioralEducational Economic Business and Industrial Engineeringvol 11 no 1 pp 129ndash138 2017
[3] N Komninos C Kakderi A Panori and P TsarchopoulosldquoSmart city planning from an evolutionary perspectiverdquoJournal of Urban Technology vol 26 no 2 pp 3ndash20 2019
[4] N P Rana S Luthra S K Mangla R Islam S Roderick andY K Dwivedi ldquoBarriers to the development of smart cities inIndian contextrdquo Information Systems Frontiers vol 21 no 3pp 503ndash525 2019
[5] G Trencher and A Karvonen ldquoStretching ldquosmartrdquo advancinghealth and well-being through the smart city agendardquo LocalEnvironment vol 24 no 7 pp 610ndash627 2019
[6] S P Mohanty U Choppali and E Kougianos ldquoEverythingyou wanted to know about smart cities the internet of thingsis the backbonerdquo IEEE Consumer Electronics Magazine vol 5no 3 pp 60ndash70 2016
[7] Y Mehmood F Ahmad I Yaqoob A Adnane M Imranand S Guizani ldquoInternet-of-things-based smart cities recentadvances and challengesrdquo IEEE Communications Magazinevol 55 no 9 pp 16ndash24 2017
[8] M Angelidou ldquo+e role of smart city characteristics in theplans of fifteen citiesrdquo Journal of Urban Technology vol 24no 4 pp 3ndash28 2017
[9] M Sookhak H Tang Y He et al ldquoSecurity and privacy ofsmart cities a survey research issues and challengesrdquo IEEECommunications Surveys amp Tutorials vol 21 no 2pp 1718ndash1743 2018
[10] S Berke ldquo+e sustainable development of data-driven smartcities citizen-centered urban governance and networkeddigital technologiesrdquo Geopolitics History and InternationalRelations vol 11 no 2 pp 122ndash127 2019
[11] M Masera E F Bompard F Profumo and N HadjsaidldquoSmart (electricity) grids for smart cities assessing roles andsocietal impactsrdquo Proceedings of the IEEE vol 106 no 4pp 613ndash625 2018
[12] A Panori A Gonzalez-Quel M Tavares et al ldquoMigration ofapplications to the Cloud a user-driven approachrdquo Journal ofSmart Cities vol 2 no 1 pp 16ndash27 2019
[13] J Xie H Tang T Huang et al ldquoA survey of blockchaintechnology applied to smart cities research issues and chal-lengesrdquo IEEE Communications Surveys amp Tutorials vol 21no 3 pp 2794ndash2830 2019
[14] E F Z Santana A P Chaves M A Gerosa et al ldquoSoftwareplatforms for smart cities concepts requirements challengesand a unified reference architecturerdquo ACM Computing Sur-veys vol 50 no 6 pp 1ndash37 2017
Unstandardized regression coefficientUnstandardized standard errorStandard coefficientt
SigToleranceVIF
0
1
2
3
4
5
Valu
es
X2 X3 X4X1Variable
Figure 9 Regression coefficient test and significance level
10 Complexity
[15] X Kong X Liu B Jedari M Li L Wan and F Xia ldquoMobilecrowdsourcing in smart cities technologies applications andfuture challengesrdquo IEEE Internet of 2ings Journal vol 6no 5 pp 8095ndash8113 2019
[16] D Eckhoff and I Wagner ldquoPrivacy in the smart cit-ymdashapplications technologies challenges and solutionsrdquoIEEE Communications Surveys amp Tutorials vol 20 no 1pp 489ndash516 2017
[17] A-M Valdez M Cook P-A Langendahl H Roby andS Potter ldquoPrototyping sustainable mobility practices user-generated data in the smart cityrdquo Technology Analysis ampStrategic Management vol 30 no 2 pp 144ndash157 2018
[18] H Xu and X Geng ldquoPeople-centric service intelligence forsmart citiesrdquo Smart Cities vol 2 no 2 pp 135ndash152 2019
[19] J Arthi ldquoBottom billion strategy-smart Villagesrdquo AsianJournal of Multidimensional Research (AJMR) vol 7 no 1pp 63ndash66 2018
[20] A Gharaibeh M A Salahuddin S J Hussini et al ldquoSmartcities a survey on data management security and enablingtechnologiesrdquo IEEE Communications Surveys amp Tutorialsvol 19 no 4 pp 2456ndash2501 2017
[21] N F Alwan andM K AL-Nuaimi ldquo+e role and important ofinternet of things in building sustainable cityrdquo Engineeringand Technology Journal vol 36 no 1 pp 22ndash29 2018
[22] M Aamir S Masroor Z A Ali and B T Ting ldquoSustainableframework for smart transportation system a case study ofkarachirdquo Wireless Personal Communications vol 106 no 1pp 27ndash40 2019
[23] S P Caird and S H Hallett ldquoTowards evaluation design forsmart city developmentrdquo Journal of Urban Design vol 24no 2 pp 188ndash209 2019
Complexity 11
beginning of Chinarsquos proposal for smart city construction tothe present day a total of three batches of smart city pilotlists have been released Increased cities are taking smartcities as their construction goals and the selected smart citythemes are not the same +e first batch of cities partici-pating in the construction Shenzhen and Nanjing weremainly led by innovation and the pilot cities that partici-pated in the construction afterwards mainly determined thedevelopment priorities according to the characteristics of theconstruction main body and future needs so as to integratethe cityrsquos established development strategic goals andachieve smart cities +e construction of the building isorganically combined +e sustainable development modelof smart city can be divided into four categories
(1) Innovative at this stage in the army of Chinarsquos smartcity construction most cities regard innovation as animportant driving force Most of these cities alreadyhave a good development foundation and strongstrength and regard the development of ldquosmartrdquoindustries as a strategy to enhance cities which arethe key factors of status and competitive strength
(2) +e urban development of smart industries in theprocess of urban development such cities havegradually cultivated and formed characteristic in-dustries with their own advantages and formed a
complete upstream and downstream ecological en-vironment in related fields+erefore these cities aremore focused on maintaining and displaying theirown advantages and intelligent industries For thedevelopment of Kunshan for example the authorsin [23] proposed focusing on accelerating the de-velopment of the Internet of +ings electronictechnology and other industries and promoting theconstruction of smart cities
(3) Developing the wisdom and livelihood such citiesfocus on the development of intelligent managementand livelihood services to promote+e wisdom of the city avoids the ldquobig city diseaserdquo insmall and medium-sized cities +e top priority ofthe development of smart cities is people-orientedand its connotation is that all ldquopeoplerdquo of peoplersquoslivelihood are the core serving the ldquopeoplerdquo in thecity as much as possible so as to realize a green safeand efficient intelligent life
(4) Development of information technology infra-structure the construction of smart cities in thiscategory focuses on information technology andinformation infrastructure For example with thehelp of advanced information technology a modernand integrated administrative decision-making
Smart city industry
ecosystem
Resource utilization Business choice
Material and financial
integration
Scientific and technological development
Smart city operations
Collaborative integration
Collaborative integration
Collaborative integration
Collaborative integration
Improve development
Promote diversity
Promote interaction
Information Industry
Financial industry Culture industry
Innovation industry
Pillar industry
Figure 3 Smart city industrial ecosystem
Complexity 5
auxiliary system has been established Operation inmajor enterprises and national administrative de-partments has proved that similar systems can ef-fectively improve managersrsquo work efficiency anddecision-making ability
+e goal of constructing an evaluation index system forsmart city sustainable development is to reflect the actualstatus of smart city sustainable development When selectingindicators pay attention to the representativeness andquantification of indicators and build a comprehensiveindicator system Quantitative analysis of targets is donethrough quantifying indicators +e specific evaluation in-dex system is shown in Table 1
3 Results Analysis
31 Model Analyses +e reliability test is to check thestability and consistency of the measurement results +isstudy uses SPSS software for internal reliability testingand reflects its reliability through Cronbachrsquos X coeffi-cient Assuming the reliability of the evaluation indexsystem and each dimension as shown in Table 2 weconsidered the overall internal consistency of the eval-uation index system Cronbachrsquos coefficient is 0386greater than 05 and Cronbachrsquos coefficients of the threecommon factors are all greater than 05 so the indexsystemmeets certain reliability requirements +e specificindicators are shown in Table 2 Starting from the scoringcoefficient matrix of exploratory factor analysis of themodel a comprehensive evaluation model is constructedwith the variance contribution rate of each commonfactor as shown in Figure 4+e score coefficient matrix ofeach common factor and the variance contribution rate ofeach factor are shown in Figure 4
According to the coupling and coordination modelsystematically calculate the coupling and coordination be-tween smart city construction industrial development andecological protection and finally determine the evaluationlevel +e results are shown in Figure 5 In terms of couplingdegree the 12 smart cities are in low-level coupling andhigh-level phases respectively In terms of coordinationdegree except for Shenzhen Beijing and Shanghai whichhave reached moderate coordination the remaining citiesare in low coordination +e overall development index ofindividual cities is low and the evaluation index of theindustrial development system is higher than that of theurban construction system indicating that industrial de-velopment has failed to drive the development of infra-structure construction in smart cities +e proportion of thesecondary industry in the industrial structure of some citiesis still higher than that of the tertiary industry which leads toa higher comprehensive evaluation index of the industrialdevelopment system than the urban construction system+is is because the proportion of emerging industries is verysmall and cannot become the pillars or leading enterprises ofthe cities +e endogenous power is insufficient and theconstruction of smart cities fails to effectively promote thedevelopment of the industry
32 Sustainability Analyses +e study used SPSS software forcase clustering and cluster analysis of the comprehensive rankingof sustainable development of smart cities in provincial ad-ministrative regions+e results are shown in Figure 6 From theresults of the above five-year factor equivalence and compre-hensive evaluation score ranking it can be intuitively found thatthe factor score and comprehensive rankings of category A-typecities are all in the top It can be concluded that such areas are atthe forefront of the country in the construction of smart citiesand a reference for the smart construction of other cities can beprovided +erefore these areas are positioned as leading areasas the ldquoleadersrdquo for the sustainable development of smart cityconstruction +e analysis of C-type cities shows that the de-velopment of cities is not stable enough and there is no con-tinuous growth It is necessary to adjust their smart cityconstruction models and other policies and find and deal withthe problems in time +e economic level of such cities lagsbehind other provincial administrative regions+e constructionof smart cities will inevitably be restricted and affectedHoweversuch regions have a strong willingness to progress and developDevelopment requires strong national support and the for-mulation of relevant policies It can be seen that such provincialadministrative regions should be positioned as catch-up areas asa ldquocatch-up armyrdquo for the sustainable development of smart cityconstruction
In the financial industry funds and securities of capitalsuppliers are continuously transmitted in the process offinancial operation through a series of investment and fi-nancing activities of financial entities Various financialentities are formed according to the relationship betweeninvestment and financing With the development of theeconomy an intricate financial ecological network has beenformed +e more the cooperation and the closer the co-operation in the financial industry the higher the value of itsproducts the richer the variety the more developed themarket and the stronger the financing ability +e morecomplex its industry is the more stable it will be +econstruction and development of smart cities should inte-grate ldquoInternet +rdquo into the financial industry integrate andutilize the development resources of the Internet financialindustry truly realize the financial innovation driven byinformation technology and use the Internet to reduceeconomic search costs and transaction costs macroscopi-cally focusing on externalities and the network effect makesuse of the Internet to promote the positive development ofmanagement mechanisms technological innovation andcorporate culture in the industry Improve the financialentities required for each development link in the financialindustry so as to provide a stable chain of funds for urbandevelopment and industrial innovation in the process ofcomplication strengthen communication and cooperationamong various financial entities and realize the financialand information industryrsquos complementary advantages andwin-win cooperation +e cityrsquos intelligent construction alsocreates a good financial environment for industrial devel-opment During the development of the industry throughthe innovation of financial institutions financial servicesfinancial mechanisms financial structures and the expan-sion of financing channels optimize the industrial
6 Complexity
upgrading model improve the services of urban financialorganizations and form a financial center with a reasonablelayoutmdashindustry as a financial entity+e characteristics andcapital advantages of Internet companies have technologicaland innovation advantages As a huge platform for thedevelopment of the financial industry cities have pipelineadvantages and many mobile terminals and ultimatelyachieve the integration of industry and city Financial ITcostanalysis is shown in Figure 7
33 Empirical Analyses +e financial cloud has the char-acteristics of huge investment in the early stage of businessdevelopment and the short-term and short-term incomemay not be fully guaranteed Many companies rely onventure capital funds or through the stock market to obtainfunds to ensure the companyrsquos sustainable developmentFigure 8 is the net profit compiled according to the smartcityrsquos financial reports in the past years We have seen anegative net profit since 2011 Now with the support offinancial cloud technology you do not have to worry aboutchanges in business cycles or customer groups that causeproblems with working capital You can calmly face changesin various market environments
To evaluate an object a certain decision-making unitneeds to be selected and certain criteria need to be adoptedin this selection +e specific selection principle shouldensure that the selected decision-making unit can achieve
the same when it is in the same external environment Tasksachieve the same input and output +ere are three ways toselect DMU in the evaluation process of smart citiesnamely vertical and horizontal comparison and verticaland horizontal comprehensive comparison +is articleuses a longitudinal comparison to select the DMU Basedon the obtained data this paper selects the comprehensiveevaluation of the urban construction in the past three yearsand analyzes the results of the smart city construction inShanghai in the past three years based on scientific resultsBased on the above analysis it can be found that the smartcity construction has achieved certain results through inputand output Pay attention to the human cost infrastructureand capital input and the corresponding output of smartgovernment smart economy smart life and smart humanliteracy is efficient With continuous investment in majorfactors the effectiveness of the governmentrsquos DMU fluc-tuates around 1 which also proves that the principle ofeffectiveness can be met in the selection of input and outputindicators In further analysis of factor productivity we cansee that all major factors are effective and the application ofnew technologies and innovations in production andmanagement has achieved good results +is is also furthershowing that if the government major financial institu-tions and other stakeholders attach importance to theinvestment in various elements they can achieve goodresults to a large extent +e regression test results areshown in Figure 9
Table 1 Evaluation index system
Smart city construction
Infrastructure
A1 investment in fixed assets of the whole society Million dollarsA2 urban road area Square kilometersA3 drainage pipe length KilometersA4 passenger traffic of the whole society Ten thousandsA5 total volume of post and telecommunications services Million dollars
UrbanizationA6 urbanization rate A7 urban population density A8 per capita disposable income of urban residents Yuan
Composite indexindustrial development
Development scale
B1 the amount of investment in fixed assets of informationtechnology and Internet companies Million dollars
B2 number of information technology and software companies PcB3 information technology software practitioners People
Industrial structure B4 tertiary industry output value as a percentage of GDP B5 the proportion of output value of secondary industry to GDP Item
Technology supportcapability
B6 patent authorization PcB7 number of national key laboratories Million dollarsB8 RampD internal expenditure PeopleB9 number of RampD researchers
Composite index ecologicalprotectioncomprehensive index
Urban ecologyC1 green coverage rate in built-up area Square meter
C2 park green area per capita Ten thousandtons
Environmentalprotection
C3 industrial solid waste generation Ten thousandtons
C4 comprehensive utilization of industrial solid waste Million dollars
Voice networkIT service deskEnd-user computingData center
0
10
20
30
40
50
60
70
80
90
100
2016 2017 2018 20192015Year
Figure 7 Distribution of IT costs
ndash300
ndash250
ndash200
ndash150
ndash100
ndash50
0
50
100
One
mill
ion
US
dolla
rs
2012 2013 2014 2015 2016 2017 2018 20192011Year
Figure 8 City net profit over the years
Complexity 9
4 Conclusion
+is article first discusses the relationship between smartcities and finance and existing models based on existingrelevant theories and literature By comparing and an-alyzing the status of smart cities and financial clouds athome and abroad this paper applies financial cloud in-telligence to build smart cities It has laid the foundationfor empirical analysis and model innovation +eoreticalresearch of financial technology and smart city is inte-grated with each other to study and analyze the health andperfect sustainable road of smart city +e problems in thesustainable development of smart cities should be solvedfrom the aspects of economy human problems envi-ronmental science urban infrastructure and ecologicalenvironment Explore the innovation of the smart citymodel based on the ldquofinancial cloudrdquo perspective Inorder to achieve the sustainable development goals ofsmart cities explore the dynamic mechanism of sus-tainable development of smart cities through the analysisof the structure and elements of smart cities and improvethe construction of financial ecological chain in order toachieve the goal of model innovation Finally this paperproposes a way to realize the integration of smart city andfinancial cloud which has certain guiding significance forthe sustainable development of the smart city
Data Availability
+e data used to support the findings of this study areavailable from the corresponding author upon request
Conflicts of Interest
+e authors declare that they have no conflicts of interestregarding the publication of this paper
Acknowledgments
+is work was supported by the General Project of theNational Natural Science Foundation of China (Nos71874037 and 71673062) and Natural Science Foundation ofGuangdong Province (No 2018A030313397)
References
[1] C Kakderi N Komninos and P Tsarchopoulos ldquoSmart citiesand cloud computing lessons from the STORM CLOUDSexperimentrdquo Journal of Smart Cities vol 1 no 2 pp 4ndash132019
[2] P Tsarchopoulos N Komninos and C Kakderi ldquoAcceler-ating the uptake of smart city applications through cloudcomputing World Academy of Science Engineering andTechnologyrdquo International Journal of Social BehavioralEducational Economic Business and Industrial Engineeringvol 11 no 1 pp 129ndash138 2017
[3] N Komninos C Kakderi A Panori and P TsarchopoulosldquoSmart city planning from an evolutionary perspectiverdquoJournal of Urban Technology vol 26 no 2 pp 3ndash20 2019
[4] N P Rana S Luthra S K Mangla R Islam S Roderick andY K Dwivedi ldquoBarriers to the development of smart cities inIndian contextrdquo Information Systems Frontiers vol 21 no 3pp 503ndash525 2019
[5] G Trencher and A Karvonen ldquoStretching ldquosmartrdquo advancinghealth and well-being through the smart city agendardquo LocalEnvironment vol 24 no 7 pp 610ndash627 2019
[6] S P Mohanty U Choppali and E Kougianos ldquoEverythingyou wanted to know about smart cities the internet of thingsis the backbonerdquo IEEE Consumer Electronics Magazine vol 5no 3 pp 60ndash70 2016
[7] Y Mehmood F Ahmad I Yaqoob A Adnane M Imranand S Guizani ldquoInternet-of-things-based smart cities recentadvances and challengesrdquo IEEE Communications Magazinevol 55 no 9 pp 16ndash24 2017
[8] M Angelidou ldquo+e role of smart city characteristics in theplans of fifteen citiesrdquo Journal of Urban Technology vol 24no 4 pp 3ndash28 2017
[9] M Sookhak H Tang Y He et al ldquoSecurity and privacy ofsmart cities a survey research issues and challengesrdquo IEEECommunications Surveys amp Tutorials vol 21 no 2pp 1718ndash1743 2018
[10] S Berke ldquo+e sustainable development of data-driven smartcities citizen-centered urban governance and networkeddigital technologiesrdquo Geopolitics History and InternationalRelations vol 11 no 2 pp 122ndash127 2019
[11] M Masera E F Bompard F Profumo and N HadjsaidldquoSmart (electricity) grids for smart cities assessing roles andsocietal impactsrdquo Proceedings of the IEEE vol 106 no 4pp 613ndash625 2018
[12] A Panori A Gonzalez-Quel M Tavares et al ldquoMigration ofapplications to the Cloud a user-driven approachrdquo Journal ofSmart Cities vol 2 no 1 pp 16ndash27 2019
[13] J Xie H Tang T Huang et al ldquoA survey of blockchaintechnology applied to smart cities research issues and chal-lengesrdquo IEEE Communications Surveys amp Tutorials vol 21no 3 pp 2794ndash2830 2019
[14] E F Z Santana A P Chaves M A Gerosa et al ldquoSoftwareplatforms for smart cities concepts requirements challengesand a unified reference architecturerdquo ACM Computing Sur-veys vol 50 no 6 pp 1ndash37 2017
Unstandardized regression coefficientUnstandardized standard errorStandard coefficientt
SigToleranceVIF
0
1
2
3
4
5
Valu
es
X2 X3 X4X1Variable
Figure 9 Regression coefficient test and significance level
10 Complexity
[15] X Kong X Liu B Jedari M Li L Wan and F Xia ldquoMobilecrowdsourcing in smart cities technologies applications andfuture challengesrdquo IEEE Internet of 2ings Journal vol 6no 5 pp 8095ndash8113 2019
[16] D Eckhoff and I Wagner ldquoPrivacy in the smart cit-ymdashapplications technologies challenges and solutionsrdquoIEEE Communications Surveys amp Tutorials vol 20 no 1pp 489ndash516 2017
[17] A-M Valdez M Cook P-A Langendahl H Roby andS Potter ldquoPrototyping sustainable mobility practices user-generated data in the smart cityrdquo Technology Analysis ampStrategic Management vol 30 no 2 pp 144ndash157 2018
[18] H Xu and X Geng ldquoPeople-centric service intelligence forsmart citiesrdquo Smart Cities vol 2 no 2 pp 135ndash152 2019
[19] J Arthi ldquoBottom billion strategy-smart Villagesrdquo AsianJournal of Multidimensional Research (AJMR) vol 7 no 1pp 63ndash66 2018
[20] A Gharaibeh M A Salahuddin S J Hussini et al ldquoSmartcities a survey on data management security and enablingtechnologiesrdquo IEEE Communications Surveys amp Tutorialsvol 19 no 4 pp 2456ndash2501 2017
[21] N F Alwan andM K AL-Nuaimi ldquo+e role and important ofinternet of things in building sustainable cityrdquo Engineeringand Technology Journal vol 36 no 1 pp 22ndash29 2018
[22] M Aamir S Masroor Z A Ali and B T Ting ldquoSustainableframework for smart transportation system a case study ofkarachirdquo Wireless Personal Communications vol 106 no 1pp 27ndash40 2019
[23] S P Caird and S H Hallett ldquoTowards evaluation design forsmart city developmentrdquo Journal of Urban Design vol 24no 2 pp 188ndash209 2019
Complexity 11
auxiliary system has been established Operation inmajor enterprises and national administrative de-partments has proved that similar systems can ef-fectively improve managersrsquo work efficiency anddecision-making ability
+e goal of constructing an evaluation index system forsmart city sustainable development is to reflect the actualstatus of smart city sustainable development When selectingindicators pay attention to the representativeness andquantification of indicators and build a comprehensiveindicator system Quantitative analysis of targets is donethrough quantifying indicators +e specific evaluation in-dex system is shown in Table 1
3 Results Analysis
31 Model Analyses +e reliability test is to check thestability and consistency of the measurement results +isstudy uses SPSS software for internal reliability testingand reflects its reliability through Cronbachrsquos X coeffi-cient Assuming the reliability of the evaluation indexsystem and each dimension as shown in Table 2 weconsidered the overall internal consistency of the eval-uation index system Cronbachrsquos coefficient is 0386greater than 05 and Cronbachrsquos coefficients of the threecommon factors are all greater than 05 so the indexsystemmeets certain reliability requirements +e specificindicators are shown in Table 2 Starting from the scoringcoefficient matrix of exploratory factor analysis of themodel a comprehensive evaluation model is constructedwith the variance contribution rate of each commonfactor as shown in Figure 4+e score coefficient matrix ofeach common factor and the variance contribution rate ofeach factor are shown in Figure 4
According to the coupling and coordination modelsystematically calculate the coupling and coordination be-tween smart city construction industrial development andecological protection and finally determine the evaluationlevel +e results are shown in Figure 5 In terms of couplingdegree the 12 smart cities are in low-level coupling andhigh-level phases respectively In terms of coordinationdegree except for Shenzhen Beijing and Shanghai whichhave reached moderate coordination the remaining citiesare in low coordination +e overall development index ofindividual cities is low and the evaluation index of theindustrial development system is higher than that of theurban construction system indicating that industrial de-velopment has failed to drive the development of infra-structure construction in smart cities +e proportion of thesecondary industry in the industrial structure of some citiesis still higher than that of the tertiary industry which leads toa higher comprehensive evaluation index of the industrialdevelopment system than the urban construction system+is is because the proportion of emerging industries is verysmall and cannot become the pillars or leading enterprises ofthe cities +e endogenous power is insufficient and theconstruction of smart cities fails to effectively promote thedevelopment of the industry
32 Sustainability Analyses +e study used SPSS software forcase clustering and cluster analysis of the comprehensive rankingof sustainable development of smart cities in provincial ad-ministrative regions+e results are shown in Figure 6 From theresults of the above five-year factor equivalence and compre-hensive evaluation score ranking it can be intuitively found thatthe factor score and comprehensive rankings of category A-typecities are all in the top It can be concluded that such areas are atthe forefront of the country in the construction of smart citiesand a reference for the smart construction of other cities can beprovided +erefore these areas are positioned as leading areasas the ldquoleadersrdquo for the sustainable development of smart cityconstruction +e analysis of C-type cities shows that the de-velopment of cities is not stable enough and there is no con-tinuous growth It is necessary to adjust their smart cityconstruction models and other policies and find and deal withthe problems in time +e economic level of such cities lagsbehind other provincial administrative regions+e constructionof smart cities will inevitably be restricted and affectedHoweversuch regions have a strong willingness to progress and developDevelopment requires strong national support and the for-mulation of relevant policies It can be seen that such provincialadministrative regions should be positioned as catch-up areas asa ldquocatch-up armyrdquo for the sustainable development of smart cityconstruction
In the financial industry funds and securities of capitalsuppliers are continuously transmitted in the process offinancial operation through a series of investment and fi-nancing activities of financial entities Various financialentities are formed according to the relationship betweeninvestment and financing With the development of theeconomy an intricate financial ecological network has beenformed +e more the cooperation and the closer the co-operation in the financial industry the higher the value of itsproducts the richer the variety the more developed themarket and the stronger the financing ability +e morecomplex its industry is the more stable it will be +econstruction and development of smart cities should inte-grate ldquoInternet +rdquo into the financial industry integrate andutilize the development resources of the Internet financialindustry truly realize the financial innovation driven byinformation technology and use the Internet to reduceeconomic search costs and transaction costs macroscopi-cally focusing on externalities and the network effect makesuse of the Internet to promote the positive development ofmanagement mechanisms technological innovation andcorporate culture in the industry Improve the financialentities required for each development link in the financialindustry so as to provide a stable chain of funds for urbandevelopment and industrial innovation in the process ofcomplication strengthen communication and cooperationamong various financial entities and realize the financialand information industryrsquos complementary advantages andwin-win cooperation +e cityrsquos intelligent construction alsocreates a good financial environment for industrial devel-opment During the development of the industry throughthe innovation of financial institutions financial servicesfinancial mechanisms financial structures and the expan-sion of financing channels optimize the industrial
6 Complexity
upgrading model improve the services of urban financialorganizations and form a financial center with a reasonablelayoutmdashindustry as a financial entity+e characteristics andcapital advantages of Internet companies have technologicaland innovation advantages As a huge platform for thedevelopment of the financial industry cities have pipelineadvantages and many mobile terminals and ultimatelyachieve the integration of industry and city Financial ITcostanalysis is shown in Figure 7
33 Empirical Analyses +e financial cloud has the char-acteristics of huge investment in the early stage of businessdevelopment and the short-term and short-term incomemay not be fully guaranteed Many companies rely onventure capital funds or through the stock market to obtainfunds to ensure the companyrsquos sustainable developmentFigure 8 is the net profit compiled according to the smartcityrsquos financial reports in the past years We have seen anegative net profit since 2011 Now with the support offinancial cloud technology you do not have to worry aboutchanges in business cycles or customer groups that causeproblems with working capital You can calmly face changesin various market environments
To evaluate an object a certain decision-making unitneeds to be selected and certain criteria need to be adoptedin this selection +e specific selection principle shouldensure that the selected decision-making unit can achieve
the same when it is in the same external environment Tasksachieve the same input and output +ere are three ways toselect DMU in the evaluation process of smart citiesnamely vertical and horizontal comparison and verticaland horizontal comprehensive comparison +is articleuses a longitudinal comparison to select the DMU Basedon the obtained data this paper selects the comprehensiveevaluation of the urban construction in the past three yearsand analyzes the results of the smart city construction inShanghai in the past three years based on scientific resultsBased on the above analysis it can be found that the smartcity construction has achieved certain results through inputand output Pay attention to the human cost infrastructureand capital input and the corresponding output of smartgovernment smart economy smart life and smart humanliteracy is efficient With continuous investment in majorfactors the effectiveness of the governmentrsquos DMU fluc-tuates around 1 which also proves that the principle ofeffectiveness can be met in the selection of input and outputindicators In further analysis of factor productivity we cansee that all major factors are effective and the application ofnew technologies and innovations in production andmanagement has achieved good results +is is also furthershowing that if the government major financial institu-tions and other stakeholders attach importance to theinvestment in various elements they can achieve goodresults to a large extent +e regression test results areshown in Figure 9
Table 1 Evaluation index system
Smart city construction
Infrastructure
A1 investment in fixed assets of the whole society Million dollarsA2 urban road area Square kilometersA3 drainage pipe length KilometersA4 passenger traffic of the whole society Ten thousandsA5 total volume of post and telecommunications services Million dollars
UrbanizationA6 urbanization rate A7 urban population density A8 per capita disposable income of urban residents Yuan
Composite indexindustrial development
Development scale
B1 the amount of investment in fixed assets of informationtechnology and Internet companies Million dollars
B2 number of information technology and software companies PcB3 information technology software practitioners People
Industrial structure B4 tertiary industry output value as a percentage of GDP B5 the proportion of output value of secondary industry to GDP Item
Technology supportcapability
B6 patent authorization PcB7 number of national key laboratories Million dollarsB8 RampD internal expenditure PeopleB9 number of RampD researchers
Composite index ecologicalprotectioncomprehensive index
Urban ecologyC1 green coverage rate in built-up area Square meter
C2 park green area per capita Ten thousandtons
Environmentalprotection
C3 industrial solid waste generation Ten thousandtons
C4 comprehensive utilization of industrial solid waste Million dollars
Voice networkIT service deskEnd-user computingData center
0
10
20
30
40
50
60
70
80
90
100
2016 2017 2018 20192015Year
Figure 7 Distribution of IT costs
ndash300
ndash250
ndash200
ndash150
ndash100
ndash50
0
50
100
One
mill
ion
US
dolla
rs
2012 2013 2014 2015 2016 2017 2018 20192011Year
Figure 8 City net profit over the years
Complexity 9
4 Conclusion
+is article first discusses the relationship between smartcities and finance and existing models based on existingrelevant theories and literature By comparing and an-alyzing the status of smart cities and financial clouds athome and abroad this paper applies financial cloud in-telligence to build smart cities It has laid the foundationfor empirical analysis and model innovation +eoreticalresearch of financial technology and smart city is inte-grated with each other to study and analyze the health andperfect sustainable road of smart city +e problems in thesustainable development of smart cities should be solvedfrom the aspects of economy human problems envi-ronmental science urban infrastructure and ecologicalenvironment Explore the innovation of the smart citymodel based on the ldquofinancial cloudrdquo perspective Inorder to achieve the sustainable development goals ofsmart cities explore the dynamic mechanism of sus-tainable development of smart cities through the analysisof the structure and elements of smart cities and improvethe construction of financial ecological chain in order toachieve the goal of model innovation Finally this paperproposes a way to realize the integration of smart city andfinancial cloud which has certain guiding significance forthe sustainable development of the smart city
Data Availability
+e data used to support the findings of this study areavailable from the corresponding author upon request
Conflicts of Interest
+e authors declare that they have no conflicts of interestregarding the publication of this paper
Acknowledgments
+is work was supported by the General Project of theNational Natural Science Foundation of China (Nos71874037 and 71673062) and Natural Science Foundation ofGuangdong Province (No 2018A030313397)
References
[1] C Kakderi N Komninos and P Tsarchopoulos ldquoSmart citiesand cloud computing lessons from the STORM CLOUDSexperimentrdquo Journal of Smart Cities vol 1 no 2 pp 4ndash132019
[2] P Tsarchopoulos N Komninos and C Kakderi ldquoAcceler-ating the uptake of smart city applications through cloudcomputing World Academy of Science Engineering andTechnologyrdquo International Journal of Social BehavioralEducational Economic Business and Industrial Engineeringvol 11 no 1 pp 129ndash138 2017
[3] N Komninos C Kakderi A Panori and P TsarchopoulosldquoSmart city planning from an evolutionary perspectiverdquoJournal of Urban Technology vol 26 no 2 pp 3ndash20 2019
[4] N P Rana S Luthra S K Mangla R Islam S Roderick andY K Dwivedi ldquoBarriers to the development of smart cities inIndian contextrdquo Information Systems Frontiers vol 21 no 3pp 503ndash525 2019
[5] G Trencher and A Karvonen ldquoStretching ldquosmartrdquo advancinghealth and well-being through the smart city agendardquo LocalEnvironment vol 24 no 7 pp 610ndash627 2019
[6] S P Mohanty U Choppali and E Kougianos ldquoEverythingyou wanted to know about smart cities the internet of thingsis the backbonerdquo IEEE Consumer Electronics Magazine vol 5no 3 pp 60ndash70 2016
[7] Y Mehmood F Ahmad I Yaqoob A Adnane M Imranand S Guizani ldquoInternet-of-things-based smart cities recentadvances and challengesrdquo IEEE Communications Magazinevol 55 no 9 pp 16ndash24 2017
[8] M Angelidou ldquo+e role of smart city characteristics in theplans of fifteen citiesrdquo Journal of Urban Technology vol 24no 4 pp 3ndash28 2017
[9] M Sookhak H Tang Y He et al ldquoSecurity and privacy ofsmart cities a survey research issues and challengesrdquo IEEECommunications Surveys amp Tutorials vol 21 no 2pp 1718ndash1743 2018
[10] S Berke ldquo+e sustainable development of data-driven smartcities citizen-centered urban governance and networkeddigital technologiesrdquo Geopolitics History and InternationalRelations vol 11 no 2 pp 122ndash127 2019
[11] M Masera E F Bompard F Profumo and N HadjsaidldquoSmart (electricity) grids for smart cities assessing roles andsocietal impactsrdquo Proceedings of the IEEE vol 106 no 4pp 613ndash625 2018
[12] A Panori A Gonzalez-Quel M Tavares et al ldquoMigration ofapplications to the Cloud a user-driven approachrdquo Journal ofSmart Cities vol 2 no 1 pp 16ndash27 2019
[13] J Xie H Tang T Huang et al ldquoA survey of blockchaintechnology applied to smart cities research issues and chal-lengesrdquo IEEE Communications Surveys amp Tutorials vol 21no 3 pp 2794ndash2830 2019
[14] E F Z Santana A P Chaves M A Gerosa et al ldquoSoftwareplatforms for smart cities concepts requirements challengesand a unified reference architecturerdquo ACM Computing Sur-veys vol 50 no 6 pp 1ndash37 2017
Unstandardized regression coefficientUnstandardized standard errorStandard coefficientt
SigToleranceVIF
0
1
2
3
4
5
Valu
es
X2 X3 X4X1Variable
Figure 9 Regression coefficient test and significance level
10 Complexity
[15] X Kong X Liu B Jedari M Li L Wan and F Xia ldquoMobilecrowdsourcing in smart cities technologies applications andfuture challengesrdquo IEEE Internet of 2ings Journal vol 6no 5 pp 8095ndash8113 2019
[16] D Eckhoff and I Wagner ldquoPrivacy in the smart cit-ymdashapplications technologies challenges and solutionsrdquoIEEE Communications Surveys amp Tutorials vol 20 no 1pp 489ndash516 2017
[17] A-M Valdez M Cook P-A Langendahl H Roby andS Potter ldquoPrototyping sustainable mobility practices user-generated data in the smart cityrdquo Technology Analysis ampStrategic Management vol 30 no 2 pp 144ndash157 2018
[18] H Xu and X Geng ldquoPeople-centric service intelligence forsmart citiesrdquo Smart Cities vol 2 no 2 pp 135ndash152 2019
[19] J Arthi ldquoBottom billion strategy-smart Villagesrdquo AsianJournal of Multidimensional Research (AJMR) vol 7 no 1pp 63ndash66 2018
[20] A Gharaibeh M A Salahuddin S J Hussini et al ldquoSmartcities a survey on data management security and enablingtechnologiesrdquo IEEE Communications Surveys amp Tutorialsvol 19 no 4 pp 2456ndash2501 2017
[21] N F Alwan andM K AL-Nuaimi ldquo+e role and important ofinternet of things in building sustainable cityrdquo Engineeringand Technology Journal vol 36 no 1 pp 22ndash29 2018
[22] M Aamir S Masroor Z A Ali and B T Ting ldquoSustainableframework for smart transportation system a case study ofkarachirdquo Wireless Personal Communications vol 106 no 1pp 27ndash40 2019
[23] S P Caird and S H Hallett ldquoTowards evaluation design forsmart city developmentrdquo Journal of Urban Design vol 24no 2 pp 188ndash209 2019
Complexity 11
upgrading model improve the services of urban financialorganizations and form a financial center with a reasonablelayoutmdashindustry as a financial entity+e characteristics andcapital advantages of Internet companies have technologicaland innovation advantages As a huge platform for thedevelopment of the financial industry cities have pipelineadvantages and many mobile terminals and ultimatelyachieve the integration of industry and city Financial ITcostanalysis is shown in Figure 7
33 Empirical Analyses +e financial cloud has the char-acteristics of huge investment in the early stage of businessdevelopment and the short-term and short-term incomemay not be fully guaranteed Many companies rely onventure capital funds or through the stock market to obtainfunds to ensure the companyrsquos sustainable developmentFigure 8 is the net profit compiled according to the smartcityrsquos financial reports in the past years We have seen anegative net profit since 2011 Now with the support offinancial cloud technology you do not have to worry aboutchanges in business cycles or customer groups that causeproblems with working capital You can calmly face changesin various market environments
To evaluate an object a certain decision-making unitneeds to be selected and certain criteria need to be adoptedin this selection +e specific selection principle shouldensure that the selected decision-making unit can achieve
the same when it is in the same external environment Tasksachieve the same input and output +ere are three ways toselect DMU in the evaluation process of smart citiesnamely vertical and horizontal comparison and verticaland horizontal comprehensive comparison +is articleuses a longitudinal comparison to select the DMU Basedon the obtained data this paper selects the comprehensiveevaluation of the urban construction in the past three yearsand analyzes the results of the smart city construction inShanghai in the past three years based on scientific resultsBased on the above analysis it can be found that the smartcity construction has achieved certain results through inputand output Pay attention to the human cost infrastructureand capital input and the corresponding output of smartgovernment smart economy smart life and smart humanliteracy is efficient With continuous investment in majorfactors the effectiveness of the governmentrsquos DMU fluc-tuates around 1 which also proves that the principle ofeffectiveness can be met in the selection of input and outputindicators In further analysis of factor productivity we cansee that all major factors are effective and the application ofnew technologies and innovations in production andmanagement has achieved good results +is is also furthershowing that if the government major financial institu-tions and other stakeholders attach importance to theinvestment in various elements they can achieve goodresults to a large extent +e regression test results areshown in Figure 9
Table 1 Evaluation index system
Smart city construction
Infrastructure
A1 investment in fixed assets of the whole society Million dollarsA2 urban road area Square kilometersA3 drainage pipe length KilometersA4 passenger traffic of the whole society Ten thousandsA5 total volume of post and telecommunications services Million dollars
UrbanizationA6 urbanization rate A7 urban population density A8 per capita disposable income of urban residents Yuan
Composite indexindustrial development
Development scale
B1 the amount of investment in fixed assets of informationtechnology and Internet companies Million dollars
B2 number of information technology and software companies PcB3 information technology software practitioners People
Industrial structure B4 tertiary industry output value as a percentage of GDP B5 the proportion of output value of secondary industry to GDP Item
Technology supportcapability
B6 patent authorization PcB7 number of national key laboratories Million dollarsB8 RampD internal expenditure PeopleB9 number of RampD researchers
Composite index ecologicalprotectioncomprehensive index
Urban ecologyC1 green coverage rate in built-up area Square meter
C2 park green area per capita Ten thousandtons
Environmentalprotection
C3 industrial solid waste generation Ten thousandtons
C4 comprehensive utilization of industrial solid waste Million dollars
Voice networkIT service deskEnd-user computingData center
0
10
20
30
40
50
60
70
80
90
100
2016 2017 2018 20192015Year
Figure 7 Distribution of IT costs
ndash300
ndash250
ndash200
ndash150
ndash100
ndash50
0
50
100
One
mill
ion
US
dolla
rs
2012 2013 2014 2015 2016 2017 2018 20192011Year
Figure 8 City net profit over the years
Complexity 9
4 Conclusion
+is article first discusses the relationship between smartcities and finance and existing models based on existingrelevant theories and literature By comparing and an-alyzing the status of smart cities and financial clouds athome and abroad this paper applies financial cloud in-telligence to build smart cities It has laid the foundationfor empirical analysis and model innovation +eoreticalresearch of financial technology and smart city is inte-grated with each other to study and analyze the health andperfect sustainable road of smart city +e problems in thesustainable development of smart cities should be solvedfrom the aspects of economy human problems envi-ronmental science urban infrastructure and ecologicalenvironment Explore the innovation of the smart citymodel based on the ldquofinancial cloudrdquo perspective Inorder to achieve the sustainable development goals ofsmart cities explore the dynamic mechanism of sus-tainable development of smart cities through the analysisof the structure and elements of smart cities and improvethe construction of financial ecological chain in order toachieve the goal of model innovation Finally this paperproposes a way to realize the integration of smart city andfinancial cloud which has certain guiding significance forthe sustainable development of the smart city
Data Availability
+e data used to support the findings of this study areavailable from the corresponding author upon request
Conflicts of Interest
+e authors declare that they have no conflicts of interestregarding the publication of this paper
Acknowledgments
+is work was supported by the General Project of theNational Natural Science Foundation of China (Nos71874037 and 71673062) and Natural Science Foundation ofGuangdong Province (No 2018A030313397)
References
[1] C Kakderi N Komninos and P Tsarchopoulos ldquoSmart citiesand cloud computing lessons from the STORM CLOUDSexperimentrdquo Journal of Smart Cities vol 1 no 2 pp 4ndash132019
[2] P Tsarchopoulos N Komninos and C Kakderi ldquoAcceler-ating the uptake of smart city applications through cloudcomputing World Academy of Science Engineering andTechnologyrdquo International Journal of Social BehavioralEducational Economic Business and Industrial Engineeringvol 11 no 1 pp 129ndash138 2017
[3] N Komninos C Kakderi A Panori and P TsarchopoulosldquoSmart city planning from an evolutionary perspectiverdquoJournal of Urban Technology vol 26 no 2 pp 3ndash20 2019
[4] N P Rana S Luthra S K Mangla R Islam S Roderick andY K Dwivedi ldquoBarriers to the development of smart cities inIndian contextrdquo Information Systems Frontiers vol 21 no 3pp 503ndash525 2019
[5] G Trencher and A Karvonen ldquoStretching ldquosmartrdquo advancinghealth and well-being through the smart city agendardquo LocalEnvironment vol 24 no 7 pp 610ndash627 2019
[6] S P Mohanty U Choppali and E Kougianos ldquoEverythingyou wanted to know about smart cities the internet of thingsis the backbonerdquo IEEE Consumer Electronics Magazine vol 5no 3 pp 60ndash70 2016
[7] Y Mehmood F Ahmad I Yaqoob A Adnane M Imranand S Guizani ldquoInternet-of-things-based smart cities recentadvances and challengesrdquo IEEE Communications Magazinevol 55 no 9 pp 16ndash24 2017
[8] M Angelidou ldquo+e role of smart city characteristics in theplans of fifteen citiesrdquo Journal of Urban Technology vol 24no 4 pp 3ndash28 2017
[9] M Sookhak H Tang Y He et al ldquoSecurity and privacy ofsmart cities a survey research issues and challengesrdquo IEEECommunications Surveys amp Tutorials vol 21 no 2pp 1718ndash1743 2018
[10] S Berke ldquo+e sustainable development of data-driven smartcities citizen-centered urban governance and networkeddigital technologiesrdquo Geopolitics History and InternationalRelations vol 11 no 2 pp 122ndash127 2019
[11] M Masera E F Bompard F Profumo and N HadjsaidldquoSmart (electricity) grids for smart cities assessing roles andsocietal impactsrdquo Proceedings of the IEEE vol 106 no 4pp 613ndash625 2018
[12] A Panori A Gonzalez-Quel M Tavares et al ldquoMigration ofapplications to the Cloud a user-driven approachrdquo Journal ofSmart Cities vol 2 no 1 pp 16ndash27 2019
[13] J Xie H Tang T Huang et al ldquoA survey of blockchaintechnology applied to smart cities research issues and chal-lengesrdquo IEEE Communications Surveys amp Tutorials vol 21no 3 pp 2794ndash2830 2019
[14] E F Z Santana A P Chaves M A Gerosa et al ldquoSoftwareplatforms for smart cities concepts requirements challengesand a unified reference architecturerdquo ACM Computing Sur-veys vol 50 no 6 pp 1ndash37 2017
Unstandardized regression coefficientUnstandardized standard errorStandard coefficientt
SigToleranceVIF
0
1
2
3
4
5
Valu
es
X2 X3 X4X1Variable
Figure 9 Regression coefficient test and significance level
10 Complexity
[15] X Kong X Liu B Jedari M Li L Wan and F Xia ldquoMobilecrowdsourcing in smart cities technologies applications andfuture challengesrdquo IEEE Internet of 2ings Journal vol 6no 5 pp 8095ndash8113 2019
[16] D Eckhoff and I Wagner ldquoPrivacy in the smart cit-ymdashapplications technologies challenges and solutionsrdquoIEEE Communications Surveys amp Tutorials vol 20 no 1pp 489ndash516 2017
[17] A-M Valdez M Cook P-A Langendahl H Roby andS Potter ldquoPrototyping sustainable mobility practices user-generated data in the smart cityrdquo Technology Analysis ampStrategic Management vol 30 no 2 pp 144ndash157 2018
[18] H Xu and X Geng ldquoPeople-centric service intelligence forsmart citiesrdquo Smart Cities vol 2 no 2 pp 135ndash152 2019
[19] J Arthi ldquoBottom billion strategy-smart Villagesrdquo AsianJournal of Multidimensional Research (AJMR) vol 7 no 1pp 63ndash66 2018
[20] A Gharaibeh M A Salahuddin S J Hussini et al ldquoSmartcities a survey on data management security and enablingtechnologiesrdquo IEEE Communications Surveys amp Tutorialsvol 19 no 4 pp 2456ndash2501 2017
[21] N F Alwan andM K AL-Nuaimi ldquo+e role and important ofinternet of things in building sustainable cityrdquo Engineeringand Technology Journal vol 36 no 1 pp 22ndash29 2018
[22] M Aamir S Masroor Z A Ali and B T Ting ldquoSustainableframework for smart transportation system a case study ofkarachirdquo Wireless Personal Communications vol 106 no 1pp 27ndash40 2019
[23] S P Caird and S H Hallett ldquoTowards evaluation design forsmart city developmentrdquo Journal of Urban Design vol 24no 2 pp 188ndash209 2019
Complexity 11
Valu
es
Comprehensive development index TCoupling degree CCoordination D
Voice networkIT service deskEnd-user computingData center
0
10
20
30
40
50
60
70
80
90
100
2016 2017 2018 20192015Year
Figure 7 Distribution of IT costs
ndash300
ndash250
ndash200
ndash150
ndash100
ndash50
0
50
100
One
mill
ion
US
dolla
rs
2012 2013 2014 2015 2016 2017 2018 20192011Year
Figure 8 City net profit over the years
Complexity 9
4 Conclusion
+is article first discusses the relationship between smartcities and finance and existing models based on existingrelevant theories and literature By comparing and an-alyzing the status of smart cities and financial clouds athome and abroad this paper applies financial cloud in-telligence to build smart cities It has laid the foundationfor empirical analysis and model innovation +eoreticalresearch of financial technology and smart city is inte-grated with each other to study and analyze the health andperfect sustainable road of smart city +e problems in thesustainable development of smart cities should be solvedfrom the aspects of economy human problems envi-ronmental science urban infrastructure and ecologicalenvironment Explore the innovation of the smart citymodel based on the ldquofinancial cloudrdquo perspective Inorder to achieve the sustainable development goals ofsmart cities explore the dynamic mechanism of sus-tainable development of smart cities through the analysisof the structure and elements of smart cities and improvethe construction of financial ecological chain in order toachieve the goal of model innovation Finally this paperproposes a way to realize the integration of smart city andfinancial cloud which has certain guiding significance forthe sustainable development of the smart city
Data Availability
+e data used to support the findings of this study areavailable from the corresponding author upon request
Conflicts of Interest
+e authors declare that they have no conflicts of interestregarding the publication of this paper
Acknowledgments
+is work was supported by the General Project of theNational Natural Science Foundation of China (Nos71874037 and 71673062) and Natural Science Foundation ofGuangdong Province (No 2018A030313397)
References
[1] C Kakderi N Komninos and P Tsarchopoulos ldquoSmart citiesand cloud computing lessons from the STORM CLOUDSexperimentrdquo Journal of Smart Cities vol 1 no 2 pp 4ndash132019
[2] P Tsarchopoulos N Komninos and C Kakderi ldquoAcceler-ating the uptake of smart city applications through cloudcomputing World Academy of Science Engineering andTechnologyrdquo International Journal of Social BehavioralEducational Economic Business and Industrial Engineeringvol 11 no 1 pp 129ndash138 2017
[3] N Komninos C Kakderi A Panori and P TsarchopoulosldquoSmart city planning from an evolutionary perspectiverdquoJournal of Urban Technology vol 26 no 2 pp 3ndash20 2019
[4] N P Rana S Luthra S K Mangla R Islam S Roderick andY K Dwivedi ldquoBarriers to the development of smart cities inIndian contextrdquo Information Systems Frontiers vol 21 no 3pp 503ndash525 2019
[5] G Trencher and A Karvonen ldquoStretching ldquosmartrdquo advancinghealth and well-being through the smart city agendardquo LocalEnvironment vol 24 no 7 pp 610ndash627 2019
[6] S P Mohanty U Choppali and E Kougianos ldquoEverythingyou wanted to know about smart cities the internet of thingsis the backbonerdquo IEEE Consumer Electronics Magazine vol 5no 3 pp 60ndash70 2016
[7] Y Mehmood F Ahmad I Yaqoob A Adnane M Imranand S Guizani ldquoInternet-of-things-based smart cities recentadvances and challengesrdquo IEEE Communications Magazinevol 55 no 9 pp 16ndash24 2017
[8] M Angelidou ldquo+e role of smart city characteristics in theplans of fifteen citiesrdquo Journal of Urban Technology vol 24no 4 pp 3ndash28 2017
[9] M Sookhak H Tang Y He et al ldquoSecurity and privacy ofsmart cities a survey research issues and challengesrdquo IEEECommunications Surveys amp Tutorials vol 21 no 2pp 1718ndash1743 2018
[10] S Berke ldquo+e sustainable development of data-driven smartcities citizen-centered urban governance and networkeddigital technologiesrdquo Geopolitics History and InternationalRelations vol 11 no 2 pp 122ndash127 2019
[11] M Masera E F Bompard F Profumo and N HadjsaidldquoSmart (electricity) grids for smart cities assessing roles andsocietal impactsrdquo Proceedings of the IEEE vol 106 no 4pp 613ndash625 2018
[12] A Panori A Gonzalez-Quel M Tavares et al ldquoMigration ofapplications to the Cloud a user-driven approachrdquo Journal ofSmart Cities vol 2 no 1 pp 16ndash27 2019
[13] J Xie H Tang T Huang et al ldquoA survey of blockchaintechnology applied to smart cities research issues and chal-lengesrdquo IEEE Communications Surveys amp Tutorials vol 21no 3 pp 2794ndash2830 2019
[14] E F Z Santana A P Chaves M A Gerosa et al ldquoSoftwareplatforms for smart cities concepts requirements challengesand a unified reference architecturerdquo ACM Computing Sur-veys vol 50 no 6 pp 1ndash37 2017
Unstandardized regression coefficientUnstandardized standard errorStandard coefficientt
SigToleranceVIF
0
1
2
3
4
5
Valu
es
X2 X3 X4X1Variable
Figure 9 Regression coefficient test and significance level
10 Complexity
[15] X Kong X Liu B Jedari M Li L Wan and F Xia ldquoMobilecrowdsourcing in smart cities technologies applications andfuture challengesrdquo IEEE Internet of 2ings Journal vol 6no 5 pp 8095ndash8113 2019
[16] D Eckhoff and I Wagner ldquoPrivacy in the smart cit-ymdashapplications technologies challenges and solutionsrdquoIEEE Communications Surveys amp Tutorials vol 20 no 1pp 489ndash516 2017
[17] A-M Valdez M Cook P-A Langendahl H Roby andS Potter ldquoPrototyping sustainable mobility practices user-generated data in the smart cityrdquo Technology Analysis ampStrategic Management vol 30 no 2 pp 144ndash157 2018
[18] H Xu and X Geng ldquoPeople-centric service intelligence forsmart citiesrdquo Smart Cities vol 2 no 2 pp 135ndash152 2019
[19] J Arthi ldquoBottom billion strategy-smart Villagesrdquo AsianJournal of Multidimensional Research (AJMR) vol 7 no 1pp 63ndash66 2018
[20] A Gharaibeh M A Salahuddin S J Hussini et al ldquoSmartcities a survey on data management security and enablingtechnologiesrdquo IEEE Communications Surveys amp Tutorialsvol 19 no 4 pp 2456ndash2501 2017
[21] N F Alwan andM K AL-Nuaimi ldquo+e role and important ofinternet of things in building sustainable cityrdquo Engineeringand Technology Journal vol 36 no 1 pp 22ndash29 2018
[22] M Aamir S Masroor Z A Ali and B T Ting ldquoSustainableframework for smart transportation system a case study ofkarachirdquo Wireless Personal Communications vol 106 no 1pp 27ndash40 2019
[23] S P Caird and S H Hallett ldquoTowards evaluation design forsmart city developmentrdquo Journal of Urban Design vol 24no 2 pp 188ndash209 2019
Voice networkIT service deskEnd-user computingData center
0
10
20
30
40
50
60
70
80
90
100
2016 2017 2018 20192015Year
Figure 7 Distribution of IT costs
ndash300
ndash250
ndash200
ndash150
ndash100
ndash50
0
50
100
One
mill
ion
US
dolla
rs
2012 2013 2014 2015 2016 2017 2018 20192011Year
Figure 8 City net profit over the years
Complexity 9
4 Conclusion
+is article first discusses the relationship between smartcities and finance and existing models based on existingrelevant theories and literature By comparing and an-alyzing the status of smart cities and financial clouds athome and abroad this paper applies financial cloud in-telligence to build smart cities It has laid the foundationfor empirical analysis and model innovation +eoreticalresearch of financial technology and smart city is inte-grated with each other to study and analyze the health andperfect sustainable road of smart city +e problems in thesustainable development of smart cities should be solvedfrom the aspects of economy human problems envi-ronmental science urban infrastructure and ecologicalenvironment Explore the innovation of the smart citymodel based on the ldquofinancial cloudrdquo perspective Inorder to achieve the sustainable development goals ofsmart cities explore the dynamic mechanism of sus-tainable development of smart cities through the analysisof the structure and elements of smart cities and improvethe construction of financial ecological chain in order toachieve the goal of model innovation Finally this paperproposes a way to realize the integration of smart city andfinancial cloud which has certain guiding significance forthe sustainable development of the smart city
Data Availability
+e data used to support the findings of this study areavailable from the corresponding author upon request
Conflicts of Interest
+e authors declare that they have no conflicts of interestregarding the publication of this paper
Acknowledgments
+is work was supported by the General Project of theNational Natural Science Foundation of China (Nos71874037 and 71673062) and Natural Science Foundation ofGuangdong Province (No 2018A030313397)
References
[1] C Kakderi N Komninos and P Tsarchopoulos ldquoSmart citiesand cloud computing lessons from the STORM CLOUDSexperimentrdquo Journal of Smart Cities vol 1 no 2 pp 4ndash132019
[2] P Tsarchopoulos N Komninos and C Kakderi ldquoAcceler-ating the uptake of smart city applications through cloudcomputing World Academy of Science Engineering andTechnologyrdquo International Journal of Social BehavioralEducational Economic Business and Industrial Engineeringvol 11 no 1 pp 129ndash138 2017
[3] N Komninos C Kakderi A Panori and P TsarchopoulosldquoSmart city planning from an evolutionary perspectiverdquoJournal of Urban Technology vol 26 no 2 pp 3ndash20 2019
[4] N P Rana S Luthra S K Mangla R Islam S Roderick andY K Dwivedi ldquoBarriers to the development of smart cities inIndian contextrdquo Information Systems Frontiers vol 21 no 3pp 503ndash525 2019
[5] G Trencher and A Karvonen ldquoStretching ldquosmartrdquo advancinghealth and well-being through the smart city agendardquo LocalEnvironment vol 24 no 7 pp 610ndash627 2019
[6] S P Mohanty U Choppali and E Kougianos ldquoEverythingyou wanted to know about smart cities the internet of thingsis the backbonerdquo IEEE Consumer Electronics Magazine vol 5no 3 pp 60ndash70 2016
[7] Y Mehmood F Ahmad I Yaqoob A Adnane M Imranand S Guizani ldquoInternet-of-things-based smart cities recentadvances and challengesrdquo IEEE Communications Magazinevol 55 no 9 pp 16ndash24 2017
[8] M Angelidou ldquo+e role of smart city characteristics in theplans of fifteen citiesrdquo Journal of Urban Technology vol 24no 4 pp 3ndash28 2017
[9] M Sookhak H Tang Y He et al ldquoSecurity and privacy ofsmart cities a survey research issues and challengesrdquo IEEECommunications Surveys amp Tutorials vol 21 no 2pp 1718ndash1743 2018
[10] S Berke ldquo+e sustainable development of data-driven smartcities citizen-centered urban governance and networkeddigital technologiesrdquo Geopolitics History and InternationalRelations vol 11 no 2 pp 122ndash127 2019
[11] M Masera E F Bompard F Profumo and N HadjsaidldquoSmart (electricity) grids for smart cities assessing roles andsocietal impactsrdquo Proceedings of the IEEE vol 106 no 4pp 613ndash625 2018
[12] A Panori A Gonzalez-Quel M Tavares et al ldquoMigration ofapplications to the Cloud a user-driven approachrdquo Journal ofSmart Cities vol 2 no 1 pp 16ndash27 2019
[13] J Xie H Tang T Huang et al ldquoA survey of blockchaintechnology applied to smart cities research issues and chal-lengesrdquo IEEE Communications Surveys amp Tutorials vol 21no 3 pp 2794ndash2830 2019
[14] E F Z Santana A P Chaves M A Gerosa et al ldquoSoftwareplatforms for smart cities concepts requirements challengesand a unified reference architecturerdquo ACM Computing Sur-veys vol 50 no 6 pp 1ndash37 2017
Unstandardized regression coefficientUnstandardized standard errorStandard coefficientt
SigToleranceVIF
0
1
2
3
4
5
Valu
es
X2 X3 X4X1Variable
Figure 9 Regression coefficient test and significance level
10 Complexity
[15] X Kong X Liu B Jedari M Li L Wan and F Xia ldquoMobilecrowdsourcing in smart cities technologies applications andfuture challengesrdquo IEEE Internet of 2ings Journal vol 6no 5 pp 8095ndash8113 2019
[16] D Eckhoff and I Wagner ldquoPrivacy in the smart cit-ymdashapplications technologies challenges and solutionsrdquoIEEE Communications Surveys amp Tutorials vol 20 no 1pp 489ndash516 2017
[17] A-M Valdez M Cook P-A Langendahl H Roby andS Potter ldquoPrototyping sustainable mobility practices user-generated data in the smart cityrdquo Technology Analysis ampStrategic Management vol 30 no 2 pp 144ndash157 2018
[18] H Xu and X Geng ldquoPeople-centric service intelligence forsmart citiesrdquo Smart Cities vol 2 no 2 pp 135ndash152 2019
[19] J Arthi ldquoBottom billion strategy-smart Villagesrdquo AsianJournal of Multidimensional Research (AJMR) vol 7 no 1pp 63ndash66 2018
[20] A Gharaibeh M A Salahuddin S J Hussini et al ldquoSmartcities a survey on data management security and enablingtechnologiesrdquo IEEE Communications Surveys amp Tutorialsvol 19 no 4 pp 2456ndash2501 2017
[21] N F Alwan andM K AL-Nuaimi ldquo+e role and important ofinternet of things in building sustainable cityrdquo Engineeringand Technology Journal vol 36 no 1 pp 22ndash29 2018
[22] M Aamir S Masroor Z A Ali and B T Ting ldquoSustainableframework for smart transportation system a case study ofkarachirdquo Wireless Personal Communications vol 106 no 1pp 27ndash40 2019
[23] S P Caird and S H Hallett ldquoTowards evaluation design forsmart city developmentrdquo Journal of Urban Design vol 24no 2 pp 188ndash209 2019
Complexity 11
4 Conclusion
+is article first discusses the relationship between smartcities and finance and existing models based on existingrelevant theories and literature By comparing and an-alyzing the status of smart cities and financial clouds athome and abroad this paper applies financial cloud in-telligence to build smart cities It has laid the foundationfor empirical analysis and model innovation +eoreticalresearch of financial technology and smart city is inte-grated with each other to study and analyze the health andperfect sustainable road of smart city +e problems in thesustainable development of smart cities should be solvedfrom the aspects of economy human problems envi-ronmental science urban infrastructure and ecologicalenvironment Explore the innovation of the smart citymodel based on the ldquofinancial cloudrdquo perspective Inorder to achieve the sustainable development goals ofsmart cities explore the dynamic mechanism of sus-tainable development of smart cities through the analysisof the structure and elements of smart cities and improvethe construction of financial ecological chain in order toachieve the goal of model innovation Finally this paperproposes a way to realize the integration of smart city andfinancial cloud which has certain guiding significance forthe sustainable development of the smart city
Data Availability
+e data used to support the findings of this study areavailable from the corresponding author upon request
Conflicts of Interest
+e authors declare that they have no conflicts of interestregarding the publication of this paper
Acknowledgments
+is work was supported by the General Project of theNational Natural Science Foundation of China (Nos71874037 and 71673062) and Natural Science Foundation ofGuangdong Province (No 2018A030313397)
References
[1] C Kakderi N Komninos and P Tsarchopoulos ldquoSmart citiesand cloud computing lessons from the STORM CLOUDSexperimentrdquo Journal of Smart Cities vol 1 no 2 pp 4ndash132019
[2] P Tsarchopoulos N Komninos and C Kakderi ldquoAcceler-ating the uptake of smart city applications through cloudcomputing World Academy of Science Engineering andTechnologyrdquo International Journal of Social BehavioralEducational Economic Business and Industrial Engineeringvol 11 no 1 pp 129ndash138 2017
[3] N Komninos C Kakderi A Panori and P TsarchopoulosldquoSmart city planning from an evolutionary perspectiverdquoJournal of Urban Technology vol 26 no 2 pp 3ndash20 2019
[4] N P Rana S Luthra S K Mangla R Islam S Roderick andY K Dwivedi ldquoBarriers to the development of smart cities inIndian contextrdquo Information Systems Frontiers vol 21 no 3pp 503ndash525 2019
[5] G Trencher and A Karvonen ldquoStretching ldquosmartrdquo advancinghealth and well-being through the smart city agendardquo LocalEnvironment vol 24 no 7 pp 610ndash627 2019
[6] S P Mohanty U Choppali and E Kougianos ldquoEverythingyou wanted to know about smart cities the internet of thingsis the backbonerdquo IEEE Consumer Electronics Magazine vol 5no 3 pp 60ndash70 2016
[7] Y Mehmood F Ahmad I Yaqoob A Adnane M Imranand S Guizani ldquoInternet-of-things-based smart cities recentadvances and challengesrdquo IEEE Communications Magazinevol 55 no 9 pp 16ndash24 2017
[8] M Angelidou ldquo+e role of smart city characteristics in theplans of fifteen citiesrdquo Journal of Urban Technology vol 24no 4 pp 3ndash28 2017
[9] M Sookhak H Tang Y He et al ldquoSecurity and privacy ofsmart cities a survey research issues and challengesrdquo IEEECommunications Surveys amp Tutorials vol 21 no 2pp 1718ndash1743 2018
[10] S Berke ldquo+e sustainable development of data-driven smartcities citizen-centered urban governance and networkeddigital technologiesrdquo Geopolitics History and InternationalRelations vol 11 no 2 pp 122ndash127 2019
[11] M Masera E F Bompard F Profumo and N HadjsaidldquoSmart (electricity) grids for smart cities assessing roles andsocietal impactsrdquo Proceedings of the IEEE vol 106 no 4pp 613ndash625 2018
[12] A Panori A Gonzalez-Quel M Tavares et al ldquoMigration ofapplications to the Cloud a user-driven approachrdquo Journal ofSmart Cities vol 2 no 1 pp 16ndash27 2019
[13] J Xie H Tang T Huang et al ldquoA survey of blockchaintechnology applied to smart cities research issues and chal-lengesrdquo IEEE Communications Surveys amp Tutorials vol 21no 3 pp 2794ndash2830 2019
[14] E F Z Santana A P Chaves M A Gerosa et al ldquoSoftwareplatforms for smart cities concepts requirements challengesand a unified reference architecturerdquo ACM Computing Sur-veys vol 50 no 6 pp 1ndash37 2017
Unstandardized regression coefficientUnstandardized standard errorStandard coefficientt
SigToleranceVIF
0
1
2
3
4
5
Valu
es
X2 X3 X4X1Variable
Figure 9 Regression coefficient test and significance level
10 Complexity
[15] X Kong X Liu B Jedari M Li L Wan and F Xia ldquoMobilecrowdsourcing in smart cities technologies applications andfuture challengesrdquo IEEE Internet of 2ings Journal vol 6no 5 pp 8095ndash8113 2019
[16] D Eckhoff and I Wagner ldquoPrivacy in the smart cit-ymdashapplications technologies challenges and solutionsrdquoIEEE Communications Surveys amp Tutorials vol 20 no 1pp 489ndash516 2017
[17] A-M Valdez M Cook P-A Langendahl H Roby andS Potter ldquoPrototyping sustainable mobility practices user-generated data in the smart cityrdquo Technology Analysis ampStrategic Management vol 30 no 2 pp 144ndash157 2018
[18] H Xu and X Geng ldquoPeople-centric service intelligence forsmart citiesrdquo Smart Cities vol 2 no 2 pp 135ndash152 2019
[19] J Arthi ldquoBottom billion strategy-smart Villagesrdquo AsianJournal of Multidimensional Research (AJMR) vol 7 no 1pp 63ndash66 2018
[20] A Gharaibeh M A Salahuddin S J Hussini et al ldquoSmartcities a survey on data management security and enablingtechnologiesrdquo IEEE Communications Surveys amp Tutorialsvol 19 no 4 pp 2456ndash2501 2017
[21] N F Alwan andM K AL-Nuaimi ldquo+e role and important ofinternet of things in building sustainable cityrdquo Engineeringand Technology Journal vol 36 no 1 pp 22ndash29 2018
[22] M Aamir S Masroor Z A Ali and B T Ting ldquoSustainableframework for smart transportation system a case study ofkarachirdquo Wireless Personal Communications vol 106 no 1pp 27ndash40 2019
[23] S P Caird and S H Hallett ldquoTowards evaluation design forsmart city developmentrdquo Journal of Urban Design vol 24no 2 pp 188ndash209 2019
Complexity 11
[15] X Kong X Liu B Jedari M Li L Wan and F Xia ldquoMobilecrowdsourcing in smart cities technologies applications andfuture challengesrdquo IEEE Internet of 2ings Journal vol 6no 5 pp 8095ndash8113 2019
[16] D Eckhoff and I Wagner ldquoPrivacy in the smart cit-ymdashapplications technologies challenges and solutionsrdquoIEEE Communications Surveys amp Tutorials vol 20 no 1pp 489ndash516 2017
[17] A-M Valdez M Cook P-A Langendahl H Roby andS Potter ldquoPrototyping sustainable mobility practices user-generated data in the smart cityrdquo Technology Analysis ampStrategic Management vol 30 no 2 pp 144ndash157 2018
[18] H Xu and X Geng ldquoPeople-centric service intelligence forsmart citiesrdquo Smart Cities vol 2 no 2 pp 135ndash152 2019
[19] J Arthi ldquoBottom billion strategy-smart Villagesrdquo AsianJournal of Multidimensional Research (AJMR) vol 7 no 1pp 63ndash66 2018
[20] A Gharaibeh M A Salahuddin S J Hussini et al ldquoSmartcities a survey on data management security and enablingtechnologiesrdquo IEEE Communications Surveys amp Tutorialsvol 19 no 4 pp 2456ndash2501 2017
[21] N F Alwan andM K AL-Nuaimi ldquo+e role and important ofinternet of things in building sustainable cityrdquo Engineeringand Technology Journal vol 36 no 1 pp 22ndash29 2018
[22] M Aamir S Masroor Z A Ali and B T Ting ldquoSustainableframework for smart transportation system a case study ofkarachirdquo Wireless Personal Communications vol 106 no 1pp 27ndash40 2019
[23] S P Caird and S H Hallett ldquoTowards evaluation design forsmart city developmentrdquo Journal of Urban Design vol 24no 2 pp 188ndash209 2019
