Abstract
This paper constructs evaluation index system of input-output of Smart City on the basis of the present evaluation researches of Smart City, urban information and urbanization from the view of input-output theory; and via utilizing related methods of Data Envelopment Analysis (DEA) takes intelligent evaluation research of the change of effectiveness and construction efficiency of Smart City construction in Wuhan among 2013–2016. The result reveals Smart City construction of Wuhan has got a certain achievements and relative efficiency. So, Wuhan should stress overall layout, achieve key breakthrough, exert local features and tamp human resources foundation, perfect relevant system, guarantee innovation enthusiasm, improve financing system and promote industrial innovation so as to improve cluster scale of urban construction input, technological innovation capability and promote the construction of Smart City.
Introduction
Under the rising tide of Smart City, the research of Smart City evaluation has aroused people’s attention gradually, and more and more scholars and experts have a strong interest in this subject. There are many kinds of researches on the evaluation index system of “smart cities” abroad, and the more representative of that is a “smart city” evaluation system designed by European smart city organization. The index system includes 3 secondary indicators and 74 tertiary indicators, which are composed of 6 aspects of intelligent economy, intelligent movement, intelligent environment, intelligent people, intelligent life and intelligent management [1]. The smart community BBS has designed an evaluation system of city and community intelligence by qualitative method, which Includes 5 aspects of broadband connectivity, knowledge workers, digital inclusion, innovation, marketing and advocacy, and can be further divided into 18 secondary evaluation indicators [2].
At present, the evaluation of “Smart City” in China is mainly based on the evaluation of city information. The relevant scholars put forward the corresponding evaluation index system of “smart city”, according to the content, characteristics, construction idea and advance mode of “smart city”. The representative index system is the “smart city index system” issued by Pudong smart city development institute in Shanghai, which consists of 5 dimensions, such as infrastructure, public management and service, information service economy development, humanistic science literacy, the subjective perception of the citizen and so on, and 64 specific indicators. From the field of city infrastructure, industries, services, wisdom and culture in four aspects, Chen Ming and Wang Qianchen, taking Nanjing city as an example for detailed analysis, have established a set of evaluation index system, including 23 evaluation indicators [3]. Zhou Yi have adopted DPSIR model to establish a set of Smart City evaluation index system from the driving force indicators, pressure indicators, state indicators, impact indicators and response indicators, containing 37 evaluation indicators [4]. Xiang Yong and Ren Hong have established a Smart City evaluation model based on ANP, and made an empirical analysis of Mian Yang, Sui Ning, Ya An and Cong Zhou in Sichuan Province by using the Smart City Evaluation Index system published in Shanghai [5]. Guo Xirong and Wu Xianfeng have assessed the development level of smart city in Pan Zhihua through the fuzzy analytic hierarchy process based on expert scoring [6]. In addition, Li Xianyi, Li Jian, Wang Lida and other scholars also have pro-posed different evaluation index system of smart city.
Generally speaking, the current research work on the evaluation of smart city is mostly evaluation of the maturity of smart city from the perspective of city information. The focus of this approach is to describe and measure the achievements of smart city construction, and then evaluate the construction maturity of smart city. But it ignores another important aspect of smart city construction— input of resources. More importantly, smart city is based on the development and application of information technology and Internet of Things (IoT), and change with them. Therefore, there is no unified and fixed technical standard for the construction of smart cities, and there is no accepted sample to measure the development of a smart city. Focusing only on the achievements of smart city construction and the mature evaluation of smart city is not comprehensive and can’t get good results.
From the perspective of input and output, this paper carried out the evaluation study of smart city construction and focused on the intelligent evaluation of the efficiency of the construction of smart city. In order to make up for the shortcomings of existing research, Wuhan was selected as a sample to evaluate the resources and achievements in the construction of Wuhan smart city. Wuhan is the capital of Hubei province and one of the most important cities in the central region in China. Wuhan formally proposed the construction of smart city in 2011, and became one of the two pilot cities in the national 863 smart city project. Benefiting from its good economic development, scientific research education, Wuhan’s smart city construction has become one of the most prominent cities in China.
Due to the great economic development, the science of education and the high-tech industry gathering in Wuhan, the smart city construction of Wuhan has become one of the most prominent cities in China’s smart cities. Therefore, based on the construction data of Wuhan smart city from 2013 to 2016, this paper made an intelligent assessment of the construction of smart cities in Wuhan, and provided reference for the development strategy of smart city in Wuhan.
The construction of smart city input-output evaluation index system
The principle of selection of evaluation indicators
In view of the core system and activities of “smart city”, and according to the construction idea of “smart city” evaluation index system in this paper, this paper puts forward four principles should be followed in the process of building smart city evaluation index system.
First, follow the principle of comprehensiveness. The comprehensive principle requires that the index system can comprehensively reflect and evaluate the effect of smart city construction, and the intelligence degree of the city’s composing system. This mainly includes three aspects: first, the index system should be able to reflect and evaluate the performance changes brought by the introduction of “smart” scheme comprehensively. Secondly, the index system should be able to measure the city characteristics and the high information characteristics of smart city. In other words, it includes the evaluation of urbanization level and the evaluation of city information level. Finally, the index system should be able to comprehensively reflect and measure the operation of the city, for example, the intelligence of economic life and management services, etc.
Second, follow the principle of representativeness. The city is composed of multiple systems, and the index system should be able to represent and reflect the actual situation of every system. This mainly includes two aspects. Firstly, the indicators must be representative in this area. Secondly, the indicator group composed of several independent indicators should reflect the overall development level of a certain field.
Third, follow the principles of operability. The operability principle requires that the indicator system can be used effectively, and the data can be collected in practical application to reflect the smart degree of the city. This contains four aspects: First of all, the indicators should be collected into the corresponding data, which are real and scientific. Secondly, the index system should be dominated by quantitative indicators, with a small number of qualitative indicators as the auxiliary. Furthermore, the indicators should be comparable to each other when applicable to different cities. Finally, the indicators should con-form to the measurements and reflect the actual conditions of the measurement.
Fourth, follow prospective principles. At present, the construction of smart cities in China has just started, and there is no real smart city. Therefore, the existing index system should be able to evaluate the potential smart cities in future, which is forward-looking. This mainly includes three aspects: First, the index system should be able to measure the degree of intelligence in the future city, which is determined by the current situation of smart city construction in China. Secondly, the indicator system should contain static indicators that reflect the current situation of the city, and also include dynamic indicators that reflect the development trend of urban development. Finally, some indicators should be flexible and can be adjusted according to the actual situation in the future.
The construction of the indicator system
According to the selection principle of evaluation index system, and based on the analyzing of the input factors and output results of smart city construction, this paper combined with the existing core content from the research on smart city evaluation research, urban information evaluation and the evaluation of urbanization level, and extracted the evaluation indicators from them, then Built the smart city input- output evaluation index system. The evaluation index system starts from the two dimensions of input and output, and includes 7 primary indicators, such as input of human capital, infrastructure construction and so on; 26 secondary indicators, such as the scale of human resources, the level of perceptual network construction and so on; 115 three-level indicators, such as wireless network coverage, information industry contribution rate and so on, as shown in Tables 1–7.
Evaluation Index System of Input of Smart City—Human capital input
Evaluation Index System of Input of Smart City—Human capital input
Evaluation Index System of Input of Smart City—Infrastructure construction
Evaluation Index System of Input of Smart City—Capital input
Evaluation Index System of Output of Smart City—Smart government
Evaluation Index System of Output of Smart City—Smart humanistic quality
Evaluation Index System of Output of Smart City—Smart life
Evaluation Index System of Output of Smart City—the development of smart economy
At present, the goal of the studies of smart city evaluation methods is the evaluation of the maturity of smart city, its methods are mostly simple methods such as arithmetic average method. And smart city, a new thing on the basis of information technology and Internet technology, is constantly developing and changing. Therefore, according to this line of thinking, the building of smart city par model and evaluation method exist defects such as weak availability, low credibility and so on. Their scientific nature is open to discussion. Compared with the above methods, data envelopment analysis (DEA) don’t need weight assumptions and data dimensionless processing and determine the explicit expression about the relationship between input and output [9], it has very strong objectivity, and it has absolute advantage in dealing with multiple input and multiple out effectiveness evaluation [10].
The choice of decision making units
In smart city input-output effectiveness evaluation, according to different evaluation purposes, the choice of decision making unit (DMU) can use the following three ways.
First, make a longitudinal comparison. Select in different periods of the same area as the DMU, DEA evaluation can examine a region’s effectiveness development trend between resources input and effect output during the process of smart city construction.
Second, make a transverse comparison. Choosing different areas of the same period as the DMU, DEA evaluation can examine different areas’ effectiveness development trend between resources input and effect output during the process of smart city construction.
Third, make the longitudinal horizontal comprehensive comparison. At different times in different areas of the selection as a DMU, DEA evaluation can examine different areas’ effectiveness development trend between resources input and effect output during the process of smart city construction.
In order to investigate the Wuhan city’s change of the smart city construction effectiveness and efficiency from 2013 to 2016, this paper chooses the way to the longitudinal comparison, and select Wuhan smart city construction situation about four years as decision making units of evaluation respectively.
The establishment of the evaluation model
In order to measure the Wuhan smart city construction efficiency, this paper firstly chose the classic C2R models to measure multiple DMU in samples comparing the static, measuring the relative effectiveness, then used Malmquist productivity index evaluation method based on DEA to measure the change of construction efficiency, and dynamic measure Wuhan smart city construction total factor productivity, technological progress during different time. Thus, we can discuss changes of Wuhan smart city construction effectiveness and efficiency, and reveal the meaning and enlightenment.
With the C2R model of Non Archimedes infinitesimal
Is equipped with n to participate in the appraisal unit for DMU (decision making units), each DMU has m input and s output. The J-TH decision-making unit of input vector and output vector are:
Output and input weights of indicators are:
Each decision-making unit corresponding evaluation index are:
Through the use of formula (4) the efficiency evaluation index model, and the DMU efficiency index hj0 (decision making units’ efficiency index h j < 1, j = 1, 2, …, n) maximization as the goal to build the initial C2R model of DEA.
In order to facilitate solving, transforming formula (5) to Charness-Cooper, building up an equivalent linear programming model.
If
Formulas (5) and (6) are equivalent, the optimal values are equal. And define one to define two presents DEA efficient conditions of judging decision making units DMU jo :
Define one: if the optimal value of linear programming (6) is μ*TY jo = 1, so DMU jo is called a weak DEA effective.
Define two: if the optimal value of linear programming (6) is μ*TY jo = 1, and meeting the optimal solution: μ* > 0, ω* > 0, so DMU jo is called a weak DEA effective.
To solve the calculation and technical difficulties, using the duality theory to dual transformation the formula (6), and introduce the concept of non-Archimedes infinitesimal, then, it gets the C2R model of Non Archimedes infinitesimal.
In the formula (7), θj0 is the effectiveness of the decision-making unit of input-output, known as the coordinated development index; ɛ is Archimedes infinitesimal. Explain: any positive number is less than and greater than zero.
If the optimal solution of model is:
Malmquist productivity index is special index of measuring the total factor productivity change. Kraft (Caves) with others set up it on the basis of the Malmquist index and the concept of distance function. Because of the complexity of the calculation, the method is rarely used in practice. With the rapid development of DEA theory, scholars put them together, then Malmquist productivity index is widely used in the calculation of productivity.
This paper use Malmquist index based on DEA method to estimate the change of total factor productivity. And the Malmquist index method is built by Färe with others, and this method can examine the total factor productivity change (TFPCH) from t to (t+1).
If is
In the same way, under the (t+1) period technology, from t to (t+1) Malmquist productivity index is:
In order to avoid error of an arbitrary choice, Färe define Malmquist index to the above two geometric average productivity index:
Färe further pointed out that if the total factor productivity during t to (t + 1) is greater than 1, this indicates that the total factor productivity during the time is positive, it is growth period. Total factor productivity index could be decomposed into technical efficiency change (EFFCH) and technological progress (TECHCH), Which has the following relationship, total factor productivity change (TFPCH) = (EFFCH) technical efficiency change X technological progress (TECHCH), explain:
When pay the same scale, technical efficiency change (EFFCH) could be further decomposed into pure technical change components PEFFCH (based on the same scale reward) and scale efficiency change component SECH (based on the variable scale reward), the product is equal to the change in the technical efficiency (EFFCH), explain: EFFCH = PEFFCH × SECH.
The pure technical efficiency change (PEFFCH) is defined as:
According to the method proposed by Färe, the above formula can be calculated using the DEA linear programming method combined with panel data. SECH > 1, which means that the change of production system input concentration scale and scale efficiency has been improved; PEFFCH > 1, which means that the efficiency of technological innovation has been improved; TECHCH > 1, which means that production technology has been improved; TFPCH > 1, which means that overall productivity has been improved, and the evaluation object is in growth period and its production activities are meaningful. On the contrary, if the above indicators are less than 1, they indicate that the corresponding efficiency deteriorates.
The principal component data of the construction of Smart City in Wuhan from 2013 to 2016
The calculation results of the decision-making units in the construction of Wuhan Smart City
Collection and processing of the date
According to the three level evaluation index system of smart city in the previous article, and by consulted the relevant statistics, collected and sorted out the relevant data, this paper has got the original data of the evaluation index system of Smart City construction in Wuhan from 2013 to 2016.
The data is from China Statistical Yearbook, China City Statistical Yearbook, China Statistical Yearbook on Science and Technology, Hubei Statistical Yearbook, and Wuhan Statistical Yearbook. Through the method of field investigation, we not only conducted a field visit to the relevant units of Hubei Provincial Comprehensive Management Office, Hubei Provincial Communication Administration, Hubei Provincial Network Communication Company, Wuhan Municipal Government, Wuhan Municipal Comprehensive Management Office, Wuhan Municipal Network Communication Company, Wuhan Municipal Bureau of Statistics, Wuhan Municipal Bureau of Human Resources and Wuhan bus General Company, but investigated the opinions of ordinary citizens on urban information and so on by handing out questionnaires. A total of 150 questionnaires were distributed and 121 questionnaires were collected, including 108 valid questionnaires, which enabled us to obtain a large amount of first-hand data.
According to the DEA principle, input and output indicators and relevant data should not be too much, so, in order to get the required panel data for calculation, this paper adopted the principal component analysis to reduce the dimensions of the original data collected. This paper carried out the principal component analysis and solved the problem for the collected data through the SPSS software [11], and the principal component data of the construction of Smart City in Wuhan from 2013 to 2016 could be obtained, which summarized as shown in Table 8.
Data calculation
Used those data screened by principal component analysis and the C2R model of Non Archimedes infinitesimal in the above type 4, the above index system model could be solved through the MaxDEA software [12], and the final results were shown in Table 9.
Result analysis
Through the data obtained above, we can draw the following 4 conclusions.
Firstly, the θ numerical value of the three decision-making units is 1, indicating that the construction of smart city in Wuhan during the period of 2013–2016 is relatively effective.
Secondly, the numerical value of the total factor productivity change (TFPCH) is 1.807, more than 1, indicating that the efficiency of construction and production of smart city in Wuhan during the period of 2013–2016 is gradually increasing, mainly due to the gradual increase of change of technological innovation (TECHCH).
Thirdly, the numerical value of the change of technical efficiency (EFFCH) is 1, indicating that during the construction of smart city in Wuhan from 2013 to 2016, the technical efficiency has not changed, of which the reason is that the pure technical efficiency and the scale efficiency have not changed. This reflects that during the four years of the construction activities of smart city in Wuhan, the construction effect by using the high technology management methods or high technology and so on has not changed.
Fourthly, the numerical value of the change of technological innovation is 1.807, more than 1, indicating that during the construction of smart city in Wuhan from 2013 to 2016, the construction effect through technological innovation is gradually increasing. This reflects that during the four years of the construction activities of smart city in Wuhan, the development and application of new technology and its influence on urban construction are gradually increasing.
Conclusions
This paper evaluates the construction of smart city from the perspective of input-output, and can directly reflect the effectiveness of the government and the whole society in building smart city. And it is of great significance for the establishment of a diversified scientific assessment system to meet the needs of residents and the service idea of governing for the people.
The smart city input-output evaluation system established in this paper is universality and maneuver-ability. It can not only evaluate the relative effectiveness of multiple objects, but also measure the total factor productivity change of the target over a period of time. Through evaluation of the construction of smart city in Wuhan from 2013 to 2016, this paper argues that Wuhan needs to pay attention to the following aspects in the process of smart city construction.
First, play the characteristics of Wuhan and create an innovative talent highland. The key factor of technological innovation is technological innovation talents. Therefore, Wuhan must make efforts to cultivate innovative talents and build the high ground of innovative talents, so as to make full use of the science and education, industrial advantages to strengthen the foundation of innovative talents.
Second, improve the financing system and build an innovative industrial base. Adequate financial support is one of the basic conditions for technological innovation. Therefore, Wuhan city must improve the financing system to provide adequate financial security for the development of the industry and the formation of high-tech industrial clusters. It is necessary to construct multi-level investment and financing system to meet the demand of technological in-novation, improve the property rights market to promote the listing of high-tech enterprises.
Third, improve the relevant system to maintain enthusiasm for innovative activities. The perfect system standard is one of the basic conditions to guarantee the enthusiasm of technological innovation. Therefore, Wuhan must improve the relevant policy system, provide effective protection for the innovation activities in the society, and guarantee the enthusiasm of social innovation activities. Concretely speaking, it is necessary to strengthen the co-operation mechanism of production, study and re-search to promote the enthusiasm of innovation activities, improve the system of intellectual property rights protection to safeguard the interests of innovative activities.
Fourth, carry out overall layout construction to achieve breakthroughs in key areas. The overall lay-out is the prerequisite for realizing the comprehensive and orderly development of smart city construction. Key construction breakthroughs in key areas and key areas will lead to the construction of smart city and improve the efficiency of city construction. Therefore, Wuhan must make detailed construction plan and select pilot construction area; select the key of smart city construction to achieve the break-through development of the area.
Footnotes
Acknowledgments
This work was supported by the philosophy and social science planning project in Henan province (2017CZZ013); Soft science research project in Henan (172400410249); Research project of Henan sci-tech think tank(HNKJZK-03B); The higher education key research project in Henan province (16A630003); The humanities and social sciences research project of Henan provincial education department (2018-ZDJH-029).
