# Dynamic Measurement Analysis of Urban Innovation Ability and Ecological Efficiency in China

Authors: Xing Li; Fuzhou Luo
Journal: Complexity
 (2022)

---

## Abstract

To establish the evaluation index system of urbanization innovation level and ecological efficiency, the entropy method is applied to measure the comprehensive index of urbanization innovation level and ecological efficiency, the VAR model is established, and empirical measurement is used to study the internal relationship and dynamic development between urbanization innovation level and ecological efficiency. The empirical results show the following: (1) The overall development of the innovation level in 30 cities in China is uneven, there is a large gap between the urban innovation level in backwards areas and economically developed areas, and there is a certain coupling effect between the urban innovation level and ecological efficiency. (2) There is a long-term dynamic equilibrium relationship between the urban innovation level and ecological efficiency, which will exert certain pressure on the ecological environment in the process of urbanization. However, the continuous improvement of urbanization will have a positive impact on the improvement of the ecological environment. (3) The improvement of ecological efficiency will also promote the improvement of the urban innovation level. Therefore, this paper puts forwards policy suggestions to promote the harmonious development of urbanization and the ecological environment and provides a reference for realizing the balanced development of urbanization and ecological efficiency in China.


---

## Body

## 1. Introduction

With the rapid development of China’s economy and the continuous improvement of the urbanization level, urban innovation ability plays an important role in promoting the transformation of China’s social and economic structure. However, due to the agglomeration effect of the urban industrial structure and large rural population migration, research on urbanization and urban innovation ability has become a hot topic. A number of scholars have studied the impact of urban expansion and industrial agglomeration on the urban ecological environment and ecological efficiency. According to statistics, China’s urbanization rate increased from 22% in 1990 to 52.57% in 2012. It exceeded 60% in 2020 and will reach approximately 66% by 2030 [1]. It can be predicted that the level of urbanization will bring certain urban ecological pressure. At the same time, the concept of a sustainable development society promoted by China also requires important changes to the process of urbanization. Therefore, we need to correctly understand the internal relationship between the innovation ability of urbanization and ecological efficiency to achieve the harmonious development of cities and the environment through analysis, judgement, coordination, and planning.


## 2. Literature Review

The development of the urban economy cannot be separated from urban population growth, the flow of economic factors, the development and utilization of resources, etc. From a sustainable development perspective, the urban ecological environment is an important part of the urban system, and the level of the urban economy should be kept in balance with the ecological system; therefore, many scholars devote themselves to the study of the relationship and development logic between the urban economic level and the urban ecological environment.First, there are concerns about the impact of urbanization on the ecological environment [2–4]. International scholars of environmental science have shown commitment to studying the relationship between urbanization and the ecological environment. Grossman and Krueger found that the evolution of the relationship between urban economic level and environmental quality presents an inverted “U” shape through econometric theory, that is, the environmental Kuznets curve (EKC curve) [5, 6]. Kharabshen also carried out corresponding mathematical statistical research on the impact of ecological factors such as urbanization and water quality degradation in the Jordan River area [7]. Schiller and Atzmon [8] analysed the impact of the increase in agricultural consumption on the environment during the period of rapid population growth.Second, the relationship between the urban governance level and the urban environment is studied. For example, Liu et al. [9] selected industrial emissions as a research variable to analyse the environmental Kuznets curve, while Hua and Yind [10] focused on the EKC curve analysis of industrial carbon emissions. Based on the analysis of the relationship between urban air pollution and the regional economy by Tiening and Lina [11], Huang and Fang [12] hold that the relationship between urbanization and the ecological environment conforms to the characteristics of a “double exponential curve.” Wen-Long Shang et al. analysed the relationship between urban management level and environment from urban transportation system, and studies big data and traffic path planning by using GIS technology, which shows the importance of information technology in urban management [13–15].Subsequently, many experts and scholars conducted in-depth research on ecology and resources [16–18], technology and economy [19, 20], and economic consumption [21–23] to obtain regional and representative results [24–29]. Many studies have illustrated a certain correlation between the regional economy and ecological environment [30–32], and in-depth demonstrations and research have been carried out to explore the internal relationship and law governing urbanization level and ecological efficiency [33–36]. Urbanization, urban spatial form, and expansion mode present different modes in different regions [8, 37, 38], and their complexity also differs, so pressure on the urban ecological environment is expected to be substantial [39–42].The above research focuses on the urban environment and urban economic relations, but China’s vast territory and economic development are also facing regional innovation imbalances and other issues. Cities are not just the birthplace and hub of regional innovation, but also the centre of spatial heterogeneity of innovation. Peng Xiao introduced the concept of an innovative city and formulated the evaluation index [43]. Zhang et al. [44] stated that the city is the basic unit for constructing an innovative country; therefore, it is necessary to strengthen the construction at the urban innovation level. Miao and Guo [45] used the entropy method to measure the level of urban innovation and found that it has a positive impact on economic development.Building an innovative country is the trend of for future urban. With continuous improvements at the economic level, the innovation level of cities also gradually improves as a result of economic support; however, a series of environmental problems accompanies these changes [46–48]. Previous studies have focused on various urban pollution indicators and regional economic indicators, but the relationship of urban innovation to the ecological environment has not been researched in depth. This study combines the evaluation index of the urban innovation level and eco-efficiency index, constructs a multidimensional observation system of the urban innovation level, economic level and eco-environment, attempts to find the internal relationship and dynamic development relationship between the urban innovation level and ecological environment change through econometric demonstration, analyses the contradictions between them in light of development, and provides a reference for the Chinese government for achieving the goal of a sustainable development society.


## 3. Materials and Methods

### 3.1. Evaluation System

Thirty provinces (cities) in China are selected as research objects (limited to data availability, excluding Hong Kong, Macau and Taiwan Province) and the sample time range used for the study is from 2006 to 2019. Variables are selected that are operable, easy to understand and frequently used and which are in line with the evaluation of urban innovation ability and ecological efficiency, and they are used to establish an index system. The evaluation of urbanization innovation ability is divided into two categories: input index and output index as the first-class index. The input indicators are specifically divided into capital investment and manpower investment, while the output indicators include three three-level indicators, namely, scientific research output, technological transformation and economic output. Ecological efficiency adopts two primary indices, namely, the input index and output index, and specifically includes three secondary indices, namely, the environmental input index, capital input index and economic output. In addition, the third-level indicators include waste discharge, general industrial waste discharge (10,000 tons), sulfur dioxide discharge (10,000 tons), energy consumption index, regional GDP and other indicators to measure and evaluate. The establishment of the index system is based on the Pressure-State-Response (P-S-R model) jointly promoted by Organization for Economic Co-operation and Development (OCED) and United Nations Environment Programme (UNEP), which is now used globally for studying the conflicts between the needs of cities and the environment. Table1 shows the evaluation indices and characteristics.Table 1
Urban innovation level and ecological efficiency index system.
Evaluation objectPrimary indicatorsSecondary indicatorsTertiary indicatorsEffectUrbanization innovation abilityInput indicatorsCapital inputInternal expenditure of research and experimental development (R&D) funds (10,000 yuan).Positive (+)R&D input intensityPositive (+)Human inputFull-time equivalent of research and experimental development (R&D) personnel (man-years)Positive (+)Output indicatorsScientific research outputPatent acceptance letterPositive (+)Technology transformationMarket technology export area (contract amount) (10,000 yuan)Positive (+)Economic outputGross regional productPositive (+)Ecological efficiencyInput indicatorsEnvironmental inputWastewater dischargeNegative(-)General industrial waste discharge (10,000 tons)Negative (−)Emission of sulfur dioxide (10,000 tons)Negative (−)Capital inputCapital stockPositive (+)Human inputUrban employed personsPositive (+)Resource inputEnergy consumptionNegative (−)Output indicatorsCapital outputComprehensive utilization rate of solid wastePositive (+)Energy outputCarbon dioxide emissionNegative (−)


### 3.2. Entropy Weight TOPSIS Method (Technique for Order Preference by Similarity to an Ideal Solution)

The widely used comprehensive evaluation methods include the factor analysis method, analytic hierarchy process, fuzzy comprehensive evaluation method, grey relation method, TOPSIS method and entropy weight method [40]. The TOPSIS method, an effective method for multiobjective decision-making uses simple calculation, small sample size requirements and reasonable results, while the entropy weight method determines the weight according to the information reflected by the variation degree of each evaluation index value, eliminating the influence of subjective factors and accurately calculating the weight of each index. This study combines the entropy weight method and the TOPSIS method to accurately measure the development level of “zero-waste cities” in various provinces in China. The calculation process is as follows:(1)
Construct the original matrix and normalize it:(1)Xij=xij−minxijmaxxij−minxijxijis a positive indicator,Xij=maxxij−xijmaxxij−minxijxijis a negative indicator,whereXij is the normalized data of the j-th evaluation index of the i-th province (city) and xij is the raw data of the j-th evaluation index of the i-th province (city).(2)
Calculate information entropy (ej):(2)ej=−1lnn∑i=1nXij∑i=1nXijlnXij∑i=1nXij1lnn>0,ej≥0,wherej is the serial number of the evaluation index; i is the serial number of the province (city); and n is the total number of provinces (cities).(3)
Determine index weight (wj):(3)wj=1−ej∑j=1m1−ej,wheremm is the total number of evaluation indices.(4)
Calculate the weighted value (yij) of the j-th evaluation index of the i-th province and obtain the optimal solution (yj+) and the worst solution (yj−):(4)yij=wj×Xij.(5)
The comprehensive score was calculated by the Euclidean distance (Ci):(5)Ci=∑j=1myij−yj−2∑j=1myij−yj+2+∑j=1myij−yj−20≤Ci≤1.The results of the calculation are shown in Table2.Table 2
Index system of urbanization innovation level and ecological efficiency.
Target layerIndicator layerEffectIndex weightUrbanization innovation abilityInternal expenditure of research and experimental development (R&D) funds (10,000 yuan)Forwards direction0.168R&D input intensityForwards direction0.221Full-time equivalent of research and experimental development (R&D) personnel (man-years)Forwards direction0.127Number of patents acceptedForwards direction0.096Market technology export area (contract amount) (10,000 yuan)Forwards direction0.1436Gross regional productForwards direction0.245Ecological efficiencyWastewater dischargeNegative direction0.098General industrial waste discharge (10,000 tons)Negative direction0.110Emission of sulfur dioxide (10,000 tons)Negative direction0.086Capital stockForwards direction0.116Urban employed personsForwards direction0.087Energy consumptionNegative direction0.128Comprehensive utilization rate of solid wasteForwards direction0.176Carbon dioxide emissionNegative direction0.199


### 3.3. Cointegration Test

Since some economic variables observed are dynamic in practice, the relevant data, such as the mean and variance, may also fluctuate, but there may be stable linear relationships among the different economic variables. This stable linear relationship between many factors is called cointegration, and the resulting system of linear equations is called the cointegration equation.It is important to note that cointegration is only possible when the time series of two variables are of the same order and single integral sequences. The cointegration cannot judge all nonstationary sequences. Before judging whether there is a cointegration relationship between two variables, a stationarity test needs to be performed, as follows.


#### 3.3.1. Unit Root Test

The time series can be tested to analyse whether the series of data has stability. If a time series has a stable mean, variance and autocovariance, it can be judged as stable; otherwise, it should be judged as unstable. The nonstationary time series can be considered a stationary series with stable mean, variance and autocovariance after d-order difference, and then the sequence is marked as d-order Integration and I (D). The unit root test is a way to show the nonstationarity of a sequence. We can use the ADF Test (Augment Dickey-Fuller Test) to test whether a variable is stable, and there is no unit root; otherwise, there is a unit root. Therefore, the H0 hypothesis of the ADF test is the existence of a unit root. If the significance test statistic is less than three (10%, 5%, 1%), then there should be (90%, 95%, 99%) confidence to reject the original hypothesis.


#### 3.3.2. Johansen Cointegration Test

When the unit root test is used to determine whether a sequence is a single-integer sequence, the cointegration relationship between the sequences is further determined. At present, the common cointegration test method is the unit root test for the residual of the regression equation. The regression test for the VAR model proposed by Johansen offers another method; this study adopts this method because it fully utilizes the benefits of the cointegration test when there are multivariable equations.


#### 3.3.3. Granger Causality Test

A significant correlation exists between the variables, however, a causal test is required to determine whether it is economically significant. The Granger causality test can determine whether variable B can be explained by variable A when considering the influence of time and whether the explanation degree can be improved; it is calculated that there is a significant relationship between variable A and variable B in the statistical correlation coefficient, which determines that variable B is caused by variable A.


#### 3.3.4. Impulse Response Analysis and Variance Decomposition

The impulse response function describes the impact of an endogenous variable on other endogenous variables in the VAR model. By analysing the contribution of each structural shock to the change in endogenous variables (usually measured by variance), the impulse response function further evaluates the importance of different structural shocks. Therefore, the variance decomposition of the impulse response function gives information about the relative importance of each random disturbance that has an effect on the variables in the VAR model.


### 3.4. Data Description

In the dynamic measurement analysis of urbanization innovation capability and ecological efficiency, it is necessary to involve urbanization innovation capability indicators and ecological environment indicators. Data from the feasibility study and analysis inform the ecological environment indicators. Among these indicators, carbon emissions do not yet have an established systematic calculation method. Therefore, the calculation method is done mainly in accordance with the 2006 IPCC report [49].


## 3.1. Evaluation System

Thirty provinces (cities) in China are selected as research objects (limited to data availability, excluding Hong Kong, Macau and Taiwan Province) and the sample time range used for the study is from 2006 to 2019. Variables are selected that are operable, easy to understand and frequently used and which are in line with the evaluation of urban innovation ability and ecological efficiency, and they are used to establish an index system. The evaluation of urbanization innovation ability is divided into two categories: input index and output index as the first-class index. The input indicators are specifically divided into capital investment and manpower investment, while the output indicators include three three-level indicators, namely, scientific research output, technological transformation and economic output. Ecological efficiency adopts two primary indices, namely, the input index and output index, and specifically includes three secondary indices, namely, the environmental input index, capital input index and economic output. In addition, the third-level indicators include waste discharge, general industrial waste discharge (10,000 tons), sulfur dioxide discharge (10,000 tons), energy consumption index, regional GDP and other indicators to measure and evaluate. The establishment of the index system is based on the Pressure-State-Response (P-S-R model) jointly promoted by Organization for Economic Co-operation and Development (OCED) and United Nations Environment Programme (UNEP), which is now used globally for studying the conflicts between the needs of cities and the environment. Table1 shows the evaluation indices and characteristics.Table 1
Urban innovation level and ecological efficiency index system.
Evaluation objectPrimary indicatorsSecondary indicatorsTertiary indicatorsEffectUrbanization innovation abilityInput indicatorsCapital inputInternal expenditure of research and experimental development (R&D) funds (10,000 yuan).Positive (+)R&D input intensityPositive (+)Human inputFull-time equivalent of research and experimental development (R&D) personnel (man-years)Positive (+)Output indicatorsScientific research outputPatent acceptance letterPositive (+)Technology transformationMarket technology export area (contract amount) (10,000 yuan)Positive (+)Economic outputGross regional productPositive (+)Ecological efficiencyInput indicatorsEnvironmental inputWastewater dischargeNegative(-)General industrial waste discharge (10,000 tons)Negative (−)Emission of sulfur dioxide (10,000 tons)Negative (−)Capital inputCapital stockPositive (+)Human inputUrban employed personsPositive (+)Resource inputEnergy consumptionNegative (−)Output indicatorsCapital outputComprehensive utilization rate of solid wastePositive (+)Energy outputCarbon dioxide emissionNegative (−)


## 3.2. Entropy Weight TOPSIS Method (Technique for Order Preference by Similarity to an Ideal Solution)

The widely used comprehensive evaluation methods include the factor analysis method, analytic hierarchy process, fuzzy comprehensive evaluation method, grey relation method, TOPSIS method and entropy weight method [40]. The TOPSIS method, an effective method for multiobjective decision-making uses simple calculation, small sample size requirements and reasonable results, while the entropy weight method determines the weight according to the information reflected by the variation degree of each evaluation index value, eliminating the influence of subjective factors and accurately calculating the weight of each index. This study combines the entropy weight method and the TOPSIS method to accurately measure the development level of “zero-waste cities” in various provinces in China. The calculation process is as follows:(1)
Construct the original matrix and normalize it:(1)Xij=xij−minxijmaxxij−minxijxijis a positive indicator,Xij=maxxij−xijmaxxij−minxijxijis a negative indicator,whereXij is the normalized data of the j-th evaluation index of the i-th province (city) and xij is the raw data of the j-th evaluation index of the i-th province (city).(2)
Calculate information entropy (ej):(2)ej=−1lnn∑i=1nXij∑i=1nXijlnXij∑i=1nXij1lnn>0,ej≥0,wherej is the serial number of the evaluation index; i is the serial number of the province (city); and n is the total number of provinces (cities).(3)
Determine index weight (wj):(3)wj=1−ej∑j=1m1−ej,wheremm is the total number of evaluation indices.(4)
Calculate the weighted value (yij) of the j-th evaluation index of the i-th province and obtain the optimal solution (yj+) and the worst solution (yj−):(4)yij=wj×Xij.(5)
The comprehensive score was calculated by the Euclidean distance (Ci):(5)Ci=∑j=1myij−yj−2∑j=1myij−yj+2+∑j=1myij−yj−20≤Ci≤1.The results of the calculation are shown in Table2.Table 2
Index system of urbanization innovation level and ecological efficiency.
Target layerIndicator layerEffectIndex weightUrbanization innovation abilityInternal expenditure of research and experimental development (R&D) funds (10,000 yuan)Forwards direction0.168R&D input intensityForwards direction0.221Full-time equivalent of research and experimental development (R&D) personnel (man-years)Forwards direction0.127Number of patents acceptedForwards direction0.096Market technology export area (contract amount) (10,000 yuan)Forwards direction0.1436Gross regional productForwards direction0.245Ecological efficiencyWastewater dischargeNegative direction0.098General industrial waste discharge (10,000 tons)Negative direction0.110Emission of sulfur dioxide (10,000 tons)Negative direction0.086Capital stockForwards direction0.116Urban employed personsForwards direction0.087Energy consumptionNegative direction0.128Comprehensive utilization rate of solid wasteForwards direction0.176Carbon dioxide emissionNegative direction0.199


## 3.3. Cointegration Test

Since some economic variables observed are dynamic in practice, the relevant data, such as the mean and variance, may also fluctuate, but there may be stable linear relationships among the different economic variables. This stable linear relationship between many factors is called cointegration, and the resulting system of linear equations is called the cointegration equation.It is important to note that cointegration is only possible when the time series of two variables are of the same order and single integral sequences. The cointegration cannot judge all nonstationary sequences. Before judging whether there is a cointegration relationship between two variables, a stationarity test needs to be performed, as follows.


### 3.3.1. Unit Root Test

The time series can be tested to analyse whether the series of data has stability. If a time series has a stable mean, variance and autocovariance, it can be judged as stable; otherwise, it should be judged as unstable. The nonstationary time series can be considered a stationary series with stable mean, variance and autocovariance after d-order difference, and then the sequence is marked as d-order Integration and I (D). The unit root test is a way to show the nonstationarity of a sequence. We can use the ADF Test (Augment Dickey-Fuller Test) to test whether a variable is stable, and there is no unit root; otherwise, there is a unit root. Therefore, the H0 hypothesis of the ADF test is the existence of a unit root. If the significance test statistic is less than three (10%, 5%, 1%), then there should be (90%, 95%, 99%) confidence to reject the original hypothesis.


### 3.3.2. Johansen Cointegration Test

When the unit root test is used to determine whether a sequence is a single-integer sequence, the cointegration relationship between the sequences is further determined. At present, the common cointegration test method is the unit root test for the residual of the regression equation. The regression test for the VAR model proposed by Johansen offers another method; this study adopts this method because it fully utilizes the benefits of the cointegration test when there are multivariable equations.


### 3.3.3. Granger Causality Test

A significant correlation exists between the variables, however, a causal test is required to determine whether it is economically significant. The Granger causality test can determine whether variable B can be explained by variable A when considering the influence of time and whether the explanation degree can be improved; it is calculated that there is a significant relationship between variable A and variable B in the statistical correlation coefficient, which determines that variable B is caused by variable A.


### 3.3.4. Impulse Response Analysis and Variance Decomposition

The impulse response function describes the impact of an endogenous variable on other endogenous variables in the VAR model. By analysing the contribution of each structural shock to the change in endogenous variables (usually measured by variance), the impulse response function further evaluates the importance of different structural shocks. Therefore, the variance decomposition of the impulse response function gives information about the relative importance of each random disturbance that has an effect on the variables in the VAR model.


## 3.3.1. Unit Root Test

The time series can be tested to analyse whether the series of data has stability. If a time series has a stable mean, variance and autocovariance, it can be judged as stable; otherwise, it should be judged as unstable. The nonstationary time series can be considered a stationary series with stable mean, variance and autocovariance after d-order difference, and then the sequence is marked as d-order Integration and I (D). The unit root test is a way to show the nonstationarity of a sequence. We can use the ADF Test (Augment Dickey-Fuller Test) to test whether a variable is stable, and there is no unit root; otherwise, there is a unit root. Therefore, the H0 hypothesis of the ADF test is the existence of a unit root. If the significance test statistic is less than three (10%, 5%, 1%), then there should be (90%, 95%, 99%) confidence to reject the original hypothesis.


## 3.3.2. Johansen Cointegration Test

When the unit root test is used to determine whether a sequence is a single-integer sequence, the cointegration relationship between the sequences is further determined. At present, the common cointegration test method is the unit root test for the residual of the regression equation. The regression test for the VAR model proposed by Johansen offers another method; this study adopts this method because it fully utilizes the benefits of the cointegration test when there are multivariable equations.


## 3.3.3. Granger Causality Test

A significant correlation exists between the variables, however, a causal test is required to determine whether it is economically significant. The Granger causality test can determine whether variable B can be explained by variable A when considering the influence of time and whether the explanation degree can be improved; it is calculated that there is a significant relationship between variable A and variable B in the statistical correlation coefficient, which determines that variable B is caused by variable A.


## 3.3.4. Impulse Response Analysis and Variance Decomposition

The impulse response function describes the impact of an endogenous variable on other endogenous variables in the VAR model. By analysing the contribution of each structural shock to the change in endogenous variables (usually measured by variance), the impulse response function further evaluates the importance of different structural shocks. Therefore, the variance decomposition of the impulse response function gives information about the relative importance of each random disturbance that has an effect on the variables in the VAR model.


## 3.4. Data Description

In the dynamic measurement analysis of urbanization innovation capability and ecological efficiency, it is necessary to involve urbanization innovation capability indicators and ecological environment indicators. Data from the feasibility study and analysis inform the ecological environment indicators. Among these indicators, carbon emissions do not yet have an established systematic calculation method. Therefore, the calculation method is done mainly in accordance with the 2006 IPCC report [49].


## 4. Dynamic Analysis of the Relationship between the Urban Innovation Level and Ecological Efficiency

### 4.1. Data Calculation

According to the characteristics of energy consumption in urban development and the relevant data of the China Statistical Yearbook, 14 kinds of energy sources, including raw coal, coke, gasoline, kerosene, diesel oil and liquefied petroleum gas, are considered. After summing the converted standard coal quantities, the carbon emissions are obtained through conversion on a unified scale, from which the energy consumption data of China from 2008 to 2019 (the data description is as follows: (1) As a result of the impact of novel coronavirus pneumonia in 2020, the data are abrupt. To maintain the stability of data and avoid affecting the dynamic fitting results, the data of 2008–2019 are selected as the basis for calculation. (2) As the public statistics of Tibet are not comprehensive enough, the relevant indicators of Tibet have not been calculated yet) (Table3), and the national carbon emissions data (Table 4) and the evaluation scores of the urban innovation level obtained after calculation are shown in Table 5.Table 3
Energy consumption data of 30 provinces and cities in China (standard coal: 10,000 tons).
Year200820092010201120122013201420152016201720182019Beijing632765706954699571786724683168036917708872707360Tianjin536458746818759882087882814583198078783279738241Hebei243222541927531294983025029664293203103731458320833218532545Shanxi156751557616808183151933619761198631902918974195812019920859Inner Mongolia141001534416820187371978617681183091878419310197632306825346Liaoning178011911220947227122352621721218032136220847213652232123749Jilin722176988297910394438645856070206886688170007132Heilongjiang99791046711234121191275811853119551110411070112581143611614Shanghai102071036711201112701136211346110851093111242113821145411696Jiangsu222322370925774275892885029205298633037431210316023163532526Zhejiang151071556716865178271807618640188261961020276210302167522393Anhui832588969707105701135811696120111230112663130191329513870Fujian825489169809106531118511190121101186312036125551313113718Jiangxi538358136355692872337583805584238730897292869665Shandong305703242034808371323889935358365113933240138400984058141390Henan189761975121438230622364721909228902234322323221622265922300Hubei128451370815138165791767515703163201547715897161801668217316Hunan123551333114880161611674414919153171451414845152001554416001Guangdong234762465426908284802914428480295933011731211323093333034142Guangxi6497707579198591915591009515980610110104561082311270Hainan113512331359160116881720182019161984208021702264Chongqing647270307856879292788049859377477982827985578889Sichuan151451632217892196962057519212198791830618756192291991620791Guizhou70847566817590689878929997099344960698461003610423Yunnan75118032867495401043410072104551042510726111641159012158Shaanxi74178044888297611062610610112221174612146125491290013478Gansu534654825923649670077287752174897300750478237818Qinghai227923482568318935243768399241254101419343644235Ningxia322933883681431645624781494654385591646171007648Xinjiang70697526829099271183113632149261566616302173861769418490Table 4
Carbon emission data of 30 provinces and cities in China (unit: 10,000 tons).
Year200820092010201120122013201420152016201720182019Beijing12136.3612308.0912390.6911480.1211327.249991.7010122.189107.558164.027915.207776.447664.45Tianjin13710.5014735.7617968.3819699.8819718.3320229.7319351.1818722.6817414.4017084.5517376.4117402.23Hebei70212.3474799.0080386.2890869.8291964.1191970.0087291.0590909.7990900.0590044.7191653.4991445.99Shanxi63481.4762757.2366859.0873590.2376655.0279381.7481200.8992819.2991873.9896797.42103383.35108664.42Inner Mongolia67185.4771163.9778973.2494575.3398569.4894040.3295561.7495355.7797835.22101886.88117364.61132622.73Liaoning43833.2745949.3949978.1854104.0055080.8153132.4453424.7251220.4150638.6852108.5952962.7055095.02Jilin24200.8025836.0028193.1931771.8331527.4130136.6529985.3324637.5324677.3623971.2324609.8024982.03Heilongjiang30305.1530636.4333754.3335956.5437983.8436856.7336189.3235440.4934903.2735391.6135986.6038633.15Shanghai25267.8026185.2328726.9529221.9128328.2129551.8228450.6428516.0829446.7529138.9629678.3229731.93Jiangsu56251.4658464.2365288.6475477.7776204.0876825.4175518.7577108.0079013.1478750.3075828.1079425.65Zhejiang32534.7733460.1735234.8337244.1036241.4536294.1836387.8636339.9735924.7937314.9636703.5835230.27Anhui26421.3330185.2533149.6434726.9936771.7438803.3742442.1643043.5043106.9043705.5745405.2947085.65Fujian17513.7919490.4220098.6523567.6223177.2822556.5622824.1721755.2620729.0522034.7524952.8025694.46Jiangxi26875.5128973.5432679.6035246.7936394.7239122.5342761.6545718.7250271.7054621.3160028.8762247.52Shandong81854.6284333.4393357.1998475.80103274.4097959.39102477.94111745.02113300.66112793.12107151.67108367.99Henan54802.2455884.1460467.5766388.1261074.7760823.3062206.5559238.0858010.3657206.6256683.8452687.31Hubei28204.3530017.4234382.5139823.6740484.8234102.6433549.0831874.2231697.4731462.6533166.0834941.76Hunan33446.5736916.5840767.8643399.1942686.2942092.0641665.1241650.7642862.3043849.8146072.1745098.65Guangdong38347.3039082.3544178.2950521.4150351.2048430.0048711.5748330.5749234.3352097.7352063.7451994.07Guangxi14726.4816139.9718557.4120648.4121526.5521185.1821347.6020374.1021371.7921573.9022821.3324285.39Hainan1737.531950.572242.862669.542918.763006.813003.733045.812921.123140.993305.213250.39Chongqing13164.4814241.2215698.7017812.7217370.3214791.6017374.6815608.9315766.2516269.6515563.2216495.12Sichuan26810.9830099.3330076.8930730.1732261.4133281.9132580.8726012.6425631.8924550.1724184.3525253.07Guizhou20937.3122954.8223135.9025578.5427988.3228979.7227927.1527889.3429154.6630524.7729757.1430541.05Yunnan26121.5028242.4230233.5130970.8132440.4431946.2529410.5126780.8725686.5325794.1426342.1026944.75Shaanxi24503.5826658.8430983.8735276.6740027.4942787.6044806.1344355.2145715.1347313.6146111.1150192.26Gansu11536.2211110.3013086.9515105.6915851.6716379.1816808.4816184.8615611.9215467.3116350.2416514.40Qinghai3842.333962.403810.624061.165878.366526.436011.855601.836786.456487.365924.355825.89Ningxia15087.2516242.4019149.0624528.5824766.0225734.6526648.4426562.2525922.7730887.4133786.3935977.90Xinjiang13905.6917302.2419088.9822976.0927924.0032857.1536466.0539364.6741730.7244892.5947395.4051147.42Table 5
Evaluation scores of the urban innovation level of 30 provinces and cities in China.
Year200820092010201120122013201420152016201720182019Beijing52.2053.1947.9250.3146.1150.7350.1150.4552.6152.5654.3053.22Tianjin37.2437.4435.8938.2934.0936.1336.6136.4934.1533.7132.1428.83Hebei22.5425.2023.2624.4426.6723.0220.8821.l420.8924.2321.9721.86Shanxi23.0124.6923.8323.1620.6821.6821.2020.6118.1717.9319.1419.82Inner Mongolia20.5321.8720.4623.4926.1823.7319.2321.4418.2218.3219.1118.14Liaoning32.1133.0228.9331.7731.2828.8527.1926.8824.4622.2622.4422.73Jilin26.6124.3722.2027.1420.7622.6420.6918.9518.5319.0020.4818.80Heilongjiang25.2827.6722.8424.0524.6123.5521.2220.6521.1619.5119.1918.53Shanghai52.9952.4446.2349.3842.2847.1846.5945.6246.0444.8146.0045.63Jiangsu48.8155.6352.2755.4953.8457.5858.8658.0157.2053.3051.7349.58Zhejiang41.0944.6141.2342.8338.4842.4041.4642.0537.9437.6638.8838.80Anhui28.5131.9228.5627.8130.0829.7530.4729.8632.0228.3628.7228.70Fujian28.1629.8624.1628.6226.4829.3328.8029.2527.2025.7726.3026.56Jiangxi24.4825.8222.0724.5224.3223.5322.8623.3421.8522.0421.6123.31Shandong37.9640.4137.3439.0436.7137.7337.9337.4936.2933.7733.6433.12Henan26.8028.4025.9627.0525.2626.2124.3325.9026.4420.5024.9125.07Hubei29.6032.7630.6129.3528.3528.7128.8228.5929.0729.3529.4529.21Hunan27.6728.9429.7929.8128.4528.2528.5929.0127.7726.6326.5926.82Guangdong52.6553.6551.8954.8849.3853.0052.4452.7153.6255.2459.5559.49Guangxi20.8722.7022.5623.4122.6723.0622.3023.6222.8121.1921.8721.17Hainan23.4021.3121.9521.4623.3024.1026.7928.0325.6822.4922.7922.90Chongqing27.4429.5329.8530.7728.0833.8832.9032.9932.0430.0530.3030.87Sichuan29.1033.6129.9531.0728.3527.1626.9826.3929.0727.5227.0428.03Guizhou21.1323.3119.0022.6220.7722.6020.4121.2225.6422.1922.2723.60Yunnan21.6924.3220.7421.7819.3721.3221.1320.3019.7220.4321.4821.11Shaanxi28.2129.1227.7929.8027.4827.6826.8627.1429.2926.0526.4927.34Gansu19.2120.9319.8322.4119.7022.2023.5821.6822.0620.8220.0520.10Qinghai18.0518.9916.3018.4117.6217.6516.1917.7115.7818.1320.9720.11Ningxia18.1220.1620.8919.7216.8020.3217.6418.5220.0420.6819.4520.94Xinjiang19.1722.9320.3820.8120.3220.3918.4918.0419.8620.0419.9318.19Figure1 shows that among the 30 provinces and cities in China, Beijing, Jiangsu and Guangdong have a higher level of urban innovation, and their evaluation scores are higher than 50 points, belonging to the top echelon. Second, Tianjin, Shanghai, Zhejiang, Shandong, and Chongqing belong to the second echelon, with scores ranging from 30 to 50. Other provinces and cities scored less than 30 points, belonging to the third echelon. The difference between the highest score (Jiangsu: 54.36) and the lowest score (Qinghai: 17.99) was 36.37 points, indicating that the overall development of the urban innovation level is unbalanced, and there is a large gap between the urban innovation level in backwards areas and economically developed areas, which needs to be paid enough attention.Figure 1
Average value of the urban innovation level of 30 provinces and cities in China (2008–2019).The score of ecological efficiency [50, 51] calculated by the entropy weight TOPSIS method is shown in Table 6.Table 6
Ecological efficiency scores of 30 provinces and cities in China.
Year200820092010201120122013201420152016201720182019Beijing0.9160.9200.8991.0000.9261.0000.9300.9280.9490.9320.9371.000Tianjin0.7630.7440.7140.6850.6720.6650.6720.6670.6860.6980.6900.529Hebei0.6500.6220.6250.6210.5900.5660.5540.5240.5250.5820.5500.499Shanxi0.5160.4580.4490.4540.4040.3330.3050.2690.2610.3080.3380.278Inner Mongolia0.5770.5790.5730.5690.5490.5090.4930.4560.4290.3550.3630.307Liaoning0.6520.6350.6420.6350.6270.6090.6070.6140.5150.5440.5480.509Jilin0.6170.5950.5960.6010.6020.5790.5780.6150.6100.6110.5920.442Heilongjiang0.6270.5550.5440.5510.5200.5070.5060.4910.4810.4780.4680.362Shanghai0.8380.8180.8120.7690.7500.7270.7330.7270.7350.7280.7250.744Jiangsu1.0000.9640.9451.0000.8840.7070.7030.6920.6940.7020.7070.714Zhejiang0.8560.8190.8200.8170.7970.7810.7610.7460.7520.7460.7560.782Anhui0.7010.6630.6530.6510.6200.5690.5400.5120.5120.5230.4840.551Fujian0.8020.7600.7510.7150.6910.6760.6590.6500.6530.6490.6310.678Jiangxi0.6600.6420.6340.6290.5960.5600.5260.4840.4660.4650.4300.430Shandong0.7390.7130.6970.6830.6630.6530.6400.6110.6090.6080.6170.571Henan0.6910.6430.6270.5930.5830.5510.5220.5120.5130.5350.5510.599Hubei0.6970.6720.6540.6220.6070.6250.6180.6250.6280.6380.6360.652Hunan0.6880.6590.6390.6330.6190.6140.6090.6060.6020.6000.5820.569Guangdong1.0000.9720.9260.8870.8620.8020.7790.7570.7590.7570.7500.757Guangxi0.7520.6960.6810.6740.6500.6330.6240.6220.6140.6300.5810.545Hainan0.8510.7970.7700.7410.7080.6780.6660.6470.6480.6420.6420.650Chongqing0.6560.6690.6350.5900.5720.5960.5360.5660.5640.5500.5500.590Sichuan0.7150.6740.6770.6750.6560.6030.6030.6330.6330.6420.6450.656Guizhou0.4660.4340.3980.3590.3150.2810.2880.2920.2800.2880.2960.298Yunnan0.5750.5020.4520.4450.3940.3610.3750.3720.3650.3670.3450.505Shaanxi0.6220.6000.5850.5730.5390.4850.4540.3960.3790.3980.4280.401Gansu0.5780.5340.5130.4840.4390.3360.3000.2250.2130.2410.2620.241Qinghai0.5090.4970.5120.4920.4190.3750.3820.3680.3270.2990.3190.271Ningxia0.3560.3790.3930.4030.3500.3000.2710.2420.2610.2460.2620.206Xinjiang0.5580.4480.4750.4400.3870.3140.2780.1890.1200.1120.2040.202According to the above data, the average distribution of ecological efficiency of 30 provinces and cities in China from 2008 to 2019 is shown in Figure2.Figure 2
Average eco-efficiency of 30 provinces and cities in China (2008–2019).Figure2 shows that the Beijing region has the highest level of ecological efficiency in China. This is because, at the microlevel, effective environmental management mechanisms were implemented in 2008, leading to changes in the urban ecological situation, and Beijing has maintained a high level of ecological efficiency since then, with carbon emissions declining each year. Therefore, we can focus on the relevant management methods and measures in this area, propagate them to similar types of cities and promote the ecological efficiency of the whole country. The ecological efficiency of Shanxi and Guizhou Provinces is low, which is mainly due to the acceleration of urbanization in these two provinces and cities since 2008, resulting in a large amount of carbon emissions in urban infrastructure construction. Although the level of urban innovation is increasing year by year, the rate of increase is slower than that of carbon emissions. Therefore, in future urban management, we should pay attention to adjusting policies to accelerate the urbanization process and at the same time realize a recyclable society and build a green economy. Ningxia and Xinjiang are the last echelon members of China’s ecological efficiency, mainly because their industrial level is still low and possesses many industrial characteristics of high energy consumption and high emissions, and the overall economic level in China is low as well. Therefore, to strengthen the ecological efficiency of these two provinces and cities, we should consider improving the production level, promoting industrial transformation and increasing economic income.To further analyse the relationship between the urban innovation level and ecological efficiency, Figure3 can be obtained by comparing the two evaluation scores:Figure 3
Comparison of the urban innovation level and ecological efficiency average in 30 provinces and cities of China ((1) Urban innovation water average (A-UIL): average value of urban innovation level of 30 provinces and cities in China. (2) average ecological efficiency (A-EE): average eco-efficiency of 30 provinces and cities in China).By comparing the average value of the urban innovation level with the average value of ecological efficiency in 30 provinces and cities in China, it can be found that there is a certain coupling effect, and the overall urban innovation level and ecological efficiency show obvious fitting. The two indicators are positively correlated to a certain extent; that is, areas with high urban innovation levels have higher ecological efficiency. However, there are some differences between the innovation level and the fluctuation of ecological efficiency in individual cities. Among them, the urban innovation level in Shanghai, Jiangsu, Guangdong, Guizhou, Ningxia and Xinjiang is higher than the ecological efficiency, which indicates that the improvement of the urban innovation level in the above six provinces and cities has not played a full driving role in promoting the ecological efficiency of the region. Among them, the trend in Jiangsu Province is the most obvious, and the coupling effect between the urban innovation level and ecological efficiency is not fully exerted.To measure and analyse the relationship between the urbanization innovation level and ecological efficiency in detail, a vector autoregression model (VAR) can be established for dynamic econometric analysis. First, the unit root test is used to determine whether the urbanization innovation level and ecological efficiency are stationary series. Then, a cointegration test is used to analyse whether there is a stable relationship between the urbanization innovation level and ecological efficiency. Second, Granger causality analysis is used to determine the causal relationship between the two comprehensive indices. Finally, impulse response and variance decomposition are used to analyse the impact of the urbanization innovation level on ecological efficiency.


### 4.2. Unit Root Test

According to the time series, whether the series of data is stable or not can be tested. If a time series has a stable mean, variance and autocovariance, it can be judged as stable; otherwise, it should be judged as unstable. Nonstationary time series can have a stable mean, variance and autocovariance after a D-fold difference and become stationary series. At this time, the series is marked asD-order Integration and I(d). Unit is a way to express the nonstationarity of a sequence, and the ADF test can be used to test whether the variables are stable or not. The ADF test judges whether a sequence has a unit root; if the sequence is stable, there is no unit root. Otherwise, there will be a unit root. Therefore, the H0 hypothesis of the ADF test is that there is a unit root. If the statistics of the significance test are less than three confidence levels (10%, 5%, 1%), the original hypothesis should be rejected with the assurance of (90%, 95%, 99%). First, the unit root test is used to determine whether the urbanization innovation level and ecological efficiency are stationary series, then the series is tested for stationarity, and the augmented Dicket-Fuller test (ADF) is used for testing [52]. Eviews software is used for calculation, and the order calculation results after automatic selection according to SIC criteria are shown in the following Table 7:Table 7
ADF test results of the urban innovation level and eco-efficiency.
Variable(C, T, L)ADF test statisticT-statistic (1%)T-statistic (5%)T-statistic (10%)Prob.∗ConclusionA-UIL(C, T, 0)−3.9561−3.6793−2.9678-2.62300.0051StableA-EE(0, 0, 0)−1.6465−3.6892−2.9719-2.62510.4464UnstableΔA-UIL(C, T, 0)−6.4652−3.6892−2.9719-2.62510.0000StableΔA-EE(0, 0, 0)−7.5123−3.6893−2.9719-2.62510.0000StableThe test results of the above unit root show that in the mean value of the urban innovation level and ecological efficiency, the sequence of the urban innovation level is stable, and the sequence of ecological efficiency is unstable at significance levels of 1%, 5%, and 10%. Therefore, the stability test of the first-order difference sequence is carried out. In the first-order difference series test, the urban innovation level series and ecological efficiency series are stable, which is a first-order single integration series and meets the prerequisite of the cointegration test.


### 4.3. Johansen Cointegration Test

The Johansen cointegration test is used to test whether there is a long-term stable relationship between the urban innovation level and ecological efficiency, and Eviews software is used for calculation. The test results are shown in Table8.Table 8
Johansen cointegration test results.
HypothesisEigenvalueTrace testMaximum eigenvalueTrace statistic0.05 critical valueProb.∗∗Max-eigen statistic0.05 critical valueProb.∗∗None∗0.485420.1081315.49470.009418.604714.26460.0097At most 10.05231.50343.84150.22011.50343.84150.2201∗means to reject the original hypothesis at the significance level of 5%.According to the above calculation results, at a significance level of 5%, there is a cointegration relationship between the urbanization innovation level and ecological efficiency. This indicates that there is a long-term dynamic equilibrium relationship between the urbanization innovation level and ecological efficiency.


### 4.4. Granger Causality Test

The Johansen cointegration test demonstrates that there is a long-term dynamic equilibrium relationship between the urbanization innovation level and ecological efficiency, and then the Granger causality test can be carried out on these two variables. The Eviews software calculation results are shown in Table9.Table 9
Granger causality test results.
Null hypothesisObsF-statisticP valueConclusionEE does not Granger cause UIL281.139220.3375AcceptEE does not Granger cause UILUIL does not Granger cause EE281.299000.2921AcceptUIL does not Granger cause EEΔEE does not Granger cause ΔUIL2718.32030.0003RejectΔEE does Granger cause ΔUILΔUIL does not Granger cause ΔEE278.719030.0069RejectΔUIL does Granger cause ΔEEAccording to the Granger causality test, EE is not the Granger cause of UIL, and UIL is not the Granger cause of EE, which shows that there is no one-way coercion between the urban innovation level and ecological efficiency. However, EE and UIL are Granger causalities after the first-order difference and show that the two factors influence each other in the view of incremental economics, the result have been shown in Figure4.Figure 4
Granger causality test of urban innovation level and ecological efficiency based on first-order difference.


### 4.5. Impulse Response Analysis

By using Eviews software, the impulse response analysis of the urban innovation level and ecological efficiency is carried out for 10 periods, and the image shown in Figure5 is obtained. The solid line indicates the impulse response coefficient, and the dashed line indicates the deviation zone of plus or minus two standard deviations. It can be seen from the figure that the urban innovation level (UIL) will have a negative direction impact on ecological efficiency in the first four stages, which means that the ecological efficiency will decline while the urbanization innovation level is improved. However, after reaching the valley value in the fourth period, it slowly rises before the seventh period, indicating that there is a forwards direction impact in this section, which means that the level of urbanization innovation will promote the improvement of ecological efficiency, and then the two enter a balanced and stable state. However, the impulse effect of ecological efficiency on the level of urbanization innovation is complicated in 10 periods of observation. First, it presents the forwards direction effect before the second period, which means that the improvement of ecological efficiency can boost the level of urban innovation. After that, it suddenly declined from the second to the third period and slowly rose from the third period until it reached a balanced and stable state.Figure 5
Impulse response of urban innovation level and ecological efficiency.


### 4.6. Variance Decomposition

On the basis of impulse response analysis, the function of variance decomposition further reflects the contribution rate of self-shocks and shocks of other variables to understand the relative importance of shocks of various variables to endogenous variables in the Model [53, 54]. The variance decomposition results are shown in Table 10.Table 10
Variance decomposition results.
Issue no.EE-variance decomposition resultsUIL-variance decomposition resultsS.E.EE (%)UIL (%)S.E.EE (%)UIL (%)10.19571000.00000.247661.525338.474720.227398.08531.91470.283168.724031.276030.250796.60273.39730.291967.133932.866140.258096.14753.85250.295266.296733.703350.265596.09733.90270.297966.894433.105660.270496.22523.77480.301067.527732.472370.274296.27503.72500.302767.888632.111480.276496.27903.72100.303768.024931.975190.278096.27533.72470.304268.127031.8730100.279196.28383.71620.304768.221231.7788From the data of variance decomposition, it can be seen that the ecological efficiency is mainly affected by itself. The first period is completely immune to other variables and then becomes stable in the probability range of more than 95% from the second phase to the tenth phase, and the impact of the urban innovation level is small. In the second phase, there is a gradual impact on EE, but the degree of impact is not high and remains stable within the level of 5% probability during 10 issues. Relatively speaking, the level of urban innovation is mainly affected by ecological efficiency, with probability intervals of more than 65%, and the contribution rate of self-impact is approximately 30%. It can be seen that ecological efficiency has an important explanatory ability for the level of urban innovation, while the level of urban innovation has less explanatory ability for the impact change of ecological efficiency.


## 4.1. Data Calculation

According to the characteristics of energy consumption in urban development and the relevant data of the China Statistical Yearbook, 14 kinds of energy sources, including raw coal, coke, gasoline, kerosene, diesel oil and liquefied petroleum gas, are considered. After summing the converted standard coal quantities, the carbon emissions are obtained through conversion on a unified scale, from which the energy consumption data of China from 2008 to 2019 (the data description is as follows: (1) As a result of the impact of novel coronavirus pneumonia in 2020, the data are abrupt. To maintain the stability of data and avoid affecting the dynamic fitting results, the data of 2008–2019 are selected as the basis for calculation. (2) As the public statistics of Tibet are not comprehensive enough, the relevant indicators of Tibet have not been calculated yet) (Table3), and the national carbon emissions data (Table 4) and the evaluation scores of the urban innovation level obtained after calculation are shown in Table 5.Table 3
Energy consumption data of 30 provinces and cities in China (standard coal: 10,000 tons).
Year200820092010201120122013201420152016201720182019Beijing632765706954699571786724683168036917708872707360Tianjin536458746818759882087882814583198078783279738241Hebei243222541927531294983025029664293203103731458320833218532545Shanxi156751557616808183151933619761198631902918974195812019920859Inner Mongolia141001534416820187371978617681183091878419310197632306825346Liaoning178011911220947227122352621721218032136220847213652232123749Jilin722176988297910394438645856070206886688170007132Heilongjiang99791046711234121191275811853119551110411070112581143611614Shanghai102071036711201112701136211346110851093111242113821145411696Jiangsu222322370925774275892885029205298633037431210316023163532526Zhejiang151071556716865178271807618640188261961020276210302167522393Anhui832588969707105701135811696120111230112663130191329513870Fujian825489169809106531118511190121101186312036125551313113718Jiangxi538358136355692872337583805584238730897292869665Shandong305703242034808371323889935358365113933240138400984058141390Henan189761975121438230622364721909228902234322323221622265922300Hubei128451370815138165791767515703163201547715897161801668217316Hunan123551333114880161611674414919153171451414845152001554416001Guangdong234762465426908284802914428480295933011731211323093333034142Guangxi6497707579198591915591009515980610110104561082311270Hainan113512331359160116881720182019161984208021702264Chongqing647270307856879292788049859377477982827985578889Sichuan151451632217892196962057519212198791830618756192291991620791Guizhou70847566817590689878929997099344960698461003610423Yunnan75118032867495401043410072104551042510726111641159012158Shaanxi74178044888297611062610610112221174612146125491290013478Gansu534654825923649670077287752174897300750478237818Qinghai227923482568318935243768399241254101419343644235Ningxia322933883681431645624781494654385591646171007648Xinjiang70697526829099271183113632149261566616302173861769418490Table 4
Carbon emission data of 30 provinces and cities in China (unit: 10,000 tons).
Year200820092010201120122013201420152016201720182019Beijing12136.3612308.0912390.6911480.1211327.249991.7010122.189107.558164.027915.207776.447664.45Tianjin13710.5014735.7617968.3819699.8819718.3320229.7319351.1818722.6817414.4017084.5517376.4117402.23Hebei70212.3474799.0080386.2890869.8291964.1191970.0087291.0590909.7990900.0590044.7191653.4991445.99Shanxi63481.4762757.2366859.0873590.2376655.0279381.7481200.8992819.2991873.9896797.42103383.35108664.42Inner Mongolia67185.4771163.9778973.2494575.3398569.4894040.3295561.7495355.7797835.22101886.88117364.61132622.73Liaoning43833.2745949.3949978.1854104.0055080.8153132.4453424.7251220.4150638.6852108.5952962.7055095.02Jilin24200.8025836.0028193.1931771.8331527.4130136.6529985.3324637.5324677.3623971.2324609.8024982.03Heilongjiang30305.1530636.4333754.3335956.5437983.8436856.7336189.3235440.4934903.2735391.6135986.6038633.15Shanghai25267.8026185.2328726.9529221.9128328.2129551.8228450.6428516.0829446.7529138.9629678.3229731.93Jiangsu56251.4658464.2365288.6475477.7776204.0876825.4175518.7577108.0079013.1478750.3075828.1079425.65Zhejiang32534.7733460.1735234.8337244.1036241.4536294.1836387.8636339.9735924.7937314.9636703.5835230.27Anhui26421.3330185.2533149.6434726.9936771.7438803.3742442.1643043.5043106.9043705.5745405.2947085.65Fujian17513.7919490.4220098.6523567.6223177.2822556.5622824.1721755.2620729.0522034.7524952.8025694.46Jiangxi26875.5128973.5432679.6035246.7936394.7239122.5342761.6545718.7250271.7054621.3160028.8762247.52Shandong81854.6284333.4393357.1998475.80103274.4097959.39102477.94111745.02113300.66112793.12107151.67108367.99Henan54802.2455884.1460467.5766388.1261074.7760823.3062206.5559238.0858010.3657206.6256683.8452687.31Hubei28204.3530017.4234382.5139823.6740484.8234102.6433549.0831874.2231697.4731462.6533166.0834941.76Hunan33446.5736916.5840767.8643399.1942686.2942092.0641665.1241650.7642862.3043849.8146072.1745098.65Guangdong38347.3039082.3544178.2950521.4150351.2048430.0048711.5748330.5749234.3352097.7352063.7451994.07Guangxi14726.4816139.9718557.4120648.4121526.5521185.1821347.6020374.1021371.7921573.9022821.3324285.39Hainan1737.531950.572242.862669.542918.763006.813003.733045.812921.123140.993305.213250.39Chongqing13164.4814241.2215698.7017812.7217370.3214791.6017374.6815608.9315766.2516269.6515563.2216495.12Sichuan26810.9830099.3330076.8930730.1732261.4133281.9132580.8726012.6425631.8924550.1724184.3525253.07Guizhou20937.3122954.8223135.9025578.5427988.3228979.7227927.1527889.3429154.6630524.7729757.1430541.05Yunnan26121.5028242.4230233.5130970.8132440.4431946.2529410.5126780.8725686.5325794.1426342.1026944.75Shaanxi24503.5826658.8430983.8735276.6740027.4942787.6044806.1344355.2145715.1347313.6146111.1150192.26Gansu11536.2211110.3013086.9515105.6915851.6716379.1816808.4816184.8615611.9215467.3116350.2416514.40Qinghai3842.333962.403810.624061.165878.366526.436011.855601.836786.456487.365924.355825.89Ningxia15087.2516242.4019149.0624528.5824766.0225734.6526648.4426562.2525922.7730887.4133786.3935977.90Xinjiang13905.6917302.2419088.9822976.0927924.0032857.1536466.0539364.6741730.7244892.5947395.4051147.42Table 5
Evaluation scores of the urban innovation level of 30 provinces and cities in China.
Year200820092010201120122013201420152016201720182019Beijing52.2053.1947.9250.3146.1150.7350.1150.4552.6152.5654.3053.22Tianjin37.2437.4435.8938.2934.0936.1336.6136.4934.1533.7132.1428.83Hebei22.5425.2023.2624.4426.6723.0220.8821.l420.8924.2321.9721.86Shanxi23.0124.6923.8323.1620.6821.6821.2020.6118.1717.9319.1419.82Inner Mongolia20.5321.8720.4623.4926.1823.7319.2321.4418.2218.3219.1118.14Liaoning32.1133.0228.9331.7731.2828.8527.1926.8824.4622.2622.4422.73Jilin26.6124.3722.2027.1420.7622.6420.6918.9518.5319.0020.4818.80Heilongjiang25.2827.6722.8424.0524.6123.5521.2220.6521.1619.5119.1918.53Shanghai52.9952.4446.2349.3842.2847.1846.5945.6246.0444.8146.0045.63Jiangsu48.8155.6352.2755.4953.8457.5858.8658.0157.2053.3051.7349.58Zhejiang41.0944.6141.2342.8338.4842.4041.4642.0537.9437.6638.8838.80Anhui28.5131.9228.5627.8130.0829.7530.4729.8632.0228.3628.7228.70Fujian28.1629.8624.1628.6226.4829.3328.8029.2527.2025.7726.3026.56Jiangxi24.4825.8222.0724.5224.3223.5322.8623.3421.8522.0421.6123.31Shandong37.9640.4137.3439.0436.7137.7337.9337.4936.2933.7733.6433.12Henan26.8028.4025.9627.0525.2626.2124.3325.9026.4420.5024.9125.07Hubei29.6032.7630.6129.3528.3528.7128.8228.5929.0729.3529.4529.21Hunan27.6728.9429.7929.8128.4528.2528.5929.0127.7726.6326.5926.82Guangdong52.6553.6551.8954.8849.3853.0052.4452.7153.6255.2459.5559.49Guangxi20.8722.7022.5623.4122.6723.0622.3023.6222.8121.1921.8721.17Hainan23.4021.3121.9521.4623.3024.1026.7928.0325.6822.4922.7922.90Chongqing27.4429.5329.8530.7728.0833.8832.9032.9932.0430.0530.3030.87Sichuan29.1033.6129.9531.0728.3527.1626.9826.3929.0727.5227.0428.03Guizhou21.1323.3119.0022.6220.7722.6020.4121.2225.6422.1922.2723.60Yunnan21.6924.3220.7421.7819.3721.3221.1320.3019.7220.4321.4821.11Shaanxi28.2129.1227.7929.8027.4827.6826.8627.1429.2926.0526.4927.34Gansu19.2120.9319.8322.4119.7022.2023.5821.6822.0620.8220.0520.10Qinghai18.0518.9916.3018.4117.6217.6516.1917.7115.7818.1320.9720.11Ningxia18.1220.1620.8919.7216.8020.3217.6418.5220.0420.6819.4520.94Xinjiang19.1722.9320.3820.8120.3220.3918.4918.0419.8620.0419.9318.19Figure1 shows that among the 30 provinces and cities in China, Beijing, Jiangsu and Guangdong have a higher level of urban innovation, and their evaluation scores are higher than 50 points, belonging to the top echelon. Second, Tianjin, Shanghai, Zhejiang, Shandong, and Chongqing belong to the second echelon, with scores ranging from 30 to 50. Other provinces and cities scored less than 30 points, belonging to the third echelon. The difference between the highest score (Jiangsu: 54.36) and the lowest score (Qinghai: 17.99) was 36.37 points, indicating that the overall development of the urban innovation level is unbalanced, and there is a large gap between the urban innovation level in backwards areas and economically developed areas, which needs to be paid enough attention.Figure 1
Average value of the urban innovation level of 30 provinces and cities in China (2008–2019).The score of ecological efficiency [50, 51] calculated by the entropy weight TOPSIS method is shown in Table 6.Table 6
Ecological efficiency scores of 30 provinces and cities in China.
Year200820092010201120122013201420152016201720182019Beijing0.9160.9200.8991.0000.9261.0000.9300.9280.9490.9320.9371.000Tianjin0.7630.7440.7140.6850.6720.6650.6720.6670.6860.6980.6900.529Hebei0.6500.6220.6250.6210.5900.5660.5540.5240.5250.5820.5500.499Shanxi0.5160.4580.4490.4540.4040.3330.3050.2690.2610.3080.3380.278Inner Mongolia0.5770.5790.5730.5690.5490.5090.4930.4560.4290.3550.3630.307Liaoning0.6520.6350.6420.6350.6270.6090.6070.6140.5150.5440.5480.509Jilin0.6170.5950.5960.6010.6020.5790.5780.6150.6100.6110.5920.442Heilongjiang0.6270.5550.5440.5510.5200.5070.5060.4910.4810.4780.4680.362Shanghai0.8380.8180.8120.7690.7500.7270.7330.7270.7350.7280.7250.744Jiangsu1.0000.9640.9451.0000.8840.7070.7030.6920.6940.7020.7070.714Zhejiang0.8560.8190.8200.8170.7970.7810.7610.7460.7520.7460.7560.782Anhui0.7010.6630.6530.6510.6200.5690.5400.5120.5120.5230.4840.551Fujian0.8020.7600.7510.7150.6910.6760.6590.6500.6530.6490.6310.678Jiangxi0.6600.6420.6340.6290.5960.5600.5260.4840.4660.4650.4300.430Shandong0.7390.7130.6970.6830.6630.6530.6400.6110.6090.6080.6170.571Henan0.6910.6430.6270.5930.5830.5510.5220.5120.5130.5350.5510.599Hubei0.6970.6720.6540.6220.6070.6250.6180.6250.6280.6380.6360.652Hunan0.6880.6590.6390.6330.6190.6140.6090.6060.6020.6000.5820.569Guangdong1.0000.9720.9260.8870.8620.8020.7790.7570.7590.7570.7500.757Guangxi0.7520.6960.6810.6740.6500.6330.6240.6220.6140.6300.5810.545Hainan0.8510.7970.7700.7410.7080.6780.6660.6470.6480.6420.6420.650Chongqing0.6560.6690.6350.5900.5720.5960.5360.5660.5640.5500.5500.590Sichuan0.7150.6740.6770.6750.6560.6030.6030.6330.6330.6420.6450.656Guizhou0.4660.4340.3980.3590.3150.2810.2880.2920.2800.2880.2960.298Yunnan0.5750.5020.4520.4450.3940.3610.3750.3720.3650.3670.3450.505Shaanxi0.6220.6000.5850.5730.5390.4850.4540.3960.3790.3980.4280.401Gansu0.5780.5340.5130.4840.4390.3360.3000.2250.2130.2410.2620.241Qinghai0.5090.4970.5120.4920.4190.3750.3820.3680.3270.2990.3190.271Ningxia0.3560.3790.3930.4030.3500.3000.2710.2420.2610.2460.2620.206Xinjiang0.5580.4480.4750.4400.3870.3140.2780.1890.1200.1120.2040.202According to the above data, the average distribution of ecological efficiency of 30 provinces and cities in China from 2008 to 2019 is shown in Figure2.Figure 2
Average eco-efficiency of 30 provinces and cities in China (2008–2019).Figure2 shows that the Beijing region has the highest level of ecological efficiency in China. This is because, at the microlevel, effective environmental management mechanisms were implemented in 2008, leading to changes in the urban ecological situation, and Beijing has maintained a high level of ecological efficiency since then, with carbon emissions declining each year. Therefore, we can focus on the relevant management methods and measures in this area, propagate them to similar types of cities and promote the ecological efficiency of the whole country. The ecological efficiency of Shanxi and Guizhou Provinces is low, which is mainly due to the acceleration of urbanization in these two provinces and cities since 2008, resulting in a large amount of carbon emissions in urban infrastructure construction. Although the level of urban innovation is increasing year by year, the rate of increase is slower than that of carbon emissions. Therefore, in future urban management, we should pay attention to adjusting policies to accelerate the urbanization process and at the same time realize a recyclable society and build a green economy. Ningxia and Xinjiang are the last echelon members of China’s ecological efficiency, mainly because their industrial level is still low and possesses many industrial characteristics of high energy consumption and high emissions, and the overall economic level in China is low as well. Therefore, to strengthen the ecological efficiency of these two provinces and cities, we should consider improving the production level, promoting industrial transformation and increasing economic income.To further analyse the relationship between the urban innovation level and ecological efficiency, Figure3 can be obtained by comparing the two evaluation scores:Figure 3
Comparison of the urban innovation level and ecological efficiency average in 30 provinces and cities of China ((1) Urban innovation water average (A-UIL): average value of urban innovation level of 30 provinces and cities in China. (2) average ecological efficiency (A-EE): average eco-efficiency of 30 provinces and cities in China).By comparing the average value of the urban innovation level with the average value of ecological efficiency in 30 provinces and cities in China, it can be found that there is a certain coupling effect, and the overall urban innovation level and ecological efficiency show obvious fitting. The two indicators are positively correlated to a certain extent; that is, areas with high urban innovation levels have higher ecological efficiency. However, there are some differences between the innovation level and the fluctuation of ecological efficiency in individual cities. Among them, the urban innovation level in Shanghai, Jiangsu, Guangdong, Guizhou, Ningxia and Xinjiang is higher than the ecological efficiency, which indicates that the improvement of the urban innovation level in the above six provinces and cities has not played a full driving role in promoting the ecological efficiency of the region. Among them, the trend in Jiangsu Province is the most obvious, and the coupling effect between the urban innovation level and ecological efficiency is not fully exerted.To measure and analyse the relationship between the urbanization innovation level and ecological efficiency in detail, a vector autoregression model (VAR) can be established for dynamic econometric analysis. First, the unit root test is used to determine whether the urbanization innovation level and ecological efficiency are stationary series. Then, a cointegration test is used to analyse whether there is a stable relationship between the urbanization innovation level and ecological efficiency. Second, Granger causality analysis is used to determine the causal relationship between the two comprehensive indices. Finally, impulse response and variance decomposition are used to analyse the impact of the urbanization innovation level on ecological efficiency.


## 4.2. Unit Root Test

According to the time series, whether the series of data is stable or not can be tested. If a time series has a stable mean, variance and autocovariance, it can be judged as stable; otherwise, it should be judged as unstable. Nonstationary time series can have a stable mean, variance and autocovariance after a D-fold difference and become stationary series. At this time, the series is marked asD-order Integration and I(d). Unit is a way to express the nonstationarity of a sequence, and the ADF test can be used to test whether the variables are stable or not. The ADF test judges whether a sequence has a unit root; if the sequence is stable, there is no unit root. Otherwise, there will be a unit root. Therefore, the H0 hypothesis of the ADF test is that there is a unit root. If the statistics of the significance test are less than three confidence levels (10%, 5%, 1%), the original hypothesis should be rejected with the assurance of (90%, 95%, 99%). First, the unit root test is used to determine whether the urbanization innovation level and ecological efficiency are stationary series, then the series is tested for stationarity, and the augmented Dicket-Fuller test (ADF) is used for testing [52]. Eviews software is used for calculation, and the order calculation results after automatic selection according to SIC criteria are shown in the following Table 7:Table 7
ADF test results of the urban innovation level and eco-efficiency.
Variable(C, T, L)ADF test statisticT-statistic (1%)T-statistic (5%)T-statistic (10%)Prob.∗ConclusionA-UIL(C, T, 0)−3.9561−3.6793−2.9678-2.62300.0051StableA-EE(0, 0, 0)−1.6465−3.6892−2.9719-2.62510.4464UnstableΔA-UIL(C, T, 0)−6.4652−3.6892−2.9719-2.62510.0000StableΔA-EE(0, 0, 0)−7.5123−3.6893−2.9719-2.62510.0000StableThe test results of the above unit root show that in the mean value of the urban innovation level and ecological efficiency, the sequence of the urban innovation level is stable, and the sequence of ecological efficiency is unstable at significance levels of 1%, 5%, and 10%. Therefore, the stability test of the first-order difference sequence is carried out. In the first-order difference series test, the urban innovation level series and ecological efficiency series are stable, which is a first-order single integration series and meets the prerequisite of the cointegration test.


## 4.3. Johansen Cointegration Test

The Johansen cointegration test is used to test whether there is a long-term stable relationship between the urban innovation level and ecological efficiency, and Eviews software is used for calculation. The test results are shown in Table8.Table 8
Johansen cointegration test results.
HypothesisEigenvalueTrace testMaximum eigenvalueTrace statistic0.05 critical valueProb.∗∗Max-eigen statistic0.05 critical valueProb.∗∗None∗0.485420.1081315.49470.009418.604714.26460.0097At most 10.05231.50343.84150.22011.50343.84150.2201∗means to reject the original hypothesis at the significance level of 5%.According to the above calculation results, at a significance level of 5%, there is a cointegration relationship between the urbanization innovation level and ecological efficiency. This indicates that there is a long-term dynamic equilibrium relationship between the urbanization innovation level and ecological efficiency.


## 4.4. Granger Causality Test

The Johansen cointegration test demonstrates that there is a long-term dynamic equilibrium relationship between the urbanization innovation level and ecological efficiency, and then the Granger causality test can be carried out on these two variables. The Eviews software calculation results are shown in Table9.Table 9
Granger causality test results.
Null hypothesisObsF-statisticP valueConclusionEE does not Granger cause UIL281.139220.3375AcceptEE does not Granger cause UILUIL does not Granger cause EE281.299000.2921AcceptUIL does not Granger cause EEΔEE does not Granger cause ΔUIL2718.32030.0003RejectΔEE does Granger cause ΔUILΔUIL does not Granger cause ΔEE278.719030.0069RejectΔUIL does Granger cause ΔEEAccording to the Granger causality test, EE is not the Granger cause of UIL, and UIL is not the Granger cause of EE, which shows that there is no one-way coercion between the urban innovation level and ecological efficiency. However, EE and UIL are Granger causalities after the first-order difference and show that the two factors influence each other in the view of incremental economics, the result have been shown in Figure4.Figure 4
Granger causality test of urban innovation level and ecological efficiency based on first-order difference.


## 4.5. Impulse Response Analysis

By using Eviews software, the impulse response analysis of the urban innovation level and ecological efficiency is carried out for 10 periods, and the image shown in Figure5 is obtained. The solid line indicates the impulse response coefficient, and the dashed line indicates the deviation zone of plus or minus two standard deviations. It can be seen from the figure that the urban innovation level (UIL) will have a negative direction impact on ecological efficiency in the first four stages, which means that the ecological efficiency will decline while the urbanization innovation level is improved. However, after reaching the valley value in the fourth period, it slowly rises before the seventh period, indicating that there is a forwards direction impact in this section, which means that the level of urbanization innovation will promote the improvement of ecological efficiency, and then the two enter a balanced and stable state. However, the impulse effect of ecological efficiency on the level of urbanization innovation is complicated in 10 periods of observation. First, it presents the forwards direction effect before the second period, which means that the improvement of ecological efficiency can boost the level of urban innovation. After that, it suddenly declined from the second to the third period and slowly rose from the third period until it reached a balanced and stable state.Figure 5
Impulse response of urban innovation level and ecological efficiency.


## 4.6. Variance Decomposition

On the basis of impulse response analysis, the function of variance decomposition further reflects the contribution rate of self-shocks and shocks of other variables to understand the relative importance of shocks of various variables to endogenous variables in the Model [53, 54]. The variance decomposition results are shown in Table 10.Table 10
Variance decomposition results.
Issue no.EE-variance decomposition resultsUIL-variance decomposition resultsS.E.EE (%)UIL (%)S.E.EE (%)UIL (%)10.19571000.00000.247661.525338.474720.227398.08531.91470.283168.724031.276030.250796.60273.39730.291967.133932.866140.258096.14753.85250.295266.296733.703350.265596.09733.90270.297966.894433.105660.270496.22523.77480.301067.527732.472370.274296.27503.72500.302767.888632.111480.276496.27903.72100.303768.024931.975190.278096.27533.72470.304268.127031.8730100.279196.28383.71620.304768.221231.7788From the data of variance decomposition, it can be seen that the ecological efficiency is mainly affected by itself. The first period is completely immune to other variables and then becomes stable in the probability range of more than 95% from the second phase to the tenth phase, and the impact of the urban innovation level is small. In the second phase, there is a gradual impact on EE, but the degree of impact is not high and remains stable within the level of 5% probability during 10 issues. Relatively speaking, the level of urban innovation is mainly affected by ecological efficiency, with probability intervals of more than 65%, and the contribution rate of self-impact is approximately 30%. It can be seen that ecological efficiency has an important explanatory ability for the level of urban innovation, while the level of urban innovation has less explanatory ability for the impact change of ecological efficiency.


## 5. Conclusions

By measuring the changes in the urbanization innovation level and ecological efficiency level in 30 provinces and cities in China and carrying out dynamic econometric analysis on the relationship between the two variables, the following conclusions are drawn:(1)
The overall development of the urban innovation level of 30 provinces and cities in China from 2008 to 2019 is uneven, and there is a large gap between the urban innovation level of backwards areas and economically developed areas. At the same time, by comparing the average ecological efficiency of the same region in this time interval, it can be found that there is a certain coupling effect between the urban innovation level and ecological efficiency. The two indicators are positively correlated to a certain extent; that is, areas with high urban innovation levels have higher ecological efficiency. However, there are some differences between the innovation level and the fluctuation of ecological efficiency in some cities, and the urban innovation level does not play a full driving role in the ecological efficiency of the region.(2)
Through dynamic econometric analysis, it is found that there is a long-term dynamic equilibrium relationship between the two variables of urban innovation level and ecological efficiency, which verifies the coupling effect of the two variables and shows that the improvement of urban innovation level has a fluctuating impact on the improvement of ecological efficiency. The improvement of the urban innovation level in the early stage will have a negative impact on ecological efficiency, which indicates that the process of urbanization will exert certain pressure on the ecological environment. However, the continuous improvement of urbanization will have a forwards direction impact on the improvement of the ecological environment.(3)
The improvement of ecological efficiency will also promote the improvement of the urban innovation level. In the long run, the contribution of ecological efficiency to the urban innovation level is significant, which means that the evaluation of ecological indicators should not be ignored in the evaluation of the urban innovation level.In summary, there is a close relationship between the urban innovation level and ecological efficiency, and it is an important indicator that cannot be ignored in the process of urbanization in China. By discovering the relationship between the urban innovation level and ecological efficiency, the following suggestions can be provided for the harmonious development of urban innovation and the ecological environment in the future:(1)
Strengthen policies and legislation to promote the construction of ecological civilization. We should firmly establish the idea that Jinshan and Yinshan are green mountains and green hills, strengthen the punishment for environmental damage by constantly improving the laws and regulations of environmental protection, establish a comprehensive evaluation index system of urbanization level and ecological environment, pay close attention to the development of index reform, dynamically adjust relevant measures, and continuously promote the coordinated development of urban innovation and ecological civilization construction.(2)
Promote industrial transformation and change the energy structure. On the path of China’s new urbanization, we will promote the optimization and transformation of traditional industries with large energy consumption and emissions, transform the industrial structure of cities, increase the proportion of tertiary industry, promote the optimization of the energy structure and improve the utilization rate of resources. This improves the relationship between urban innovation, urbanization level, ecological civilization, environmental protection and efficient utilization of resources and forms a forwards direction effect.(3)
Promote regional integration. China has a vast territory and uneven urbanization level. Therefore, the regional economy can be constructed by regional integration, planning docking, policy coordination, industrial cooperation and other methods to promote the benign and high-level development of the regional economy, enhance the radiation-driven capacity of the central cities, and develop the agglomeration areas from point to point, thus contributing to the realization of China’s comprehensive goal of new urbanization and resource-saving society.To achieve sustainable social development, development of the urban economy should maintain a close relationship with the ecological environment, and the ecological environment should maintain dynamic equilibrium with the urban economy; therefore, in the future, it is necessary to carry out research exploring the endogenous problems between economic development and the ecological environment to improve the level of urban governance through urban innovation to reduce the destruction of the urban ecological environment.



---

*Source: 6049629-2022-03-10.xml*