# Competitiveness Evaluation and Obstacle Factor Analysis of Urban Green and Low-Carbon Development in Beijing-Tianjin-Hebei Cities

Authors: Kai Yuan; Biao Hu; Tingyun Niu; Binglong Zhu; Liang Zhang; Yuqiong Guan
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

Based on the PSR conceptual evaluation model of urban green and low-carbon development competitiveness, this paper evaluates the green and low-carbon development competitiveness of 13 cities in Beijing-Tianjin-Hebei from 2010 to 2019 and reveals their spatial effects and obstacle factors combined with the spatial autocorrelation and obstacle model. The results show the following: (1) In terms of the overall situation, the evaluation index of urban green and low-carbon development competitiveness in the Beijing-Tianjin-Hebei region shows a fluctuating upward trend as a whole from 2010 to 2019. Among them, the comprehensive evaluation indexes of Beijing, Zhangjiakou, and Tianjin are very competitive, while Hengshui, Handan, and Xingtai are relatively weak. (2) In terms of spatial data analysis, the global and local Moran`s I of Beijing-Tianjin-Hebei cities from 2010 to 2019 were positive and passed the 1% significant level test. This shows that the comprehensive evaluation index of urban green low-carbon development competitiveness of Beijing-Tianjin-Hebei cities has a positive spatial correlation, and the spatial agglomeration effect is significant. On the whole, it presents a spatial agglomeration pattern of “high in the north, partial jump, and low in the south”. (3) In the aspect of obstacle factor analysis, the proportion of the tertiary industry output value in GDP, energy consumption of ten thousand yuan of GDP, the urban green technology innovation level, and industrial soot emission of ten thousand yuan of GDP are the main obstacle factors restricting the improvement of urban green low-carbon development competitiveness in Beijing-Tianjin-Hebei cities.


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## Body

## 1. Introduction

In recent years, with the rapid development of the economy, the contradiction between environmental carrying capacity and economic growth has become increasingly prominent, so the concept of urban green and low-carbon development has attracted more and more attention. The development of the goal of “double carbon” put forward by President XI is the inevitable requirement of China’s ecological civilization construction and high-quality economic development and vigorously promoting the construction of China’s urban green, and the low-carbon development level is an important measure and direction to achieve the “double carbon” goal. The Beijing-Tianjin-Hebei region, as the region with the largest economic scale and the strongest development vitality in northern China, has the essential strategic position [1], and vigorously promoting the coordinated development among Beijing-Tianjin-Hebei regions is the general trend in the future. Therefore, how to apply the concept of “green development” to the whole process of coordinated development in the Beijing-Tianjin-Hebei region is a key problem to be solved urgently.As an important breakthrough to achieve sustainable economic, social, and environmental development, green low-carbon development is gradually becoming the consensus of the global sustainable development goals (SDGs) and the development of all mankind. As an important unit of China’s economic development, cities and urban agglomerations have the problems of high energy consumption and low output, which seriously affect the efficiency of urban resource utilization and hinder the realization of urban green and low-carbon development. In view of this phenomenon, domestic and foreign scholars first conducted extensive demonstration and research on China’s implementation of urban green low-carbon development strategy, policies and related driving, and restrictive factors [2–8]. Through the research and analysis of the concept and connotation, development path and mode transformation, strategy, countermeasures, drivers, restrictive factors, measurement and evaluation index system, theory and application methods of urban green, and low-carbon development, a more systematic theoretical system has been formed [9–12].Through analysis, it is found that these studies put the previous concepts, connotations, and other top-level designs in place, and systematically explore the implementation of the green low-carbon development model, regional differences, industry, and enterprise green low-carbon development heterogeneity and other issues in China. However, from the perspective of the relationship between green and low-carbon development and urban competitiveness, the research on the level of urban green and low-carbon development is still rare. Therefore, it is necessary to further explore the following: what are the time series changes of the comprehensive evaluation index of green and low-carbon development and the overall level of urban competitiveness? What are the spatial distribution characteristics and the trend of spatial evolution? What are the obstacles to urban green and low-carbon development and the improvement of urban competitiveness? Driven by these research issues, this paper synthesizes the research results and enlightenment of previous scholars, comprehensively applies the conceptual model of PSR, and brings the pressure, state, and response of green and low-carbon development into the analysis framework. The competitiveness evaluation model of urban green and low-carbon development based on combined weight is constructed to avoid the one-sidedness of subjective and objective evaluation. With the comprehensive use of spatial autocorrelation analysis and the obstacle model, this paper reveals the spatial agglomeration characteristics of the green and low-carbon development level in the Beijing-Tianjin-Hebei region and the main obstacle factors restricting the improvement of urban competitiveness. On the basis of this, targeted suggestions and measures are put forward in order to provide the reference value for promoting the coordinated governance goal of green and low-carbon development in the Beijing-Tianjin-Hebei region.The remainder of this paper is organized as follows: The first part introduces the review of the relevant literature, and the second part introduces the basic research methods, model setting, and evaluation index selection. The third part is empirical result analysis, which analyzes the comprehensive evaluation index of green and low-carbon development and the time series change, spatio-temporal evolution trend, spatial autocorrelation analysis, and main obstacle factors of urban competitiveness. The fourth part draws the research conclusion and puts forward the pertinent opinions and measures.


## 2. Literature Review

This part reviews the relevant literature on green and low-carbon development in cities and urban agglomerations in recent years. It mainly focuses on the measurement of green and low-carbon development level and efficiency, regional differences, and influencing factors.


### 2.1. Measurement of Level and Efficiency

The key to the core concept of green and low-carbon development is to improve the efficiency of green and low-carbon development. In the calculation of the efficiency of green and low-carbon development, scholars’ selection of input indicators basically revolves around three aspects: manpower, capital, and energy [13]. Expected output indicators often select economic growth, technological progress, and so on. Studies often take environmental pollution as an indicator of unexpected output [14]. In the selection of methods for measuring the efficiency of green and low-carbon development, nonparametric methods such as information entropy model, principal component analysis, F-integral comprehensive evaluation model, SUPER-SBM model, and PSR geographic detector model are often used, especially focusing on data envelopment analysis (DEA) and its various extended models, which are good at dealing with multi-input and multi-output variables. Some scholars put forward the directional distance function, which brings pollutants as negative output to the environment into the efficiency analysis framework, which provides methodological support for measuring efficiency under environmental constraints [8]. With regard to the convergence of green low-carbon development efficiency, the current research is mainly focused on the convergence of green low-carbon efficiency or green innovation ability. Foreign scholars have investigated the evolution trend of the green and low-carbon development level in OECD countries based onβconvergence and the spatio-temporal model. Chinese scholars have analyzed the convergence of regional green and low-carbon development efficiency in China from the perspective of spatial economics [15].


### 2.2. Regional Differences

There are differences in geographical location, economic level, resource endowment, and development planning among different cities. It is necessary to study the temporal and spatial differences of green development efficiency between cities and urban agglomerations. On the whole, the difference of green and low-carbon development efficiency in Chinese cities is high in the east and low in the west [16]. The green development efficiency of urban agglomeration has evolved from “low in Northwest, North China, Central China, and high in Southeast China” to “low in Northwest, Central China, high in North China, East Coast, and Southwest” [17]. Some scholars have calculated the green development efficiency of the four major eastern coastal urban agglomerations, and summarized the spatio-temporal evolution characteristics of the green development efficiency of the urban agglomeration as a whole and within the urban agglomeration. It is pointed out that except for the ecological efficiency of Beijing-Tianjin-Hebei urban agglomeration, the ecological efficiency of the four major coastal urban agglomerations shows a V-shaped evolution trend of first decline and then rise [18]. There are also scholars who regard environmental load as unexpected output and use various extended DEA models to measure the efficiency of green and low-carbon development in China. They all find that the efficiency of green and low-carbon development in China is “high in the east and low in the west” [19]. Other scholars have analyzed the trend of regional unbalanced development with the main indicators such as the degree of urban single-center and multi-center, the spatial concentration and dispersion of each index, the degree of industrial diversification, and the intensity of urban extroverted function [20].


### 2.3. Influencing Factors

In terms of the influencing factors and driving mechanism of green and low-carbon development efficiency, many scholars mainly analyze the industrial structure, scientific and technological innovation, environmental regulation, urbanization level, marketization level, foreign investment, and so on. Some scholars have pointed out that there is an inverted “U-shaped” relationship between the city size and the efficiency of urban green and low-carbon development. With the expansion of the city scale, the efficiency of green development first increases and then decreases [21]. Other scholars have found that there is a “U-shaped” relationship between industrial agglomeration and green development efficiency. With the gradual enhancement of industrial agglomeration, green development efficiency first decreases and then increases [22]. Some scholars also analyze the direct and indirect effects of foreign investment and environmental regulation on the efficiency of urban green development in the Yangtze River Economic Belt. It is pointed out that the spillover effects of foreign investment on the green development efficiency of the Yangtze river economic belt are alternating environmental regulation “bottom-to-bottom competition” and “yardstick competition” [23]. Based on the R&D-driven theory, some scholars have made empirical analysis on the influencing factors of regional green and low-carbon development, pointing out that technological progress is the main factor to promote green and low-carbon development. The level of regional economic development, FDI, urbanization, and industrial structure are closely related to green and low-carbon development, but the influencing factors have significant differences in the development of different regions and cities [24].Through the literature review, it is found that green low-carbon development and its efficiency has become a key concern in the research field of cities and urban agglomerations. However, the comparative analysis of the efficiency of green and low-carbon development of urban agglomeration and internal cities, the level of urban competitiveness, regional evolution characteristics, and obstacle factors need to be further discussed. Therefore, this paper takes the Beijing-Tianjin-Hebei urban agglomeration as the research object, to evaluate the green and low-carbon development level and urban competitiveness of the Beijing-Tianjin-Hebei region from 2010 to 2019. With the help of the obstacle model, this paper identifies the specific obstacles of the evaluation index of urban green and low-carbon development, and puts forward targeted measures to provide theoretical reference for improving the level of urban green and low-carbon development. It has certain theoretical and practical significance for the green and low-carbon development model and economic development transformation of Chinese cities and urban agglomerations.


## 2.1. Measurement of Level and Efficiency

The key to the core concept of green and low-carbon development is to improve the efficiency of green and low-carbon development. In the calculation of the efficiency of green and low-carbon development, scholars’ selection of input indicators basically revolves around three aspects: manpower, capital, and energy [13]. Expected output indicators often select economic growth, technological progress, and so on. Studies often take environmental pollution as an indicator of unexpected output [14]. In the selection of methods for measuring the efficiency of green and low-carbon development, nonparametric methods such as information entropy model, principal component analysis, F-integral comprehensive evaluation model, SUPER-SBM model, and PSR geographic detector model are often used, especially focusing on data envelopment analysis (DEA) and its various extended models, which are good at dealing with multi-input and multi-output variables. Some scholars put forward the directional distance function, which brings pollutants as negative output to the environment into the efficiency analysis framework, which provides methodological support for measuring efficiency under environmental constraints [8]. With regard to the convergence of green low-carbon development efficiency, the current research is mainly focused on the convergence of green low-carbon efficiency or green innovation ability. Foreign scholars have investigated the evolution trend of the green and low-carbon development level in OECD countries based onβconvergence and the spatio-temporal model. Chinese scholars have analyzed the convergence of regional green and low-carbon development efficiency in China from the perspective of spatial economics [15].


## 2.2. Regional Differences

There are differences in geographical location, economic level, resource endowment, and development planning among different cities. It is necessary to study the temporal and spatial differences of green development efficiency between cities and urban agglomerations. On the whole, the difference of green and low-carbon development efficiency in Chinese cities is high in the east and low in the west [16]. The green development efficiency of urban agglomeration has evolved from “low in Northwest, North China, Central China, and high in Southeast China” to “low in Northwest, Central China, high in North China, East Coast, and Southwest” [17]. Some scholars have calculated the green development efficiency of the four major eastern coastal urban agglomerations, and summarized the spatio-temporal evolution characteristics of the green development efficiency of the urban agglomeration as a whole and within the urban agglomeration. It is pointed out that except for the ecological efficiency of Beijing-Tianjin-Hebei urban agglomeration, the ecological efficiency of the four major coastal urban agglomerations shows a V-shaped evolution trend of first decline and then rise [18]. There are also scholars who regard environmental load as unexpected output and use various extended DEA models to measure the efficiency of green and low-carbon development in China. They all find that the efficiency of green and low-carbon development in China is “high in the east and low in the west” [19]. Other scholars have analyzed the trend of regional unbalanced development with the main indicators such as the degree of urban single-center and multi-center, the spatial concentration and dispersion of each index, the degree of industrial diversification, and the intensity of urban extroverted function [20].


## 2.3. Influencing Factors

In terms of the influencing factors and driving mechanism of green and low-carbon development efficiency, many scholars mainly analyze the industrial structure, scientific and technological innovation, environmental regulation, urbanization level, marketization level, foreign investment, and so on. Some scholars have pointed out that there is an inverted “U-shaped” relationship between the city size and the efficiency of urban green and low-carbon development. With the expansion of the city scale, the efficiency of green development first increases and then decreases [21]. Other scholars have found that there is a “U-shaped” relationship between industrial agglomeration and green development efficiency. With the gradual enhancement of industrial agglomeration, green development efficiency first decreases and then increases [22]. Some scholars also analyze the direct and indirect effects of foreign investment and environmental regulation on the efficiency of urban green development in the Yangtze River Economic Belt. It is pointed out that the spillover effects of foreign investment on the green development efficiency of the Yangtze river economic belt are alternating environmental regulation “bottom-to-bottom competition” and “yardstick competition” [23]. Based on the R&D-driven theory, some scholars have made empirical analysis on the influencing factors of regional green and low-carbon development, pointing out that technological progress is the main factor to promote green and low-carbon development. The level of regional economic development, FDI, urbanization, and industrial structure are closely related to green and low-carbon development, but the influencing factors have significant differences in the development of different regions and cities [24].Through the literature review, it is found that green low-carbon development and its efficiency has become a key concern in the research field of cities and urban agglomerations. However, the comparative analysis of the efficiency of green and low-carbon development of urban agglomeration and internal cities, the level of urban competitiveness, regional evolution characteristics, and obstacle factors need to be further discussed. Therefore, this paper takes the Beijing-Tianjin-Hebei urban agglomeration as the research object, to evaluate the green and low-carbon development level and urban competitiveness of the Beijing-Tianjin-Hebei region from 2010 to 2019. With the help of the obstacle model, this paper identifies the specific obstacles of the evaluation index of urban green and low-carbon development, and puts forward targeted measures to provide theoretical reference for improving the level of urban green and low-carbon development. It has certain theoretical and practical significance for the green and low-carbon development model and economic development transformation of Chinese cities and urban agglomerations.


## 3. Research and Design

### 3.1. Research Methods

Entropy method, as an objective weighting method with high credibility and accuracy, can effectively overcome the overlap of index information. Combine entropy method with AHP method for calculation and analysis, avoid the one-sidedness of subjective and objective evaluation, and improve the accuracy of judgment [25].


#### 3.1.1. Standardization of the Evaluation Index

Based on the problem of different dimensions and orders of magnitude of each evaluation index, it is necessary to homogenize the heterogeneous index. Therefore, the extreme value method is used to standardize the index data.Positive indicator, the calculation formula is(1)yij=xij−minxijmaxxij−minxij.Negative index, the calculation formula is(2)yij=maxxij−xijmaxxij−minxij.In the formula:i represents the city and j represents the evaluation index.


#### 3.1.2. Determine the Weight of Indicators

Step 1.
The entropy of itemj is calculated as follows:(3)Hj=−k∑i=1mpijlnpij.
In the formula:k=1/lnm, k>0; ln is a natural logarithm, pij=yij/∑i=1myij (pij represents the proportion of the index j; Hj represents the entropy value of the item j).Step 2.
Calculate the coefficient of difference of itemj, Gj, the greater the difference in index values, the greater the impact on the evaluation results: Gj=1−Hj.Step 3.
AHP determines the subjective weight of thej index Πj.Step 4.
Determine the weight of the itemj. According to the combined weight method Wj, the calculation formula is as follows:(4)Wj=aGj∑j=1nGj+1−aΠj,n=1,2,3,…,30.
In the formula,0≤a≤1, referring to the previous research [26], we take a = 0.5. According to the evaluation index system, the comprehensive weight of each evaluation index is calculated (see Table 1).Table 1
Weights of the competitiveness index for urban green and low-carbon development.
IndexWj1Wj2WjRankingP0.280.640.16—P10.040.030.0314P20.080.030.057P30.030.040.0413P40.030.150.092P50.020.110.075P60.020.060.0411P70.050.220.141S0.420.260.34—S10.090.010.058S20.060.060.066S30.080.090.093S40.050.010.0315S50.060.030.0410S60.040.020.0316S70.050.040.059R0.310.110.21—R10.030.010.0219R20.040.010.0217R30.120.040.084R40.020.030.0218R50.020.010.0121R60.030.010.0220R70.060.040.0412Step 5.
We determine the comprehensive evaluation index matrix of urban green and low-carbon development competitiveness of Beijing-Tianjin-Hebei cities.(5)vij=∑j=1nWj×yij.


#### 3.1.3. Exploratory Spatial Data Analysis (ESDA)

(1) Global Autocorrelation Analysis. The global spatial autocorrelation analysis method is used to identify the horizontal spatial agglomeration of urban green development in the Beijing-Tianjin-Hebei region. Global Moran’s I is generally used to reflect the distribution effect of regional units [27].(6)I=n∑i=1n∑j=1nxi−x−xj−x−÷∑i=1nxi−x−2∑i=1n∑j=1nwij,where n is the number of spatial units in the study area, xi and xj the observations represent the region i and region j, and the x− values represent the average value. wij is a spatial weight matrix, I ∈ [−1, 1], if I ＞ 0, it shows that the regional green and low-carbon development presents an agglomeration trend in space; on the contrary, it means that the competitiveness of regional green and low-carbon development shows a negative correlation in space.(2) Local Autocorrelation Analysis. Global Moran’s I may ignore local spatial heterogeneity, so the local autocorrelation index (LISA) is adopted. The calculation formula is as follows:(7)I=nCj−C−∑j=1nwijCi−C−2∑j=1nCi−C−2.


#### 3.1.4. Obstacle Degree Model

On the basis of measuring the competitiveness level of urban green and low-carbon development, to further study the obstacle factors to improve the competitiveness of urban green and low-carbon development of Beijing-Tianjin-Hebei cities, the obstacle model is introduced [28].Obstacle degree model contains factor contribution degreeOij, index deviation degree Dij, obstacle degree Pij three parts. Among them, Oij is the weight of the j index to the competitiveness of urban green and low-carbon development, that is Wj; Dij is the gap between the j index and the competitiveness target of urban green and low-carbon development, that is 1−yij. Pij is the obstacle of the j index to the competitiveness of urban green and low-carbon development.The formula is(8)pij=Oij×Dij∑i=1mOij×Dij×100%.


### 3.2. Construction of the Evaluation Index System

The PSR model is a framework proposed by the organization for economic cooperation and development and the United Nations Environment Programme to study the causality of the green environment. It uses the thinking logic of “pressure-state-response”, and each subsystem can be divided into several indicators [29]. The competitiveness of urban green and low-carbon development is affected by many factors. Using the PSR model, we can comprehensively evaluate the pressure, current situation, and government response of urban green and low-carbon development, and construct a PSR conceptual model for evaluating the competitiveness of urban green and low-carbon development (see Figure 1).Figure 1
The PSR conceptual model.Based on the PSR conceptual model for evaluating the competitiveness of urban green and low-carbon development, and combined with the current situation and previous research progress of urban green and low-carbon development in 13 cities in Beijing-Tianjin-Hebei. A total of 21 evaluation indicators are selected under the three subsystems of pressure (P), status (S), and response (R) to construct a comprehensive evaluation index system of urban green and low-carbon development competitiveness of Beijing-Tianjin-Hebei cities (see Table2). Among them, the evaluation of the urban green technology innovation capability index selects the full-time equivalent of Redd personnel (person/year), the proportion of per capita expenditure (%), the proportion of per capita patent transaction in green technology market to GDP, and the proportion of sales income of urban green innovative products to GDP, which are characterized by four dimensions and calculated comprehensively according to the entropy method.Table 2
Comprehensive evaluation index system.
Target layerCriterion layerIndex layerUnitIndicator codeIndex propertyEvaluation index system of urban green and low-carbon development competitiveness of Beijing-Tianjin-Hebei citiesPressure subsystemPopulation density人·Km−2P1(−)Natural population growth rate%P2(−)Ten thousand yuan GDP energy consumptiont·yuan−1P3(−)Industrial sulfur dioxide emissions of ten thousand yuan GDPt·yuan−1P4(−)Discharge of industrial wastewater from ten thousand yuan GDPt·yuan−1P5(−)Ten thousand yuan GDP water consumptionm3·yuan−1P6(−)Industrial dust emission of ten thousand yuan GDPt·yuan−1P7(−)State subsystemUrban green technology innovation ability%S1(+)Annual mean value of urban PM2.5 concentrationμg·m−3S2(−)Annual mean value of urban SO2 concentrationμg·m−3S3(−)Annual mean value of urban NO2 concentrationμg·m−3S4(−)Proportion of per capita carbon emissions%S5(−)Pollution rate of urban water resources%S6(−)Urbanization rate%S7(−)Response subsystemPer capita green area coverage%R1(+)The proportion of the output value of the tertiary industry in GDP%R2(+)Comprehensive utilization rate of industrial solid waste%R3(+)Centralized treatment rate of municipal sewage%R4(+)Harmless treatment rate of municipal solid waste%R5(+)Proportion of investment in environment, water conservancy and public facilities%R6(+)Growth rate of per capita consumption of urban green low-carbon products%R7(+)Note. (+) indicates that the indicator has a positive impact on the target layer; (−) indicates that the indicator has a negative impact on the target layer.


### 3.3. Flow Chart and Data Source

The research idea and process of this artical is shown in Figure2). The study takes the panel data of Beijing, Tianjin, Shijiazhuang, Chengde, Zhangjiakou, Qinhuangdao, Tangshan, Langfang, Baoding, Cangzhou, Hengshui, Xingtai, and Handan from 2010 to 2019, as samples to ensure the reliability and authority of the data. The research data come from China Statistical Yearbook, China Science and Technology Statistical Yearbook, China Energy Statistical Yearbook, China Environmental Statistical Yearbook, Beijing, Tianjin, and Hebei Statistical Yearbooks, statistical bulletins, and environmental bulletins from 2011 to 2020. The lack of data in individual years is supplemented by mobile interpolation.Figure 2
Flow chart of step guidance for research and analysis.


## 3.1. Research Methods

Entropy method, as an objective weighting method with high credibility and accuracy, can effectively overcome the overlap of index information. Combine entropy method with AHP method for calculation and analysis, avoid the one-sidedness of subjective and objective evaluation, and improve the accuracy of judgment [25].


### 3.1.1. Standardization of the Evaluation Index

Based on the problem of different dimensions and orders of magnitude of each evaluation index, it is necessary to homogenize the heterogeneous index. Therefore, the extreme value method is used to standardize the index data.Positive indicator, the calculation formula is(1)yij=xij−minxijmaxxij−minxij.Negative index, the calculation formula is(2)yij=maxxij−xijmaxxij−minxij.In the formula:i represents the city and j represents the evaluation index.


### 3.1.2. Determine the Weight of Indicators

Step 1.
The entropy of itemj is calculated as follows:(3)Hj=−k∑i=1mpijlnpij.
In the formula:k=1/lnm, k>0; ln is a natural logarithm, pij=yij/∑i=1myij (pij represents the proportion of the index j; Hj represents the entropy value of the item j).Step 2.
Calculate the coefficient of difference of itemj, Gj, the greater the difference in index values, the greater the impact on the evaluation results: Gj=1−Hj.Step 3.
AHP determines the subjective weight of thej index Πj.Step 4.
Determine the weight of the itemj. According to the combined weight method Wj, the calculation formula is as follows:(4)Wj=aGj∑j=1nGj+1−aΠj,n=1,2,3,…,30.
In the formula,0≤a≤1, referring to the previous research [26], we take a = 0.5. According to the evaluation index system, the comprehensive weight of each evaluation index is calculated (see Table 1).Table 1
Weights of the competitiveness index for urban green and low-carbon development.
IndexWj1Wj2WjRankingP0.280.640.16—P10.040.030.0314P20.080.030.057P30.030.040.0413P40.030.150.092P50.020.110.075P60.020.060.0411P70.050.220.141S0.420.260.34—S10.090.010.058S20.060.060.066S30.080.090.093S40.050.010.0315S50.060.030.0410S60.040.020.0316S70.050.040.059R0.310.110.21—R10.030.010.0219R20.040.010.0217R30.120.040.084R40.020.030.0218R50.020.010.0121R60.030.010.0220R70.060.040.0412Step 5.
We determine the comprehensive evaluation index matrix of urban green and low-carbon development competitiveness of Beijing-Tianjin-Hebei cities.(5)vij=∑j=1nWj×yij.


### 3.1.3. Exploratory Spatial Data Analysis (ESDA)

(1) Global Autocorrelation Analysis. The global spatial autocorrelation analysis method is used to identify the horizontal spatial agglomeration of urban green development in the Beijing-Tianjin-Hebei region. Global Moran’s I is generally used to reflect the distribution effect of regional units [27].(6)I=n∑i=1n∑j=1nxi−x−xj−x−÷∑i=1nxi−x−2∑i=1n∑j=1nwij,where n is the number of spatial units in the study area, xi and xj the observations represent the region i and region j, and the x− values represent the average value. wij is a spatial weight matrix, I ∈ [−1, 1], if I ＞ 0, it shows that the regional green and low-carbon development presents an agglomeration trend in space; on the contrary, it means that the competitiveness of regional green and low-carbon development shows a negative correlation in space.(2) Local Autocorrelation Analysis. Global Moran’s I may ignore local spatial heterogeneity, so the local autocorrelation index (LISA) is adopted. The calculation formula is as follows:(7)I=nCj−C−∑j=1nwijCi−C−2∑j=1nCi−C−2.


### 3.1.4. Obstacle Degree Model

On the basis of measuring the competitiveness level of urban green and low-carbon development, to further study the obstacle factors to improve the competitiveness of urban green and low-carbon development of Beijing-Tianjin-Hebei cities, the obstacle model is introduced [28].Obstacle degree model contains factor contribution degreeOij, index deviation degree Dij, obstacle degree Pij three parts. Among them, Oij is the weight of the j index to the competitiveness of urban green and low-carbon development, that is Wj; Dij is the gap between the j index and the competitiveness target of urban green and low-carbon development, that is 1−yij. Pij is the obstacle of the j index to the competitiveness of urban green and low-carbon development.The formula is(8)pij=Oij×Dij∑i=1mOij×Dij×100%.


## 3.1.1. Standardization of the Evaluation Index

Based on the problem of different dimensions and orders of magnitude of each evaluation index, it is necessary to homogenize the heterogeneous index. Therefore, the extreme value method is used to standardize the index data.Positive indicator, the calculation formula is(1)yij=xij−minxijmaxxij−minxij.Negative index, the calculation formula is(2)yij=maxxij−xijmaxxij−minxij.In the formula:i represents the city and j represents the evaluation index.


## 3.1.2. Determine the Weight of Indicators

Step 1.
The entropy of itemj is calculated as follows:(3)Hj=−k∑i=1mpijlnpij.
In the formula:k=1/lnm, k>0; ln is a natural logarithm, pij=yij/∑i=1myij (pij represents the proportion of the index j; Hj represents the entropy value of the item j).Step 2.
Calculate the coefficient of difference of itemj, Gj, the greater the difference in index values, the greater the impact on the evaluation results: Gj=1−Hj.Step 3.
AHP determines the subjective weight of thej index Πj.Step 4.
Determine the weight of the itemj. According to the combined weight method Wj, the calculation formula is as follows:(4)Wj=aGj∑j=1nGj+1−aΠj,n=1,2,3,…,30.
In the formula,0≤a≤1, referring to the previous research [26], we take a = 0.5. According to the evaluation index system, the comprehensive weight of each evaluation index is calculated (see Table 1).Table 1
Weights of the competitiveness index for urban green and low-carbon development.
IndexWj1Wj2WjRankingP0.280.640.16—P10.040.030.0314P20.080.030.057P30.030.040.0413P40.030.150.092P50.020.110.075P60.020.060.0411P70.050.220.141S0.420.260.34—S10.090.010.058S20.060.060.066S30.080.090.093S40.050.010.0315S50.060.030.0410S60.040.020.0316S70.050.040.059R0.310.110.21—R10.030.010.0219R20.040.010.0217R30.120.040.084R40.020.030.0218R50.020.010.0121R60.030.010.0220R70.060.040.0412Step 5.
We determine the comprehensive evaluation index matrix of urban green and low-carbon development competitiveness of Beijing-Tianjin-Hebei cities.(5)vij=∑j=1nWj×yij.


## 3.1.3. Exploratory Spatial Data Analysis (ESDA)

(1) Global Autocorrelation Analysis. The global spatial autocorrelation analysis method is used to identify the horizontal spatial agglomeration of urban green development in the Beijing-Tianjin-Hebei region. Global Moran’s I is generally used to reflect the distribution effect of regional units [27].(6)I=n∑i=1n∑j=1nxi−x−xj−x−÷∑i=1nxi−x−2∑i=1n∑j=1nwij,where n is the number of spatial units in the study area, xi and xj the observations represent the region i and region j, and the x− values represent the average value. wij is a spatial weight matrix, I ∈ [−1, 1], if I ＞ 0, it shows that the regional green and low-carbon development presents an agglomeration trend in space; on the contrary, it means that the competitiveness of regional green and low-carbon development shows a negative correlation in space.(2) Local Autocorrelation Analysis. Global Moran’s I may ignore local spatial heterogeneity, so the local autocorrelation index (LISA) is adopted. The calculation formula is as follows:(7)I=nCj−C−∑j=1nwijCi−C−2∑j=1nCi−C−2.


## 3.1.4. Obstacle Degree Model

On the basis of measuring the competitiveness level of urban green and low-carbon development, to further study the obstacle factors to improve the competitiveness of urban green and low-carbon development of Beijing-Tianjin-Hebei cities, the obstacle model is introduced [28].Obstacle degree model contains factor contribution degreeOij, index deviation degree Dij, obstacle degree Pij three parts. Among them, Oij is the weight of the j index to the competitiveness of urban green and low-carbon development, that is Wj; Dij is the gap between the j index and the competitiveness target of urban green and low-carbon development, that is 1−yij. Pij is the obstacle of the j index to the competitiveness of urban green and low-carbon development.The formula is(8)pij=Oij×Dij∑i=1mOij×Dij×100%.


## 3.2. Construction of the Evaluation Index System

The PSR model is a framework proposed by the organization for economic cooperation and development and the United Nations Environment Programme to study the causality of the green environment. It uses the thinking logic of “pressure-state-response”, and each subsystem can be divided into several indicators [29]. The competitiveness of urban green and low-carbon development is affected by many factors. Using the PSR model, we can comprehensively evaluate the pressure, current situation, and government response of urban green and low-carbon development, and construct a PSR conceptual model for evaluating the competitiveness of urban green and low-carbon development (see Figure 1).Figure 1
The PSR conceptual model.Based on the PSR conceptual model for evaluating the competitiveness of urban green and low-carbon development, and combined with the current situation and previous research progress of urban green and low-carbon development in 13 cities in Beijing-Tianjin-Hebei. A total of 21 evaluation indicators are selected under the three subsystems of pressure (P), status (S), and response (R) to construct a comprehensive evaluation index system of urban green and low-carbon development competitiveness of Beijing-Tianjin-Hebei cities (see Table2). Among them, the evaluation of the urban green technology innovation capability index selects the full-time equivalent of Redd personnel (person/year), the proportion of per capita expenditure (%), the proportion of per capita patent transaction in green technology market to GDP, and the proportion of sales income of urban green innovative products to GDP, which are characterized by four dimensions and calculated comprehensively according to the entropy method.Table 2
Comprehensive evaluation index system.
Target layerCriterion layerIndex layerUnitIndicator codeIndex propertyEvaluation index system of urban green and low-carbon development competitiveness of Beijing-Tianjin-Hebei citiesPressure subsystemPopulation density人·Km−2P1(−)Natural population growth rate%P2(−)Ten thousand yuan GDP energy consumptiont·yuan−1P3(−)Industrial sulfur dioxide emissions of ten thousand yuan GDPt·yuan−1P4(−)Discharge of industrial wastewater from ten thousand yuan GDPt·yuan−1P5(−)Ten thousand yuan GDP water consumptionm3·yuan−1P6(−)Industrial dust emission of ten thousand yuan GDPt·yuan−1P7(−)State subsystemUrban green technology innovation ability%S1(+)Annual mean value of urban PM2.5 concentrationμg·m−3S2(−)Annual mean value of urban SO2 concentrationμg·m−3S3(−)Annual mean value of urban NO2 concentrationμg·m−3S4(−)Proportion of per capita carbon emissions%S5(−)Pollution rate of urban water resources%S6(−)Urbanization rate%S7(−)Response subsystemPer capita green area coverage%R1(+)The proportion of the output value of the tertiary industry in GDP%R2(+)Comprehensive utilization rate of industrial solid waste%R3(+)Centralized treatment rate of municipal sewage%R4(+)Harmless treatment rate of municipal solid waste%R5(+)Proportion of investment in environment, water conservancy and public facilities%R6(+)Growth rate of per capita consumption of urban green low-carbon products%R7(+)Note. (+) indicates that the indicator has a positive impact on the target layer; (−) indicates that the indicator has a negative impact on the target layer.


## 3.3. Flow Chart and Data Source

The research idea and process of this artical is shown in Figure2). The study takes the panel data of Beijing, Tianjin, Shijiazhuang, Chengde, Zhangjiakou, Qinhuangdao, Tangshan, Langfang, Baoding, Cangzhou, Hengshui, Xingtai, and Handan from 2010 to 2019, as samples to ensure the reliability and authority of the data. The research data come from China Statistical Yearbook, China Science and Technology Statistical Yearbook, China Energy Statistical Yearbook, China Environmental Statistical Yearbook, Beijing, Tianjin, and Hebei Statistical Yearbooks, statistical bulletins, and environmental bulletins from 2011 to 2020. The lack of data in individual years is supplemented by mobile interpolation.Figure 2
Flow chart of step guidance for research and analysis.


## 4. Results and Analysis

Based on the statistical panel data, through the above formula, the comprehensive evaluation index of urban green and low-carbon development competitiveness of 13 cities in Beijing-Tianjin-Hebei from 2010 to 2019 can be calculated (see Table3). According to Table 3, the comprehensive evaluation index of urban green and low-carbon development competitiveness in the Beijing-Tianjin-Hebei region shows a fluctuating upward trend as a whole from 2010 to 2019. Among them, Beijing, Zhangjiakou, and Tianjin are the top three in the comprehensive evaluation index, while Hengshui, Handan, and Xingtai are in the bottom three.Table 3
Evaluation results of urban green and low-carbon development competitiveness.
YearProjectBJTJSJZCDZJKQHDTSLFBDCZHSXTHD2010Evaluation index0.620.570.320.470.490.460.380.430.350.520.340.180.27Ranking121154687931013122011Evaluation index0.640.610.360.490.530.510.420.480.380.540.370.190.33Ranking121164587931013122012Evaluation index0.680.590.410.480.580.570.400.520.420.510.390.230.35Ranking129734105861113122013Evaluation index0.740.540.440.510.610.580.450.570.460.560.430.270.28Ranking161072394851113122014Evaluation index0.720.560.420.580.630.610.410.550.430.550.410.290.29Ranking1584239676910102015Evaluation index0.760.580.460.620.590.570.490.520.460.540.440.250.37Ranking14923587961012112016Evaluation index0.840.620.470.510.630.610.460.530.470.520.410.310.37Ranking13872495861012112017Evaluation index0.820.570.520.620.650.620.470.590.490.560.450.290.36Ranking15732394861012112018Evaluation index0.860.640.490.540.670.650.480.560.450.530.420.270.41Ranking148623951071113122019Evaluation index0.710.580.480.640.670.640.430.580.470.590.460.330.39Ranking156323957481110Evaluation index0.740.590.440.550.610.580.440.530.440.540.410.260.34Comprehensive ranking138524878691110With regard to the classification of the evaluation index of urban green and low-carbon development competitiveness, the study draws lessons from the practices of previous scholars [30] and combines the results of the evaluation index into five levels: very competitive (0.64–0.75), strong competitiveness (0.52–0.63), medium competitiveness (0.40–0.51), weak competitiveness (0.28–0.39), and uncompetitive (0.15–0.27). Analysis from two dimensions, through the horizontal comparison with other cities, we can find out the differences in the competitiveness of green and low-carbon development of each city. Through the vertical comparison with different years, we can get the development trend of green and low-carbon development competitiveness of each city.


### 4.1. Analysis on the Changing Trend of the Urban Green and Low-Carbon Development Competitiveness Index

In order to further study the change trend of the urban green low-carbon development competitiveness index of cities in the Beijing-Tianjin-Hebei region from 2010 to 2019, draw the line chart of the urban green low-carbon development competitiveness index of Beijing-Tianjin-Hebei cities (see Figure3). According to Figure 3, the evaluation index of urban green and low-carbon development competitiveness of Beijing-Tianjin-Hebei cities in 2010–2019 shows a fluctuating upward trend.Figure 3
Trend chart of the urban green and low-carbon development competitiveness index.Among them, Zhangjiakou, Tangshan, Chengde, and Qinhuangdao had higher growth rates in 2018, up 9.8%, 6.7%, 5.9%, and 12.1%, respectively, over 2013. These cities are concentrated in the northern Beijing-Tianjin-Hebei region and play a good supporting role in improving the quality of urban green and low-carbon development in the northern region. However, the comprehensive index of urban green and low-carbon development competitiveness of Beijing, Tianjin, and Xingtai cities is declining in the fluctuation. Among them, the evaluation index of Tianjin and Beijing in 2019 dropped sharply by 9.4% and 17.4% compared with 2018, and has been in a fluctuating downward trend in the past three years. The green and low-carbon development of these three cities should be paid great attention to. In addition, the competitiveness of urban green and low-carbon development of neighboring cities is relatively close, which indicates that there may be a certain spatial correlation in the competitiveness of urban green and low-carbon development. In order to explore this hypothesis, the spatial correlation analysis of the urban green low-carbon development competitiveness index of Beijing-Tianjin-Hebei cities is carried out in Section3.3.


### 4.2. Analysis on the Spatio-Temporal Evolution of Urban Green and Low-Carbon Development Competitiveness

In order to express the spatio-temporal change trend of urban green and low-carbon development competitiveness more intuitively, 2013 and 2019 are selected as representatives, and ArcGIS10.2 is used to make spatial difference bitmap (see Figure4). Overall, the spatial pattern of urban green and low-carbon development competitiveness of Beijing-Tianjin-Hebei cities changed greatly from 2010 to 2019, showing a spatial pattern of “high in the north, low in the south, and local jump”. In the five levels of competitiveness, the overall upper-low level gap between Beijing-Tianjin-Hebei cities is gradually narrowing, which is closely related to the implementation of the national strategy for the coordinated development of Beijing-Tianjin-Hebei integration.Figure 4
Spatio-temporal difference map of urban green and low-carbon development competitiveness.Specifically, in terms of a very competitive level, it was only Beijing in 2013, while by 2019 there were Beijing, Zhangjiakou, Chengde, and Qinhuangdao, an increase of 23.08% over 2013. In terms of a strong competitiveness level, there are Zhangjiakou, Qinhuangdao, Langfang, Cangzhou, and Tianjin in 2013, and Langfang, Cangzhou, and Tianjin in 2019, 15.38% less than in 2013. In terms of a medium competitiveness level, there are Chengde, Tangshan, Baoding, Shijiazhuang, and Hengshui in 2013, and Tangshan, Baoding, Shijiazhuang, and Hengshui in 2019, 7.69% less than in 2013. In terms of a weak competitiveness level, there are Handan in 2013, while by 2019 there were Handan and Xingtai, an increase of 7.69% over 2013. In terms of an uncompetitive level, there was Xingtai in 2013, and there will be no cities at this level by 2019, 7.69% less than in 2013.


### 4.3. Spatial Autocorrelation Analysis of Urban Green and Low-Carbon Development in Beijing-Tianjin-Hebei Cities

#### 4.3.1. Global Spatial Autocorrelation Analysis

According to the spatial difference map of urban green and low-carbon development competitiveness of Beijing-Tianjin-Hebei cities, we can see that the comprehensive evaluation index of adjacent cities is relatively close. In order to verify whether there is spatial correlation, Stata software is used to test the green low-carbon development competitiveness index of Beijing-Tianjin-Hebei cities from 2010 to 2019, and the global Moran’s I of the green low-carbon development competitiveness index of Beijing-Tianjin-Hebei cities is calculated (see Table4). The results show that: (1) From 2010 to 2019, the global Moran’s I of Beijing-Tianjin-Hebei cities was positive, and the P value passed the 1% significant level test. This shows that the comprehensive evaluation index of urban green and low-carbon development competitiveness of Beijing-Tianjin-Hebei cities has a positive spatial correlation, and the spatial agglomeration effect is significant. (2) During the study period, the global Moran`s I value first decreased and then increased with the passage of time, showing a “U-shaped” evolutionary fluctuation, indicating that the characteristics of urban spatial agglomeration in Beijing-Tianjin-Hebei showed an unstable distribution trend of evolutionary fluctuation. Specifically, Moran`s I showed a downward trend as a whole from 2010 to 2015. Investigate the reason? After consulting the relevant data, we can see that the Beijing-Tianjin-Hebei region developed rapidly from 2010 to 2015, showing an extensive economic growth model of “high input, high consumption, and high emissions”, the industrial structure was relatively single, and the level of urban green and low-carbon innovation technology was low. The serious “Matthew effect” in the development between cities and the “siphon effect” between Beijing and Tianjin as the core and the surrounding cities have increasingly led to the “polarization” of urban spatial agglomeration. From 2016 to 2019, Moran`s I began to show an upward trend. The survey found that in October 2015, the Fifth Plenary Session of the 18th CPC Central Committee put forward five major development concepts of “green, innovation, coordination, openness, and sharing”. We promoted the establishment of a green and low-carbon circular development industrial system, economic system, market-oriented green technology innovation system, and energy system, and advocated a green and low-carbon way of life. The Beijing-Tianjin-Hebei region has vigorously responded to the national policy by adjusting the industrial structure, optimizing the energy structure, changing the economic development model, promoting the transformation and upgrading of traditional industries, and vigorously developing strategic emerging industries. It has promoted the formation of a green and low-carbon industrial system with “low input, low consumption, and low emissions”. And in recent years, the outline of Beijing-Tianjin-Hebei coordinated development plan has been officially adopted. Relying on their own development advantages, Beijing and Tianjin continue to increase spatial radiation, overall coordination, and spatial spillover effects. Efforts have been made to form a new pattern of coordinated development of Beijing-Tianjin-Hebei in the same direction, integrated measures, complementary advantages, and mutual benefit, which has promoted the continuous improvement of the urban positive spatial agglomeration effect.Table 4
The Global Moran’s I of urban green low-carbon development competitiveness.
Year2010201120122013201420152016201720182019Moran’s I0.2130.2090.1960.3750.2740.1940.2680.3220.4940.561Z value2.0641.9921.9872.5262.2841.9722.2762.3782.8543.442P value0.0030.0050.0070.0010.0040.0070.0040.0040.0030.001


#### 4.3.2. Local Spatial Autocorrelation Analysis

In order to further reveal the spatial agglomeration characteristics of the competitiveness index of urban green low-carbon development in Beijing-Tianjin-Hebei cities, 2013 and 2019 were selected as examples for local spatial autocorrelation analysis (see Figure5). It reflects the green and low-carbon development competitiveness of 13 cities in Beijing-Tianjin-Hebei and the agglomeration or differentiation characteristics of their neighboring cities. Among them, the distribution in the first quadrant (HH) and the third quadrant (LL) shows that its spatial distribution has strong positive correlation and spatial aggregation. The distribution in the second quadrant (LH) and the fourth quadrant (HL) shows that its spatial distribution has strong negative correlation and spatial differentiation.Figure 5
Scattered map of urban green and low-carbon development competitiveness in 2013 and 2019.As can be seen from Figure5, during the study period, the local Moran’s I was positive and passed the 1% significant level test. It shows that the 13 cities in the Beijing-Tianjin-Hebei region show the characteristics of positive spatial agglomeration as a whole, and the number of cities in the first and third quadrant shows an increasing trend. Specifically, (1) compared with 2013, the green and low-carbon development competitiveness index of Beijing-Tianjin-Hebei cities in 2019 is 15.38% higher than that in the first and third quadrants. This shows that there is a strong positive correlation and spatial agglomeration in the spatial distribution of the urban green low-carbon development competitiveness index in these two quadrants. (2) The cities distributed in the second quadrant (LH) and the fourth quadrant (HL) reduce 15.38%. This reflects that the competitiveness index of urban green and low-carbon development of some cities in this region has strong negative correlation and differentiation characteristics, and the competitiveness index of urban green and low-carbon development has the phenomenon of “polarization”. Especially in the second quadrant (LH) cities, how to undertake industrial transfer in high-value areas on the premise of ensuring the improvement of their competitiveness index of urban green and low-carbon development, and at the same time strengthen environmental regulations related to green and low-carbon development is the key to prevent it from becoming a “pollution paradise”.


### 4.4. Analysis of Key Obstacle Factors

On the basis of the above overall evaluation, formula (8) is applied to test the degree of the factors restricting the improvement of urban green and low-carbon development competitiveness of Beijing-Tianjin-Hebei cities and the main obstacle factors are discussed. Taking 2013, 2016, and 2019 as examples, the results are shown (see Table 5). Due to too many indicators, only the first three obstacle factors are listed for description and analysis.Table 5
Obstacle degree of the urban green and low-carbon development competitiveness index.
CityItem2013 year2016 year2019 year123123123BJObstacle indexS2P3P1P3S2P1P3P1S1Obstacle degree14.213.812.212.212.111.411.810.99.7TJObstacle indexP7P4R2P7P4S2P4P7S1Obstacle degree17.312.79.713.510.78.59.79.57.8SJZObstacle indexS2S1R2S2S1R2R2S2S1Obstacle degree13.09.89.412.38.98.210.810.47.3CDObstacle indexR2P3P7R2P7P4R2P4P7Obstacle degree21.718.815.819.814.913.416.412.79.8ZJKObstacle indexP3R2P7P4R2P7R2P7P3Obstacle degree22.520.818.321.519.318.616.913.511.3QHDObstacle indexP7R2P3P7R2S4R2P7S4Obstacle degree18.414.211.716.712.910.611.411.29.5TSObstacle indexP3R2S2R2P2S2R2P3S4Obstacle degree15.313.89.712.411.88.710.39.58.5LFObstacle indexP3S2P7R2S2P2R2P7P2Obstacle degree11.911.610.316.710.610.314.410.110.1BDObstacle indexR2S2S7S2R2S1R2S2S1Obstacle degree13.912.48.711.411.010.39.59.58.7CZObstacle indexR2P3P2R2S2P2R2S2P3Obstacle degree15.712.511.712.612.410.611.510.810.6HSObstacle indexP7R2S2P7S2R2P7R2S3Obstacle degree17.611.810.415.510.110.012.211.210.3XTObstacle indexP7S2R2P7S2R2P7R2S2Obstacle degree16.311.211.015.511.110.414.311.310.7HDObstacle indexP7P3R2P7S2R2P7P3R2Obstacle degree19.716.89.916.312.89.111.410.48.9As can be seen from Table5, on the whole, the proportion of the output value of the tertiary industry to GDP (R2), the annual average concentration of urban PM2.5 (S2), and the industrial soot emission of ten thousand yuan of GDP (P7) are the main obstacles restricting the improvement of urban green and low-carbon development competitiveness of Beijing-Tianjin-Hebei cities.In terms of stages, the frequencies of R2, S2, P7, and P3 in 2013 were 11, 7, 8, and 6, respectively, covering more than half of the cities, indicating that the proportion of the tertiary industry output value in GDP, energy consumption of ten thousand yuan GDP, urban average annual PM2.5 concentration, and industrial soot emissions of ten thousand yuan GDP were the main factors restricting the improvement of urban green and low-carbon development competitiveness of Beijing-Tianjin-Hebei cities at that time. In 2016, the frequencies of indicators R2, S2, and P7 are 11, 10, and 7, respectively. The proportion of the tertiary industry output value in GDP, the annual average concentration of urban PM2.5, and industrial soot emissions of ten thousand yuan of GDP have become the main factors restricting the improvement of urban green and low-carbon development competitiveness of Beijing-Tianjin-Hebei cities. In 2019, the frequencies of indicators R2, S2, P7, and P3 were 11, 4, 8, and 5, respectively. With the promotion of iron and steel and other high energy-consuming industries to eliminate excess capacity, the energy consumption of ten thousand yuan of GDP is declining, and the effect of energy saving and emission reduction is obvious. However, the proportion of the output value of the tertiary industry in GDP is still the most important factor hindering the improvement of the competitiveness of urban green and low-carbon development, indicating that the adjustment and upgrading of the industrial structure is imminent, and there is an urgent need to improve the level of urban green technology innovation.Specifically, the annual average PM2.5 concentration, population density, and energy consumption of ten thousand yuan of GDP in Beijing are still the main factors hindering the improvement of competitiveness of green and low-carbon development. Therefore, Beijing should adhere to the strategy of clean energy, and speed up the development of the modern service industry. Doing a good job in the transfer of industry and resident population to Tianjin and Hebei. Relieve the pressure of urban population, reduce energy consumption, and thus enhance the competitiveness of green and low-carbon development. Industrial soot emissions, SO2 concentration and the proportion of the tertiary industry are still the key factors restricting the competitiveness of green and low-carbon development in Tianjin. Therefore, Tianjin should speed up the transformation of the energy structure, adjust the industrial structure, and promote the transformation and upgrading of the economic development model with the help of the development of the digital economy. The resistance of green and low-carbon development in Zhangjiakou is concentrated in the concentration of SO2 pollutants. Therefore, the treatment of bulk coal should be taken as the primary task. The proportion of the output value of the tertiary industry in GDP in Chengde, Tangshan, Langfang, and Cangzhou is the primary obstacle to improve the competitiveness of green and low-carbon development. It should actively cooperate with Beijing to complete the industrial transfer, and at the same time take industrial transformation upgrading and improving the scale of service-oriented industries as the main work plan. In 2017, the PM2.5 concentrations of Shijiazhuang and Baoding were 86 μg·m3 and 84 μg·m3, respectively, ranking in the forefront of 13 cities. These two cities should take the comprehensive control of coal burning and industrial pollution as the starting point to carry out special renovation and control. Qinhuangdao, Hengshui, Xingtai, and Handan are mainly subject to ten thousand yuan of GDP industrial soot, emissions seriously exceed the standard, hindering the competitiveness of green and low-carbon development. Therefore, it is necessary to strengthen industrial soot emission supervision and industrial production end control and give full play to the emission reduction effect of green technology innovation. We will gradually eliminate backward industries, vigorously develop strategic emerging industries, such as green and new energy, and strive to pursue green, and high-quality economic development.


## 4.1. Analysis on the Changing Trend of the Urban Green and Low-Carbon Development Competitiveness Index

In order to further study the change trend of the urban green low-carbon development competitiveness index of cities in the Beijing-Tianjin-Hebei region from 2010 to 2019, draw the line chart of the urban green low-carbon development competitiveness index of Beijing-Tianjin-Hebei cities (see Figure3). According to Figure 3, the evaluation index of urban green and low-carbon development competitiveness of Beijing-Tianjin-Hebei cities in 2010–2019 shows a fluctuating upward trend.Figure 3
Trend chart of the urban green and low-carbon development competitiveness index.Among them, Zhangjiakou, Tangshan, Chengde, and Qinhuangdao had higher growth rates in 2018, up 9.8%, 6.7%, 5.9%, and 12.1%, respectively, over 2013. These cities are concentrated in the northern Beijing-Tianjin-Hebei region and play a good supporting role in improving the quality of urban green and low-carbon development in the northern region. However, the comprehensive index of urban green and low-carbon development competitiveness of Beijing, Tianjin, and Xingtai cities is declining in the fluctuation. Among them, the evaluation index of Tianjin and Beijing in 2019 dropped sharply by 9.4% and 17.4% compared with 2018, and has been in a fluctuating downward trend in the past three years. The green and low-carbon development of these three cities should be paid great attention to. In addition, the competitiveness of urban green and low-carbon development of neighboring cities is relatively close, which indicates that there may be a certain spatial correlation in the competitiveness of urban green and low-carbon development. In order to explore this hypothesis, the spatial correlation analysis of the urban green low-carbon development competitiveness index of Beijing-Tianjin-Hebei cities is carried out in Section3.3.


## 4.2. Analysis on the Spatio-Temporal Evolution of Urban Green and Low-Carbon Development Competitiveness

In order to express the spatio-temporal change trend of urban green and low-carbon development competitiveness more intuitively, 2013 and 2019 are selected as representatives, and ArcGIS10.2 is used to make spatial difference bitmap (see Figure4). Overall, the spatial pattern of urban green and low-carbon development competitiveness of Beijing-Tianjin-Hebei cities changed greatly from 2010 to 2019, showing a spatial pattern of “high in the north, low in the south, and local jump”. In the five levels of competitiveness, the overall upper-low level gap between Beijing-Tianjin-Hebei cities is gradually narrowing, which is closely related to the implementation of the national strategy for the coordinated development of Beijing-Tianjin-Hebei integration.Figure 4
Spatio-temporal difference map of urban green and low-carbon development competitiveness.Specifically, in terms of a very competitive level, it was only Beijing in 2013, while by 2019 there were Beijing, Zhangjiakou, Chengde, and Qinhuangdao, an increase of 23.08% over 2013. In terms of a strong competitiveness level, there are Zhangjiakou, Qinhuangdao, Langfang, Cangzhou, and Tianjin in 2013, and Langfang, Cangzhou, and Tianjin in 2019, 15.38% less than in 2013. In terms of a medium competitiveness level, there are Chengde, Tangshan, Baoding, Shijiazhuang, and Hengshui in 2013, and Tangshan, Baoding, Shijiazhuang, and Hengshui in 2019, 7.69% less than in 2013. In terms of a weak competitiveness level, there are Handan in 2013, while by 2019 there were Handan and Xingtai, an increase of 7.69% over 2013. In terms of an uncompetitive level, there was Xingtai in 2013, and there will be no cities at this level by 2019, 7.69% less than in 2013.


## 4.3. Spatial Autocorrelation Analysis of Urban Green and Low-Carbon Development in Beijing-Tianjin-Hebei Cities

### 4.3.1. Global Spatial Autocorrelation Analysis

According to the spatial difference map of urban green and low-carbon development competitiveness of Beijing-Tianjin-Hebei cities, we can see that the comprehensive evaluation index of adjacent cities is relatively close. In order to verify whether there is spatial correlation, Stata software is used to test the green low-carbon development competitiveness index of Beijing-Tianjin-Hebei cities from 2010 to 2019, and the global Moran’s I of the green low-carbon development competitiveness index of Beijing-Tianjin-Hebei cities is calculated (see Table4). The results show that: (1) From 2010 to 2019, the global Moran’s I of Beijing-Tianjin-Hebei cities was positive, and the P value passed the 1% significant level test. This shows that the comprehensive evaluation index of urban green and low-carbon development competitiveness of Beijing-Tianjin-Hebei cities has a positive spatial correlation, and the spatial agglomeration effect is significant. (2) During the study period, the global Moran`s I value first decreased and then increased with the passage of time, showing a “U-shaped” evolutionary fluctuation, indicating that the characteristics of urban spatial agglomeration in Beijing-Tianjin-Hebei showed an unstable distribution trend of evolutionary fluctuation. Specifically, Moran`s I showed a downward trend as a whole from 2010 to 2015. Investigate the reason? After consulting the relevant data, we can see that the Beijing-Tianjin-Hebei region developed rapidly from 2010 to 2015, showing an extensive economic growth model of “high input, high consumption, and high emissions”, the industrial structure was relatively single, and the level of urban green and low-carbon innovation technology was low. The serious “Matthew effect” in the development between cities and the “siphon effect” between Beijing and Tianjin as the core and the surrounding cities have increasingly led to the “polarization” of urban spatial agglomeration. From 2016 to 2019, Moran`s I began to show an upward trend. The survey found that in October 2015, the Fifth Plenary Session of the 18th CPC Central Committee put forward five major development concepts of “green, innovation, coordination, openness, and sharing”. We promoted the establishment of a green and low-carbon circular development industrial system, economic system, market-oriented green technology innovation system, and energy system, and advocated a green and low-carbon way of life. The Beijing-Tianjin-Hebei region has vigorously responded to the national policy by adjusting the industrial structure, optimizing the energy structure, changing the economic development model, promoting the transformation and upgrading of traditional industries, and vigorously developing strategic emerging industries. It has promoted the formation of a green and low-carbon industrial system with “low input, low consumption, and low emissions”. And in recent years, the outline of Beijing-Tianjin-Hebei coordinated development plan has been officially adopted. Relying on their own development advantages, Beijing and Tianjin continue to increase spatial radiation, overall coordination, and spatial spillover effects. Efforts have been made to form a new pattern of coordinated development of Beijing-Tianjin-Hebei in the same direction, integrated measures, complementary advantages, and mutual benefit, which has promoted the continuous improvement of the urban positive spatial agglomeration effect.Table 4
The Global Moran’s I of urban green low-carbon development competitiveness.
Year2010201120122013201420152016201720182019Moran’s I0.2130.2090.1960.3750.2740.1940.2680.3220.4940.561Z value2.0641.9921.9872.5262.2841.9722.2762.3782.8543.442P value0.0030.0050.0070.0010.0040.0070.0040.0040.0030.001


### 4.3.2. Local Spatial Autocorrelation Analysis

In order to further reveal the spatial agglomeration characteristics of the competitiveness index of urban green low-carbon development in Beijing-Tianjin-Hebei cities, 2013 and 2019 were selected as examples for local spatial autocorrelation analysis (see Figure5). It reflects the green and low-carbon development competitiveness of 13 cities in Beijing-Tianjin-Hebei and the agglomeration or differentiation characteristics of their neighboring cities. Among them, the distribution in the first quadrant (HH) and the third quadrant (LL) shows that its spatial distribution has strong positive correlation and spatial aggregation. The distribution in the second quadrant (LH) and the fourth quadrant (HL) shows that its spatial distribution has strong negative correlation and spatial differentiation.Figure 5
Scattered map of urban green and low-carbon development competitiveness in 2013 and 2019.As can be seen from Figure5, during the study period, the local Moran’s I was positive and passed the 1% significant level test. It shows that the 13 cities in the Beijing-Tianjin-Hebei region show the characteristics of positive spatial agglomeration as a whole, and the number of cities in the first and third quadrant shows an increasing trend. Specifically, (1) compared with 2013, the green and low-carbon development competitiveness index of Beijing-Tianjin-Hebei cities in 2019 is 15.38% higher than that in the first and third quadrants. This shows that there is a strong positive correlation and spatial agglomeration in the spatial distribution of the urban green low-carbon development competitiveness index in these two quadrants. (2) The cities distributed in the second quadrant (LH) and the fourth quadrant (HL) reduce 15.38%. This reflects that the competitiveness index of urban green and low-carbon development of some cities in this region has strong negative correlation and differentiation characteristics, and the competitiveness index of urban green and low-carbon development has the phenomenon of “polarization”. Especially in the second quadrant (LH) cities, how to undertake industrial transfer in high-value areas on the premise of ensuring the improvement of their competitiveness index of urban green and low-carbon development, and at the same time strengthen environmental regulations related to green and low-carbon development is the key to prevent it from becoming a “pollution paradise”.


## 4.3.1. Global Spatial Autocorrelation Analysis

According to the spatial difference map of urban green and low-carbon development competitiveness of Beijing-Tianjin-Hebei cities, we can see that the comprehensive evaluation index of adjacent cities is relatively close. In order to verify whether there is spatial correlation, Stata software is used to test the green low-carbon development competitiveness index of Beijing-Tianjin-Hebei cities from 2010 to 2019, and the global Moran’s I of the green low-carbon development competitiveness index of Beijing-Tianjin-Hebei cities is calculated (see Table4). The results show that: (1) From 2010 to 2019, the global Moran’s I of Beijing-Tianjin-Hebei cities was positive, and the P value passed the 1% significant level test. This shows that the comprehensive evaluation index of urban green and low-carbon development competitiveness of Beijing-Tianjin-Hebei cities has a positive spatial correlation, and the spatial agglomeration effect is significant. (2) During the study period, the global Moran`s I value first decreased and then increased with the passage of time, showing a “U-shaped” evolutionary fluctuation, indicating that the characteristics of urban spatial agglomeration in Beijing-Tianjin-Hebei showed an unstable distribution trend of evolutionary fluctuation. Specifically, Moran`s I showed a downward trend as a whole from 2010 to 2015. Investigate the reason? After consulting the relevant data, we can see that the Beijing-Tianjin-Hebei region developed rapidly from 2010 to 2015, showing an extensive economic growth model of “high input, high consumption, and high emissions”, the industrial structure was relatively single, and the level of urban green and low-carbon innovation technology was low. The serious “Matthew effect” in the development between cities and the “siphon effect” between Beijing and Tianjin as the core and the surrounding cities have increasingly led to the “polarization” of urban spatial agglomeration. From 2016 to 2019, Moran`s I began to show an upward trend. The survey found that in October 2015, the Fifth Plenary Session of the 18th CPC Central Committee put forward five major development concepts of “green, innovation, coordination, openness, and sharing”. We promoted the establishment of a green and low-carbon circular development industrial system, economic system, market-oriented green technology innovation system, and energy system, and advocated a green and low-carbon way of life. The Beijing-Tianjin-Hebei region has vigorously responded to the national policy by adjusting the industrial structure, optimizing the energy structure, changing the economic development model, promoting the transformation and upgrading of traditional industries, and vigorously developing strategic emerging industries. It has promoted the formation of a green and low-carbon industrial system with “low input, low consumption, and low emissions”. And in recent years, the outline of Beijing-Tianjin-Hebei coordinated development plan has been officially adopted. Relying on their own development advantages, Beijing and Tianjin continue to increase spatial radiation, overall coordination, and spatial spillover effects. Efforts have been made to form a new pattern of coordinated development of Beijing-Tianjin-Hebei in the same direction, integrated measures, complementary advantages, and mutual benefit, which has promoted the continuous improvement of the urban positive spatial agglomeration effect.Table 4
The Global Moran’s I of urban green low-carbon development competitiveness.
Year2010201120122013201420152016201720182019Moran’s I0.2130.2090.1960.3750.2740.1940.2680.3220.4940.561Z value2.0641.9921.9872.5262.2841.9722.2762.3782.8543.442P value0.0030.0050.0070.0010.0040.0070.0040.0040.0030.001


## 4.3.2. Local Spatial Autocorrelation Analysis

In order to further reveal the spatial agglomeration characteristics of the competitiveness index of urban green low-carbon development in Beijing-Tianjin-Hebei cities, 2013 and 2019 were selected as examples for local spatial autocorrelation analysis (see Figure5). It reflects the green and low-carbon development competitiveness of 13 cities in Beijing-Tianjin-Hebei and the agglomeration or differentiation characteristics of their neighboring cities. Among them, the distribution in the first quadrant (HH) and the third quadrant (LL) shows that its spatial distribution has strong positive correlation and spatial aggregation. The distribution in the second quadrant (LH) and the fourth quadrant (HL) shows that its spatial distribution has strong negative correlation and spatial differentiation.Figure 5
Scattered map of urban green and low-carbon development competitiveness in 2013 and 2019.As can be seen from Figure5, during the study period, the local Moran’s I was positive and passed the 1% significant level test. It shows that the 13 cities in the Beijing-Tianjin-Hebei region show the characteristics of positive spatial agglomeration as a whole, and the number of cities in the first and third quadrant shows an increasing trend. Specifically, (1) compared with 2013, the green and low-carbon development competitiveness index of Beijing-Tianjin-Hebei cities in 2019 is 15.38% higher than that in the first and third quadrants. This shows that there is a strong positive correlation and spatial agglomeration in the spatial distribution of the urban green low-carbon development competitiveness index in these two quadrants. (2) The cities distributed in the second quadrant (LH) and the fourth quadrant (HL) reduce 15.38%. This reflects that the competitiveness index of urban green and low-carbon development of some cities in this region has strong negative correlation and differentiation characteristics, and the competitiveness index of urban green and low-carbon development has the phenomenon of “polarization”. Especially in the second quadrant (LH) cities, how to undertake industrial transfer in high-value areas on the premise of ensuring the improvement of their competitiveness index of urban green and low-carbon development, and at the same time strengthen environmental regulations related to green and low-carbon development is the key to prevent it from becoming a “pollution paradise”.


## 4.4. Analysis of Key Obstacle Factors

On the basis of the above overall evaluation, formula (8) is applied to test the degree of the factors restricting the improvement of urban green and low-carbon development competitiveness of Beijing-Tianjin-Hebei cities and the main obstacle factors are discussed. Taking 2013, 2016, and 2019 as examples, the results are shown (see Table 5). Due to too many indicators, only the first three obstacle factors are listed for description and analysis.Table 5
Obstacle degree of the urban green and low-carbon development competitiveness index.
CityItem2013 year2016 year2019 year123123123BJObstacle indexS2P3P1P3S2P1P3P1S1Obstacle degree14.213.812.212.212.111.411.810.99.7TJObstacle indexP7P4R2P7P4S2P4P7S1Obstacle degree17.312.79.713.510.78.59.79.57.8SJZObstacle indexS2S1R2S2S1R2R2S2S1Obstacle degree13.09.89.412.38.98.210.810.47.3CDObstacle indexR2P3P7R2P7P4R2P4P7Obstacle degree21.718.815.819.814.913.416.412.79.8ZJKObstacle indexP3R2P7P4R2P7R2P7P3Obstacle degree22.520.818.321.519.318.616.913.511.3QHDObstacle indexP7R2P3P7R2S4R2P7S4Obstacle degree18.414.211.716.712.910.611.411.29.5TSObstacle indexP3R2S2R2P2S2R2P3S4Obstacle degree15.313.89.712.411.88.710.39.58.5LFObstacle indexP3S2P7R2S2P2R2P7P2Obstacle degree11.911.610.316.710.610.314.410.110.1BDObstacle indexR2S2S7S2R2S1R2S2S1Obstacle degree13.912.48.711.411.010.39.59.58.7CZObstacle indexR2P3P2R2S2P2R2S2P3Obstacle degree15.712.511.712.612.410.611.510.810.6HSObstacle indexP7R2S2P7S2R2P7R2S3Obstacle degree17.611.810.415.510.110.012.211.210.3XTObstacle indexP7S2R2P7S2R2P7R2S2Obstacle degree16.311.211.015.511.110.414.311.310.7HDObstacle indexP7P3R2P7S2R2P7P3R2Obstacle degree19.716.89.916.312.89.111.410.48.9As can be seen from Table5, on the whole, the proportion of the output value of the tertiary industry to GDP (R2), the annual average concentration of urban PM2.5 (S2), and the industrial soot emission of ten thousand yuan of GDP (P7) are the main obstacles restricting the improvement of urban green and low-carbon development competitiveness of Beijing-Tianjin-Hebei cities.In terms of stages, the frequencies of R2, S2, P7, and P3 in 2013 were 11, 7, 8, and 6, respectively, covering more than half of the cities, indicating that the proportion of the tertiary industry output value in GDP, energy consumption of ten thousand yuan GDP, urban average annual PM2.5 concentration, and industrial soot emissions of ten thousand yuan GDP were the main factors restricting the improvement of urban green and low-carbon development competitiveness of Beijing-Tianjin-Hebei cities at that time. In 2016, the frequencies of indicators R2, S2, and P7 are 11, 10, and 7, respectively. The proportion of the tertiary industry output value in GDP, the annual average concentration of urban PM2.5, and industrial soot emissions of ten thousand yuan of GDP have become the main factors restricting the improvement of urban green and low-carbon development competitiveness of Beijing-Tianjin-Hebei cities. In 2019, the frequencies of indicators R2, S2, P7, and P3 were 11, 4, 8, and 5, respectively. With the promotion of iron and steel and other high energy-consuming industries to eliminate excess capacity, the energy consumption of ten thousand yuan of GDP is declining, and the effect of energy saving and emission reduction is obvious. However, the proportion of the output value of the tertiary industry in GDP is still the most important factor hindering the improvement of the competitiveness of urban green and low-carbon development, indicating that the adjustment and upgrading of the industrial structure is imminent, and there is an urgent need to improve the level of urban green technology innovation.Specifically, the annual average PM2.5 concentration, population density, and energy consumption of ten thousand yuan of GDP in Beijing are still the main factors hindering the improvement of competitiveness of green and low-carbon development. Therefore, Beijing should adhere to the strategy of clean energy, and speed up the development of the modern service industry. Doing a good job in the transfer of industry and resident population to Tianjin and Hebei. Relieve the pressure of urban population, reduce energy consumption, and thus enhance the competitiveness of green and low-carbon development. Industrial soot emissions, SO2 concentration and the proportion of the tertiary industry are still the key factors restricting the competitiveness of green and low-carbon development in Tianjin. Therefore, Tianjin should speed up the transformation of the energy structure, adjust the industrial structure, and promote the transformation and upgrading of the economic development model with the help of the development of the digital economy. The resistance of green and low-carbon development in Zhangjiakou is concentrated in the concentration of SO2 pollutants. Therefore, the treatment of bulk coal should be taken as the primary task. The proportion of the output value of the tertiary industry in GDP in Chengde, Tangshan, Langfang, and Cangzhou is the primary obstacle to improve the competitiveness of green and low-carbon development. It should actively cooperate with Beijing to complete the industrial transfer, and at the same time take industrial transformation upgrading and improving the scale of service-oriented industries as the main work plan. In 2017, the PM2.5 concentrations of Shijiazhuang and Baoding were 86 μg·m3 and 84 μg·m3, respectively, ranking in the forefront of 13 cities. These two cities should take the comprehensive control of coal burning and industrial pollution as the starting point to carry out special renovation and control. Qinhuangdao, Hengshui, Xingtai, and Handan are mainly subject to ten thousand yuan of GDP industrial soot, emissions seriously exceed the standard, hindering the competitiveness of green and low-carbon development. Therefore, it is necessary to strengthen industrial soot emission supervision and industrial production end control and give full play to the emission reduction effect of green technology innovation. We will gradually eliminate backward industries, vigorously develop strategic emerging industries, such as green and new energy, and strive to pursue green, and high-quality economic development.


## 5. Conclusions and Revelation

### 5.1. Conclusion

Based on the panel data of 13 prefecture-level cities in Beijing-Tianjin-Hebei urban agglomeration from 2010 to 2019. This paper constructs a PSR conceptual evaluation model based on combined weight to evaluate the competitiveness level of green and low-carbon development of each city. Combined with exploratory spatial data analysis and the obstacle model, the spatial effects and obstacle factors are revealed. The results show the following:(1)
On the whole, the evaluation index of urban green and low-carbon development competitiveness in the Beijing-Tianjin-Hebei region shows a fluctuating upward trend from 2010 to 2019. Among them, the average annual growth rate of Zhangjiakou, Tangshan, Chengde, and Qinhuangdao is higher. The comprehensive indexes of green and low-carbon development competitiveness of Beijing, Tianjin, and Xingtai cities are declining in the fluctuation. Among them, the evaluation index of Tianjin and Beijing dropped 9.4% and 17.4%, respectively, in 2019 compared with 2018. The rest of the cities basically maintained a steady and a fluctuating upward trend.(2)
In terms of spatial disequilibrium analysis, the evolution and distribution characteristics of the spatio-temporal pattern show that the spatial pattern of urban green and low-carbon development competitiveness of Beijing-Tianjin-Hebei cities has changed greatly from 2010 to 2019, showing an overall spatial distribution pattern of “high in the north, low in the south, and local jump”. Among them, in the five levels of competitiveness, the number of cities with strong competitiveness in the Beijing-Tianjin-Hebei urban agglomeration increased by 23.08%, while the number of cities with weak competitiveness decreased by 7.69%. And on the whole, the level gap of green and low-carbon development competitiveness of Beijing-Tianjin-Hebei urban agglomeration is gradually narrowing.(3)
With the application of exploratory spatial data analysis, this paper analyzes the spatial autocorrelation characteristics of the green and low-carbon development of Beijing-Tianjin-Hebei urban agglomeration from the time and spatial dimensions, and reveals the evolution process of its spatial development pattern. The results show that the green low-carbon development of Beijing-Tianjin-Hebei urban agglomeration shows a positive autocorrelation in space, and the distribution characteristics of spatial agglomeration are significant. The local spatial autocorrelation regularity is obvious. The overall spatial pattern shows a development trend of “high in the north and low in the south”, and the gap between the south and the north is gradually narrowing. Therefore, the high and high agglomeration areas should continue to maintain a good momentum of development and play the role of technology diffusion and radiation. Low and low agglomeration areas should further strive for national policy support. Increasing investment in research and development. We should pay attention to the digestion and absorption of imported technology. Actively learning advanced experience and technology. In order to achieve corner overtaking, it is necessary to dock industrial gradient transfer, and we should make use of scientific and technological innovation to change the mode of economic development. High-low agglomeration areas and low-high agglomeration areas should strengthen cross-regional cooperation, enhance soft and hard power, and give full play to spatial spillover effects to improve the competitiveness of their own green and low-carbon development.(4)
In the aspect of obstacle factor analysis, due to the difference of urban geographical location and resource endowment, the factors restricting the competitiveness of urban green and low-carbon development are also different. Specifically, the annual average PM2.5 concentration, population density, and energy consumption of ten thousand of GDP in Beijing are still the main factors hindering the improvement of competitiveness of green and low-carbon development. Tianjin’s obstacle factors need to control industrial soot emissions, reduce SO2 concentration, and increase the proportion of the tertiary industry, which is still the main direction to improve the competitiveness of green and low-carbon development in Tianjin. The development resistance of green and low-carbon development in Zhangjiakou focuses on the concentration of SO2 pollutants, energy consumption of ten thousand of GDP, industrial soot emissions, and so on. The proportion of the output value of the tertiary industry in GDP in Chengde, Tangshan, Langfang, and Cangzhou is the primary obstacle factor restricting the improvement of urban green and low-carbon development competitiveness. The concentration of PM2.5 in Shijiazhuang and Baoding is in the forefront, so it is necessary to carry out special improvement work on the ecological environment. Qinhuangdao, Hengshui, Xingtai, and Handan are subject to excessive industrial soot emissions of ten thousand yuan of GDP, which seriously hinders the improvement of competitiveness of green and low-carbon development. To sum up, seizing the opportunity of the transfer of Beijing’s non-capital functions to Tianjin and Hebei, and making full use of their respective comparative advantages and misplaced development is the key to enhance the competitiveness of green and low-carbon development of Beijing-Tianjin-Hebei urban agglomeration [31].


### 5.2. Revelation

Through the above research, we can see that adjusting the industrial structure, accelerating green technology innovation, promoting industrial transformation, and upgrading are the only way to enhance the competitiveness of urban green and low-carbon development in Beijing-Tianjin-Hebei cities. At the same time, it is suggested that “regional governance” should be implemented, and four areas should be divided into “core protected areas,” “key compensation areas,” “strict control and diffusion areas,” and “vigilant differentiation areas,” which should be changed from passive protection to active guiding construction. From hierarchical governance to regional governance. We pay close attention to the two major opportunities of Beijing’s non-capital function relief and Beijing-Tianjin-Hebei industrial transfer to realize the organic combination of “blood transfusion” compensation and “hematopoiesis” compensation. Based on this, the following measures are put forward.(1)
We adjust the industrial structure, we change the mode of economic development, and we construct the ecological industrial system of urban green and low-carbon development. In the Beijing-Tianjin-Hebei region, most cities are still dominated by secondary industries with high energy consumption and high emissions, which adds great pressure to the development of the green and low-carbon economy in Beijing-Tianjin-Hebei cities. Therefore, it is urgent to develop green innovation technology, develop new environmental protection products, and do a good job in the promotion of green low-carbon development and commercial innovation. We will promote the transformation and upgrading of traditional industries, support the development of strategic emerging industries, and expand the scale and efficiency of the tertiary industry. We will establish an industrial carbon emission management system and a carbon emissions trading mechanism, and explore the formation of a number of waste disposal models compatible with urban green and low-carbon development [32]. We will speed up industrial digital empowerment, carry out digital upgrading of industrial functional areas, create a number of digital upgrading demonstrations, such as intelligent factories and smart parks, and promote the deep integration of emerging technologies, such as the Internet and artificial intelligence, with green and low-carbon industries.(2)
We carry out pilot demonstration of urban green and low-carbon development. We explore the low-carbon management model of industrial parks, strengthen the supervision of enterprise pollution control, and increase the proportion of renewable energy consumption. We will speed up the low-carbon transformation of key energy-consuming industries, such as iron and steel, building materials, non-ferrous metals, petrochemical, and chemical industries, we adjust the scale of factor input, we optimize the structure of factor input, and we cultivate a number of green and low-carbon enterprises [33]. We need to break down geographical division and administrative barriers, strengthen regional cooperation, and build a Beijing-Tianjin-Hebei collaborative governance mechanism and benefit-sharing mechanism. We explore the benefit-sharing mechanism through cooperation in taxation, education, health care, finance, and other fields. We will participate in the construction of low-carbon society and promote the formation of a green and low-carbon lifestyle. We carry out energy conservation publicity week, national low-carbon day, green travel promotion month, popularize the concept of green and low-carbon development, advocate green lifestyle, and so on.(3)
We attach great importance to the spatial correlation and nonequilibrium characteristics of green low-carbon development in the Beijing-Tianjin-Hebei region, and give full play to the spatial spillover effect. We will build a cross-regional cooperation platform for green technology innovation big data, and encourage technological exchanges and cooperation between enterprises, universities, and scientific research institutes. We should establish a sharing mechanism of green technology innovation resources, give full play to the comparative advantages of various regions, and avoid homogenization and vicious competition of industries in the same region. The construction of urban green low-carbon development growth pole, through domination effect, multiplier effect, spillover, and diffusion effect, has a radiative driving effect on the green and low-carbon development activities of surrounding cities [34], and turns the “siphon effect” into the “radiation effect”. We need to actively promote the surrounding backward cities, narrow the development gap between the north and the south, improve its catch-up effect, and then enhance the overall level of regional development.


## 5.1. Conclusion

Based on the panel data of 13 prefecture-level cities in Beijing-Tianjin-Hebei urban agglomeration from 2010 to 2019. This paper constructs a PSR conceptual evaluation model based on combined weight to evaluate the competitiveness level of green and low-carbon development of each city. Combined with exploratory spatial data analysis and the obstacle model, the spatial effects and obstacle factors are revealed. The results show the following:(1)
On the whole, the evaluation index of urban green and low-carbon development competitiveness in the Beijing-Tianjin-Hebei region shows a fluctuating upward trend from 2010 to 2019. Among them, the average annual growth rate of Zhangjiakou, Tangshan, Chengde, and Qinhuangdao is higher. The comprehensive indexes of green and low-carbon development competitiveness of Beijing, Tianjin, and Xingtai cities are declining in the fluctuation. Among them, the evaluation index of Tianjin and Beijing dropped 9.4% and 17.4%, respectively, in 2019 compared with 2018. The rest of the cities basically maintained a steady and a fluctuating upward trend.(2)
In terms of spatial disequilibrium analysis, the evolution and distribution characteristics of the spatio-temporal pattern show that the spatial pattern of urban green and low-carbon development competitiveness of Beijing-Tianjin-Hebei cities has changed greatly from 2010 to 2019, showing an overall spatial distribution pattern of “high in the north, low in the south, and local jump”. Among them, in the five levels of competitiveness, the number of cities with strong competitiveness in the Beijing-Tianjin-Hebei urban agglomeration increased by 23.08%, while the number of cities with weak competitiveness decreased by 7.69%. And on the whole, the level gap of green and low-carbon development competitiveness of Beijing-Tianjin-Hebei urban agglomeration is gradually narrowing.(3)
With the application of exploratory spatial data analysis, this paper analyzes the spatial autocorrelation characteristics of the green and low-carbon development of Beijing-Tianjin-Hebei urban agglomeration from the time and spatial dimensions, and reveals the evolution process of its spatial development pattern. The results show that the green low-carbon development of Beijing-Tianjin-Hebei urban agglomeration shows a positive autocorrelation in space, and the distribution characteristics of spatial agglomeration are significant. The local spatial autocorrelation regularity is obvious. The overall spatial pattern shows a development trend of “high in the north and low in the south”, and the gap between the south and the north is gradually narrowing. Therefore, the high and high agglomeration areas should continue to maintain a good momentum of development and play the role of technology diffusion and radiation. Low and low agglomeration areas should further strive for national policy support. Increasing investment in research and development. We should pay attention to the digestion and absorption of imported technology. Actively learning advanced experience and technology. In order to achieve corner overtaking, it is necessary to dock industrial gradient transfer, and we should make use of scientific and technological innovation to change the mode of economic development. High-low agglomeration areas and low-high agglomeration areas should strengthen cross-regional cooperation, enhance soft and hard power, and give full play to spatial spillover effects to improve the competitiveness of their own green and low-carbon development.(4)
In the aspect of obstacle factor analysis, due to the difference of urban geographical location and resource endowment, the factors restricting the competitiveness of urban green and low-carbon development are also different. Specifically, the annual average PM2.5 concentration, population density, and energy consumption of ten thousand of GDP in Beijing are still the main factors hindering the improvement of competitiveness of green and low-carbon development. Tianjin’s obstacle factors need to control industrial soot emissions, reduce SO2 concentration, and increase the proportion of the tertiary industry, which is still the main direction to improve the competitiveness of green and low-carbon development in Tianjin. The development resistance of green and low-carbon development in Zhangjiakou focuses on the concentration of SO2 pollutants, energy consumption of ten thousand of GDP, industrial soot emissions, and so on. The proportion of the output value of the tertiary industry in GDP in Chengde, Tangshan, Langfang, and Cangzhou is the primary obstacle factor restricting the improvement of urban green and low-carbon development competitiveness. The concentration of PM2.5 in Shijiazhuang and Baoding is in the forefront, so it is necessary to carry out special improvement work on the ecological environment. Qinhuangdao, Hengshui, Xingtai, and Handan are subject to excessive industrial soot emissions of ten thousand yuan of GDP, which seriously hinders the improvement of competitiveness of green and low-carbon development. To sum up, seizing the opportunity of the transfer of Beijing’s non-capital functions to Tianjin and Hebei, and making full use of their respective comparative advantages and misplaced development is the key to enhance the competitiveness of green and low-carbon development of Beijing-Tianjin-Hebei urban agglomeration [31].


## 5.2. Revelation

Through the above research, we can see that adjusting the industrial structure, accelerating green technology innovation, promoting industrial transformation, and upgrading are the only way to enhance the competitiveness of urban green and low-carbon development in Beijing-Tianjin-Hebei cities. At the same time, it is suggested that “regional governance” should be implemented, and four areas should be divided into “core protected areas,” “key compensation areas,” “strict control and diffusion areas,” and “vigilant differentiation areas,” which should be changed from passive protection to active guiding construction. From hierarchical governance to regional governance. We pay close attention to the two major opportunities of Beijing’s non-capital function relief and Beijing-Tianjin-Hebei industrial transfer to realize the organic combination of “blood transfusion” compensation and “hematopoiesis” compensation. Based on this, the following measures are put forward.(1)
We adjust the industrial structure, we change the mode of economic development, and we construct the ecological industrial system of urban green and low-carbon development. In the Beijing-Tianjin-Hebei region, most cities are still dominated by secondary industries with high energy consumption and high emissions, which adds great pressure to the development of the green and low-carbon economy in Beijing-Tianjin-Hebei cities. Therefore, it is urgent to develop green innovation technology, develop new environmental protection products, and do a good job in the promotion of green low-carbon development and commercial innovation. We will promote the transformation and upgrading of traditional industries, support the development of strategic emerging industries, and expand the scale and efficiency of the tertiary industry. We will establish an industrial carbon emission management system and a carbon emissions trading mechanism, and explore the formation of a number of waste disposal models compatible with urban green and low-carbon development [32]. We will speed up industrial digital empowerment, carry out digital upgrading of industrial functional areas, create a number of digital upgrading demonstrations, such as intelligent factories and smart parks, and promote the deep integration of emerging technologies, such as the Internet and artificial intelligence, with green and low-carbon industries.(2)
We carry out pilot demonstration of urban green and low-carbon development. We explore the low-carbon management model of industrial parks, strengthen the supervision of enterprise pollution control, and increase the proportion of renewable energy consumption. We will speed up the low-carbon transformation of key energy-consuming industries, such as iron and steel, building materials, non-ferrous metals, petrochemical, and chemical industries, we adjust the scale of factor input, we optimize the structure of factor input, and we cultivate a number of green and low-carbon enterprises [33]. We need to break down geographical division and administrative barriers, strengthen regional cooperation, and build a Beijing-Tianjin-Hebei collaborative governance mechanism and benefit-sharing mechanism. We explore the benefit-sharing mechanism through cooperation in taxation, education, health care, finance, and other fields. We will participate in the construction of low-carbon society and promote the formation of a green and low-carbon lifestyle. We carry out energy conservation publicity week, national low-carbon day, green travel promotion month, popularize the concept of green and low-carbon development, advocate green lifestyle, and so on.(3)
We attach great importance to the spatial correlation and nonequilibrium characteristics of green low-carbon development in the Beijing-Tianjin-Hebei region, and give full play to the spatial spillover effect. We will build a cross-regional cooperation platform for green technology innovation big data, and encourage technological exchanges and cooperation between enterprises, universities, and scientific research institutes. We should establish a sharing mechanism of green technology innovation resources, give full play to the comparative advantages of various regions, and avoid homogenization and vicious competition of industries in the same region. The construction of urban green low-carbon development growth pole, through domination effect, multiplier effect, spillover, and diffusion effect, has a radiative driving effect on the green and low-carbon development activities of surrounding cities [34], and turns the “siphon effect” into the “radiation effect”. We need to actively promote the surrounding backward cities, narrow the development gap between the north and the south, improve its catch-up effect, and then enhance the overall level of regional development.



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*Source: 5230314-2022-07-21.xml*