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With the development of global economy, the evaluation of industrial clusters has become an important method to scientifically analyze the advantages and disadvantages of industrial development. The current research model uses the traditional evaluation method, which leads to the problem of unsatisfactory evaluation results and low effect. This paper proposes an evaluation model based on the BP neural network combined with the LM algorithm, which has the advantages of fast convergence speed and strong application ability. The comprehensive evaluation model of industrial clusters is put forward from the comprehensive application of the scale, benefit, and 27 related evaluation indexes of industrial clusters. This paper takes Beijing, Tianjin, and Hebei industrial clusters for BP-LM evaluation and analysis, which fully illustrates the advantages of this method. The evaluation results show that the evaluation target value and the average error of the overall parity have obvious advantages compared with those of other models, which provides application guidance for local economic development and policy formulation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The normal supply of energy is related to the stable development of the economy and society. Forecasting energy consumption helps prepare for the normal supply of energy. In the study of energy consumption forecasting, different scholars have used different forecasting models. This paper uses five-year energy consumption data in the Beijing-Tianjin-Hebei region and uses the grey fractional FGM(1, 1) model to analyze the next six years. Then, the energy consumption of three places is predicted. The advantage of the grey score FGM(1, 1) model is that it can get more accurate prediction results based on a small amount of information. In this study, relatively outdated information affects the accuracy of prediction results. However, other prediction models have great limitations on data. Choosing the grey number fractional model for prediction research can get a more reasonable prediction result. We use the FGM(1, 1) model to make predictions and get the prediction results. In Beijing, the growth rate of natural gas consumption has slowed down and will be basically stable by 2023. The average annual deceleration of coal consumption is 32%. The average annual deceleration of coke consumption is 10%. Crude oil consumption decreased by 6.3% annually. Gasoline consumption is slowly increasing. The consumption of kerosene increased about 8% annually. Diesel consumption is slowly decreasing. Fuel oil consumption is reduced by 17% annually. The average annual growth rate of power consumption exceeds 6%. In Tianjin, the annual growth rate of natural gas consumption is about 5%. Coal consumption is reduced by about 8% every year. The average annual deceleration of coke consumption is 7%. Crude oil consumption decreased by 2.4% annually. Gasoline consumption is slowly decreasing. The consumption of kerosene has increased by about 20% annually. Diesel consumption is slowly decreasing. Fuel oil consumption is reduced by 20% annually. Electricity consumption is slowly increasing. In Hebei Province, the annual growth rate of natural gas consumption is about 15%. Annual coal consumption is reduced by about 3%. Coke consumption remained stable. Crude oil consumption is reduced by 3% annually. Gasoline consumption is slowly increasing, and kerosene consumption has increased by about 31% annually. Diesel consumption is reduced by about 3% annually. Fuel oil consumption remained stable. Electricity consumption is slowly increasing. [ABSTRACT FROM AUTHOR]

Research on ecological total-factor energy efficiency (ETFEE) is conducive to energy conservation, emission reduction, and ecological protection. This paper focuses on the measurement and decomposition of the ETFEE in the Beijing-Tianjin-Hebei (BTH) region in China. In order to measure the ETFEE values, the window technology is combined with a nonradial and nonoriented SBM-undesirable model considering undesirable outputs to overcome the defect of insufficient data of research objects and ensure the calculation process to be implemented. The findings show that Beijing and Tianjin are DEA-efficient, while Hebei is not. The technological progress rates of Beijing and Tianjin reach up to 11.92% and 14.96%, while that of Hebei retrogresses by 4.47%. The scale efficiencies of Beijing, Tianjin, and Hebei are 97.75%, 86.60%, and 93.81%, respectively, which means that there are potentials for further optimization in the energy structures. The impulse response results between the energy structure and the ETFEE show that the proportions of coal and petroleum have negative effects on the ETFEE, while that of natural gas has a positive effect. The research results can provide reference for decision makers to formulate regional development plans. [ABSTRACT FROM AUTHOR]

Two global cities of China, Beijing and Shanghai, have completely different development statuses with their hinterlands. There is a large economic development gap between Beijing and its hinterland, Hebei Province, while Shanghai has formed a highly integrated region with its hinterland, the Yangtze River Delta. According to the Outline of Collaborative Development of Beijing, Tianjin, and Hebei Province, enterprise linkages between Beijing and Hebei should be strengthened in order to narrow the economic gap between the center and the hinterland. On this basis, this paper proposes two hypotheses. (1) The economic gap between Beijing and Hebei has not really changed, because more enterprise linkages of Beijing are nonlocal. (2) The gradual narrowing of the economic gap between Shanghai and Jiangsu as well as Zhejiang is due to the fact that more enterprise linkages of Shanghai are local. We have used enterprise data on business registration from the State Administration for Industry and Commerce of China and have selected two indicators, namely, the number of headquarters-branches offices and the amount of enterprise investment, to examine the enterprise linkages between Beijing/Shanghai and their hinterlands as well as the hinterworld, and have verified these two hypotheses. The results show that nonlocal enterprise linkages based on the hinterworld are the main form in Beijing, while local enterprise linkages based on the hinterland are the main form in Shanghai. In addition, we have analyzed the mechanisms of formation of the enterprises linkages of the two cities and have put forward some policy suggestions. [ABSTRACT FROM AUTHOR]

For a deep understanding of Beijing-Tianjin-Hebei (BTH) collaborative innovation, we detected and visualized the communities of innovation in BTH Urban Agglomeration based on the patent cooperation network. China Patent Database was connected with Business Registration Database and the Tianyan Check to achieve the geographical information of organizational innovators. Spinglass algorithm was applied and ultimately 12 communities of innovation were detected. Based on the different structure characteristics, we further clustered the 12 communities into four typical structures that are hierarchical, single-center, polycentric, and flat structures. The hierarchical structure is usually large in scale and the cooperative intensity is relatively high. Single-center structure has a center with a high proportion of centrality and the cooperative intensity is relatively low. Polycentric structure has multiple centers with similar proportions of centrality. Flat structure is usually small in scale and has no obvious network center. In the patent cooperative network of BTH Urban Agglomeration, universities and state-owned enterprises occupied the centers and acted important roles to connect other organizations. Some communities of innovation showed significant industry characteristics, mainly involving six industry fields that are electric power, construction, petroleum, metallurgy and materials, municipal transportation, and railway. From the geographical perspective, some communities manifested local attributes and some demonstrated cooperation between regions. Beijing was the center of the Beijing-Tianjin-Hebei patent cooperation network. Compared with the pair of Beijing-Tianjin and the pair of Beijing-Hebei, Tianjin and Hebei were not closely connected. In the future, Beijing-Tianjin-Hebei collaborative innovation should strengthen cooperation between Tianjin and Hebei. [ABSTRACT FROM AUTHOR]

This study constructs a theoretical model and empirical framework concerning how spatial structure affects economic efficiency using data on the Beijing-Tianjin-Hebei (BTH) megaregion between 2008 and 2017. The study finds the following: ① the development of the internal spatial structure of the BTH urban agglomeration is unequal. The populations of most cities in the urban agglomeration are still in a dispersed state. Although urban populations have tended to agglomerate around multiple subcenter units in the cities, the trend towards population agglomeration around city centers is not found to be significant. ② The total factor productivity (TFP) of the BTH urban agglomeration was not high in most years between 2008 and 2017, showing a fluctuating downward trend overall. The TFP of the urban agglomeration showed differential regional patterns. The decline of TFP growth in the BTH urban agglomeration is mainly due to declining technological progress, technological efficiency, and scale efficiency. Resource input remains the major driving force behind the development of the BTH megaregion. ③ Concerning how the spatial structure of the urban agglomeration affected economic efficiency, the study finds that primacy, urban Gini index, urban population size, human capital, informatization level, industrial structure, and science and technology levels have positive effects on economic efficiency, whereas dispersion, governmental role, economic openness, and land input have negative effects. This study has several policy implications. Achieving coordinated and integrated development of the BTH urban agglomeration will require constructing a scientific and regional spatial system, improving the development levels of regional central cities, divesting Beijing of noncapital functions, and reshaping the industrial layout of the BTH megaregion in an orderly manner, while continuously improving the internal hierarchical structure of urban agglomeration and strengthening intercity economic connections. [ABSTRACT FROM AUTHOR]

The Beijing–Tianjin–Hebei (BTH) region is a top urban agglomeration of China but has the problem of severe environmental pollution. Most of the current researches on the sustainable development of this region only concentrate on the environmental pollution itself and ignore its relationship to the socioeconomic development. In this research, an entropy-based coupling model, a polynomial equation with partial least squares algorithm, and socioeconomic and environmental data in 2006–2015 were used to measure and fit the above relationship. Empirical analysis led to the following conclusions. (1) Beijing, Tianjin, and Hebei presented similar socioeconomic development modes but different environmental pollution modes. (2) The social economy of the BTH region has been developing at the expense of environmental pollution, but the environmental cost has been decreasing year by year. (3) At present, the BTH region has huge potential to improve its environment. (4) Increasing the investment in the treatment of industrial pollution in Tianjin and mitigating the soot (dust) emissions in Tianjin and Hebei are the major environmental policy directions. (5) Controlling the development of smelting and pressing of ferrous metals and other building material sectors in Hebei is the major economic policy direction. [ABSTRACT FROM AUTHOR]