Search

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]

Does green image impact enterprise environmental protection investment? How does the green image affect enterprise environmental protection investment? In order to solve the above problems, this paper uses an empirical analysis to explore the relationship among regional economic development level, green image, and enterprise environmental protection investment based on the empirical data of A-share manufacturing listed companies from 2007 to 2015 in China. The research results show that good green image has a positive role to promote enterprise environmental protection investment, and regional economic development level partially mediates green image and enterprise environmental protection investment. The article confirms that a good green image can send positive signals to stakeholders and increase their loyalty and satisfaction with the enterprise's products or services. The level of regional economic development provides opportunities for the green development of enterprises. With the improvement in the level of regional economic development, stakeholders' awareness of environmental protection has increased, and they have become more sensitive to enterprise environmental protection investment behaviors. The research results also show that the environmental awareness of corporate stakeholders plays an important role in actively fulfilling, and it provides new ideas for companies to conduct production and management in a green-oriented model. [ABSTRACT FROM AUTHOR]

China's economic development has shifted from high-speed to high-quality. The fundamental of promoting the high-quality development of the real economy lies in real enterprises. Therefore, it is urgent to measure the high-quality development of real enterprises scientifically and reasonably. Herein, we construct a comprehensive evaluation index system for the high-quality development of real enterprises based on the existing research and combined with the characteristics of real enterprises. Then, the high-quality development index for real enterprises is created using factor analysis and AHP. Moreover, we measure and analyze the high-quality development of real enterprises in 2020. The results show that the high-quality development level of Chinese real enterprises remains to be further upgraded. Meanwhile, there are obvious contrasts in the high-quality development index of real enterprises in industries and regions. Finally, we put forward some related policy recommendations, such as enhancing policy relevance, insisting on innovative development, and increasing financial sustainability. [ABSTRACT FROM AUTHOR]

This study is based on the unsupervised learning-based enterprise spatial structure evolution and economic coupling coordination relationship situation assessment method. Pattern recognition has high-precision characteristics, but it is necessary to train the evaluation model for the enterprise spatial structure evolution in advance and then carry out economic coupling coordination based on the trained model. The conclusions are as follows: (1) through the RC1, RC2, RC3, RC4, RC5, and RC6 evaluation indicators to evaluate the situation evaluation method based on the unsupervised learning of the evolution of the enterprise spatial structure and the economic coupling and coordination relationship, it is found that the main component characteristics as a whole meet the standard. The optimal RC is RC6: profit = −0.0885, highest = −0.0809, lowest = −0.0932, WR.WR2 = 0.0038, MA.MA3 = −0.0782, MTM.MTM = −0.0427, OSC.OSC = −0.0355, ROC.MAROC = 0.0105, SKDJ.D = −0.0268, BIAS-QL.BIAS = −0.01, WIDTH.WIDTH = 0.2408, CYD.CYDN = −0.0961, FSL.SWL = −0.0868ADTM.ADTM = −0.0379, ATR.ATR = −0.0278, DMA.DIFMA = −0.0358, DMI.ADX = 0.8516, DMI.ADXR = 0.854, EMV.EMV = −0.0942, VMACD.DIF = 0.3312, and UOS.MAUOS = −0.0846.2. Based on the deep learning model of the coupling and coordination relationship between the evolution of the spatial structure of the enterprise, the time-dependent matrix comparison experiment is divided into directed + self, directed, undirected + self, and undirected time for comparison. The experimental results on directed + self are the best; with various indicators, the upward improvement is above 10%: CP = 0.8611, CR = 0.9353, C–F1 = 0.8967, EP = 0.8865, ER = 0.857, E–F1 = 0.917, OP = 0.856, OR = 0.9845, and O–F1 = 0.99.3. The time cost, profit, and transaction volume data of the company are collected for a certain period of time, and simulation experiments are conducted to get a small difference between the predicted result and the actual data. The January data are closest to the true value: cost = 30.78, profit = 30.11, highest = 30.1, lowest = 29.7, WR.WR1 = 81.21, WR.WR2 = 45.62, AMV.AMV2 = 32.67, AMV.AMV3 = 34.95, and MCST. MCST = 36.08.4. In the model score, the best performance of LSTM data is CP = 0.3829, CR = 0.3664, C–F1 = 0.3744, EP = 0.3726, ER = 0.3004, E–F1 = 0.3326, OP = 0.9155, OR = 0.9316, and O–F1 = 0.9234, which is better than the BiLSTM model with CP = 0.3648, CR = 0.3319, C–F1 = 0.3392, EP = 0.4402, ER = 0.391, E–F1 = 0.4145, OP = 0.9215, OR = 0.9318, and O–F1 = 0.9266. [ABSTRACT FROM AUTHOR]

Small- and medium-sized enterprises (SMEs) are considered to have potential innovation capabilities and can create new market opportunities. Venture capital can financially support entrepreneurial activities for economic growth and governs and nurtures the growth of the SMEs. The aim of this study is to investigate the influence mechanism of venture capital on the development of SMEs in agri-food industry. Based on the enterprise growth theory, this study constructed an evaluation model, consisting of technological innovation, profitability, development capability, and solvency, to examine the effect of venture capital on the growth of agricultural SMEs. Using data of 40 agricultural SEMs from the SME and ChiNext boards in China, the empirical analysis has been conducted with the multivariate regression analysis method. The results show that the venture capital can significantly improve the technology innovation, profitability, and growth ability of SMEs. For the solvency of SMEs, the promoting role of venture capital is not obvious. Finally, the practical implications of this study for venture capitalists, entrepreneurs, and regulators are discussed. [ABSTRACT FROM AUTHOR]