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Deepening production-education integration (PEI) is a major strategy of China to reinforce education reform and promote industry transformation. Different from the traditional research perspective of colleges on the supple side, this paper chooses to evaluate the PEI performance of the high-tech industry from the angle of industry on the demand side. Firstly, the action mechanisms of the education system and industry system in PEI were expounded, and the design framework of the evaluation index system (EIS) was derived from the interactions between two elements (technologies and talents) and two market relationships (supply and demand), forming a two-dimensional four-quadrant EIS. On this basis, the correlation-entropy composite matter-element (CECME) model and coupling coordination degree (CCD) model were introduced to empirically compare the PEI performance of the high-tech industry between Guangdong-Hong Kong-Macao (GHKM) Greater Bay Area, Beijing-Tianjin-Hebei (BTH) Urban Agglomeration, and Yangtze River Delta (YRD) Urban Agglomeration. The main conclusions are as follows: (1) the GHKM Greater Bay Area is far better in education and industry development than YRD Urban Agglomeration and BTH Urban Agglomeration; (2) the PEI performance of the high-tech industry in GHKM Greater Bay Area was evaluated as high level with industry in lead, that in YRD Urban Agglomeration as moderate level with education in lead, and that in BTH Urban Agglomeration high-tech industry PEI as moderate level with industry in lag. Finally, several countermeasures were proposed for the urban agglomerations to further enhance the PEI performance of the high-tech industry: GHKM Greater Bay Area should improve the weak link of education development; YRD Urban Agglomeration should give full play to its advantages in higher education; BTH Urban Agglomeration should focus on high-quality industry development. From the practical level, this paper objectively evaluates the actual performance of PEI and provides a reference for scientific policymaking of PEI development. [ABSTRACT FROM AUTHOR]

The mystery of China's economic growth has been widely concerned by the world. However, little attention has been paid to the transformation of the economic growth momentum of Guangdong Province, which has long occupied the top in China's economic growth. This paper analyzes the evolution and the transformation of the momentum source of economic growth in Guangdong Province from 1979 to 2019, using Total Factor Productivity Growth Decomposition method. The results show that capital investment contributes the most to economic growth, the contributions of total factor productivity, and labor input rank second and third, respectively; the change of the contribution of each momentum source to GDP growth mainly comes from the impact of the secondary industry. Both industrial capital productivity and capital allocation structure play a negative role in driving the overall capital productivity, and both industrial labor productivity and labor allocation structure play a positive role in driving the overall labor productivity. Industrial factor productivity and industrial factor allocation structure play a major positive role and a weak negative role in promoting economic growth, respectively. Our findings provide some ideas on how to maintain economic growth in the new era especially for the developing countries. [ABSTRACT FROM AUTHOR]

Global warming caused by carbon emissions has become increasingly prominent. As the world's second-largest economy, China is under enormous pressure to cut down its carbon dioxide emissions. It is urgent to seek effective methods to forecast carbon emissions and put forward the pointed and effective measures to reduce emissions. In this paper, we first use the software Eviews to make an analysis of randomness on data of carbon emissions in the four representative provinces and city, Beijing, Henan, Guangdong, and Zhejiang, in terms of their carbon emissions data from 1997 to 2017. Then, according to their distinct characteristics, the methods of stationary processing of the difference, moving average, and substituting strong impact points, respectively, are adopted to perform the data preprocessing. Then, model identification, parameter estimation, and model test are carried out to establish the model of ARIMA(p , d, q) for the prediction of the carbon emissions of the four regions, respectively. Finally, the model is used to forecast the data and analyze their tendency for their carbon emissions in the next three years. The results can provide guidance for decision-makers to set reasonable carbon emission reduction targets and take appropriate energy conservation and emission reduction measures. [ABSTRACT FROM AUTHOR]

For the purpose of accurate measurement of regional systemic financial risks and prevention of regional economic turmoil, this paper proposes a new measure called the CoCVaR model, based on the tail mean loss, which is applied to measure the impact of stock returns of each listed company on the overall stock returns in Guangdong Province, China, from January 2010 to December 2020. It is found that there are significant CoVaR and CoCVaR for real estate, finance, utilities, and energy companies, while the risk spillover to the real economy market in Guangdong Province is more significant when companies in these industries are in extreme situations. There are insignificant CoCVaR for daily consumption, information technology, and health care. The risk spillover to the real economy market in Guangdong Province is smaller when companies in these industries are in crisis. [ABSTRACT FROM AUTHOR]

The average death and injury intensity on sharp horizontal curves (SHCs) are much higher than those of straight sections of the expressway in China. In this paper, the statistics of crashes from 2008 to 2012 on 2200 km expressways in Guangdong province are collected, and the relationships between the radius of plane curves and the crash rate are analyzed. After that, the curved expressway section with radius equal to or less than 1000 m is defined as SHCs. According to the results of the test of the operating speed, the heart rate change of drivers, and the vehicle acceleration, the distribution patterns of driving risks on the certain SHCs were theoretically analyzed. Hence, the driving risk affected areas on adjacent line units of SHCs are determined as 200 m sections before entering or after exiting the SHCs. Combining with surveyed data, the spatial distribution of crashes on SHCs is analyzed, and the driving risk distribution function of SHCs in expressway is finally deduced. The result of this research provides a theoretical basis to enhance expressway safety management and to improve the driving safety on SHCs. [ABSTRACT FROM AUTHOR]