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Research on urban innovation efficiency of Guangdong-Hong Kong-Macao Greater Bay Area based on DEA-Malmquist model.

ABSTRACT: In order to scientifically evaluate the allocation efficiency of science and technology innovation system and build a good science and technology financial environment, this paper constructs an evaluation index system based on the panel data of 11 cities in Guangdong-Hong Kong-Macao Greater Bay Area from 2010 to 2019. By using the Three-stage DEA model and Malmquist model, this paper makes a static and dynamic analysis on the efficiency of scientific and technological innovation, calculates the efficiency of scientific and technological innovation, and reveals its internal reasons. The results show that: the productivity of scientific and technological innovation in Guangdong-Hong Kong-Macao Greater Bay Area has been steadily improved; technological progress is the key factor to promote the productivity of scientific and technological innovation; there are differences in the efficiency of scientific and technological innovation in the same city, and the efficiency of scale has inhibited the efficiency of scientific and technological innovation to a certain extent.

SUBMITTER: Hu S

PROVIDER: S-EPMC8862699 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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Research on urban innovation efficiency of Guangdong-Hong Kong-Macao Greater Bay Area based on DEA-Malmquist model.

Hu Shanshan S Kim Hyung-Ho HH

Annals of operations research 20220222

In order to scientifically evaluate the allocation efficiency of science and technology innovation system and build a good science and technology financial environment, this paper constructs an evaluation index system based on the panel data of 11 cities in Guangdong-Hong Kong-Macao Greater Bay Area from 2010 to 2019. By using the Three-stage DEA model and Malmquist model, this paper makes a static and dynamic analysis on the efficiency of scientific and technological innovation, calculates the ...[more]

PMID: 35221418

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Project description:The Guangdong-Hong Kong-Macao Greater Bay Area urban agglomeration is an urban agglomeration with some of the most intensive urbanization since 1980s. A large amount of cultivated land, forest land, water bodies and other land types in the region has been occupied by construction land, resulting in fragmented ecological landscapes and biodiversity in the region and causing many other ecological problems. Based on this, this paper takes the Guangdong-Hong Kong-Macao Greater Bay Area as a case study, constructs an ecological network of the dispersion scale of five species from 1990 to 2020 based on a morphological spatial pattern analysis (MSPA) method, identifies the ecological groups in the network and uses the core node-based community evolution path tracking algorithm to analyze the ecological groups in order to explore the changes of ecological network connectivity at different scales in the region and to reveal the overall and local characteristics and changes of the migratory space of terrestrial mammals with different dispersion capabilities. The research results show that: (1) From 1990 to 2020, the area of construction land in the Guangdong-Hong Kong-Macao Greater Bay Area increased sharply, with good connectivity in the northwest, southwest and eastern regions and poor connectivity in the central region. (2) There are obvious differences between the overall and local changes in the connectivity trends of multi-scale regional ecological networks. On the whole, the overall ecological connectivity of the ecological network at each scale showed a gradual upward trend, and the overall connectivity index IIC and the possible connectivity index PC gradually increased with the increase of the maximum dispersal distance of species. From the perspective of local patches, the larger the species dispersion scale, the larger the value of the revised betweenness centrality index and the patch possible connectivity index. (3) The distribution of ecological groups at different species dispersion scales is different, and the smaller the dispersal scale of the species, the greater the distribution of ecological groups. Small-scale species are limited by the maximum dispersal distance, and the range of their ecological groups is generally small. Small-scale (3 km), mesoscale (10 km) and large-scale (30 km) core nodes of ecological groups show a gradual increase trend, and the overall connectivity of ecological groups has improved. However, the core nodes of the extra-large-scale (60 km) and ultra-large-scale (100 km) ecological groups show a trend of decreasing fluctuations, and the overall connectivity within the ecological group has declined. This study is helpful to clarify the structural characteristics of regional ecological space and provide a theoretical basis for regional ecological planning.

Project description:The Guangdong-Hong Kong-Macao Greater Bay Area has attracted attention for its extraordinary pace of economic development and is considered to be leading the way in China's transformation from a manufacturing to an innovation cluster. However, due to rapid economic expansion and rapid urbanization, the Great Bay Area still struggles with low energy efficiency and environmental degradation, which has slowed down the pace of development. Therefore, in order to alleviate energy pressure, promote the country's sustainable development and gain a competitive advantage in the global market, researching energy efficiency and improving energy utilization efficiency is crucial. In this study, macro-level energy efficiency indicators are constructed using energy consumption data from various cities in the Greater Bay Area for the period from 2000 to 2020, and the spatio-temporal evolution of energy efficiency is analysed. The results show that all cities in the Greater Bay Area experienced an increasing trend in energy efficiency from 2000 to 2019, with significant variation in growth rates and magnitudes between cities. Compared to the nine cities in Guangdong province, Hong Kong and Macao exhibited significantly superior energy efficiency, with Foshan recording the highest growth rate of 14%. In 2020, most cities experienced a decline in energy efficiency due to the COVID-19 pandemic, with Macao experiencing the greatest decrease at 57%. Hong Kong and Macao are both in the "low consumption and high efficiency" target region, while Guangzhou, Shenzhen and Zhuhai are consistently in the "both high" region. Changes in the industrial upgrading index correspond significantly with changes in energy efficiency trajectories, with the transition from primary to secondary and tertiary industries playing a more substantial role. There is no significant association found between the strength of environmental regulation and changes in energy efficiency. The study's findings indicate that the most effective way to achieve economic transformation in the majority of China's regions is to combine adequate environmental legislation with industrial structural adjustment.

Dataset Information

Research on urban innovation efficiency of Guangdong-Hong Kong-Macao Greater Bay Area based on DEA-Malmquist model.

Publications

Research on urban innovation efficiency of Guangdong-Hong Kong-Macao Greater Bay Area based on DEA-Malmquist model.

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