Baifa Zhang

Dr. Baifa Zhang


Research Focus & Metrics

Academic Career Length

5Years (2022-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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8 May 2022 in Remote Sensing
Verified
Journal Article
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BZBaifa Zhang
Baifa Zhang
Jing ZhangJing Zhang
Jing Zhang
Changhong MiaoChanghong Miao
Changhong Miao
Urbanization level is a key indicator for socioeconomic development and policy making, but the measurement data and methods need to be discussed further due to the limitation of a single index and the availability and accuracy of statistical data. China is urbanizing rapidly, but the urbanization level at the county scale remains a mystery due to its complexity and lack of unified and effective measurement indicators. In this paper, we proposed a new urbanization index to measure the Chinese urbanization level at the county scale by integrating population, land, and economic factors; by fusing remote sensing data and traditional demographic data, we investigated the multi-dimensional unbalanced development patterns and the driving mechanism from 1995 to 2015. Results indicate that: The average comprehensive urbanization level at the Chinese county scale has increased from 31.06% in 1995 to 45.23% in 2015, and the urbanization level in the permanent population may overestimate China’s urbanization process. There were significant but different spatial and temporal dynamic patterns in population, land, and economic levels as well as at a comprehensive urbanization level. The comprehensive urbanization level shows the pattern of being high in the south-east and low in the north-west, divided by “Hu line”. The urbanization of registered populations presents high in the northern border and the eastern coastal areas, which is further strengthened over time. Economic urbanization based on lighting data presents high in the east and low in the west. Land urbanization based on remote sensing data shows high in the south and low in the north. The registered population urbanization level is lower than economic and land urbanization. County urbanization was driven by large population size, reasonable industrial structure, and strong government capacity; 38% and 59% of urbanization levels can be regarded as the key nodes of the urbanization process. When the urbanization rate is lower than 38%, the secondary industry plays a strong role in powering urbanization; when the urbanization rate is higher than 38% but less than 59%, the promotion effect of the tertiary industry is more obvious, and the secondary industry is gradually weakened. When the urbanization rate exceeds 59%, the tertiary industry becomes the major driver.

11 November 2022 in Sustainability
Verified
Journal Article
Open access
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Jing ZhangJing Zhang
Jing Zhang
YFYuhan Fu
Yuhan Fu
BZBaifa Zhang
Baifa Zhang
Exploring the spatial distribution and influencing factors of firms’ location choices in the service-oriented digital industry is of great significance for achieving a new round of rapid development for the digital economy in the context of a post-epidemic situation. Based on the micro perspective of firms, this paper explores the spatial distribution and influencing factors of the location choice of the service-oriented digital industry in the Yangtze River Delta of China. The research shows that the number of the service-oriented digital industry firms varies greatly among cities in the Yangtze River Delta region of China, showing an obvious core–periphery structure. There are many firms in municipalities directly under the central government and provincial capitals, which are the core areas; the number of firms in non-provincial capital cities is small and they are marginal areas. The influencing factors include: per capita GDP, industrial structure upgrading, whether it is a municipality directly under the Central Government or a provincial capital city, whether it has opened high-speed rail, information transmission, number of employees in the computer service industry, number of patent applications, Internet infrastructure level, and scientific fiscal expenditure have a significant positive impact on its location choice, while the per capita total social consumer goods have a significant negative impact on its location choice.