Spatial Pattern of Technological Innovation in the Yangtze River Delta Region and Its Impact on Water Pollution

Int J Environ Res Public Health. 2022 Jun 17;19(12):7437. doi: 10.3390/ijerph19127437.

Abstract

The impact of technological innovation on water pollution is an important parameter to determine and monitor while promoting and furthering a region's economic development. Here, exploratory spatial data analysis was used to analyze: the spatial patterns of technological innovation and water pollution in the Yangtze River, the changes in technical innovation and the resulting changes in water pollution, and the impact of technological innovation on water pollution. The following major inferences were drawn from the obtained results: (1) The spatial pattern of innovation input has a single-center structure that tends to spread. The patent innovation output has evolved, from a single spatial pattern with Shanghai as the core to a diffusion structure with three cores-Hangzhou, Shanghai, and Nanjing. (2) The aggregation mode of water pollution has evolved from the original "Z" mode to a new mode of core agglomeration, and water pollution is constantly being reduced. (3) The trends of change in patent innovation output and innovation input are roughly the same, while the trends of both and that of water pollution are contrary to each other. (4) The correlations between innovation input, patented innovation output, and water pollution are relatively low. From the perspective of spatial distribution, the number of cities with medium and high levels of gray correlation with water pollution is the same.

Keywords: YRD; gray relational analysis; spatial pattern; technological innovation; water pollution.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • China
  • Cities
  • Environmental Monitoring*
  • Inventions*
  • Rivers
  • Water Pollution

Grants and funding

This research was supported by the Project for Young Backbone Teachers in Higher Education Institutions of Henan Province under Grant (2020GGJS187), the National Natural Science Foundation of China under Grant (41971160), and the Natural Science Foundation of Zhejiang Province under Grant (LY19D010009). Support was also provided by the Shenzhen Philosophy and Social Science Planning Project (SZ2021C003) and the Wuhan Science and Technology Innovation Think Tank Construction Research Project (WHKX202204).