The extraction of urban cognizable synergistic features could be
regarded as a form of dimensionality reduction in 23 X factors.
Therefore, we compared urban cognizable features with the results of a
principal component analysis, which is a widely used technique in
machine learning (Monedero et al. 2019). There were 6 principal
components, which all contained no more than 2 factors (Table S3).
However, we could not give definite practical meaning to the principal
components, which meant that the extraction of urban cognizable features
has unique advantages as a new dimensionality reduction method.
Conclusion
The study proposed deep learning as a new, more effective approach to
understanding the patterns, dynamics, and driving factors of ESV that
are crucial for coping with sustainability challenges. The findings of
the model analysis suggested that underlying social and economic
conditions presumably influence regional ecological functions through
ESV.
Regarding Nanjing City, although the outputs of the
1st, 2nd and 3rdindustries all showed a decreasing trend in ESV, the
“2nd industry output value” had the highest
influence intensity, indicating the urgency and necessity of controlling
its proportion. We propose that economic development, urbanization, and
tourism should be further accelerated and enhanced in Nanjing, because
“GDP”, “light index”, “tourism output” and “residential
electricity consumption” all have positive influences on ESV. In
addition, there should be singleness in the urban function, which means
that city space needs to be separated to serve different functions. The
extraction of high-level urban cognizable factors related to ESV in the
penultimate layer may be a new dimensionality reduction method, and the
analysis suggested that the city scale of Nanjing can truly affect the
ESV. As a result, it is possible for decision-makers to provide policy
guidance and adjust urban features to realize the coordinated
development of the regional economy and ecological functions. For
instance, the most suitable city scale can be found that is within the
regional ecological carrying capacity.
In this work, the relationship between human socioeconomic development
and ESV on the urban scale is at the heart of our research. We built a
deep learning model based on the limited socioeconomic factors (X) to
cognize it and obtained interesting and meaningful results. Furthermore,
our point of view is that there are likely to be obvious differences in
the driving mechanisms under diverse regional and scale contexts.
Therefore, an important direction for further research is the
investigation of more influence patterns and mechanisms on diverse
spatial scales and levels of socioeconomic development affecting the
change in regional ESV.
Acknowledge
This work was supported by the Environmental Protection Research Project
of Jiangsu Province(No. 2018008)and the Environmental Science and
Technology Project of Nanjing(No.201904). The authors thank Prof.
Jiangang Xu for valuable comments and discussion.
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