Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Metropolitan areas are being challenged by the disparity between growing societal needs and dwindling natural resource provision. Understanding the supply-demand mismatches of ecosystem services (ES) and their drivers is essential for landscape planning and decision-making. However, integrating such information into spatial planning remains challenging due to the complex nature of urban ecosystems and their intrinsic interactions. In this study, we first assessed and mapped the supply, demand, and mismatches of six typical ES in Tianjin, China. We then clustered numerous townships based on their corresponding spatial characteristic of ES supply-demand mismatches. We also used Random Forest regression to examine the relative importance of drivers and applied Partial Least Squares structural equation modelling to decouple their interactions. The results showed that, the distribution of ES supply and demand showed obvious spatial heterogeneity, with a common surplus of ES supply in highly natural mountainous region and an excess of demand in urban centre. Additionally, all towns were classified into four spatial clusters with homogeneous states of supply-demand mismatches, serving as basic units for spatial optimization. Moreover, the interactions between drivers affected ES supply-demand mismatches in a coupled manner, including the direct effects of the socioeconomic factor (-0.821) and landscape composition (0.234), as well as the indirect effects of the biophysical factor (0.151) and landscape configuration (0.082). Finally, we discussed the utility of analysing the spatial mismatches between ES supply and demand for integrated territorial planning and coordinated decision-making.
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Source |
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http://dx.doi.org/10.1016/j.scitotenv.2023.165067 | DOI Listing |
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