Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&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: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
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
This research presents the methods that are used to examine the dynamics and potential spillover effects of various global environmental conservation programs. We specifically show the data and models that we use to analyze the interactions and mutual influences between the U.S.'s Conservation Reserve Program (CRP) and Environmental Quality Incentives Program (EQIP), as well as those between China's Grain-to-Green Program (GTGP) and Forest Ecological Benefit Compensation (FEBC). Additionally, this study illustrates information about global initiatives, their interconnected impacts, and the associated policy strategies for environmental conservation. By utilizing multivariate regression, logistic regression, eigenvector spatial filtering, and scenario modeling, the research aims to understand the collective influence of these initiatives on broader environmental objectives. The findings of this study provide valuable insights for improving conservation policy designs and effectiveness.•Multivariate and logistic regression analyses to dissect global environmental conservation program interactions and mutual influences.•Eigenvector spatial filtering to address spatial autocorrelation and enhance the accuracy of the model results and our interpretations.•Scenario modeling to project potential future outcomes and impacts.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11067531 | PMC |
http://dx.doi.org/10.1016/j.mex.2024.102672 | DOI Listing |
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