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: 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
To address global sustainability challenges, (public) policy interventions are needed to induce or accelerate technological change. While most policy interventions occur on the local level, their innovation effects can spill over to other jurisdictions, potentially having global impact. These spillovers can increase or reduce the incentive for interventions. Lacking to date are computational models that capture these spillover dynamics. Here, we devise a conceptual and methodological approach to quantify ex ante the effects of local demand-side interventions on global competition between incumbent and novel technologies. We introduce two factors that moderate global spillovers-relative size of selection environments and relative innovation potential of competing technologies. Our approach incorporates both factors in a techno-economic discrete choice model that evaluates technology competition over time through endogenized technological learning. We apply this modeling framework to the case of road freight. Different demand-pull interventions and shocks are modeled to assess spillover effects. In the case of road freight, electric vehicles experience growth in most application segments but can still be accelerated substantially through public policy intervention-spillovers occur if strong public interventions are introduced in large regions or in multiple combined regions under club policy interventions. These findings are discussed in the context of club policy interventions and a modeled geopolitical shock in China. A full sensitivity analysis of model input parameters and intervention or shock dynamics reveals high model robustness. Finally, we discuss the implications of the road-freight case study as it might inform the progress of other niche technologies in transitioning sectors.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10589666 | PMC |
http://dx.doi.org/10.1073/pnas.2215684120 | DOI Listing |
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