Artikel

How can cities learn from each other? Evidence from China's five-year plans

International organizations such as the United Nations, the World Bank, the Organization for Economic Cooperation and Development, and the International Monetary Fund routinely organize cross-learning programs on specific topics for their member governments. Likewise, many national governments organize offers little theoretical or practical guidance on how best to organize such cross-learning activities. One fundamental question is whether to proceed on the basis of cohort- or task-oriented programs, where a cohort-based approach would emphasize shared, institutionalized learning over time amongst local governments with shared planning priorities. To assess this question, we use a case study comparing 286 cities and their avowed priorities for China's 11th and 12th Five-Year Plans. The evidence from our case study supports a task-rather than a cohort-oriented approach. Moreover, because of China's unique administrative structure, with an integrated approach entailing proactive national level guidance and directives, we conclude that for most other countries a cohort-oriented approach would be even less effective. The practical implication of these results is that a task-oriented approach to cross-learning is more advisable.

Sprache
Englisch

Erschienen in
Journal: Journal of Urban Management ; ISSN: 2226-5856 ; Volume: 9 ; Year: 2020 ; Issue: 2 ; Pages: 216-227

Klassifikation
Landschaftsgestaltung, Raumplanung
Thema
Institutional learning
Issue-based clustering approach
Cross-learning
Five-year plan
Chinese cities

Ereignis
Geistige Schöpfung
(wer)
Xu, Ying
Heikkila, Eric J.
Ereignis
Veröffentlichung
(wer)
Elsevier
(wo)
Amsterdam
(wann)
2020

DOI
doi:10.1016/j.jum.2020.04.002
Handle
Letzte Aktualisierung
10.03.2025, 11:42 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Xu, Ying
  • Heikkila, Eric J.
  • Elsevier

Entstanden

  • 2020

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