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Article

Dynamic analysis of spatial convergence of green total factor productivity in China’s primary provinces along its Belt and Road Initiative

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Pages 101-112 | Received 25 Dec 2018, Accepted 26 Mar 2019, Published online: 15 May 2019
 

ABSTRACT

This paper focuses on China’s primary provinces along its Belt and Road Initiative, measures and analyses the provincial green total factor productivity (GTFP) development by GML index based on SBM directional distance function, discusses the temporal evolution characteristics of spatial convergence of the provincial GTFP under multiple spatial weight matrices, and estimates the net effect of China’s Belt and Road Initiative on the development gap of provincial GTFP by regression discontinuity. The research shows that the provincial GTFP is generally good while the internal gap is relatively large. There is spatial absolute βconvergence and condition βconvergence in provincial GTFP, and the convergence speed has been significantly accelerated since the construction of China’s Belt and Road Initiative. The current low economy level and infrastructure allocation efficiency of primary provinces restrict the GTFP development, while government expenditure has promoted the GTFP development, trade between provinces and countries along China’s Belt and Road Initiative is negative to GTFP. China’s Belt and Road Initiative has significantly narrowed the gap of provincial GTFP development with the net effect of −0.016.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This paper is supported by Social science planning project in Shandong Province “Study on the Growth Effect of Import and Export Trade Between Shandong Province and Countries along the Belt and Road” [Grant number: 17CJJJ33]; The 48th batch of scientific research fund projects for returned students studying abroad initiated by the ministry of education “Study on the Impact of Carbon Tariff on Economic Development of Shandong Province and Countermeasures” [Grant number: [2014]1685].

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