Abstract
Improving green total factor productivity (GTFP) is an important theme. Whether collaborative agglomeration between logistics industry and manufacturing (LMCA) can effectively promote GTFP is worth further research. Based on the panel data of 284 cities in China from 2005 to 2018, GTFP is calculated by using the Biennial Malmquist-Luenberger productivity index (BMLPI), and this research investigates the impact of LMCA on GTFP by adopting the spatial Durbin model (SDM) and threshold regressive model (TRM). First, LMCA plays a significant role in promoting the improvement of GTFP in the local and surrounding areas through the knowledge spillover effect, scale economy effect, resource allocation effect and symbiotic economic effect, and the spillover effect is greater than the local effect. Second, the positive direct effect of LMCA on GTFP comes mainly from technological progress, and the positive indirect effect of LMCA on GTFP comes mainly from the positive spillover effect of technological progress and technical efficiency improvement. Finally, the Williamson hypothesis exists significantly in the collaborative agglomeration scenario of the logistics industry and manufacturing of China. With the improvement of the level of economic development, the impact of LMCA on GTFP changes from insignificant to promoting. However, when it is further improved, the promoting effect turns into an inhibiting effect, and this change is dominated mainly by the impact of LMCA on technical change.
Highlights
The report of the 19th National Congress of the Communist Party of China pointed out that China's economic development must adhere to the principle of quality first and efficiency first and improve total factor productivity (TFP) by promoting quality reform, efficiency reform and dynamic changes in economic development
Many scholars have discussed the impact of industrial collaborative agglomeration on economic growth, environmental pollution and TFP from various aspects and there are a few studies on the impact of manufacturing and producer services collaborative agglomeration on Green total factor productivity (GTFP), there is a lack of research on the relationship between LMCA and GTFP, and the literature often ignores the spatial spillover effect or nonlinear effect
When PGDP was not more than 9490 yuan per year, the estimated coefficient of lnLMCA was –0.054, but it is not significant, which denotes that LMCA has no impact on GTFP when LMCA is lower than the first threshold, possibly because when the economic development level is low, the degree of industrial specialization is low, and the synergy effect is weak, which leads to the failure of the economic growth effect
Summary
The report of the 19th National Congress of the Communist Party of China pointed out that China's economic development must adhere to the principle of quality first and efficiency first and improve total factor productivity (TFP) by promoting quality reform, efficiency reform and dynamic changes in economic development. The unique phenomenon of the coexistence of "complementarity" and "extrusion" characteristics of collaborative agglomeration between logistics industry and manufacturing (LMCA) has attracted increasing attention [9] Whether this unique form of agglomeration can effectively improve GTFP is worthy of further study. The marginal contribution of this paper is as follows: First, from the perspective of spatial spillover, this paper constructs a theoretical model between LMCA and GTFP for the first time, which provides a theoretical analysis framework for revealing the impact mechanism of industrial collaborative agglomeration on GTFP Considering both the spatial spillover effect and threshold effect, based on panel data of 284 cities in China from 2005 to 2018, this paper constructs a spatial Durbin model (SDM) and threshold regression model (TRM), respectively, which provides more robust empirical evidence for the relationship between industrial agglomeration and GTFP. The other sections are organized as follows: Section II shows a literature review; Section III constructs the mechanism analysis framework; Section IV introduces the research design, which includes data, methods and variable selection; Section V gives empirical analysis results and discussion; and Section VI draws research conclusions and policy implications
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