Research On The Promotion Path Of Logistics Industry And Manufacturing Industry Collaborative Agglomeration

The logistics industry is a fundamental,strategic,and leading industry that supports the development of the national economy,while the manufacturing industry is the mainstay of the national economy and the main source of social logistics demand.Both are also the "main battlefield" of the supply-side structural reform.Promoting the deep integration of the logistics industry and the manufacturing industry(referred to as "two industries")is of great significance for deepening the supply-side structural reform and realizing the construction of a modern economic system as proposed in the report of the 20 th National Congress of the Communist Party of China.The synergistic aggregation of the logistics industry and the manufacturing industry(referred to as "two industries synergistic aggregation”)is a concrete manifestation of the deep integration of the two.Therefore,exploring and analysing the promotion path of the synergistic aggregation of the two industries is not only conducive to promoting the deep integration of the two,but also to achieving high-quality development of the social economy.Based on this,first,this dissertation constructs a theoretical analysis framework based on configurational thinking and panel multiple regression analysis principles,on the basis of sorting out relevant theories,concepts,and literature.Secondly,the synergistic aggregation of the two industries is measured,and its evolutionary characteristics are analysed from both temporal and spatial dimensions.Thirdly,using the dynamic qualitative comparative analysis method(QCA),explore the high/non-high paths of the synergistic aggregation of the two industries from both temporal and spatial dimensions.Finally,based on the principles of panel multiple regression analysis,the marginal effects of the various high synergistic aggregation enhancement paths of the two industries are quantitatively analysed to deepen the results of the configuration path.The main research conclusions of this dissertation are as follows:Firstly,the overall level of synergistic aggregation between China’s logistics and manufacturing industries is relatively low,displaying an "N"-shaped development trend.The level of agglomeration in the logistics industry is significantly lower than that in the manufacturing industry,and there exists a clear regional mismatch between the two,leading to poor synergy between the logistics and manufacturing industries in many provinces and cities in China.The agglomeration of synergy exhibits certain characteristics such as path dependence,hierarchical heterogeneity,and same-level contiguity between geographically adjacent provinces and cities in the temporal and spatial evolution,presenting an "N"-shaped development trend,which has been continuously rising in recent years after experiencing two significant ups and downs.Secondly,the synergistic aggregation of the two industries are the complex results of multiple factors.A single factor does not constitute a necessary condition the synergistic aggregation of the two industries.From an inter-group perspective,although the information technology level does not constitute a necessary single-factor condition for impacting the path of high level of synergistic aggregation of the two industries,its importance is increasing year by year,showing a significant time effect.From an intra-group perspective,social innovation level,the degree of openness,and weak level of transportation infrastructure have obvious individual differences among some provinces.Thirdly,the path of the synergistic aggregation of the two industries exhibits diversified characteristics.This study has identified seven high synergistic aggregation enhancement paths,further refined into four configurations: pure government support-driven,government supportdriven under the absence of environmental regulation,social environment compensation under the absence of environmental regulation,and two-way policy guidance and social environmentdriven.Additionally,four non-high synergistic aggregation paths are also identified.Overall,the level of government support is a core condition in all seven high synergistic aggregation enhancement paths,while it is entirely missing in all four non-high synergistic aggregation paths,and there is no significant time effect or individual difference among all seven high synergistic aggregation enhancement paths.However,in the time dimension,the consistency of the high synergistic aggregation enhancement paths between 2018 and 2020 showed significant fluctuations,presenting an "outer trumpet-shaped" trend.In the spatial dimension,through the single-factor variance analysis and Kruskal-Wallis rank sum test,it is found that there exist significant regional differences in the case coverage distribution of some high synergistic aggregation enhancement paths,such as paths 4 and 6.Fourthly,among the high synergistic aggregation enhancement paths,the marginal effect of paths H5-H7 is the highest,followed by paths H3-H4,and the marginal effect of paths H1-H2 is the lowest.The marginal effect of each high synergistic aggregation enhancement path is greater than the net effect of each univariate variable included in that path,indicating that the organic combination and joint effort of each preceding independent variable are more conducive to the improvement of the high synergistic aggregation of the two industries.The main innovative points of this article are:Firstly,based on the configuration perspective,constructing a theoretical analysis framework for the synergistic aggregation enhancement paths of the two industries.Due to the limitations of traditional quantitative research paradigms in revealing the impact effects of various independent variables on the dependent variable and the difficulty in observing the causal complexity between the independent and dependent variables as a whole,this article,based on the configurational perspective,constructs the core theoretical analysis framework of the "two industries" collaborative agglomeration promotion path,taking into account the possible multiple concurrent reasons caused by the mutual combination of various independent variables on the results.To deepen the configuration results,based on the principles of panel multiple regression,the configurational effects of the enhancement path of high synergistic aggregation of the two industries were quantitatively analyzed,providing a new perspective for the analysis of the enhancement path of high synergistic aggregation of the two industries.Secondly,analysing the enhancement path of the synergistic aggregation of the two industries based on the dynamic QCA analysis method.Given the limitations of traditional QCA analysis methods in cross-sectional data,this article breaks through the barrier between panel data and traditional QCA analysis methods by using R language software and adopts the dynamic QCA analysis method to explore the configurational path of the synergistic aggregation of the two industries from the temporal and spatial dimensions.Meanwhile,the enhanced standard analysis(ESA)is used throughout to improve the configurational accuracy.The configurational path is analysed from three dimensions: inter-group,intra-group,and overall,and the consistency adjustment distance is used to capture the changes in consistency in the temporal and spatial dimensions.Thirdly,constructing a panel multiple regression model to examine the marginal effects of the high synergistic aggregation enhancement paths of the two industries,and a quantitative test was conducted to deepen the configurational path results.Based on the configurational results of high synergistic aggregation enhancement paths,a panel multiple regression model is constructed with the coordinated agglomeration level of the "two industries" as the only dependent variable and 8 influencing factors such as "industry association level" as independent variables.The regression analysis is carried out using the time-individual fixed effects model.Based on the 7 high synergistic aggregation enhancement paths,and the regression coefficients of each preceding independent variables as weights,the preceding independent variables in each high synergistic aggregation enhancement path are integrated into one configurational variable and brought into the model together with other preceding independent variables not appearing in the path for quantitative analysis of the high synergistic aggregation enhancement path configurational effects,thereby deepening the configurational path results.