Abstract

Environmental factors in time and space play a critical role in advancing the sustainable development of the fresh agricultural product supply chain. This paper, availing the panel data of 31 Chinese provinces from 2008 to 2019, constructs a system of indicators assessing the development of the fresh agricultural product supply chain, and obtains the comprehensive development level in the Entropy Weight Method (EWM). Furthermore, it establishes a comparison between optimal solutions generated by the Instrumental Variables Method (IVM) and the Generalized Method of Moments (GMM) over the endogeneity issue of variables, creates the comparison between the weighted regression methods of Geographically Weighted Regression (GWR) and Multi-scale Geographic Weighted Regression (MGWR), and obtains the relationship among the 14 environmental factors in their spatio-temporal impacts on the development of the fresh agricultural product supply chain. The results indicate that: (1) the environmental influencing factors in this paper have significant endogenous problems and various environmental factors impact on the fresh agricultural product supply chain in different trends and to different degrees. (2) With different bandwidths, the environmental factors could impact the fresh agricultural product supply chain to greatly varied degrees, demonstrating a strong attribute of regional correlation.

Highlights

  • In recent years, fresh agricultural products have been taking an increasingly larger proportion of people’s daily consumption

  • The average retail price index of fresh agricultural products and the average Consumer Price Index (CPI) of fresh agricultural products fall into the category of downstream indicators. The former indicates the average value of the ratios of the retail price of various fresh agricultural products in the current year to that of the previous year and represents average changes in the retail prices of various fresh agricultural products, and reflects the stability of the supply chain. The latter refers to the average value of the ratios of the consumer price of various fresh agricultural products in the current year to that of the previous year, which could reflect the changes in the power of money in purchasing various fresh agricultural products from the perspective of consumers, and reveal the efficiency of the supply chain management

  • As the socialism with Chinese characteristics has crossed the threshold into a new era, the Chinese population has grown increasingly demanding for food, which makes it more urgent to shatter the traditional operation model of supply chains and proactively promote the integrated innovation of the fresh agricultural product supply chain (FAPSC)

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Summary

Introduction

Fresh agricultural products have been taking an increasingly larger proportion of people’s daily consumption. The performance of the food supply chain, including food security and nutrition, is subject to the influence of various factors, such as the climate, economy and human activities [2] On this basis, the fresh agricultural product supply chain (FAPSC) faces greater uncertainty in value and quality [3]. This paper, availing the panel data of 31 Chinese provinces from 2008 to 2019, constructs a system of indicators assessing the development of the FAPSC and obtains the comprehensive development level in the EWM It avails four model methods, namely the IVM, the GMM, the GWR and the MGWR, to analyze the relationship among the impacts of the natural, economic, and social environment on various indicators of the upstream, mid-stream and downstream of the FAPSC. This paper is of significance mainly in that it provides a theoretical basis for the coordinated development between environment and the FAPSC and for the differentiated, sustainable development among regions

Model Methods
Development Assessment Indicators for the FAPSC
Indicators of Environmental Factors
Data Interpretation
Weight Calculation for Indicators of the Development of China’s FAPSC
Analysis of the Development of China’s FAPSC
Average
Model Specifications
PCDIUH
Test for Endogeneity and Selection of Instrumental Variables
Two-Stage Least Squares
Model Design
Optimal GMM Estimation
Weak Instrumental Variable Test
Over Identification Test
Model Comparison
Scale Analysis
Indicator Analysis
Spatial
Findings
Conclusions and Suggestions
Full Text
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