A Gravity Model Study on the Impact of the "Belt and Road" Trade Facilitation on Agricultural Products Trade
DOI: 10.54647/economics790388 104 Downloads 161802 Views
Author(s)
Abstract
In order to solve the problem that China's agricultural trade prospects in the "the Belt and Road economic belt" are unclear, this paper proposes a prediction system based on the stochastic frontier gravity model.This process examines the current situation of agricultural industry and the change of economic structure in China and the five Central Asian countries from three aspects of import and export. Model agricultural products and country models, and assess the agricultural potential of both sides in the current state. Comparative advantage index and market complementarity index are used to analyze competition and integration. Then, from the country level and product level of China's agricultural exports to the five Central Asian countries, the market analysis and market value calculation. The results show that in China's export market, the second type of agricultural products are mainly exported to the five Central Asian countries, accounting for more than 50%, and about 1/4% of the first and fourth types of agricultural products. ; The average export efficiency of China's agricultural products to the five Central Asian countries is 71.8%. The average import efficiency of agricultural products between China and the five Central Asian countries was 79.6%. This proves the reliability of the framework proposed in this paper for forecasting changes in China's agricultural and export markets through economic support.
Keywords
The Belt and Road; Agricultural products trade; Random frontier gravitational model; Trade efficiency
Cite this paper
Hongyi Sun,
A Gravity Model Study on the Impact of the "Belt and Road" Trade Facilitation on Agricultural Products Trade
, SCIREA Journal of Economics.
Volume 8, Issue 3, June 2023 | PP. 132-155.
10.54647/economics790388
References
[ 1 ] | Springer, C. H. , Evans, S. , & Fei, T. . (2021). An empirical analysis of the environmental performance of china's overseas coal plants. Environmental Research Letters, 16(5), 054062 (11pp). |
[ 2 ] | Li, X. , Liu, C. , Wang, F. , Ge, Q. , & Hao, Z. . (2020). The effect of chinese investment on reducing co2 emission for the belt and road countries. Journal of Cleaner Production, 288(1), 125125. |
[ 3 ] | Li, Z. , Deng, X. , & Zhang, Y. . (2021). Evaluation and convergence analysis of socio-economic vulnerability to natural hazards of belt and road initiative countries. Journal of Cleaner Production, 282(5), 125406. |
[ 4 ] | Tritto, A. . (2021). China's belt and road initiative: from perceptions to realities in indonesia's coal power sector. Energy Strategy Reviews, 34(12), 100624. |
[ 5 ] | Tuninetti, M. , Ridolfi, L. , & Laio, F. . (2020). Charting out the future agricultural trade and its impact on water resources. The Science of the Total Environment, 714(Apr.20), 136626.1-136626.12. |
[ 6 ] | Zhang, R. , Xing, D. , & Wang, C. . (2021). Pancreatic triglyceride lipase inhibitors derived from natural products: how to dig into the truth. Journal of agricultural and food chemistry, 69(22), 6097-6099. |
[ 7 ] | Deng, G. , Lu, F. , Wu, L. , & Xu, C. . (2020). Social network analysis of virtual water trade among major countries in the world. Science of The Total Environment, 753(1), 142043. |
[ 8 ] | Sunge, R. , & Ngepah, N. . (2020). Agricultural trade liberalization, regional trade agreements and agricultural technical efficiency in africa:. Outlook on Agriculture, 49(1), 66-76. |
[ 9 ] | Liu, Y. , Zhuo, L. , Varis, O. , Fang, K. , & Wu, P. . (2021). Enhancing water and land efficiency in agricultural production and trade between central asia and china. Science of The Total Environment, 780(4), 146584. |
[ 10 ] | Li, L. , & Zhu, H. . (2020). Analysis on trade effect of green barriers and on agricultural product export and maritime transport in china. Journal of Coastal Research, 115(sp1), 477. |
[ 11 ] | Rosario, Z. D. , Rupp, M. , Kim, Y. , Antono, E. , & Ling, J. . (2020). Assessing the frontier: active learning, model accuracy, and multi-objective candidate discovery and optimization. The Journal of Chemical Physics, 153(2), 024112. |
[ 12 ] | Gorard, J. . (2020). Some relativistic and gravitational properties of the wolfram model. Complex Systems, 29(2), 599-654. |
[ 13 ] | Leite, D. , Pessanha, J. , Simes, P. , Calili, R. , & Souza, R. . (2020). A stochastic frontier model for definition of non-technical loss targets. Energies, 13(12), 3227. |
[ 14 ] | Karakoc, D. B. , & Konar, M. . (2021). A complex network framework for the efficiency and resilience trade-off in global food trade. Environmental Research Letters, 16(10), 105003-. |
[ 15 ] | Peng, H. R. , Qi, S. Z. , & Zhang, Y. J. . (2021). Does trade promote energy efficiency convergence in the belt and road initiative countries?. Journal of Cleaner Production, 322(5), 129063. |
[ 16 ] | Xue, X. , & Chen, J. . (2020). Optimizing sensor ontology alignment through compact co-firefly algorithm. Sensors, 20(7), 2056. |
[ 17 ] | Martinez, C. , Abro, T. , & Martinez, A. . (2021). Energy and spectral efficiency trade-off in ocdma-pon assisted by non-linear programming methods. Computer Networks, 189(4), 107920. |
[ 18 ] | Jyotsna Dogra, Shruti Jain, Ashutosh Sharma, Rajiv Kumar and Meenakshi Sood.(2020).Brain Tumor Detection from MR Images Employing Fuzzy Graph Cut Technique.Recent Advances in Computer Science and Communications,13(3),362 - 369. |
[ 19 ] | P., Ajay & J., Jaya. (2022). Bi-level energy optimization model in smart integrated engineering systems using WSN. Energy Reports. 8. 2490-2495. |
[ 20 ] | Zhao, X. L. , Liu, X. , Liu, J. , Chen, J. , Fu, S. , & Zhong, F. . (2019). The effect of ionization energy and hydrogen weight fraction on the non-thermal plasma vocs removal efficiency. Journal of Physics D Applied Physics. |
[ 21 ] | R. Huang, X. Yang, "The application of TiO2 and noble metal nanomaterials in tele materials," Journal of Ceramic Processing Research, vol. 23, no. 2, pp. 213–220, 2022. |
[ 22 ] | Zhan, X., Mu, Z., Kumar, R. & Shabaz, M. (2021). Research on speed sensor fusion of urban rail transit train speed ranging based on deep learning. Nonlinear Engineering, 10(1), 363-373. |