Analysis of Green Total Factor Productivity of Grain and Its Dynamic Distribution: Evidence from Poyang Lake Basin, China

Bingfei Bao, Shengtian Jin, Lilian Li, Kaifeng Duan and Xiaomei Gong
Additional contact information
Bingfei Bao: Institute of Statistics and Applied Mathematics, Anhui University of Finance & Economics, Bengbu 233000, China
Shengtian Jin: School of Finance, Anhui University of Finance & Economics, Bengbu 233000, China
Lilian Li: School of Economics and Management, Tongji University, Shanghai 200092, China
Kaifeng Duan: School of Economics and Management, Tongji University, Shanghai 200092, China
Xiaomei Gong: School of Public Economics and Administration, Shanghai University of Finance and Economics, Shanghai 200433, China

Agriculture, 2021, vol. 12, issue 1, 1-16

Abstract: Based on the grain production data of the counties (cities, districts) in Poyang Lake Basin, this paper uses the productivity index of Epsilon Based Measure of Malmquist Luenberger (EBM-ML Index) to analyse the green total factor productivity (GTFP) of grain in Poyang Lake Basin. Kernel density function and Markov analysis are used to discuss the dynamic evolution process of the distribution of GTFP of grain. The results show the following: (1) From the time dimension, the GTFP of grain is on the rise and fluctuates more frequently from 2001 to 2017, and its trend of change is determined by the combination of technical efficiency and technological progress. Moreover, from a spatial dimension, the number of counties (cities, districts) with GTFP of grain greater than 1.0 has shown an overall increase, indicating that the overall level of GTFP of grain is increasing. (2) According to the kernel density estimation results, the crest of the main peak of the kernel density curve corresponding to the GTFP of grain in Poyang Lake Basin shifts to the right, and the area formed by the right part of the GTFP of grain corresponding to the crest of the main peak of its kernel density curve gradually increases. The peak of the kernel density curve changes from “multi-peak mode” to “single-peak mode,” and the height of the main peak of the kernel density curve of GTFP of grain shows an overall decrease. Meanwhile, the right tail of the kernel density curve shows an overall extending trend. (3) According to the estimation results of the Markov chain, the GTFP of grain in Poyang Lake Basin is highly mobile from 2001 to 2017, and the counties (cities, districts) have a certain degree of agglomeration in the low, medium-low, medium-high and high levels. In other words, the long-term equilibrium state of growth of GTFP of grain remains dispersed in the state space of four level types, indicating that the divergence state of GTFP of grain in counties (cities, districts) of Poyang Lake Basin will continue for a long time in the future. The study reveals the evolution and dynamic change of GTFP of grain in Poyang Lake Basin, which has important theoretical significance and practical value for optimizing the spatial pattern and realizing the balanced development of GTFP among counties (cities, districts) of Poyang Lake Basin and consolidating China’s food security strategy.

Keywords: GTFP of grain; kernel density function; Markov chain; Poyang Lake Basin (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/2077-0472/12/1/8/pdf (application/pdf)
https://www.mdpi.com/2077-0472/12/1/8/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:12:y:2021:i:1:p:8-:d:708725

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().