Abstract
Continuously improving freshwater aquaculture efficiency will promote the sustainable development of freshwater aquaculture, which is crucial to ensure aquatic food supply. In particular, measuring the total factor productivity (TFP) of freshwater aquaculture to find ways to improve its efficiency is of great significance to sustainable development of freshwater aquaculture industry. Therefore, based on directional distance function, this paper constructs a meta-frontier Malmquist index (MMI) model by considering the regional heterogeneity to evaluate the TFP of freshwater aquaculture of China from 2004 to 2019. The results show that (1) from the perspective of time, TFP fluctuated significantly from 2004 to 2012, while after 2013, TFP remained around 1 with small fluctuation. In other words, freshwater aquaculture in China began to maintain a relatively negative state of development. (2) From a regional point of view, this study found that freshwater aquaculture TFP of inland region is better than the TFP of coastal region. (3) From the decomposition index, the variation of freshwater aquaculture TFP was driven by the combined effect of technology change (TC) and technical efficiency change (EC). In addition, the decomposition index efficiency shows that the technical efficiency decreases and the management efficiency changes little. (4) The gap of freshwater aquaculture technology in coastal areas is very small, and close to the optimal technical level. While in inland region, on the contrary, there is more room for improvement. According to the above empirical results, this paper finally gives some policy suggestions to improve the TFP to ensure the sustainable development of freshwater aquaculture.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
References
Aigner D, Lovell CAK, Schmidt P (1977) Formulation and estimation of stochastic frontier production function models. J Econ 6(1):21–37. https://doi.org/10.1016/0304-4076(77)90052-5
Asche F, Guttormsen AG, Nielsen R (2013) Future challenges for the maturing Norwegian salmon aquaculture industry: an analysis of total factor productivity change from 1996 to 2008. Aquaculture 396-399:43–50. https://doi.org/10.1016/j.aquaculture.2013.02.015
Battese GE, Coelli TJ (1995) A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empir Econ 20(2):325–332. https://doi.org/10.1007/BF01205442
Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2(6):429–444. https://doi.org/10.1016/0377-2217(78)90138-8
Chung YH, Färe R, Grosskopf S (1997) Productivity and undesirable outputs: a directional distance function approach. J Environ Manag 51(3):229–240. https://doi.org/10.1006/jema.1997.0146
Coelli T (1998) A multi-stage methodology for the solution of orientated DEA models. Oper Res Lett 23(3):143–149. https://doi.org/10.1016/S0167-6377(98)00036-4
Chinese Academy of Fishery Sciences (2020) Fisheries statistical yearbook of China 2020. China Agricultural Press, Beijing
Färe R, Grosskopf G, Lovell CAK, Pasurka C (1989) Multilateral productivity comparisons when some outputs are undesirable: a nonparametric approach. Rev Econ Stat 71(1):90–98. https://doi.org/10.2307/1928055
Färe R, Grosskopf S, Norris M, Zhang ZY (1994) Productivity growth, technical progress, and efficiency change in industrialized countries. Am Econ Rev 84(1):66–83
FAO (2018) The state of world fisheries and aquaculture. Rome, Italy
FAO (2020) The state of world fisheries and aquaculture. Rome, Italy
Hayami Y, Ruttan VW (1971) Agricultural development:an International perspective. Baltimore, London
Iliyasu A, Mohamed ZA, Ismail MM, Amin AM, Mazuki H (2016) Technical efficiency of cage fish farming in Peninsular Malaysia: a stochastic frontier production approach. Aquac Res 47(1):101–113. https://doi.org/10.1111/are.12474
Iliyasu A, Mohamed ZA (2016) Evaluating contextual factors affecting the technical efficiency of freshwater pond culture systems in Peninsular Malaysia: a two-stage DEA approach. Aquaculture Reports 3:12–17. https://doi.org/10.1016/j.aqrep.2015.11.002
Jamnia AR, Mazloumzadeh SM, Keikha AA (2013) Estimate the technical efficiency of fishing vessels operating in Chabahar region, Southern Iran. J Saudi Soc Agric Sci 14(1):26–32. https://doi.org/10.1016/j.jssas.2013.04.005
Jennings S, Stentiford GD, Leocadio AM, Jeffery KR, Metcalfe JD, Katsiadaki I, Auchterlonie NA, Mangi SC, Pinnegar JK, Ellis T, Peeler EJ, Luisetti T, Baker-Austin C, Brown M, Catchpole TL, Clyne FJ, Dye SR, Edmonds NJ, Hyder K et al (2016) Aquatic food security: insights into challenges and solutions from an analysis of interactions between fisheries, aquaculture, food safety, human health, fish and human welfare, economy and environment. Fish and fisheries (Oxford, England) 17(4):893–938. https://doi.org/10.1111/faf.12152
Ji JY, Wang PP (2015) Research on China’s aquaculture efficiency evaluation and influencing factors with undesirable outputs. J Ocean Univ China 14(3):569–574. https://doi.org/10.1007/s11802-015-2679-9
Kumaran M, Anand PR, Kumar JA, Ravisankar T, Johnson P, Vasagam KPK, Vimala DD, Raja KA (2017) Is Pacific white shrimp (Penaeus vannamei) farming in India is technically efficient? — a comprehensive study. Aquaculture 468:262–270. https://doi.org/10.1016/j.aquaculture.2016.10.019
Kumbhakar SC, Lovell CAK (2000) Stochastic frontier analysis. U.K, Cambridge
Lin BQ, Ge JM (2019) Carbon sinks and output of China’s forestry sector: an ecological economic development perspective. Sci Total Environ 655:1169–1180. https://doi.org/10.1016/j.scitotenv.2018.11.219
Malmquist S (1953) Index numbers and indifference surfaces. Trab Estad 4:209–242. https://doi.org/10.1007/bf03006863
Meeuseen W, Van den Broeck J (1977) Efficiency measurement from CobbDouglass production functions with composed error. Int Econ Rev 18:435–444. https://doi.org/10.2307/2525757
Ministry of Agriculture of the PRC (2013) Interpretation of China fisheries statistical yearbook of 2013 by the Ministry of Agriculture. http://www.gov.cn/gzdt/2013-06/13/content_2425505.htm
Mitra S, Khan MA, Nielsen R, Islam N (2020) Total factor productivity and technical efficiency differences of aquaculture farmers in Bangladesh: do environmental characteristics matter? J World Aquacult Soc 51(4):918–930. https://doi.org/10.1111/jwas.12666
National Bureau of Statistic of China (2009) Shanxi’s fishery is gradually developing in the process of reform and opening up. http://www.stats.gov.cn/ztjc/ztfx/dfxx/200904/t20090410_34734.html
O'Donnell C, Rao D, Battese G (2008) Metafrontier frameworks for the study of firm-level efficiencies and technology ratios. Empir Econ 34(2):231–255. https://doi.org/10.1007/s00181-007-0119-4
Oh DH, Lee JD (2010) A metafrontier approach for measuring Malmquist productivity index. Empir Econ 38(1):47–64. https://doi.org/10.1007/s00181-009-0255-0
Pastor JT, Lovell CK (2005) A global Malmquist productivity index. Econ Lett 88(2):266–271. https://doi.org/10.1016/j.econlet.2005.02.013
Pipitone V, Colloca F (2018) Recent trends in the productivity of the Italian trawl fishery: the importance of the socio-economic context and overexploitation. Mar Policy 87:135–140. https://doi.org/10.1016/j.marpol.2017.10.017
Sarker MAA, Arshad FM, Alam MF, Mohamed ZA, Khan MA (2016) Stochastic modeling of production risk and technical efficiency of Thai koi (Anabas testudineus) farming in Northern Bangladesh. Aquac Econ Manag 20(2):165–184. https://doi.org/10.1080/13657305.2016.1156189
Simar L, Wilson PW (1998) Sensitivity analysis of efficiency scores: how to bootstrap in nonparametric frontier models. Manag Sci 44(1):49–61. https://doi.org/10.1287/mnsc.44.1.49
Song ML, Zheng WP (2016) Computational analysis of thermoelectric enterprises’ environmental efficiency and Bayesian estimation of influence factors. Soc Sci J (Fort Collins) 53(1):88–99. https://doi.org/10.1016/j.soscij.2015.10.002
Squires D (1992) Productivity measurement in common property resource industries: an application to the Pacific Coast trawl fishery. RAND J Econ 23(2):221–236. https://doi.org/10.2307/2555985
Troell M, Naylor RL, Metian M, Beveridge M, Tyedmers PH, Folke C, Arrow KJ, Barrett S, Crepin AS, Ehrlich P, Gren R, Kautsky N, Levin SA, Nyborg K, Osterblom H, Polasky S, Scheffer M, Walker BH, Xepapadeas T, Zeeuw de A (2014) Does aquaculture add resilience to the global food system? Proceedings of the National Academy of Sciences - PNAS 111(37):13257–13263. https://doi.org/10.1073/pnas.1404067111
Wang PP, Ji JY (2017) Research on China’s mariculture efficiency evaluation and influencing factors with undesirable outputs—an empirical analysis of China’s ten coastal regions. Aquac Int 25(4):1521–1530. https://doi.org/10.1007/s10499-017-0131-4
Xu JJ (2020) Research on the efficiency of marine aquaculture production in China’s coastal provinces based on the DEA-Malmquist index. J Guizhou Provincial Party Sch 01:57–68. https://doi.org/10.16436/j.cnki.52-5023/d.2020.01.008(inChinese)
Yang J, Cheng JX, Huang SJ (2020) CO2 emissions performance and reduction potential in China’s manufacturing industry: a multi-hierarchy meta-frontier approach. J Clean Prod 255:120226. https://doi.org/10.1016/j.jclepro.2020.120226
Yin XL, Zhu XL, Zhou H, Li ZY, Wang AM, Liao XJ (2017) Technical efficiency of carp polyculture production in Jiangsu, China: a ray stochastic frontier production approach. Aquac Res 48(4):1629–1637. https://doi.org/10.1111/are.12998
Yu X, Hu QG, Shen MH (2020) Provincial differences and dynamic changes in mariculture efficiency in China: based on Super-SBM model and global Malmquist index. Biology (Basel, Switzerland) 9(1):18. https://doi.org/10.3390/biology9010018
Zhong S, Wang HL, Wen HM, Li J (2020) The total factor productivity index of science and technology innovations in the coastal regions of China between 2006 and 2016. Environ Sci Pollut Res Int 28:40555–40567. https://doi.org/10.1007/s11356-020-09300-3
Zheng LY, Wang HH, Lv F (2019) Study on the measurement and influencing factors of fishery economic efficiency in China—environmental perspective. J Coast Res 94(94):701–706. https://doi.org/10.2112/SI94-139.1
Acknowledgements
The authors are also grateful to anonymous referees for providing helpful comments and suggestions to improve this study.
Funding
This work was supported by the National Social Science Foundation Project of China (No. 18JL094).
Author information
Authors and Affiliations
Contributions
Shen Zhong: Conceptualization, Methodology, Software. Aizhi Li: Investigation, Data curation, Formal analysis, Writing—Original draft. Jing Wu: Validation, Writing—Reviewing and Editing.
Corresponding author
Ethics declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Additional information
Responsible Editor: Philippe Garrigues
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zhong, S., Li, A. & Wu, J. The total factor productivity index of freshwater aquaculture in China: based on regional heterogeneity. Environ Sci Pollut Res 29, 15664–15680 (2022). https://doi.org/10.1007/s11356-021-16504-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-021-16504-8