Research on Efficiency of Marine Green Aquaculture in China: Regional Disparity, Driving Factors, and Dynamic Evolution
Abstract
:1. Introduction
2. Research Methods and Index Processing
2.1. Research Methods
2.1.1. Measurement of the Efficiency of Marine Green Aquaculture in China
2.1.2. Regional Gap Analysis Method for the Efficiency of Marine Green Aquaculture in China
- (1)
- Kernel density estimation
- (2)
- Dagum Gini coefficient and decomposition
- (3)
- Center of gravity standard deviation ellipse
2.1.3. Dynamic Evolution Analysis Method of Marine Green Aquaculture Efficiency in China
2.1.4. Driving Factors of Regional Gap in Marine Green Aquaculture Efficiency in China
2.2. Data Sources
3. Empirical Analysis
3.1. Analysis of Regional Gap in Efficiency of Marine Green Aquaculture in China
3.1.1. Efficiency Measurement and Spatial–Temporal Characteristics Analysis of Marine Green Aquaculture in China
3.1.2. Extent and Decomposition of Regional Gap in the Efficiency of Marine Green Aquaculture in China
3.1.3. Spatial Distribution Pattern of Marine Green Aquaculture Efficiency in China
3.2. Dynamic Evolution of Marine Green Aquaculture Efficiency in China
3.2.1. Static Analysis Results of Spatial Markov Chain
3.2.2. Dynamic Analysis Results of Spatial Markov Chain
3.3. Analysis of Driving Factors of Regional Gap in Marine Green Aquaculture Efficiency in China
4. Conclusions and Suggestions
4.1. Conclusions
4.2. Suggestions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Grealis, E.; Hynes, S.; O’Donoghue, C.; Vega, A.; Van Osch, S.; Twomey, C. The Economic Impact of Aquaculture Expansion: An Input-Output Approach. Mar. Policy 2017, 81, 29–36. [Google Scholar] [CrossRef]
- Naylor, R.L.; Hardy, R.W.; Buschmann, A.H.; Bush, S.R.; Cao, L.; Klinger, D.H.; Little, D.C.; Lubchenco, J.; Shumway, S.E.; Troell, M. A 20-Year Retrospective Review of Global Aquaculture. Nature 2021, 591, 551–563. [Google Scholar] [CrossRef] [PubMed]
- Bjørndal, T.; Dey, M.; Tusvik, A. Economic Analysis of the Contributions of Aquaculture to Future Food Security. Aquaculture 2024, 578, 740071. [Google Scholar] [CrossRef]
- Ton Nu Hai, A.; Speelman, S. Economic-Environmental Trade-Offs in Marine Aquaculture: The Case of Lobster Farming in Vietnam. Aquaculture 2020, 516, 734593. [Google Scholar] [CrossRef]
- Votsi, N.E. Pathways to Protect Marine Biodiversity: Could Marine Protected Areas (MPAs) Be Benefited by Landscape Ecology? Mar. Pollut. Bull. 2023, 191, 114942. [Google Scholar] [CrossRef] [PubMed]
- Gomes Ferreira, R.; Ferreira, J.G.; Boogert, F.-J.; Corner, R.A.; Nunes, J.P.; Grant, J.; Johansen, J.; Dewey, W.F. A Multimetric Investor Index for Aquaculture: Application to the European Union and Norway. Aquaculture 2020, 516, 734600. [Google Scholar] [CrossRef]
- Puszkarski, J.; Śniadach, O. Instruments to Implement Sustainable Aquaculture in the European Union. Mar. Policy 2022, 144, 105215. [Google Scholar] [CrossRef]
- Guillen, J.; Asche, F.; Carvalho, N.; Fernández Polanco, J.M.; Llorente, I.; Nielsen, R.; Nielsen, M.; Villasante, S. Aquaculture Subsidies in the European Union: Evolution, Impact and Future Potential for Growth. Mar. Policy 2019, 104, 19–28. [Google Scholar] [CrossRef]
- Carter, C. Actor Intentions Implementing ‘Ecosystem Europe’: The Contested Case of Aquaculture. Environ. Sci. Policy 2021, 124, 305–312. [Google Scholar] [CrossRef]
- Olin, P.G.; Smith, J.; Nabi, R. Regional Review on Status and Trends in Aquaculture Development in North America: Canada and the United States of America—2010. FAO. UC San Diego: California Sea Grant College Program. 13 January 2012. Available online: https://escholarship.org/uc/item/1946b7nm (accessed on 5 December 2023).
- Fujita, R.; Brittingham, P.; Cao, L.; Froehlich, H.; Thompson, M.; Voorhees, T. Toward an Environmentally Responsible Offshore Aquaculture Industry in the United States: Ecological Risks, Remedies, and Knowledge Gaps. Mar. Policy 2023, 147, 105351. [Google Scholar] [CrossRef]
- Yamane, T. Capture Fisheries and Aquaculture in Japan and the World: Current Status and Future Concern. In Marine Productivity: Perturbations and Resilience of Socio-Ecosystems; Ceccaldi, H.-J., Hénocque, Y., Koike, Y., Komatsu, T., Stora, G., Tusseau-Vuillemin, M.-H., Eds.; Springer International Publishing: Cham, Switzerland, 2015; pp. 271–281. [Google Scholar]
- Jang, H.G.; Yamazaki, S. Community-Level Analysis of Correlated Fish Production in Fisheries and Aquaculture: The Case of Japan. Mar. Policy 2020, 122, 104240. [Google Scholar] [CrossRef]
- Bergland, H.; Burlakov, E.; Pedersen, P.A.; Wyller, J. Aquaculture, Pollution and Fishery—Dynamics of Marine Industrial Interactions. Ecol. Complex. 2020, 43, 100853. [Google Scholar] [CrossRef]
- Gaspar, M.B.; Carvalho, S.; Cúrdia, J.; dos Santos, M.N.; Vasconcelos, P. Restoring Coastal Ecosystems from Fisheries and Aquaculture Impacts. In Reference Module in Earth Systems and Environmental Sciences; Elsevier: Amsterdam, The Netherlands, 2023; ISBN 978-0-12-409548-9. [Google Scholar]
- Liu, G.; Xu, Y.; Ge, W.; Yang, X.; Su, X.; Shen, B.; Ran, Q. How Can Marine Fishery Enable Low Carbon Development in China? Based on System Dynamics Simulation Analysis. Ocean Coast. Manag. 2023, 231, 106382. [Google Scholar] [CrossRef]
- Zhang, F.; He, Y.; Xie, S.; Shi, W.; Zheng, M.; Wang, Y. Research on the Game of Fishermen’s Cooperative Behavior in Developing Marine Carbon Sink Fisheries from a Complex Network Perspective. Ocean Coast. Manag. 2023, 244, 106832. [Google Scholar] [CrossRef]
- Nielsen, M.; Ravensbeck, L.; Nielsen, R. Green Growth in Fisheries. Mar. Policy 2014, 46, 43–52. [Google Scholar] [CrossRef]
- Fu, X.-M.; Wu, W.-Y.; Lin, C.-Y.; Ku, H.-L.; Wang, L.-X.; Lin, X.-H.; Liu, Y. Green Innovation Ability and Spatial Spillover Effect of Marine Fishery in China. Ocean Coast. Manag. 2022, 228, 106310. [Google Scholar] [CrossRef]
- Yan, W.; Zhong, C. The Coordination of Aquaculture Development with Environment and Resources: Based on Measurement of Provincial Eco-Efficiency in China. Int. J. Environ. Res. Public Health 2022, 19, 8010. [Google Scholar] [CrossRef]
- Shi, X.; Xu, Y.; Dong, B.; Nishino, N. Mariculture Carbon Sequestration Efficiency in China: Its Measurement and Socio-Economic Factor Analysis. Sustain. Prod. Consum. 2023, 40, 101–121. [Google Scholar] [CrossRef]
- Guo, W.; Dong, S.; Qian, J.; Lyu, K. Measuring the Green Total Factor Productivity in Chinese Aquaculture: A Zofio Index Decomposition. Fishes 2022, 7, 269. [Google Scholar] [CrossRef]
- Ji, J.; Liu, L.; Xu, Y.; Zhang, N. Spatio-Temporal Disparities of Mariculture Area Production Efficiency Considering Undesirable Output: A Case Study of China’s East Coast. Water 2022, 14, 324. [Google Scholar] [CrossRef]
- Xu, Y.; Ji, J.; Xu, Y. Spatial Disequilibrium of Mariculture Areas Utilization Efficiency in China and Causes. Resour. Sci. 2020, 42, 2158–2169. [Google Scholar] [CrossRef]
- Ji, J.; Sun, Q.; Ren, W.; Wang, P. The Spatial Spillover Effect of Technical Efficiency and Its Influencing Factors for China’s Mariculture—Based on the Partial Differential Decomposition of a Spatial Durbin Model in the Coastal Provinces of China. Iran. J. Fish. Sci. 2020, 19, 921–933. [Google Scholar]
- Liu, X.; Chen, S. Has Environmental Regulation Facilitated the Green Transformation of the Marine Industry? Mar. Policy 2022, 144, 105238. [Google Scholar] [CrossRef]
- Sun, Y.; Ji, J.; Wei, Z. Can Environmental Regulation Promote the Green Output Bias in China’s Mariculture? Env. Sci. Pollut. Res. 2023, 30, 31116–31129. [Google Scholar] [CrossRef] [PubMed]
- Sun, L.; Yang, Z.; Wang, Q.; Peng, L.; Zhang, Z.; Liu, D.; Li, S. Evaluation of the Green Development Efficiency of Marine Fish Culture in China. Front. Sustain. Food Syst. 2023, 7, 1274224. [Google Scholar] [CrossRef]
- Okabe, A.; Satoh, T.; Sugihara, K. A Kernel Density Estimation Method for Networks, Its Computational Method and a GIS-based Tool. Int. J. Geogr. Inf. Sci. 2009, 23, 7–32. [Google Scholar] [CrossRef]
- Dabo-Niang, S.; Hamdad, L.; Ternynck, C.; Yao, A.-F. A Kernel Spatial Density Estimation Allowing for the Analysis of Spatial Clustering. Application to Monsoon Asia Drought Atlas Data. Stoch. Environ. Res. Risk Assess. 2014, 28, 2075–2099. [Google Scholar] [CrossRef]
- Li, Z.-W.; He, P. Data-Based Optimal Bandwidth for Kernel Density Estimation of Statistical Samples. Commun. Theor. Phys. 2018, 70, 728. [Google Scholar] [CrossRef]
- Chen, Z.; Li, X.; Xia, X. Measurement and Spatial Convergence Analysis of China’s Agricultural Green Development Index. Env. Sci. Pollut. Res. 2021, 28, 19694–19709. [Google Scholar] [CrossRef]
- Ma, T.; Liu, Y.; Yang, M. Spatial-Temporal Heterogeneity for Commercial Building Carbon Emissions in China: Based the Dagum Gini Coefficient. Sustainability 2022, 14, 5243. [Google Scholar] [CrossRef]
- Xiao, W.; He, M. Characteristics, Regional Differences, and Influencing Factors of China’s Water-Energy-Food (W-E–F) Pressure: Evidence from Dagum Gini Coefficient Decomposition and PGTWR Model. Env. Sci. Pollut. Res. 2023, 30, 66062–66079. [Google Scholar] [CrossRef]
- Wachowicz, M.; Liu, T. Finding Spatial Outliers in Collective Mobility Patterns Coupled with Social Ties. Int. J. Geogr. Inf. Sci. 2016, 30, 1806–1831. [Google Scholar] [CrossRef]
- Yuan, W.; Sun, H.; Chen, Y.; Xia, X. Spatio-Temporal Evolution and Spatial Heterogeneity of Influencing Factors of SO2 Emissions in Chinese Cities: Fresh Evidence from MGWR. Sustainability 2021, 13, 12059. [Google Scholar] [CrossRef]
- Zhang, Y.; Jiang, P.; Cui, L.; Yang, Y.; Ma, Z.; Wang, Y.; Miao, D. Study on the Spatial Variation of China’s Territorial Ecological Space Based on the Standard Deviation Ellipse. Front. Environ. Sci. 2022, 10, 982734. [Google Scholar] [CrossRef]
- Li, W.; Zhang, C. Some Further Clarification on Markov Chain Random Fields and Transiograms. Int. J. Geogr. Inf. Sci. 2013, 27, 423–430. [Google Scholar] [CrossRef]
- Pu, Y.; Ma, R.; Ge, Y.; Huang, X. Spatial-Temporal Dynamics of Regional Convergence at County Level in Jiangsu. Chin. Geograph.Sc. 2005, 15, 113–119. [Google Scholar] [CrossRef]
- Tsionas, M.; Sakkas, S.; Baltas, N.C. Regional Convergence in Greece (1995–2005): A Dynamic Panel Perspective. Econ. Res. Int. 2014, 2014, e385038. [Google Scholar] [CrossRef]
- Du, Q.; Wu, M.; Xu, Y.; Lu, X.; Bai, L.; Yu, M. Club Convergence and Spatial Distribution Dynamics of Carbon Intensity in China’s Construction Industry. Nat. Hazards 2018, 94, 519–536. [Google Scholar] [CrossRef]
- Luo, W.; Jasiewicz, J.; Stepinski, T.; Wang, J.; Xu, C.; Cang, X. Spatial Association between Dissection Density and Environmental Factors over the Entire Conterminous United States. Geophys. Res. Lett. 2016, 43, 692–700. [Google Scholar] [CrossRef]
- Song, Y.; Wang, J.; Ge, Y.; Xu, C. An Optimal Parameters-Based Geographical Detector Model Enhances Geographic Characteristics of Explanatory Variables for Spatial Heterogeneity Analysis: Cases with Different Types of Spatial Data. GIScience Remote Sens. 2020, 57, 593–610. [Google Scholar] [CrossRef]
- Yang, L.; Yang, X.; Wei, W.; Pan, J. Spatio-Temporal Evolution and Influencing Factors of Water Resource Carrying Capacity in Shiyang River Basin: Based on the Geographical Detector Method. Water Supply 2020, 20, 1409–1424. [Google Scholar] [CrossRef]
- Xu, J.; Han, L.; Yin, W. Research on the Ecologicalization Efficiency of Mariculture Industry in China and Its Influencing Factors. Mar. Policy 2022, 137, 104935. [Google Scholar] [CrossRef]
- Guan, H.; Sun, Z.; Zhao, A. Spatio-Temporal Evolution and Influencing Factors of Net Carbon Sink in Marine Aquaculture in China. Front. Environ. Sci. 2022, 10, 1344. [Google Scholar] [CrossRef]
- Yu, J.; Han, Q. Food Security of Mariculture in China: Evolution, Future Potential and Policy. Mar. Policy 2020, 115, 103892. [Google Scholar] [CrossRef]
- Zhang, H. Fisheries Cooperation in the South China Sea: Evaluating the Options. Mar. Policy 2018, 89, 67–76. [Google Scholar] [CrossRef]
- Liu, L.-J.; Creutzig, F.; Yao, Y.-F.; Wei, Y.-M.; Liang, Q.-M. Environmental and Economic Impacts of Trade Barriers: The Example of China–US Trade Friction. Resour. Energy Econ. 2020, 59, 101144. [Google Scholar] [CrossRef]
- Liu, Y.; Han, L.; Pei, Z.; Jiang, Y. Evolution of the Coupling Coordination between the Marine Economy and Urban Resilience of Major Coastal Cities in China. Mar. Policy 2023, 148, 105456. [Google Scholar] [CrossRef]
- Cuilleret, M.; Doyen, L.; Gomes, H.; Blanchard, F. Resilience Management for Coastal Fisheries Facing with Global Changes and Uncertainties. Econ. Anal. Policy 2022, 74, 634–656. [Google Scholar] [CrossRef]
- Zhang, Y.-Z.; Xue, C.; Wang, N.; Chen, G. A Comparative Study on the Measurement of Sustainable Development of Marine Fisheries in China. Ocean Coast. Manag. 2024, 247, 106911. [Google Scholar] [CrossRef]
Index | Variables | Instructions | |
---|---|---|---|
Input | Fixed assets per unit of breeding area | Marine motor fishing vessels (production fishing vessels) at the end of the year/mariculture area | |
Farming area per unit of labor force | Mariculture area/number of mariculture professionals | ||
Fishery seedlings per unit of cultivation area | Number of mariculture seedlings/mariculture area | ||
Labor force per unit of breeding area | Number of professionals in mariculture/number of professionals in fisheries | ||
Technical training intensity | Number of fishermen in technical training × number of professional mariculture practitioners/number of professional fishery aquaculture practitioners | ||
Output | Expectations | Economic output per unit of labor | Mariculture output value/number of mariculture professionals |
Carbon sequestration per unit of farming area | Carbon sequestration in mariculture/mariculture area | ||
Not expected | Nitrogen and phosphorus pollution per unit of farming area | Nitrogen and phosphorus pollution output/mariculture area | |
Carbon emissions per unit of farming area | Mariculture carbon emissions/mariculture area |
Variables | Instructions | Units |
---|---|---|
x1 | Number of professional employees in mariculture | people |
x2 | Number of aquatic technology extension institutions | pcs |
x3 | Year-end ownership of mariculture motor fishing boats (production fishing boats) | Kilowatt |
x4 | Mariculture area | hectare |
x5 | Number of seawater seedlings | 100 million tails |
x6 | Output value of mariculture | CNY 100 million |
x7 | Per capita disposable income of fishermen | CNY 10 thousand |
x8 | Mariculture yield | Tons |
x9 | Number of fishermen in technical training | people |
x10 | Carbon sequestration capacity | — |
Year | Total G | Within a Group | Between Groups | Hypervariable Density | |||
---|---|---|---|---|---|---|---|
Gw | Rate of Contribution (%) | Gb | Rate of Contribution (%) | Gt | Rate of Contribution (%) | ||
2006 | 0.040 | 0.011 | 0.274 | 0.011 | 0.288 | 0.017 | 0.437 |
2007 | 0.032 | 0.008 | 0.250 | 0.018 | 0.568 | 0.006 | 0.182 |
2008 | 0.130 | 0.026 | 0.199 | 0.102 | 0.790 | 0.001 | 0.011 |
2009 | 0.133 | 0.029 | 0.221 | 0.101 | 0.763 | 0.002 | 0.016 |
2010 | 0.098 | 0.028 | 0.286 | 0.041 | 0.421 | 0.029 | 0.293 |
2011 | 0.106 | 0.030 | 0.286 | 0.032 | 0.304 | 0.044 | 0.410 |
2012 | 0.080 | 0.021 | 0.261 | 0.041 | 0.507 | 0.019 | 0.232 |
2013 | 0.096 | 0.026 | 0.273 | 0.021 | 0.217 | 0.049 | 0.509 |
2014 | 0.086 | 0.025 | 0.290 | 0.010 | 0.121 | 0.051 | 0.589 |
2015 | 0.092 | 0.026 | 0.283 | 0.023 | 0.252 | 0.043 | 0.465 |
2016 | 0.159 | 0.040 | 0.249 | 0.070 | 0.440 | 0.049 | 0.311 |
2017 | 0.156 | 0.041 | 0.263 | 0.085 | 0.542 | 0.030 | 0.194 |
2018 | 0.155 | 0.042 | 0.271 | 0.070 | 0.452 | 0.043 | 0.276 |
2019 | 0.096 | 0.028 | 0.294 | 0.046 | 0.481 | 0.021 | 0.225 |
Year | Barycentric Coordinates | Direction | Distance Traveled/km | Angle of Turn θ/° | Major Half Axis/km | Short Half Axis/km | Area of Ellipse/10 Thousand km2 |
---|---|---|---|---|---|---|---|
2006 | (116.06° E, 29.89° N) | 15.020 | 1289.036 | 383.353 | 155.208 | ||
2010 | (115.72° E, 29.92° N) | West by north | 37.880 | 16.430 | 1323.330 | 391.208 | 162.602 |
2015 | (115.79° E, 29.89° N) | East by south | 8.560 | 16.640 | 1294.946 | 385.547 | 156.812 |
2019 | (115.60° E, 29.62° N) | South by west | 36.860 | 16.730 | 1299.942 | 388.466 | 158.610 |
Spatial Lag | t/t + 1 | Frequency | Low Level | Lower Level | Higher Level | High Level |
---|---|---|---|---|---|---|
No lag | Low level | 211 | 0.919 | 0.081 | 0 | 0 |
Lower level | 210 | 0.005 | 0.862 | 0.133 | 0 | |
Higher level | 206 | 0 | 0.010 | 0.879 | 0.112 | |
High level | 193 | 0 | 0 | 0.016 | 0.985 | |
1 | Low level | 36 | 0.917 | 0.083 | 0 | 0 |
Lower level | 29 | 0 | 0.966 | 0.035 | 0 | |
Higher level | 5 | 0 | 0 | 0.800 | 0.200 | |
High level | 1 | 0 | 0 | 0 | 1 | |
2 | Low level | 136 | 0.934 | 0.066 | 0 | 0 |
Lower level | 109 | 0 | 0.881 | 0.119 | 0 | |
Higher level | 88 | 0 | 0 | 0.932 | 0.068 | |
High level | 26 | 0 | 0 | 0.077 | 0.923 | |
3 | Low level | 34 | 0.853 | 0.147 | 0 | 0 |
Lower level | 67 | 0.015 | 0.821 | 0.164 | 0 | |
Higher level | 78 | 0 | 0.013 | 0.859 | 0.128 | |
High level | 58 | 0 | 0 | 0.017 | 0.983 | |
4 | Low level | 5 | 1.000 | 0 | 0 | 0 |
Lower level | 5 | 0 | 0.400 | 0.600 | 0 | |
Higher level | 35 | 0 | 0.029 | 0.800 | 0.171 | |
High level | 108 | 0 | 0 | 0 | 1.000 |
Year | Factor of Interaction |
---|---|
2006 | x1∩x3; x1∩x10; x2∩x3; x3∩x8; x7∩x8* |
2008 | x1∩x3; x1∩x4; x1∩x7*; x1∩x8; x1∩x9; x1∩x10; x2∩x3; x2∩x4; x2∩x7; x2∩x8*; x2∩x9; x2∩x10; x3∩x7*; x3∩x8*; x4∩x8*; x5∩x8*; x6∩x8*; x7∩x8*; x7∩x9*; x8∩x9*; x8∩x10*; x9∩x10 |
2010 | x1∩x9; x2∩x9; x4∩x9* |
2012 | x1∩x4; x1∩x10*; x2∩x4 |
2014 | x1∩x7; x1∩x9; x2∩x9 |
2016 | x1∩x2; x2∩x10; x3∩x6; x3∩x9 |
2019 | x1∩x8*; x2∩x3*; x2∩x6*; x2∩x8*; x5∩x8; x6∩x7; x7∩x8 |
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Wang, W.; Mao, W.; Zhu, J.; Wu, R.; Yang, Z. Research on Efficiency of Marine Green Aquaculture in China: Regional Disparity, Driving Factors, and Dynamic Evolution. Fishes 2024, 9, 11. https://doi.org/10.3390/fishes9010011
Wang W, Mao W, Zhu J, Wu R, Yang Z. Research on Efficiency of Marine Green Aquaculture in China: Regional Disparity, Driving Factors, and Dynamic Evolution. Fishes. 2024; 9(1):11. https://doi.org/10.3390/fishes9010011
Chicago/Turabian StyleWang, Wei, Wei Mao, Jianzhen Zhu, Renhong Wu, and Zhenbo Yang. 2024. "Research on Efficiency of Marine Green Aquaculture in China: Regional Disparity, Driving Factors, and Dynamic Evolution" Fishes 9, no. 1: 11. https://doi.org/10.3390/fishes9010011