地方政府补贴下渔业碳汇供需侧演化博弈研究

doi:10.12052/gdutxb.230085

摘要/Abstract

摘要： 碳汇渔业是生产蓝碳，助力实现碳中和的重要手段，具有可观的经济与环境效益。然而，碳汇渔业的实施涉及到多方主体的利益，是一个复杂的动态博弈问题。现有研究很少以碳汇渔业为切入点将政府、养殖商及企业同时纳入系统进行分析。在连江、莆田接连取得试点成功的背景下，基于碳汇渔业的视角探讨蓝碳交易有利于为后续的渔业碳汇交易试点提供科学的建议。因此，本文构建了涉及地方政府、水产养殖商和企业的三方演化博弈模型，分析了每个主体的策略选择对系统稳定性的影响。研究结果表明：(1) 碳汇渔业补贴对演化进程具有双重影响。在一定范围内，适度的碳汇渔业补贴可以激励水产养殖商向碳汇渔业转变，但过高的碳汇渔业补贴可能会引发沉重的财政负担而迫使政府选择不补贴。(2) 补贴后的蓝碳单价与碳税的差异是影响企业策略选择的关键因素，当补贴后的蓝碳单价小于碳税时，企业趋向于购买蓝碳，并且补贴后的蓝碳单价越低，企业的响应速度越快。(3) 参照初始情景，适度提高地方政府的补贴水平可以促使三方演化博弈达到理想状态。在这种补贴水平下，无论三方主体的初始概率如何，均不会影响理想状态的实现及延续。

关键词: 碳汇渔业, 蓝碳, 三方演化博弈, 补贴, 碳税

Abstract: Carbon sink fisheries are an important means of producing blue carbon and contributing to carbon neutrality, with significant economic and environmental benefits. However, the implementation of carbon sink fisheries involves multiple parties and is a dynamic game. Existing studies have rarely analyzed carbon sink fisheries as an entry point to include governments, aquaculturists, and enterprises in the system at the same time. Against the background of the success of the pilot projects in Lianjiang and Putian, exploring blue carbon trading based on the perspective of carbon sink fisheries will help to provide scientific recommendations. Therefore, a tripartite evolutionary game model involving local governments, aquaculturists, and enterprises was constructed, and the impact of each subject's strategy choice on the stability of the system was analyzed. The results show that (1) subsidies for carbon sink fisheries have a dual effect on the evolutionary process. Within a certain range, moderate subsidies for carbon sink fisheries can motivate aquaculturists to shift to carbon sink fisheries, but too high subsidies level can cause a heavy financial burden and force the government to give up. (2) The difference between the price of subsidized blue carbon and the carbon tax is a key factor affecting the strategy choice of enterprises. When the price of subsidized blue carbon is lower than the carbon tax, enterprises tend to purchase blue carbon, and the lower the price of subsidized blue carbon, the faster the response of enterprises. (3) Based on the initial scenario, a moderate increase in the subsidy level of the local government can lead the three-party evolutionary game to the ideal outcome. Under this subsidy level, no matter what the initial probability of each party is, it will not affect the realization and continuation of the ideal outcome.

Key words: carbon sink fisheries, blue carbon, tripartite evolutionary game, subsidies, carbon tax

中图分类号:

P741

毛文俊, 谭倩. 地方政府补贴下渔业碳汇供需侧演化博弈研究[J]. 广东工业大学学报,doi: 10.12052/gdutxb.230085

Mao Wen-jun, Tan Qian. An Evolutionary Game of the Supply and Demand Side of Carbon Sinks in Fisheries under Local Government Subsidies[J]. Journal of Guangdong University of Technology, doi: 10.12052/gdutxb.230085

参考文献

[1] HUANG R, ZHANG S, WANG P. Key areas and pathways for carbon emissions reduction in Beijing for the "Dual Carbon" targets [J]. Energy Policy, 2022, 164: 112873.
[2] WANG Y, YANG H, SUN R. Effectiveness of China's provincial industrial carbon emission reduction and optimization of carbon emission reduction paths in "lagging regions": efficiency-cost analysis [J]. Journal of Environmental Management, 2020, 275: 111221.
[3] YANG W, MIN Z, YANG M, et al. Exploration of the implementation of carbon neutralization in the field of natural resources under the background of sustainable development—an overview [J]. International Journal of Environmental Research and Public Health, 2022, 19(21): 14109.
[4] JIANG L, YANG T, YU J. Global trends and prospects of blue carbon sinks: a bibliometric analysis [J]. Environmental Science and Pollution Research, 2022, 29(44): 65924-65939.
[5] SUN W, LIU X. Review on carbon storage estimation of forest ecosystem and applications in China [J]. Forest Ecosystems, 2020, 7(1): 37-50.
[6] ZHANG M, CHENG Y Y, BAO Y, et al. Seasonal to decadal spatiotemporal variations of the global ocean carbon sink [J]. Global Change Biology, 2022, 28(5): 1786-1797.
[7] BERTRAM C, QUAAS M, REUSCH T B H, et al. The blue carbon wealth of nations [J]. Nature Climate Change, 2021, 11(10): 704-709.
[8] 易思亮. 中国海岸带蓝碳价值评估[D]. 厦门: 厦门大学, 2017.
[9] 高宇. 中国典型红树林湿地沉积物碳库分布特征及控制因子研究[D]. 北京: 清华大学, 2019.
[10] GRUBER N, CLEMENT D, CARTER B R, et al. The oceanic sink for anthropogenic CO₂ from 1994 to 2007 [J]. Science, 2019, 363(6432): 1193-1199.
[11] MCLEOD E, CHMURA G L, BOUILLON S, et al. A blueprint for blue carbon: toward an improved understanding of the role of vegetated coastal habitats in sequestering CO₂ [J]. Frontiers in Ecology and the Environment, 2011, 9(10): 552-560.
[12] TANG J, YE S, CHEN X, et al. Coastal blue carbon: concept, study method, and the application to ecological restoration [J]. Science China-Earth Sciences, 2018, 61(6): 637-646.
[13] LIU Y, ZANG Y, YANG Y. China's rural revitalization and development: theory, technology and management [J]. Journal of Geographical Sciences, 2020, 30(12): 1923-1942.
[14] LI X W, MIAO H Z. How to incorporate blue carbon into the China certified emission reductions scheme: legal and policy perspectives [J]. Sustainability, 2022, 14(17): 10567.
[15] ZHENG S, YU L. The government's subsidy strategy of carbon-sink fishery based on evolutionary game [J]. Energy, 2022, 254(PB): 124282.
[16] ZHENG H, LI J, ZHAO X. How does financial policy support the development of China's fishery? Characteristics, experience and prospects [J]. Marine Policy, 2021, 132: 104678.
[17] KUWAE T, WATANABE A, YOSHIHARA S, et al. Implementation of blue carbon offset crediting for seagrass meadows, macroalgal beds, and macroalgae farming in Japan [J]. Marine Policy, 2022, 138: 104996.
[18] ZHAO C, SUN J, GONG Y, et al. Research on the blue carbon trading market system under blockchain technology [J]. Energies, 2022, 15(9): 3134.
[19] CAO Y, KANG Z, BAI J, et al. How to build an efficient blue carbon trading market in China? A study based on evolutionary game theory [J]. Journal of Cleaner Production, 2022, 367: 132867.
[20] PENG H, LIU Y. How government subsidies promote the growth of entrepreneurial companies in clean energy industry: an empirical study in China [J]. Journal of Cleaner Production, 2018, 188: 508520.
[21] YU J K, WANG Y L. Exploring the goals and objectives of policies for marine ranching management: performance and prospects for China [J]. Marine Policy, 2020, 122: 104255.
[22] ZHANG S, WANG C, YU C. The evolutionary game analysis and simulation with system dynamics of manufacturer's emissions abatement behavior under cap-and-trade regulation [J]. Applied Mathematics and Computation, 2019, 355: 343-355.
[23] CHONG D, SUN N. Explore emission reduction strategy and evolutionary mechanism under central environmental protection inspection system for multi-agent based on evolutionary game theory [J]. Computer Communications, 2020, 156: 77-90.
[24] CHEN W, HU Z H. Using evolutionary game theory to study governments and manufacturers' behavioral strategies under various carbon taxes and subsidies [J]. Journal of Cleaner Production, 2018, 201: 123-141.
[25] WAN X, LI Q, QIU L, et al. How do carbon trading platform participation and government subsidy motivate blue carbon trading of marine ranching? A study based on evolutionary equilibrium strategy method [J]. Marine Policy, 2021, 130: 104567.
[26] WAN X, XIAO S, LI Q, et al. Evolutionary policy of trading of blue carbon produced by marine ranching with media participation and government supervision [J]. Marine Policy, 2021, 124: 104302.
[27] HE Y, ZHANG F. A game study on the implementation of marine carbon sink fisheries in the context of carbon neutrality—analysis of the tripartite behavior of fishery practitioners, research institutions, and the government [J]. Marine Policy, 2023, 147: 105365.
[28] 杨建君. 辽宁海洋碳汇渔业发展问题研究[D]. 大连: 大连海洋大学, 2022.
[29] WANG Y, GUO T, CHENG T C E, et al. Evolution of blue carbon trading of China's marine ranching under the blue carbon special subsidy mechanism [J]. Ocean & Coastal Management, 2022, 222: 106123.
[30] HE Y, ZHANG F, WANG Y. How to facilitate efficient blue carbon trading? A simulation study using the game theory to find the optimal strategy for each participant [J]. Energy, 2023, 276: 107521.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed