Carbon emission reduction is a crucial link of the low-carbon transformation of fishery, and clarifying the marginal carbon emission cost of fishery is the most important part. Based on this view, the paper estimates the marginal carbon emission cost of fishery in 28 provinces of China from 2003 to 2019 using the fishery carbon shadow price model, and further explores their evolution trend and influencing factors. The results show as follows. On the whole, the marginal emission cost of fishery fluctuates and rises, with most provinces continuously increasing and a few provinces no significant changing or slight declining. There are significant differences in marginal costs between regions, and the coastal is much higher than the inland. In terms of dynamic evolution, the kernel density curve shows a downward trend and a rightward shift, with the carbon emission reduction cost evolving to the high value area and the emission reduction space gradually shrinking, the regional difference characteristics are enhanced, and the distortion of resource allocation is aggravated. From the influencing factors, the impacts of industrial structure, energy intensity, fishery development level, fishery scale and specialization degree on the marginal carbon abatement cost are significantly negative, while the impacts of factor endowment and science and technology promotion are significantly positive. Accordingly, each province should clarify its regional advantages and characteristics, formulate differentiated emission reduction policies to reduce the marginal carbon emission cost of fishery, and make efforts from multiple levels such as technical support and industrial structure to promote the overall emission reduction and efficiency improvement of China 's fishery.
Key words
marginal cost /
fishery carbon emission reduction /
directional distance function /
Spatial Durbin Model
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
References
[1] 曾冰. 长江经济带渔业经济碳排放效率空间格局及影响因素研究[J].当代经济管理,2019,41(2):44-48.
[2] 刘明磊,朱磊,范英.我国省级碳排放绩效评价及边际减排成本估计:基于非参数距离函数方法[J].中国软科学,2011(3):106-114.
[3] 蒋伟杰,张少华.中国工业二氧化碳影子价格的稳健估计与减排政策[J].管理世界,2018,34(7):32-49,183-184.
[4] 吴贤荣.中国农业碳排放边际减排成本:参数法测度与时空分析[J].世界农业,2021(1):46-56,129-130.
[5] 胡剑波,李潇潇,王蕾.效率视角下中国产业部门隐含碳配额及边际减排成本研究[J].中国软科学,2023(12):134-142.
[6] 赵巧芝,闫庆友.中国省域二氧化碳边际减排成本的空间演化轨迹[J].统计与决策,2019,35(14):128-132.
[7] 陈胜涛,张开华,张岳武.农业碳排放绩效的测量与脱钩效应[J].统计与决策,2021,37(22):85-88.
[8] 魏丽莉,侯宇琦.中国工业二氧化碳边际减排成本测算与行业碳达峰预测[J].经济理论与经济管理,2023,43(2):63-77.
[9] Hao Y,Liao H,Wei Y M.Is China's carbon reduction target allocation reasonable? An analysis based on carbon intensity convergence[J].Applied Energy,2015(142):229-239.
[10] 王文举,陈真玲.中国省级区域初始碳配额分配方案研究——基于责任与目标、公平与效率的视角[J].管理世界,2019,35(3):81-98.
[11] 魏楚. 中国城市CO2边际减排成本及其影响因素[J].世界经济,2014,37(7):115-141.
[12] Boussemart J P,Leleu H,Shen Z.Worldwide carbon shadow prices during 1990-2011[J].Energy Policy,2017,109:288-296.
[13] Cheng S,Lu K,Liu W,et al.Efficiency and marginal abatement cost of PM2.5 in China: A parametric approach[J].Journal of Cleaner Production,2019(235):57-68.
[14] 杨子晖,陈里璇,罗彤.边际减排成本与区域差异性研究[J].管理科学学报,2019,22(2):1-21.
[15] Jiang Y,Luo T,Wu Z,et al.The driving factors in the corporate proactivity of carbon emissions abatement:Empirical evidence from China[J].Journal of Cleaner Production, 2021(288):125549.
[16] 茹雪,雷鹏飞,刘培.二氧化碳动态边际减排成本及其影响机制[J].中国人口·资源与环境,2022,32(11):43-57.
[17] Färe R,Grosskopf S,Lovell C A K,et al.Derivation of Shadow Prices for Undesirable Outputs: A Distance Function Approach[J].The Review of Economics and Statistics,1993,75(2):374-380.
[18] Färe R,Grosskopf S,Weber W L.Shadow Prices of Missouri Public Conservation Land[J].Public Finance Review,2001,29(6):444-460.
[19] Hailu A,Veeman T S.Environmentally Sensitive Productivity Analysis of the Canadian Pulp and Paper Industry,1959-1994:An Input Distance Function Approach[J].Journal of Environmental Economics & Management,2000,40(3):251-274.
[20] Vardanyan M,Noh D.Approximating pollution abatement costs via alternative specifications of a multi-output production technology:A case of the US electric utility industry[J].Journal of Environmental Management,2006,80(2):177-190.
[21] Färe R,Martins-Filho C,Vardanyan M.On Functional form Representation of Multi-Output Production Technologies[J].Journal of Productivity Analysis,2010,33(2):81-96.
[22] Elhorst J P.Spatial econometrics:from cross-sectional data panels[M].Berlin:Springer,2014.
[23] 张军,吴桂英,张吉鹏.中国省际物质资本存量估算:1952—2000[J].经济研究,2004(10):35-44.
[24] 李晨,栾佩园,李昊玉.基于福利绩效的我国渔业碳排放区域差异研究[J].山东财经大学学报,2023,35(4):79-94.
[25] 邵桂兰,孔海峥,于谨凯等.基于LMDI法的我国海洋渔业碳排放驱动因素分解研究[J].农业技术经济,2015(6):119-128.
[26] 岳冬冬,王鲁民,方海等.基于碳平衡的中国海洋渔业产业发展对策探析[J].中国农业科技导报,2016,18(4):1-8.
[27] LeSage J,Pace R K. Introduction to Spatial Econometrics[M]. New York: Chapman and Hall/CRC,2009.
PDF(1003 KB)
