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摘要: 海洋渔业碳汇是海洋碳汇的主要组成部分, 是实现海洋碳增汇的有效途径之一。在碳达峰与碳中和背景下, 海洋渔业兼具“碳源”与“碳汇”的双重属性。利用《中国渔业统计年鉴》《国内机动渔船油价补助用油量测算参考标准》和《中国统计年鉴》数据, 计算了2006—2020年辽宁省渔业净碳汇量和渔业碳汇价值量; 运用时间序列三次指数平滑模型, 预测2021—2030年渔业碳汇量和渔业碳汇价值量; 基于灰色关联模型分析了辽宁省海洋渔业碳源碳汇量变化及其价值量变化的主要驱动要素。结果表明: 2006—2020年辽宁省海洋渔业碳汇收支盈余态势逐年减少, 海洋渔业碳汇赤字情况逐年加剧, 海洋渔业碳源碳汇最大顺差为256.36万t, 最大逆差29.99万t, 其平均差额为116.66万t·a−1。其中, 海洋捕捞鱼类碳汇总量3976.04万t, 但自2016年起急剧下降, 并呈持续下降趋势; 贝藻类碳汇总量241.67万t, 养殖占83%, 变化不大; 海洋捕捞碳排放量为164.52万t·a−1, 其中拖网捕捞渔业占比近50%。2017年后海洋捕捞碳汇量不能补偿碳排放量。辽宁省海洋渔业碳汇价值总量274.23亿元, 年均18.28亿元。辽宁省渔业碳汇总量和渔业碳汇价值总量持续下降, 碳汇价值量与碳汇量呈正相关。海洋渔业碳汇量与海洋捕捞渔获物产量、养殖贝藻类产量呈正相关。基于时间序列预测模型分析显示, 2020—2030年辽宁省海洋渔业碳汇赤字将持续加剧, 海洋渔业碳汇量逐年降低。辽宁省海洋渔业碳汇受国家政策、捕捞渔获物产量、从业人员数量、贝藻类养殖面积和海洋捕捞渔船总功率等因素影响。辽宁省海洋渔业碳源排放量受海洋捕捞渔船总功率、渔业专业户数量和技术推广机构数量影响明显。建议多种养殖方式深度融合, 减少高能耗、低产量捕捞作业方式, 保护海洋生物多样性, 并加强高排放渔船监管, 以促进辽宁省海洋渔业发展。Abstract: Marine fisheries are valuable oceanic carbon sinks that store and sequester carbon. They act as both “carbon sources” and “carbon sinks”, and this is particularly important to achieve the established carbon peak and carbon neutrality goals. The amount of carbon sequestered by fisheries and its economic value in Liaoning Province from 2006 to 2020 were calculated based on the China Fisheries Statistical Yearbook, the Calculation Reference of Oil Consumption for Oil Price Subsidy of Domestic Fishing Vessels, and the China Statistical Yearbook. Then, a cubic exponential smoothing method was applied to a time-series forecasting model to predict the same parameters for 2021–2030, and the factors controlling the amount and economic value of carbon sequestered in fisheries in Liaoning Province were examined using gray correlation analysis. The results showed that 1) the surplus of income and expenditure for carbon sequestration in marine fisheries in the region decreased each year from 2006 to 2020, and the deficit is predicted to intensify in 2021–2030. 2) The maximum surplus of carbon (sequestration minus emissions) was 256.36×104 tons and the maximum deficit was 29.99×104 tons, with an average of 116.66×104 tons per year. 3) The total amount of carbon sequestered by shellfish and algae was 241.67×104 tons, 83% of which was attributed to the aquaculture industry, with little change. 4) The average amount of carbon emissions form marine fishing was 164.52×104 tons per year, almost 50% of which was attributed to trawling. The amount of carbon sequestered from marine fishing could not compensate for carbon emissions after 2017. 5) The total economic value of sequestered carbon of marine fisheries of Liaoning Province was 27.423 billion Yuan, with an annual average of 1.828 billion Yuan. 6) The total amount and economic value of carbon sequestered in marine fisheries continued to decline and were positively correlated. 7) The amount of sequestered carbon was also positively correlated with fishing yields, shellfish production, and macroalgal culture. The amount and economic value of carbon sequestered in marine fisheries in Liaoning Province were significantly influenced by national policies, fishing yield, number of employees, area of shellfish and macroalgal aquaculture sites, and the total power of fishing vessels (which determined the vessels’ carbon emissions). To protect marine biodiversity and promote the sustainable development of marine fisheries in the area, it is recommended to integrate multiple aquaculture systems, reduce high-energy-consuming fishing operations, and strengthen the monitoring of highly polluting fishing vessels.
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全球气候变化已引起全世界对气候变暖影响和应对方案的探讨, 碳减排增汇是实现“碳中和”的主要渠道之一。国际上对“碳中和”问题的关注源于1997年《京都议定书》的签署[1]。被称为“蓝碳”的海洋碳汇是碳增汇的重要途径之一。2009年, 联合国环境规划署(UNEP)、粮农组织(FAO)和教科文组织政府兼海洋学委员会(IOC/UNESCO)联合发布的《蓝碳: 健康海洋固碳作用的评估报告》中提出“蓝碳是指利用海洋活动及海洋生物吸收大气中的CO2, 并将其固定在海洋中的过程、活动和机制”[2]。我国在2015年首次提出的《生态文明体制改革总体方案》中强烈呼吁构建有利于提升世界海洋碳汇竞争力的综合有效机制, 在增加海洋碳汇的同时加强应对气候变化环境下的国际合作。海洋是地球上最富活跃的碳库, 海洋碳储量约为陆地碳储量总量的20倍, 为大气碳储量的50倍[3-4], 占全球碳总量的55%[1]。海洋蓝碳最早关注海岸带生态系统、渔业碳汇和微型生物碳汇[5-6]。因具有高效碳汇, 海洋固碳能力约为陆地生态系统10倍以上[7], 最早开始研究的有滨海沼泽、红树林以及海草床生态系统[2], 它们是海洋碳汇的主要贡献者, 生物量仅为陆生植物的0.05%, 但两者的碳储量相当[2]。淡水和海洋生态系统中的渔业碳汇也被称为“可产业化的蓝碳”和“可移出的碳汇”。鉴于一部分渔业产业活动具备碳汇功能, 因此将具有碳汇功能、可直接或间接降低大气CO2浓度的渔业生产活动称为“碳汇渔业”, 可囊括为: 贝藻类养殖、滤食性鱼类养殖、渔业增殖、海洋牧场和捕捞渔业等生产活动[8]。碳汇渔业被定义为“能够发挥生物碳汇功能、具有直接或间接降低CO2浓度的渔业生产活动, 是绿色可持续发展理念在渔业领域的具体体现”[9]。
渔业碳汇在海洋碳汇核算以及应对气候变化中发挥积极作用, 中国的海洋渔业和水产养殖业有望实现4.6×108 t∙a−1的固碳量, 约为每年10%的碳减排量[8], 海水养殖是碳“海洋负排放”的践行途径[10]。大型海藻含有其干重28.3%的碳, 滤食性贝类软组织和贝壳平均碳含量分别为其干重的44.3%和11.6%, 据此估算的2002年养殖大型海藻可形成约3.3×105 t∙a−1的渔业碳汇量, 养殖贝类可形成约8.6×105 t∙a−1的渔业碳汇量, 其中贝壳中的约6.7×105 t∙a−1的碳汇量是持久性碳汇[11]。此外, 岳冬冬等[12]估算2006—2010年增养殖海水贝类可形成9.29×105 t∙a−1的渔业碳汇量。孙康等[13]估算2008—2017年增养殖大型海藻可形成1.05×105 t∙a−1的渔业碳汇量, 增养殖海水贝类可形成1.09×106 t∙a−1的渔业碳汇量。张波等[14]估算1980—2010年渤海捕捞业可形成(0.283~1.008)×107 t∙a−1的渔业碳汇量、黄海捕捞业可形成(0.361~2.613)×107 t∙a−1的渔业碳汇量。但是, 中国海洋捕捞业2006—2011年平均温室气体排放量增长2.666×105 t∙a−1, 平均年增长率为1.54%[15]。
尽管渔业活动可以产生净碳汇, 但是渔业生产中的碳足迹仍是碳源汇过程, 即除产生碳汇外, 也产生碳排放, 甚至有学者认为商业捕捞是全球海洋生物碳汇的干扰[16]。因此, 为实施海洋负排放, 提出渔业碳汇扩增对策建议, 践行渔业碳汇扩增战略[2]和“碳中和”战略[17], 渔业碳汇机制研究仍待亟需加强[10]。本文通过对辽宁省海洋渔业碳源汇的核算, 分析近15年来辽宁省海洋渔业碳源汇变化趋势; 通过其驱动要素, 预测海洋渔业碳源汇未来发展态势, 促进辽宁省海洋渔业可持续科学发展。相关研究表明, 渔业是隐藏碳足迹的, 需要“从源头到养殖场(cradle to farm-gate)”量化水产养殖的温室气体排放[18], 也需要从能源消耗量化捕捞渔业的温室气体排放[19-24]。此外, 因海洋捕捞大型海洋鱼类, 其尸体未沉至深海底被封存而被认为渔业提取了海洋蓝碳[25]。
辽宁省兼具海洋大省、渔业大省的双重身份, 据《2020年中国渔业统计年鉴》统计, 2019年辽宁省渔业产品产量4.55×106 t, 占全国7.02%, 其中海水养殖产量2.95×106 t, 占全国14.27%; 海洋捕捞产量0.49×106 t, 占全国4.87%; 远洋渔业产量0.26×106 t, 占全国12.21%。2019年辽宁省渔业产值652.22亿元, 占全国5.04%, 其中海水养殖产值353.11亿元, 占全国9.88%; 海洋捕捞产值110.46亿元, 占全国5.22%。因此, 有必要分析辽宁省海洋渔业碳汇收支情况。本研究基于2006—2020年辽宁省海洋捕捞船只碳排放量、海洋捕捞鱼类、捕捞贝藻类和养殖贝藻类, 计算海洋渔业碳源汇量, 运用灰色关联度模型与时间序列预测模型, 分析其海洋渔业碳源汇驱动要素, 预测未来其碳源汇发展态势。以期全面了解辽宁省海洋渔业碳源汇过程与机制, 使海洋渔业产业积极响应国家低碳、绿色的发展理念, 促进产业升级, 从本质上促进海洋渔业供给侧结构性改革, 充分发挥渔业碳汇作用, 寻求海洋碳负排放途径, 制定海洋渔业碳增汇策略。
1. 数据来源与计算方法
1.1 数据来源
计算辽宁省海洋渔业碳汇数据指标来源于2007—2021年《中国渔业统计年鉴》《国内机动渔船油价补助用油测算参考标准》和《中国统计年鉴》中第一产业能源消耗指标数据。
1.2 海水贝藻类碳汇计算
海水贝藻类碳汇采用辽宁省几种常见的养殖贝藻类进行计算。其中, 贝类碳汇量按表1中公式(1)计算, 海水养殖贝类碳汇总量按表1中公式(2)计算, 参数取值见表2; 藻类碳汇量按表1中公式(3)计算, 参数取值见表3; 海水养殖贝藻类年碳汇总量计算公式见表1中公式(4)。
表 1 辽宁省海洋渔业碳汇计算公式Table 1. Calculation formula of marine fishery carbon sink in Liaoning Province类别
Category公式
Formula符号说明
Symbol description参考文献
Reference贝类碳汇
Carbon sink of shellfish1 Ci=Qi×γ×µ1×ρ1+Qi×
γ×µ2×ρ2$ {C}_{i} $为第i种贝类碳汇量; $ {Q}_{i} $为第i种贝类年总产量; $ \gamma $为贝类干湿转换系数; $ {\mu }_{1} $为贝壳比; $ {\rho }_{1} $为贝壳固碳系数; $ {\mu }_{2} $为软组织比; $ {\rho }_{2} $为软组织固碳系数, 参数取值见表2
$ {C}_{i} $ is the carbon fixed by i shellfish; $ {Q}_{i} $ is the annual yield of i shellfish; $ \gamma $ is the conversion coefficient wet to dry weight of shellfish; $ {\mu }_{1} $ is the ratio of shell to body weight; $ {\rho }_{1} $ is the carbon fixation coefficient of shell; $ {\mu }_{2} $ is the ratio of soft tissue to body weight; $ {\rho }_{2} $ is the carbon fixation coefficient of soft tissue, whose value is shown in the table 2[4] 贝类碳汇总量
Total carbon sink of shellfish2 ${C}_{{\rm{s}}}=\displaystyle\sum _{i=1}^{n}{C}_{i}$ ${C}_{{\rm{s}}}$为海水养殖贝类碳汇总量
${C}_{{\rm{s}}}$ is the total amount of carbon of maricultured shellfish藻类碳汇
Carbon sink of macroalgae3 $ {C}_{{\rm{e}}}=\displaystyle\sum _{i=1}^{m}\left({P}_{i}\times {W}_{i}\right)\times 20 {\text{%}} $ $ {C}_{{\rm{e}}} $为不同种类藻类碳汇总量; $ {P}_{i} $为i种藻类的年总产量; $ {W}_{i} $为i种藻类的碳含量百分比, 参数取值见表3
$ {C}_{{\rm{e}}} $ is the carbon fixed by maricultural macroalgae; $ {P}_{i} $ is the annual harvest of i macroalgae; $ {W}_{i} $ is the percentage of carbon by i macroalgae, whose value is shown in the table 3贝藻类碳汇总量
Total carbon sink of shellfish and macroalgae4 $ C={C}_{{\rm{e}}}+{C}_{{\rm{s}}} $ $ C $为海水养殖贝藻类年碳汇总量
$ C $ is the annual carbon totals by maricultural shellfish and macroalgae鱼类营养级
Fish nutritional level5 ${T}_{i}=1+\displaystyle\sum _{j=1}^{n}{D}_{i j}\times {T}_{j}$ $ {T}_{i} $为生物i的营养级; $ {T}_{j} $为生物i摄食的食物j的营养级; ${D}_{i j}$为食物j在生物i的食物中所占的比例
$ {T}_{i} $ is the trophic level of i fish; $ {T}_{j} $ is the trophic level of j organism being fed on i fish; ${D}_{i j}$ is the proportion of j prey in the food of i predator[29] 被捕食者生物量
Biomass of prey6 ${B}_{j}={Y}_{0} /\left({E}_{ {L}_{0}-1}\times {E}_{ {L}_{0}-2}\right)$ ${B}_{j} $为被捕食者的生物量; $ {Y}_{0} $为渔获量; $ {L}_{0} $为渔获物平均营养级; $ {E}_{{L}_{0}-1} $为营养级$ ({L}_{0}-1) $的生态转换效率; $ {E}_{{L}_{0}-2} $为营养级$ ({L}_{0}-2) $的生态转换效率
${B}_{j} $ is the biomass of the prey; $ {Y}_{0} $ is the yield; $ {L}_{0} $ is the average trophic level in catch; $ {E}_{{L}_{0}-1} $ is the ecological conversion efficiency of trophic level $ ({L}_{0}-1) $; $ {E}_{{L}_{0}-2} $ is the ecological conversion efficiency of trophic level $ ({L}_{0}-2) $[14] 摄食浮游植物和有机碎屑的比例
Proportion of phytoplankton and organic detritus in diet7 1×Q+2×(100%−Q) =
L0−2, 即Q = 4−L0$ Q $为摄食浮游植物和有机碎屑的比例; L0为渔获物平均营养级
$ Q $ is the proportion of phytoplankton and organic detritus in diet. L0 is the average trophic level in catch摄食浮游植物和有机碎屑的生物量
Biomas of phytoplankton and organic detritus in diet8 B0=(B1×Q)+B1×
(100%−Q)/EL=1$ {B}_{0} $为摄食浮游植物和有机碎屑的生物量; $ {E}_{L=1} $为初级消费者摄食浮游植物和有机碎屑的生态转换效率
$ {B}_{0} $ is the biomass of phytoplankton and organic detritus in diets; $ {E}_{L=1} $ is the ecological conversion efficiency of primary consumers to ingest phytoplankton and organic detritus被捕食的浮游植物碳含量
Carbon content of phytoplankton9 $ {C}_{1}=4.49 {\text{%}}\times {B}_{0} $ $ {C}_{1} $被捕食的浮游植物的现存碳含量
$ {C}_{1} $ is the carbon content of phytoplankton[30] 摄食浮游植物固碳量
Carbon sequestration by feeding phytoplankton10 $ {C}_{\mathrm{T}}=45\times {C}_{1} $ $ {C}_{\mathrm{T}} $摄食浮游植物的固碳量
$ {C}_{\mathrm{T}} $ is the carbon sequestration by feeding phytoplankton[31] 化石燃料燃烧排放CO2量
CO2 emissions from fossil fuel combustion11 $ {G}_{k}={T}_{k}\times f\times h $ $ {G}_{k} $为渔船燃烧k燃料时的碳排放量; $ {T}_{k} $为消耗k燃料的量; f为有效氧化分数, h为燃料k的平均含碳量
$ {G}_{k} $ is the carbon emissions of fishing vessels burning k fuel; $ {T}_{k} $ is the amount of k fuel consumed; f is the effective oxidation fraction; h is the average carbon content of k fuel[32] 海洋渔船产碳量
Carbon production of marine fishing vessel12 ${G}_{n}={T}_{n}\times s\times w\times z\times \varphi$ $ {G}_{n} $为渔船燃烧碳量; $ {T}_{n} $为渔船作业燃油消耗量; s为折标准煤系数1.4571; w为有效氧化分数0.982; z为每吨标煤含碳量0.732 57; $\varphi$为0.813, 即在获得相同热能的条件下, 燃油释放CO2与燃煤释放CO2之间的比值
$ {G}_{n} $ is the amount of carbon burned by n fishing vessels; $ {T}_{n} $ is the fuel consumption of fishing vessel n operation; s is the standard coal coefficient (1.4571); w is the effective oxidation fraction (0.982); z is the carbon content per ton of standard coal (0.732 57); $\varphi$ is 0.813, the ratio of CO2 released between from fuel oil and from coal under the same thermal energy conditions[33] 海洋捕捞渔船CO2排放量
CO2 emissions from marine fishing vessels13 ${C}_{ {\mathrm{c}\mathrm{o} }_{2} }={G}_{n}\times \partial$ $ {C}_{{\mathrm{c}\mathrm{o}}_{2}} $为$ {\mathrm{C}\mathrm{O}}_{2} $排 放量; $ \partial $为碳换算为CO2的常数(3.67)
$ {C}_{{\mathrm{c}\mathrm{o}}_{2}} $ is the $ {\mathrm{C}\mathrm{O}}_{2} $ emission; $ \partial $ is the constant from carbon to carbon dioxide (3.67)燃油消耗量
Fuel consumption14 $ {T}_{n}={\displaystyle\sum }_{j=1}^{m}\left({P}_{j}{\times F}_{j}\right) $ $ {P}_{j} $为海洋捕捞$\mathrm{作}\mathrm{业}\mathrm{功}\mathrm{率},{F}_{j}$为海洋捕捞作业${用}{油}{系}{数}$
$ {P}_{j} $ is the power of j marine fishing operation; $ {F}_{j} $ is the oil consumption coefficient of j marine fishing operation渔业碳汇价值量预测
Prediction of economic value of carbon sink by fisheries15 ${Y}_{t+T}={A}_{t}+{B}_{t}+{C}_{t}\times {T}^{2}$ $ {Y}_{t+T} $为碳汇量预测值; T为预测期数; $ {A}_{t} $、$ {B}_{t} $、$ {C}_{t} $分别为t年预测系数; $ {L}_{t}^{\left(1\right)} $、$ {L}_{t}^{\left(2\right)} $、$ {L}_{t}^{\left(3\right)} $分别为t年一次、二次、三次平滑预测值; $ \omega $为平滑系数; $ {X}_{t} $为t年碳汇量原始值; $ {L}_{0}^{\left(1\right)} $、$ {L}_{0}^{\left(2\right)} $、$ {L}_{0}^{\left(3\right)} $分别为$ {L}_{t}^{\left(1\right)} $、$ {L}_{t}^{\left(2\right)} $、$ {L}_{t}^{\left(3\right)} $的初始值
$ {Y}_{t+T} $ is the predicted value of carbon sinks; T is the forecast period; $ {A}_{t} $, ${B}_{t}\;and\;$ $ {C}_{t} $ are t year prediction coefficients; $ {L}_{t}^{\left(1\right)} $, $ {L}_{t}^{\left(2\right)} $ and $ {L}_{t}^{\left(3\right)} $ are the first, second and third smoothing predictions in t year, respectively; $ \omega $ is the smoothing coefficient; $ {X}_{t} $ is the original value of the carbon sink in t year; $ {L}_{0}^{\left(1\right)} $, $ {L}_{0}^{\left(2\right)} $, and $ {L}_{0}^{\left(3\right)} $ are the initial values of $ {L}_{t}^{\left(1\right)} $, $ {L}_{t}^{\left(2\right)} $, and $ {L}_{t}^{\left(3\right)} $, respectively[34-35] $ {A}_{t}=3{L}_{t}^{\left(1\right)}-3{L}_{t}^{\left(2\right)}+{L}_{t}^{\left(3\right)} $ $B_t={\dfrac{\omega }{2\left(1-\omega \right)^2 }}$$\left[\left(6-5\omega \right){L}_{t}^{\left(1\right)}-2\left(5-4\omega \right){L}_{t}^{\left(2\right)}+\left(4-3\omega \right){L}_{t}^{\left(3\right)}\right] $ $ {C}_{t}=\dfrac{\omega ^2}{2\left(1-\omega \right)^2}\left[{L}_{t}^{\left(1\right)}-2{L}_{t}^{\left(2\right)}+{L}_{t}^{\left(3\right)}\right] $ $ {L}_{t}^{\left(1\right)}=\omega {\times X}_{t}+\left(1-\omega \right)\times {L}_{t-1}^{\left(1\right)} $ $ {L}_{t}^{\left(2\right)}=\omega {\times L}_{t}^{\left(1\right)}+\left(1-\omega \right)\times {L}_{t-1}^{\left(2\right)} $ $ {L}_{t}^{\left(3\right)}=\omega {\times L}_{t}^{\left(2\right)}+\left(1-\omega \right){\times L}_{t-1}^{\left(3\right)} $ $ {L}_{0}^{\left(1\right)}={L}_{0}^{\left(2\right)}={L}_{0}^{\left(3\right)}=\dfrac{{X}_{1}+{X}_{2}+{X}_{3}}{3} $ 渔业碳汇价值
Economic value of fishery carbon sink16 ${O}_{_ {\rm{CSV} } }={O}_{_ {\rm{FS} } }\times {C}_{ {\rm{rec} } }$ ${O}_{_ {\rm{CSV} } }$为渔业碳汇价值量, ${O}_{_ {\rm{FS} } }$为渔业碳汇量, $ {C}_{{\rm{rec}}} $为单位的碳减排经济成本价值
${O}_{_ {\rm{CSV} } }$ is the value of fishery carbon sink; ${O}_{_ {\rm{FS} } }$ is the amount of fishery carbon sink; $ {C}_{{\rm{rec}}} $ is the economic cost value of carbon emission reduction per unit[36-39] 关联系数
Correlation coefficient17 $\delta_{i j}=\dfrac{\min _j \times \min _k \times \Delta_{i j}(k)+\beta \times \max _j \times \max _k \times \Delta_{i j}(k)}{\Delta_{i j}(k)+\beta \times \max _j \times \max _k \times \Delta_{i j}(k)}$ $\delta_{i j} $为灰色关联系数; $\Delta_{ i j}\left(k\right)={x}_{i}\left(k\right)-{x}_{j}\left(k\right)$为序列i$ \left\{{x}_{i}\left(k\right)\right\} $与序列j$ \left\{{x}_{j}\left(k\right)\right\} $在第k点的绝对差; ${\min}_{j}\times{\min}_{k}\times{\Delta }_{ij}\left(k\right)$为两极最小差; ${\max}_{j}\times{\max}_{k}\times{\Delta }_{ij}\left(k\right)$为两极最大差; $ \beta $为分辨系数, 其值为 0~1, 取$ \beta $ = 0.5
$\delta_{i j} $ is grey correlation coefficient; $\Delta_{ i j}\left(k\right)={x}_{i}\left(k\right)-{x}_{j}\left(k\right)$ is the absolute difference between sequence i$ \left\{{x}_{i}\left(k\right)\right\} $ and sequence j$ \left\{{x}_{j}\left(k\right)\right\} $ at point k; ${\min}_{j}\times{\min}_{k}\times{\Delta }_{ij}\left(k\right)$ is the minimum difference between two poles; ${\max}_{j}{\max}_{k}{\Delta }_{ij}\left(k\right)$ is the maximum difference between the two poles; $ \beta $ is the resolution coefficient, whose value is 0.5[40-41] 灰色关联度
Grey correlation18 $ {R}_{ij}=\dfrac{1}{n}\displaystyle\sum _{k=1}^{n}{\delta }_{ij}\left(k\right)k=\mathrm{1,2},3,\cdots ,n $ Rij为灰色关联度
Rij is the grey correlation degree表 2 海水养殖贝类固碳计算参数Table 2. Calculation parameters of carbon sequestration in mariculture shellfish种类
Shellfish干重比
Dry weight ratio (%)质量比 Mass ratio (%) 碳汇系数 Carbon sink coefficient (% dry weight) 软体组织 Soft tissue 贝壳 Shell 软体组织 Soft tissue 贝壳 Shell 牡蛎 Oyster 65.10 6.14 93.86 45.89 12.68 贻贝 Mussel 75.28 8.47 91.53 44.40 11.76 扇贝 Scallop 63.89 14.35 85.65 43.90 11.40 蛤 Clam 52.55 1.98 98.02 44.90 11.52 蛏 Razor clam 70.48 3.26 96.74 44.90 13.24 其他贝类 Other shellfish 64.21 11.41 88.59 43.87 11.44 表 3 海水养殖藻类固碳计算参数(藻类干重比为20%)Table 3. Calculation parameters of carbon sink in mariculture macroalgae (dry weight ratio of algae is 20%)种类
Macroalgae碳汇系数
Carbon sink coefficient (% dry weight)海带 Kelp 31.20 裙带菜 Wakame 26.40 其他藻类 Other algae 27.76 1.3 海洋捕捞渔业碳汇量计算
采用海洋食物网能量效率转换法计算海洋捕捞渔获物中碳移出量, 基于渔获物营养级和其他营养级的生态转换效率, 由海洋捕捞渔获物种类及其营养级逆推各营养级生物的被捕食量和需要摄食浮游植物生物量, 计算被摄食的浮游植物光合作用碳汇量[14]。
1.3.1 估算海洋捕捞渔获物营养级
大多数海洋生物的食物具有广泛性, 海洋捕捞渔获物通常是消费者功能群海洋生物, 其营养级具有物种和阶段特性, 其营养级是非整数的[26]。我国海洋捕捞渔获物营养级通常分为3~4级[27-28], 碎屑和浮游植物等初级生产者的营养级为1(unity)级, 所有其他类群生物的营养级(平均加权的)被定义为1加上被捕食者营养级乘以其在食物中的比例之和, 营养级计算公式见表1中公式(5)[29]。
1.3.2 估算被捕食者生物量
通过两营养层级转换估算平均营养级1~2之间被捕食者生物量
$ {B}_{j} $ , 计算公式见表1中公式(6)。1.3.3 估算初级消费者生物量
依据营养级计算公式(5), 计算被捕食者中浮游植物的有机碎屑物(营养级为1)占比Q, 计算公式见表1中公式(7), 由此估算摄食浮游植物和有机碎屑的生物量
$ {B}_{0} $ , 计算公式见表1中公式(8)。1.3.4 估算碳汇量
浮游植物对生物碳汇的贡献是初级生产过程与透明胞外聚合颗粒物(TEP)形成的凝聚网沉降过程[30]。因此, 有机碎屑物碳汇量等同于浮游植物碳汇量。按照10种浮游植物碳含量平均值4.49%, 估算被捕食的浮游植物的现存碳含量(
$ {C}_{1} $ ), 计算公式见表1中公式(9)。参考孙军等[31]研究的浮游植物固碳量为浮游植物碳含量的45倍, 估算摄食浮游植物的固碳量($ {C}_{\mathrm{T}} $ ), 计算公式见表1中公式(10)。1.4 海洋渔业船只碳排放计算
依据美国橡树岭实验室在1989年提出的化石燃料燃烧排放二氧化碳计算公式[见表1中公式(11)[32]]计算渔船碳排放量, 计算公式见表1中公式(12); 海洋捕捞渔船CO2排放量计算公式见表1中公式(13)。海洋捕捞渔船作业方式不同用油系数不同。拖网、围网、刺网、其他、张网和钓具用油系数分别为: 0.480、0.492、0.451、0.312和0.328 (张网和钓具), 燃油消耗量计算公式见表1中公式(14)[33]。
1.5 渔业碳汇量预测
渔业碳汇数据波动受外界影响较大, 即基于时间序列三次指数平滑分析模型[34-35], 对辽宁省未来十年渔业碳汇价值量进行预测, 分析辽宁省海洋渔业碳汇变化。预测模型公式见表1中公式(15)。
1.6 渔业碳汇价值量估算
基于《京都议定书》中预估的CO2减排的成本约为150~600美元·t−1, 参考2020年美元兑人民币平均汇率(1美元=6.9元), 折合人民币1035~4140元·t−1。我国造林减排的成本为2001—2020年的平均成本264.2元·t−1 [36-38], 取CO2减排成本下限与我国造林成本均值, 估算出我国单位碳减排经济成本约为649.6元·t−1 [39], 计算渔业碳汇价值量, 公式见表1中公式(16)。
1.7 海洋碳汇驱动因素分析
灰色关联分析法是采用一定方法计算各因子之间的相似度, 比较各因子之间是否存在相互关联的关系[40-41]。对数据进行归一化处理, 把碳源、碳汇、鱼类和贝藻类碳汇分别作为参考序列, 选取渔业经济总产值、渔民人均纯收入、渔业占农业产值比重、技术推广经费、家庭总收入、海洋机动渔船年末拥有量、专业从业人员、技术推广机构、渔业专业户、海水养殖面积和捕捞产量等作为比较序列。关联系数
$ {\mathit{\delta }}_{ij} $ , 计算公式见表1中公式(17)。由聚集灰色关联系数$ {\mathit{\delta }}_{ij} $ 在各点k的值, 得灰色关联度$ {R}_{ij} $ , 计算公式见表1中公式(18)。2. 结果与分析
2.1 海洋捕捞鱼类碳汇
图1为辽宁省2006—2020年捕捞鱼类碳汇。总碳汇量为3976.04万t, 2006年为碳汇最大值377.71万t, 2020年碳汇为最低值126.63万t, 15年间碳汇量下降254.08万t。2006—2009年碳汇量持续下降, 2010—2016年碳汇量稳定在300万t左右, 2017年较上一年下降155.82万t, 2018—2020年碳汇量持续下降, 最低降至126.63万t。
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图 1 辽宁省2006—2020年捕捞鱼类碳汇量Figure 1. Carbon sink of fish catched in Liaoning Province from 2006 to 2020表4为捕捞鱼类固碳量。小黄鱼(Larimichthys polyactis)固碳量为1017.49万t, 鲅鱼(Scomberomorus niphonius) 835.27万t, 鳀鱼(Engraulis japonicus)和鲐鱼(Scomber japonica)固碳量为620.17万t和479.91万t, 共占捕捞渔业碳汇量的74%; 大黄鱼(Larimichthys crocea)、带鱼(Trichiutus lepturus)和梭鱼(Liza haematocheila)碳汇高于100万t, 占碳汇量的16%; 梅童鱼(Collichthys lucidus)、玉筋鱼(Ammodytes personatus)、鲻鱼(Mugil cephalus)、黄姑鱼(Nibea albiflora)、石斑鱼(Epinephelus sp.)和鲳鱼(Pampus gargenteus)等固碳量在30万t至65万t, 占碳汇量的7%; 竹筴鱼(Trachurus japonicus)、沙丁鱼(Sardina pilchardus)、白姑鱼(Argyrosomus argentatus)和鮸鱼(Miichthys miiuy)的固碳量在10万t至30万t, 占碳汇量的2%; 其他鱼类占碳汇总量的1%。
表 4 辽宁省2006—2020年不同鱼类碳汇量Table 4. Carbon sinks of different fish species catched in Liaoning Province from 2006 to 2020次序
Order鱼类
Fish species碳汇量
Carbon sink次序
Order鱼类
Fish species碳汇量
Carbon sink×104 t 1 小黄鱼 Larimichthys polyactis 1017.49 14 竹筴鱼 Trachurus japonicus 28.05 2 鲅鱼(蓝点马鲛) Scomberomorus niphonius 835.27 15 沙丁鱼 Sardina pilchardus 23.08 3 鳀鱼 Engraulis japonicus 620.17 16 白姑鱼 Argyrosomus argentatus 19.64 4 鲐鱼(日本鲭) Scomber japonica 479.91 17 鮸鱼 Miichthys miiuy 10.03 5 大黄鱼 Larimichthys crocea 295.22 18 金枪鱼 Thunnus thynnus 9.18 6 带鱼 Trichiutus lepturus 228.94 19 马面鲀 Navodon modestus 8.45 7 梭鱼 Liza haematocheila 107.48 20 海鳗 Muraenesox cinereus 7.19 8 梅童鱼 Collichthys lucidus 64.92 21 鳓鱼 Ilisha elongata 3.85 9 玉筋鱼 Ammodytes personatus 61.83 22 金线鱼 Nemipterus virgatus 2.14 10 鲻鱼 Mugil cephalus 39.91 23 方头鱼 Branchiostegus japonicus 1.83 11 黄姑鱼 Nibea albiflora 38.51 24 鲷鱼 Pagrus pagrus 0.93 12 石斑鱼 Epinephelus sp. 36.79 25 蓝圆鯵 Decapterus maruadsi 0.24 13 鲳鱼 Pampus gargenteus 34.80 26 鲱鱼 Clupea pallasi 0.17 2.2 养殖贝藻类碳汇
如图2a所示, 牡蛎、扇贝、蛤、蚶、海带和裙带菜等碳汇量逐年增加, 养殖贻贝和蛏固碳量逐年下降。蛤在近15年贝藻类碳汇量贡献量最大, 占比43%; 其次是扇贝(23%)、牡蛎(11%)、海带(8%)、裙带菜(6%)、贻贝(3%)、蚶和蛏(3%和2%)。
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图 2 辽宁省2006—2020年不同种类(a)和不同生产方式(b)的贝藻类碳汇量Figure 2. Carbon sinks of different species shellfish and macroalgae (a) with different production methods (b) in Liaoning Province from 2006 to 2020如图2b所示, 2006—2020年贝藻类碳汇总量为228.83万t, 2019年为最大值19.07万t, 2007年为最低值10.39万t, 年均碳汇量15.26万t。养殖贝类碳汇量为200.59万t, 占比超过80%; 养殖藻类碳汇量维持在2.5万t左右; 捕捞贝类碳汇量下降1.1万t, 捕捞藻类碳汇量无明显变化, 维持在均值0.002万t。
2.3 海洋捕捞碳排放
图3为2006—2020年辽宁省海洋捕捞碳排放量。碳排放总量为2467.87万t, 拖网碳排放量为1208.75万t, 占比49%; 刺网碳排放量为1037.57万t, 占比42%; 其余4种作业方式碳排放量处于小范围波动。拖网、刺网和钓业等作业方式的碳排放量逐年上升, 围网和张网碳排放量呈下降趋势。
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图 3 辽宁省2006—2020年海洋捕捞不同作业方式碳排放量Figure 3. Carbon emissions of different marine fishing operations in Liaoning Province from 2006 to 20202.4 海洋渔业碳源碳汇
图4为近15年海洋渔业碳源碳汇量变化。海洋渔业碳汇为4217.71万t, 年均碳汇量281.18万t; 海洋捕捞碳源排放量为2467.87万t, 年均排放量164.52万t。2006年碳汇为最大值391.8万t, 2020年为最低值139.9万t; 碳源排放在2015年达最大值185.03万t, 2006年为最小值135.45万t。海洋渔业碳源碳汇最大顺差为256.36万t, 最大逆差29.99万t, 平均差额为116.66万t·a−1。海洋渔业碳源碳汇量之比最大为2.80, 最小为0.75, 除2010年波动回到2.14、2013年1.95和2016年1.89之外, 呈现连续波动的下降趋势。
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图 4 辽宁省2006—2020年海洋捕捞碳源碳汇Figure 4. Source and sink of carbon from marine fishing in Liaoning Province from 2006 to 20202.5 海洋渔业碳汇价值
图5为辽宁省渔业碳汇价值量。渔业碳汇价值总量为114.4亿元, 年均价值量7.63亿元; 渔业碳汇价值量为274.63亿元, 年均价值18.31亿元; 碳源价值量为160.28亿元, 年均价值量10.68亿元。2006—2017年渔业碳汇价值总量中, 碳汇价值量高于碳源价值量, 2018—2020年碳源价值量高于碳汇价值量。在预测的10年期间渔业碳汇碳源价值总量为209.45亿元, 渔业碳汇价值量为94.08亿元, 碳源价值量为115.37亿元。在预测期间捕捞鱼类碳汇价值量呈指数型下降, 贝藻类碳汇价值量和碳源价值量稳定增长。
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图 5 辽宁省2006—2020年海洋渔业碳汇价值量Figure 5. Economic values of carbon sequestration of harvested marine products in Liaoning Province from 2006 to 20202.6 海洋渔业碳汇驱动要素
表5和表6为海洋渔业碳汇驱动要素关联度排名。渔业碳汇关联度前3要素是捕捞产量、技术推广机构数量、海洋捕捞渔船总功率。渔业碳源主要关联要素分别是渔业占农业产值比重、海洋捕捞渔船总功率、渔业专业户数量、专业从业人员数量和技术推广机构数量(表5)。捕捞渔业碳汇主要关联度分别是捕捞产量、技术推广机构数量、海洋捕捞渔船总功率、渔业专业户数量和专业从业人员数量(表6)。养殖贝藻类碳汇主要关联要素分别是渔民人均纯收入、渔业经济总产值、渔业占农业产值比重、海洋捕捞渔船总功率和海水养殖面积(表6)。
表 5 辽宁省2006—2020年海洋渔业碳源和碳汇的驱动要素Table 5. Driving factors for carbon sources and carbon sinks of marine fisheries in Liaoning Province from 2006 to 2020碳源 Carbon source 碳汇 Carbon sink 评价项
Evaluation item关联度Correlation 排名Ranking 评价项
Evaluation item关联度 Correlation 排名Ranking 渔业占农业产值比重
Proportion of fishery in agricultural output value0.938 1 捕捞产量
Catch yield0.950 1 海洋捕捞渔船总功率
Total power of marine fishing vessels0.935 2 技术推广机构数量
Number of technology extension agencies0.888 2 渔业专业户数量
Number of specialized fishery households0.919 3 海洋捕捞渔船总功率
Total power of marine fishing vessels0.862 3 专业从业人员数量
Number of employees0.889 4 渔业专业户数量
Number of specialized fishery households0.862 4 技术推广机构数量
Number of technology extension agencies0.856 5 专业从业人员数量
Number of employees0.851 5 海水养殖面积
Mariculture area0.848 6 渔业占农业产值比重
Proportion of fishery in agricultural output value0.845 6 渔业经济总产值
Gross economic output value of fisheries0.847 7 海水养殖面积
Mariculture area0.811 7 渔民人均纯收入
Net income per fisherman0.828 8 渔业经济总产值
Gross economic output value of fisheries0.794 8 捕捞产量
Catch yield0.796 9 渔民人均纯收入
Net income per fisherman0.770 9 技术推广经费
Technology promotion funds0.704 10 技术推广经费
Technology promotion funds0.682 10 家庭总收入
Total household income0.630 11 家庭总收入
Total household income0.623 11 表 6 辽宁省2006—2020年贝藻类以及捕捞渔业碳汇关联度排名Table 6. Ranking of gray correlation between shellfish, capture fisheries and carbon sink in Liaoning Province from 2006 to 2020捕捞渔业 Capture fisheries 贝藻类 Shell algae 评价项
Evaluation item关联度Correlation 排名Ranking 评价项
Evaluation item关联度 Correlation 排名Ranking 捕捞产量
Catch yield0.952 1 渔民人均纯收入
Net income per fisherman0.922 1 技术推广机构数量
Number of technology extension agencies0.883 2 渔业经济总产值
Gross economic output value of fisheries0.896 2 海洋捕捞渔船总功率
Total power of marine fishing vessels0.854 3 渔业占农业产值比重
Proportion of fishery in agricultural output value0.859 3 渔业专业户数量
Number of specialized fishery households0.852 4 海洋捕捞渔船总功率
Total power of marine fishing vessels0.857 4 专业从业人员数量
Number of employees0.843 5 海水养殖面积
Mariculture area0.844 5 渔业占农业产值比重
Proportion of fishery in agricultural output value0.837 6 渔业专业户数量
Number of specialized fishery households0.837 6 海水养殖面积
Mariculture area0.804 7 技术推广机构数量
Number of technology extension agencies0.827 7 渔业经济总产值
Gross economic output value of fisheries0.788 8 专业从业人员数量
Number of employees0.818 8 渔民人均纯收入
Net income per fisherman0.763 9 捕捞产量
Catch yield0.779 9 技术推广经费
Technology promotion funds0.677 10 技术推广经费
Technology promotion funds0.773 10 家庭总收入
Total household income0.619 11 家庭总收入
Total household income0.696 11 3. 讨论
由于海洋生态系统的复杂性和动态性, 同时海洋渔业又具有“碳源”与“碳汇”的双重属性, 海洋渔业碳汇总量变化是受多种要素驱动的, 确定多要素驱动的耦合机制及各要素对其影响程度面临挑战。农业部在2006年印发关于《全国渔业发展第十一个五年规划(2006—2010年)的通知》, 坚持“以养为主” 的渔业发展方针, 加强渔业资源和生态环境保护。故中国海洋渔业捕捞产量在2006—2010年间平均减幅3.8%。2017年农业部制定《2017年渔业渔政工作要点》, 推进产业转型升级, 加大捕捞渔民转产转业力度。2017年以后辽宁省年均渔业碳汇总量下降至174.45万t, 2010年以后年均渔业碳汇总量恢复到300万t左右。可见, 国家海洋政策方针的调整对海洋渔业年均碳汇总量的变化是一个巨大的影响因素。
2009年广东省贝藻类碳汇总量约为11万t, 在贝藻类养殖海区投放大量的养殖设施, 对海域生境造成胁迫[42], 该养殖模式不利于海区贝藻类的长久增产增收, 不利于海区生态环境的长久动态平衡。2005—2019年浙江省贝藻碳汇总量年均为6.7万t, 主要得益于养殖贝藻品种结构的科学合理, 但是冷冻、台风等造成其产量下降[43]。2014—2019年福建省贝藻类碳汇总量10.7万t∙a−1, 海带、牡蛎、扇贝及蛤等为碳汇总量的主要贡献者, 其养殖产量受气候、养殖面积及市场等因素影响。2006—2020年辽宁省年均贝藻类碳汇总量16.36万t∙a−1, 扇贝、牡蛎、蛤、海带和裙带菜等为碳汇总量的主要贡献者, 其碳汇总量受养殖品种结构、养殖面积和海洋开发等损害海岸生态系统的因素限制。在“双碳”的背景下, 节能减排、节约成本和科学养殖成为海水养殖贝藻类的主流发展模式, 粗放的贝类养殖模式对海域生境造成下行胁迫, 大规模的养殖藻类会扰乱养殖海域生态系统平衡[44-45], 同时大规模的养殖模式将会被逐渐代替。在海洋贝藻类混合养殖模式中, 贝类滤食产生的排泄物和无机氨氮为藻类生长提供营养物质, 藻类光合作用吸收二氧化碳转化为氧气促进贝类生长代谢, 贝藻类在保持各自固碳能力同时, 相互促进对方固碳潜力提升[46], 加快养殖海区碳循环, 维持养殖海区生态系统平衡, 缓解海洋酸化和富营养化。
辽宁省海洋渔业碳汇价值总量在逐年下降。海洋捕捞渔业碳汇价值总量随海洋捕捞渔获物产量的减少而下降, 海水养殖贝藻类碳汇价值总量随海水养殖贝藻类产量的提升而增加。其海洋渔业碳源价值总量保持在年均10.68亿元。在2006—2020年期间海洋渔业碳汇价值总量中, 碳源的价值总量小于碳汇价值总量, 渔业碳汇前中期处于收支盈余水平。2020—2030年海洋碳汇价值总量小于碳源价值的总量, 渔业碳汇的赤字加剧。基于灰色关联度分析, 以及福建省和广东省的海洋贝藻类养殖概况以及其影响要素分析, 辽宁省海洋渔业碳汇总量的变化主要受海洋捕捞渔获物产量、渔船捕捞作业碳排放量、海水贝藻类养殖面积、海水贝藻类养殖品种构成、海水贝藻类养殖模式、国家海洋渔业政策、渔业经济总价值、专业从业人员和技术推广等影响。表明海洋渔业碳汇价值总量受多种要素驱动影响, 并导致海洋渔业碳汇价值总量下降, 这也是对海洋渔业碳汇收支平衡的响应。
因此, 从海洋渔业碳增汇的角度, 辽宁省渔业发展建议如下: 1)开展多种养殖模式深度融合。例如, 扩大鱼类和贝藻类的养殖规模和不同品种的养殖比例变化, 建设人工鱼礁和海洋牧场。2)从节能减排的角度出发, 减少高能耗、低产量的捕捞作业方式。针对捕捞渔船的排放量问题, 加快渔船改造升级, 减少海洋捕捞船只碳排放, 加快实现海洋渔业碳平衡。3)保护海洋物种的丰富性与完整性。改变海洋渔业捕捞方式、降低渔网间隔密度, 减少对幼鱼的捕捞; 增殖放流, 设置禁渔区等。4)发挥政府机构的主导作用, 针对高污染、高排放渔船实行监管政策, 推进渔业低碳化、高效化发展; 完善渔民生活的社会保障体系, 增强渔民自主碳减排意识, 促进辽宁省海洋碳汇渔业可持续发展。
4. 结论
1)辽宁省近15年海洋渔业的碳汇收支盈余, 渔业碳汇总量受海洋捕捞渔获物和养殖贝藻类碳汇总量影响。海洋捕捞碳排放平稳, 各种捕捞方式碳排放存在差异。在研究期间海洋渔业碳汇价值总量持续下降, 捕捞渔业碳汇价值总量持续降低。
2)辽宁省2006—2020年海洋渔业碳汇变化明显, 基于关联度分析, 海洋渔业碳汇下降与海洋捕捞渔获物产量、养殖贝藻类产量、渔业碳排放、捕捞船只数量、渔业专业户养殖规模和专业人员等密切相关。
3)在预测的10年间, 辽宁省海洋渔业碳汇总量与价值量持续下降, 其中捕捞鱼类碳汇量持续性下降, 养殖贝藻类碳汇总量与碳排放总量小幅度升高。
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图 1 辽宁省2006—2020年捕捞鱼类碳汇量
Figure 1. Carbon sink of fish catched in Liaoning Province from 2006 to 2020
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图 2 辽宁省2006—2020年不同种类(a)和不同生产方式(b)的贝藻类碳汇量
Figure 2. Carbon sinks of different species shellfish and macroalgae (a) with different production methods (b) in Liaoning Province from 2006 to 2020
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图 3 辽宁省2006—2020年海洋捕捞不同作业方式碳排放量
Figure 3. Carbon emissions of different marine fishing operations in Liaoning Province from 2006 to 2020
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图 4 辽宁省2006—2020年海洋捕捞碳源碳汇
Figure 4. Source and sink of carbon from marine fishing in Liaoning Province from 2006 to 2020
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图 5 辽宁省2006—2020年海洋渔业碳汇价值量
Figure 5. Economic values of carbon sequestration of harvested marine products in Liaoning Province from 2006 to 2020
表 1 辽宁省海洋渔业碳汇计算公式
Table 1 Calculation formula of marine fishery carbon sink in Liaoning Province
类别
Category公式
Formula符号说明
Symbol description参考文献
Reference贝类碳汇
Carbon sink of shellfish1 Ci=Qi×γ×µ1×ρ1+Qi×
γ×µ2×ρ2$ {C}_{i} $为第i种贝类碳汇量; $ {Q}_{i} $为第i种贝类年总产量; $ \gamma $为贝类干湿转换系数; $ {\mu }_{1} $为贝壳比; $ {\rho }_{1} $为贝壳固碳系数; $ {\mu }_{2} $为软组织比; $ {\rho }_{2} $为软组织固碳系数, 参数取值见表2
$ {C}_{i} $ is the carbon fixed by i shellfish; $ {Q}_{i} $ is the annual yield of i shellfish; $ \gamma $ is the conversion coefficient wet to dry weight of shellfish; $ {\mu }_{1} $ is the ratio of shell to body weight; $ {\rho }_{1} $ is the carbon fixation coefficient of shell; $ {\mu }_{2} $ is the ratio of soft tissue to body weight; $ {\rho }_{2} $ is the carbon fixation coefficient of soft tissue, whose value is shown in the table 2[4] 贝类碳汇总量
Total carbon sink of shellfish2 ${C}_{{\rm{s}}}=\displaystyle\sum _{i=1}^{n}{C}_{i}$ ${C}_{{\rm{s}}}$为海水养殖贝类碳汇总量
${C}_{{\rm{s}}}$ is the total amount of carbon of maricultured shellfish藻类碳汇
Carbon sink of macroalgae3 $ {C}_{{\rm{e}}}=\displaystyle\sum _{i=1}^{m}\left({P}_{i}\times {W}_{i}\right)\times 20 {\text{%}} $ $ {C}_{{\rm{e}}} $为不同种类藻类碳汇总量; $ {P}_{i} $为i种藻类的年总产量; $ {W}_{i} $为i种藻类的碳含量百分比, 参数取值见表3
$ {C}_{{\rm{e}}} $ is the carbon fixed by maricultural macroalgae; $ {P}_{i} $ is the annual harvest of i macroalgae; $ {W}_{i} $ is the percentage of carbon by i macroalgae, whose value is shown in the table 3贝藻类碳汇总量
Total carbon sink of shellfish and macroalgae4 $ C={C}_{{\rm{e}}}+{C}_{{\rm{s}}} $ $ C $为海水养殖贝藻类年碳汇总量
$ C $ is the annual carbon totals by maricultural shellfish and macroalgae鱼类营养级
Fish nutritional level5 ${T}_{i}=1+\displaystyle\sum _{j=1}^{n}{D}_{i j}\times {T}_{j}$ $ {T}_{i} $为生物i的营养级; $ {T}_{j} $为生物i摄食的食物j的营养级; ${D}_{i j}$为食物j在生物i的食物中所占的比例
$ {T}_{i} $ is the trophic level of i fish; $ {T}_{j} $ is the trophic level of j organism being fed on i fish; ${D}_{i j}$ is the proportion of j prey in the food of i predator[29] 被捕食者生物量
Biomass of prey6 ${B}_{j}={Y}_{0} /\left({E}_{ {L}_{0}-1}\times {E}_{ {L}_{0}-2}\right)$ ${B}_{j} $为被捕食者的生物量; $ {Y}_{0} $为渔获量; $ {L}_{0} $为渔获物平均营养级; $ {E}_{{L}_{0}-1} $为营养级$ ({L}_{0}-1) $的生态转换效率; $ {E}_{{L}_{0}-2} $为营养级$ ({L}_{0}-2) $的生态转换效率
${B}_{j} $ is the biomass of the prey; $ {Y}_{0} $ is the yield; $ {L}_{0} $ is the average trophic level in catch; $ {E}_{{L}_{0}-1} $ is the ecological conversion efficiency of trophic level $ ({L}_{0}-1) $; $ {E}_{{L}_{0}-2} $ is the ecological conversion efficiency of trophic level $ ({L}_{0}-2) $[14] 摄食浮游植物和有机碎屑的比例
Proportion of phytoplankton and organic detritus in diet7 1×Q+2×(100%−Q) =
L0−2, 即Q = 4−L0$ Q $为摄食浮游植物和有机碎屑的比例; L0为渔获物平均营养级
$ Q $ is the proportion of phytoplankton and organic detritus in diet. L0 is the average trophic level in catch摄食浮游植物和有机碎屑的生物量
Biomas of phytoplankton and organic detritus in diet8 B0=(B1×Q)+B1×
(100%−Q)/EL=1$ {B}_{0} $为摄食浮游植物和有机碎屑的生物量; $ {E}_{L=1} $为初级消费者摄食浮游植物和有机碎屑的生态转换效率
$ {B}_{0} $ is the biomass of phytoplankton and organic detritus in diets; $ {E}_{L=1} $ is the ecological conversion efficiency of primary consumers to ingest phytoplankton and organic detritus被捕食的浮游植物碳含量
Carbon content of phytoplankton9 $ {C}_{1}=4.49 {\text{%}}\times {B}_{0} $ $ {C}_{1} $被捕食的浮游植物的现存碳含量
$ {C}_{1} $ is the carbon content of phytoplankton[30] 摄食浮游植物固碳量
Carbon sequestration by feeding phytoplankton10 $ {C}_{\mathrm{T}}=45\times {C}_{1} $ $ {C}_{\mathrm{T}} $摄食浮游植物的固碳量
$ {C}_{\mathrm{T}} $ is the carbon sequestration by feeding phytoplankton[31] 化石燃料燃烧排放CO2量
CO2 emissions from fossil fuel combustion11 $ {G}_{k}={T}_{k}\times f\times h $ $ {G}_{k} $为渔船燃烧k燃料时的碳排放量; $ {T}_{k} $为消耗k燃料的量; f为有效氧化分数, h为燃料k的平均含碳量
$ {G}_{k} $ is the carbon emissions of fishing vessels burning k fuel; $ {T}_{k} $ is the amount of k fuel consumed; f is the effective oxidation fraction; h is the average carbon content of k fuel[32] 海洋渔船产碳量
Carbon production of marine fishing vessel12 ${G}_{n}={T}_{n}\times s\times w\times z\times \varphi$ $ {G}_{n} $为渔船燃烧碳量; $ {T}_{n} $为渔船作业燃油消耗量; s为折标准煤系数1.4571; w为有效氧化分数0.982; z为每吨标煤含碳量0.732 57; $\varphi$为0.813, 即在获得相同热能的条件下, 燃油释放CO2与燃煤释放CO2之间的比值
$ {G}_{n} $ is the amount of carbon burned by n fishing vessels; $ {T}_{n} $ is the fuel consumption of fishing vessel n operation; s is the standard coal coefficient (1.4571); w is the effective oxidation fraction (0.982); z is the carbon content per ton of standard coal (0.732 57); $\varphi$ is 0.813, the ratio of CO2 released between from fuel oil and from coal under the same thermal energy conditions[33] 海洋捕捞渔船CO2排放量
CO2 emissions from marine fishing vessels13 ${C}_{ {\mathrm{c}\mathrm{o} }_{2} }={G}_{n}\times \partial$ $ {C}_{{\mathrm{c}\mathrm{o}}_{2}} $为$ {\mathrm{C}\mathrm{O}}_{2} $排 放量; $ \partial $为碳换算为CO2的常数(3.67)
$ {C}_{{\mathrm{c}\mathrm{o}}_{2}} $ is the $ {\mathrm{C}\mathrm{O}}_{2} $ emission; $ \partial $ is the constant from carbon to carbon dioxide (3.67)燃油消耗量
Fuel consumption14 $ {T}_{n}={\displaystyle\sum }_{j=1}^{m}\left({P}_{j}{\times F}_{j}\right) $ $ {P}_{j} $为海洋捕捞$\mathrm{作}\mathrm{业}\mathrm{功}\mathrm{率},{F}_{j}$为海洋捕捞作业${用}{油}{系}{数}$
$ {P}_{j} $ is the power of j marine fishing operation; $ {F}_{j} $ is the oil consumption coefficient of j marine fishing operation渔业碳汇价值量预测
Prediction of economic value of carbon sink by fisheries15 ${Y}_{t+T}={A}_{t}+{B}_{t}+{C}_{t}\times {T}^{2}$ $ {Y}_{t+T} $为碳汇量预测值; T为预测期数; $ {A}_{t} $、$ {B}_{t} $、$ {C}_{t} $分别为t年预测系数; $ {L}_{t}^{\left(1\right)} $、$ {L}_{t}^{\left(2\right)} $、$ {L}_{t}^{\left(3\right)} $分别为t年一次、二次、三次平滑预测值; $ \omega $为平滑系数; $ {X}_{t} $为t年碳汇量原始值; $ {L}_{0}^{\left(1\right)} $、$ {L}_{0}^{\left(2\right)} $、$ {L}_{0}^{\left(3\right)} $分别为$ {L}_{t}^{\left(1\right)} $、$ {L}_{t}^{\left(2\right)} $、$ {L}_{t}^{\left(3\right)} $的初始值
$ {Y}_{t+T} $ is the predicted value of carbon sinks; T is the forecast period; $ {A}_{t} $, ${B}_{t}\;and\;$ $ {C}_{t} $ are t year prediction coefficients; $ {L}_{t}^{\left(1\right)} $, $ {L}_{t}^{\left(2\right)} $ and $ {L}_{t}^{\left(3\right)} $ are the first, second and third smoothing predictions in t year, respectively; $ \omega $ is the smoothing coefficient; $ {X}_{t} $ is the original value of the carbon sink in t year; $ {L}_{0}^{\left(1\right)} $, $ {L}_{0}^{\left(2\right)} $, and $ {L}_{0}^{\left(3\right)} $ are the initial values of $ {L}_{t}^{\left(1\right)} $, $ {L}_{t}^{\left(2\right)} $, and $ {L}_{t}^{\left(3\right)} $, respectively[34-35] $ {A}_{t}=3{L}_{t}^{\left(1\right)}-3{L}_{t}^{\left(2\right)}+{L}_{t}^{\left(3\right)} $ $B_t={\dfrac{\omega }{2\left(1-\omega \right)^2 }}$$\left[\left(6-5\omega \right){L}_{t}^{\left(1\right)}-2\left(5-4\omega \right){L}_{t}^{\left(2\right)}+\left(4-3\omega \right){L}_{t}^{\left(3\right)}\right] $ $ {C}_{t}=\dfrac{\omega ^2}{2\left(1-\omega \right)^2}\left[{L}_{t}^{\left(1\right)}-2{L}_{t}^{\left(2\right)}+{L}_{t}^{\left(3\right)}\right] $ $ {L}_{t}^{\left(1\right)}=\omega {\times X}_{t}+\left(1-\omega \right)\times {L}_{t-1}^{\left(1\right)} $ $ {L}_{t}^{\left(2\right)}=\omega {\times L}_{t}^{\left(1\right)}+\left(1-\omega \right)\times {L}_{t-1}^{\left(2\right)} $ $ {L}_{t}^{\left(3\right)}=\omega {\times L}_{t}^{\left(2\right)}+\left(1-\omega \right){\times L}_{t-1}^{\left(3\right)} $ $ {L}_{0}^{\left(1\right)}={L}_{0}^{\left(2\right)}={L}_{0}^{\left(3\right)}=\dfrac{{X}_{1}+{X}_{2}+{X}_{3}}{3} $ 渔业碳汇价值
Economic value of fishery carbon sink16 ${O}_{_ {\rm{CSV} } }={O}_{_ {\rm{FS} } }\times {C}_{ {\rm{rec} } }$ ${O}_{_ {\rm{CSV} } }$为渔业碳汇价值量, ${O}_{_ {\rm{FS} } }$为渔业碳汇量, $ {C}_{{\rm{rec}}} $为单位的碳减排经济成本价值
${O}_{_ {\rm{CSV} } }$ is the value of fishery carbon sink; ${O}_{_ {\rm{FS} } }$ is the amount of fishery carbon sink; $ {C}_{{\rm{rec}}} $ is the economic cost value of carbon emission reduction per unit[36-39] 关联系数
Correlation coefficient17 $\delta_{i j}=\dfrac{\min _j \times \min _k \times \Delta_{i j}(k)+\beta \times \max _j \times \max _k \times \Delta_{i j}(k)}{\Delta_{i j}(k)+\beta \times \max _j \times \max _k \times \Delta_{i j}(k)}$ $\delta_{i j} $为灰色关联系数; $\Delta_{ i j}\left(k\right)={x}_{i}\left(k\right)-{x}_{j}\left(k\right)$为序列i$ \left\{{x}_{i}\left(k\right)\right\} $与序列j$ \left\{{x}_{j}\left(k\right)\right\} $在第k点的绝对差; ${\min}_{j}\times{\min}_{k}\times{\Delta }_{ij}\left(k\right)$为两极最小差; ${\max}_{j}\times{\max}_{k}\times{\Delta }_{ij}\left(k\right)$为两极最大差; $ \beta $为分辨系数, 其值为 0~1, 取$ \beta $ = 0.5
$\delta_{i j} $ is grey correlation coefficient; $\Delta_{ i j}\left(k\right)={x}_{i}\left(k\right)-{x}_{j}\left(k\right)$ is the absolute difference between sequence i$ \left\{{x}_{i}\left(k\right)\right\} $ and sequence j$ \left\{{x}_{j}\left(k\right)\right\} $ at point k; ${\min}_{j}\times{\min}_{k}\times{\Delta }_{ij}\left(k\right)$ is the minimum difference between two poles; ${\max}_{j}{\max}_{k}{\Delta }_{ij}\left(k\right)$ is the maximum difference between the two poles; $ \beta $ is the resolution coefficient, whose value is 0.5[40-41] 灰色关联度
Grey correlation18 $ {R}_{ij}=\dfrac{1}{n}\displaystyle\sum _{k=1}^{n}{\delta }_{ij}\left(k\right)k=\mathrm{1,2},3,\cdots ,n $ Rij为灰色关联度
Rij is the grey correlation degree
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表 2 海水养殖贝类固碳计算参数
Table 2 Calculation parameters of carbon sequestration in mariculture shellfish
种类
Shellfish干重比
Dry weight ratio (%)质量比 Mass ratio (%) 碳汇系数 Carbon sink coefficient (% dry weight) 软体组织 Soft tissue 贝壳 Shell 软体组织 Soft tissue 贝壳 Shell 牡蛎 Oyster 65.10 6.14 93.86 45.89 12.68 贻贝 Mussel 75.28 8.47 91.53 44.40 11.76 扇贝 Scallop 63.89 14.35 85.65 43.90 11.40 蛤 Clam 52.55 1.98 98.02 44.90 11.52 蛏 Razor clam 70.48 3.26 96.74 44.90 13.24 其他贝类 Other shellfish 64.21 11.41 88.59 43.87 11.44
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表 3 海水养殖藻类固碳计算参数(藻类干重比为20%)
Table 3 Calculation parameters of carbon sink in mariculture macroalgae (dry weight ratio of algae is 20%)
种类
Macroalgae碳汇系数
Carbon sink coefficient (% dry weight)海带 Kelp 31.20 裙带菜 Wakame 26.40 其他藻类 Other algae 27.76
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表 4 辽宁省2006—2020年不同鱼类碳汇量
Table 4 Carbon sinks of different fish species catched in Liaoning Province from 2006 to 2020
次序
Order鱼类
Fish species碳汇量
Carbon sink次序
Order鱼类
Fish species碳汇量
Carbon sink×104 t 1 小黄鱼 Larimichthys polyactis 1017.49 14 竹筴鱼 Trachurus japonicus 28.05 2 鲅鱼(蓝点马鲛) Scomberomorus niphonius 835.27 15 沙丁鱼 Sardina pilchardus 23.08 3 鳀鱼 Engraulis japonicus 620.17 16 白姑鱼 Argyrosomus argentatus 19.64 4 鲐鱼(日本鲭) Scomber japonica 479.91 17 鮸鱼 Miichthys miiuy 10.03 5 大黄鱼 Larimichthys crocea 295.22 18 金枪鱼 Thunnus thynnus 9.18 6 带鱼 Trichiutus lepturus 228.94 19 马面鲀 Navodon modestus 8.45 7 梭鱼 Liza haematocheila 107.48 20 海鳗 Muraenesox cinereus 7.19 8 梅童鱼 Collichthys lucidus 64.92 21 鳓鱼 Ilisha elongata 3.85 9 玉筋鱼 Ammodytes personatus 61.83 22 金线鱼 Nemipterus virgatus 2.14 10 鲻鱼 Mugil cephalus 39.91 23 方头鱼 Branchiostegus japonicus 1.83 11 黄姑鱼 Nibea albiflora 38.51 24 鲷鱼 Pagrus pagrus 0.93 12 石斑鱼 Epinephelus sp. 36.79 25 蓝圆鯵 Decapterus maruadsi 0.24 13 鲳鱼 Pampus gargenteus 34.80 26 鲱鱼 Clupea pallasi 0.17
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表 5 辽宁省2006—2020年海洋渔业碳源和碳汇的驱动要素
Table 5 Driving factors for carbon sources and carbon sinks of marine fisheries in Liaoning Province from 2006 to 2020
碳源 Carbon source 碳汇 Carbon sink 评价项
Evaluation item关联度Correlation 排名Ranking 评价项
Evaluation item关联度 Correlation 排名Ranking 渔业占农业产值比重
Proportion of fishery in agricultural output value0.938 1 捕捞产量
Catch yield0.950 1 海洋捕捞渔船总功率
Total power of marine fishing vessels0.935 2 技术推广机构数量
Number of technology extension agencies0.888 2 渔业专业户数量
Number of specialized fishery households0.919 3 海洋捕捞渔船总功率
Total power of marine fishing vessels0.862 3 专业从业人员数量
Number of employees0.889 4 渔业专业户数量
Number of specialized fishery households0.862 4 技术推广机构数量
Number of technology extension agencies0.856 5 专业从业人员数量
Number of employees0.851 5 海水养殖面积
Mariculture area0.848 6 渔业占农业产值比重
Proportion of fishery in agricultural output value0.845 6 渔业经济总产值
Gross economic output value of fisheries0.847 7 海水养殖面积
Mariculture area0.811 7 渔民人均纯收入
Net income per fisherman0.828 8 渔业经济总产值
Gross economic output value of fisheries0.794 8 捕捞产量
Catch yield0.796 9 渔民人均纯收入
Net income per fisherman0.770 9 技术推广经费
Technology promotion funds0.704 10 技术推广经费
Technology promotion funds0.682 10 家庭总收入
Total household income0.630 11 家庭总收入
Total household income0.623 11
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表 6 辽宁省2006—2020年贝藻类以及捕捞渔业碳汇关联度排名
Table 6 Ranking of gray correlation between shellfish, capture fisheries and carbon sink in Liaoning Province from 2006 to 2020
捕捞渔业 Capture fisheries 贝藻类 Shell algae 评价项
Evaluation item关联度Correlation 排名Ranking 评价项
Evaluation item关联度 Correlation 排名Ranking 捕捞产量
Catch yield0.952 1 渔民人均纯收入
Net income per fisherman0.922 1 技术推广机构数量
Number of technology extension agencies0.883 2 渔业经济总产值
Gross economic output value of fisheries0.896 2 海洋捕捞渔船总功率
Total power of marine fishing vessels0.854 3 渔业占农业产值比重
Proportion of fishery in agricultural output value0.859 3 渔业专业户数量
Number of specialized fishery households0.852 4 海洋捕捞渔船总功率
Total power of marine fishing vessels0.857 4 专业从业人员数量
Number of employees0.843 5 海水养殖面积
Mariculture area0.844 5 渔业占农业产值比重
Proportion of fishery in agricultural output value0.837 6 渔业专业户数量
Number of specialized fishery households0.837 6 海水养殖面积
Mariculture area0.804 7 技术推广机构数量
Number of technology extension agencies0.827 7 渔业经济总产值
Gross economic output value of fisheries0.788 8 专业从业人员数量
Number of employees0.818 8 渔民人均纯收入
Net income per fisherman0.763 9 捕捞产量
Catch yield0.779 9 技术推广经费
Technology promotion funds0.677 10 技术推广经费
Technology promotion funds0.773 10 家庭总收入
Total household income0.619 11 家庭总收入
Total household income0.696 11
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