中国渔业生产系统隐含碳排放结构特征及驱动因素分解

doi:10.18402/resci.2021.06.09

摘要/Abstract

摘要：

面对新的发展形势,传统的渔业生产方式已不适应当前社会转型要求,面临着发展方式粗放、效益低迷及影响生态环境等诸多挑战。渔业经济的低碳化发展是中国渔业可持续发展必然选择,是保障国家食物安全、促进农（渔）民增收和经济社会发展的现实需要,对于从根本上推进中国渔业供给侧结构性改革意义重大。基于投入产出模型和扩展Kaya恒等式,对2002—2017年中国渔业生产系统隐含碳排放变动趋势及结构特征进行研究,并对隐含碳排放的驱动因素进行分解。结果表明：①中国渔业生产系统隐含碳排放整体呈现上升—下降—再上升—再下降的变化趋势,渔业第一产业始终处于渔业隐含碳排放首位,但渔业第二与第三产业的隐含碳排放占比在逐渐上升。②渔业养殖与捕捞、水产加工和水产流通是渔业隐含碳排放的主要产业;渔业三大产业及所包含的12个细分产业的单位产值隐含碳排放水平都呈现下降趋势。③渔业经济增长、一般渔业技术进步对渔业隐含碳排放具有正向的推动作用,渔业经济增长是拉动中国渔业隐含碳排放增长的主要因素;渔业人口和低碳渔业技术进步效应对渔业隐含碳排放具有一定程度的抑制作用,低碳渔业技术进步效应是抑制中国渔业隐含碳排放的最大因素。因此,调整渔业产业结构,转变渔业增长方式,提升渔业低碳技术的应用水平是有效抑制中国渔业隐含碳排放、实现渔业低碳发展的关键。

关键词: 渔业生产系统, 隐含碳排放, 排放结构特征, 驱动因素分解, 投入产出模型, Kaya恒等式, 中国

Abstract:

In the face of the new development situation, the traditional mode of fishery production no longer can meet the requirements of the current social transformation. The low-carbon development of the fishery industry is an inevitable choice for its sustainable development in China, a practical need to ensure the national food security and promote the income increase of farmers (fishermen) and economic and social development, and is of great significance for fundamentally promoting the supply side structural reform of China’s marine fisheries. Based on the input-output model and the extended Kaya identity, this study examined the changing trend and structural characteristics of embodied carbon emissions in China’s fishery production system, and decomposed the driving factors of the embodied carbon emissions. The results show that: (1) Embodied carbon emission of fishery production system in China as a whole showed a trend of rising, slightly decreasing, rising again, and falling, the primary fishery sector always had the highest embodied carbon emissions, but the embodied carbon emissions of the secondary and tertiary fishery sectors increased gradually. (2) Fishery capture and aquaculture, processing, and circulation are the main sectors of the embodied carbon emissions in the fishery industry. The embodied carbon emission level per unit output value of the three fishery sectors and the 12 sub-sectors all showed a downward trend, among which the embodied carbon emission level per unit output value of fishery drug, aquatic transportation, and fishery construction sub-sectors was the highest, which had a great impact on the embodied carbon emissions of the fishery industry. (3) The fishery economic growth and general fishery technological progress have positive impact on the embodied carbon emissions of the industry, and fishery economic growth is the main factor that drives the growth of the embodied carbon emissions. Fishery population, fishery economic structure, and low-carbon fishery technology have a certain degree of restraining effect to the embodied carbon emissions. Low-carbon fishery technologies are the biggest factor to restrain the embodied carbon emissions of fisheries in China. Therefore, adjusting the structure of the fishery industry, changing the mode of fishery growth, and improving the application level of low-carbon fishery technologies are the key to effectively restraining the embodied carbon emissions and realize the low-carbon development of the fishery industry in China.

Key words: fishery production system, embodied carbon emissions, emission structural characteristics, driving factor decomposition, input-output model, Kaya identity, China

李晨, 李昊玉, 孔海峥, 冯伟. 中国渔业生产系统隐含碳排放结构特征及驱动因素分解[J]. 资源科学, 2021, 43(6): 1166-1177.

LI Chen, LI Haoyu, KONG Haizheng, FENG Wei. Structural characteristics and driving factors of embodied carbon emissions from fishery production system in China[J]. Resources Science, 2021, 43(6): 1166-1177.

图/表 7

表1

表2

表3

图1

图2

图3

表4

参考文献 31

[1]	陈柔, 何艳秋, 朱思宇, 等. 我国农业碳排放双重性及其与经济发展的协调性研究[J]. 软科学, 2020, 34(1):132-138.
	[ Chen R, He Y Q, Zu S Y, et al. Duality of agricultural carbon emissions and coordination with economic development in China[J]. Soft Science, 2020, 34(1):132-138.]
[2]	胡婉玲, 张金鑫, 王红玲. 中国农业碳排放特征及影响因素研究[J]. 统计与决策, 2020, 36(5):56-62.
	[ Hu W L, Zhang J X, Wang H L. Characteristics and influencing factors of agricultural carbon emission in China[J]. Statistics & Decision, 2020, 36(5):56-62.]
[3]	徐皓. 我国渔业节能减排基本情况研究报告[J]. 渔业现代化, 2008, 35(4):1-7.
	[ Xu H. Report on fishery industry energy conservation and emissions reduction research in China[J]. Fishery Modernization, 2008, 35(4):1-7.]
[4]	李晨, 冯伟, 邵桂兰. 中国省域渔业全要素碳排放效率时空分异[J]. 经济地理, 2018, 38(5):179-187.
	[ Li C, Feng W, Shao G L. Spatio-temporal difference of total carbon emission efficiency of fishery in China[J]. Economic Geography, 2018, 38(5):179-187.]
[5]	陈红敏. 包含工业生产过程碳排放的产业部门隐含碳研究[J]. 中国人口·资源与环境, 2009, 19(3):25-30.
	[ Chen H M. Analysis on embodied CO₂ emissions including industrial process emissions [J]. China Population, Resources and Environment, 2009, 19(3):25-30.]
[6]	Howard T O. Environmental Accounting: Emergy and Environmental Decision Making[M]. New York: Wiley, 1996.
[7]	Ahmadn W A. Carbon dioxide emissions embodied in international trade of goods[J]. OECD Science Technology and Industry Working Papers, 2003, 25(4):1-22.
[8]	Nakano S A, Okamuran A, Sakurai N, et al. The Measurement of CO₂ Embodiments in International Trade: Evidence from the Harmonized Input-output and Bilateral Trade Database[R]. Paris: OECD, STIW Working Paper, 2009.
[9]	Peters G P, Hertwich E G. CO₂ embodied in international trade with implications for global climate policy[J]. Environmental Science & Technology, 2008, 42(5):1401-1407. doi: 10.1021/es072023k
[10]	Liu X B, Ishikawa M, Wang C, et al. Analyses of CO₂ emissions embodied in Japan-China trade[J]. Energy Policy, 2010, 38(3):1510-1518. doi: 10.1016/j.enpol.2009.11.034
[11]	Leontief W W. Quantitative input-output relations in the economic system[J]. The Review of Economic Statistics, 1936, 18(3):105-125. doi: 10.2307/1927837
[12]	项莹, 赵静. 中国省际高技术产业非竞争型投入产出表编制及应用研究[J]. 数量经济技术经济研究, 2020, 37(1):122-140.
	[ Xiang Y, Zhao J. Complication and application of non-competitive input-output table of high-tech industries in China’s provinces[J]. The Journal of Quantitative & Technical Economics, 2020, 37(1):122-140.]
[13]	韩梦瑶, 熊焦, 刘卫东. 中国跨境能源贸易及隐含能源流动对比: 以 “一带一路” 能源合作为例[J]. 自然资源学报, 2020, 35(11):2674-2686.
	[ Han M Y, Xiong J, Liu W D. China’ s cross-border energy relations between direct trade and embodied transfers: Based on “the Belt and Road” energy cooperation[J]. Journal of Natural Resources, 2020, 35(11):2674-2686.]
[14]	李艳梅, 牛苗苗, 张红丽. 京津冀区域内增加值贸易的经济收益和隐含碳排放比较[J]. 资源科学, 2019, 41(9):1619-1629.
	[ Li Y M, Niu M M, Zhang H L. Comparison of economic benefits and embodied carbon emissions of intraregional value-added trade in the Beijing-Tianjin-Hebei region[J]. Resources Science, 2019, 41(9):1619-1629.]
[15]	Schaeffer R, de Sá A. The embodiment of carbon associated with Brazilian imports and exports[J]. Energy Conversion and Management, 1996, (6):955-960.
[16]	Weber C L, Matthews H S. Quantifying the global and distributional aspects of American household carbon footprint[J]. Ecological Economics, 2008, 66(2):379-391. doi: 10.1016/j.ecolecon.2007.09.021
[17]	曲英, 刘炜, 周文杰. 辽宁省污染密集型产业绿色增长效率测度研究[J]. 科技与管理, 2017, 19(1):1-7.
	[ Qu Y, Liu W, Zhou W J. Research on green growth efficiency measurement of pollution-intensive industries taking Liaoning Province as a case[J]. Science-Technology and Management, 2017, 19(1):1-7.]
[18]	徐博, 杨来科, 钱志权. 全球价值链分工地位对于碳排放水平的影响[J]. 资源科学, 2020, 42(3):527-535.
	[ Xu B, Yang L K, Qian Z Q. The impact of global value chain position on carbon emissions[J]. Resources Science, 2020, 42(3):527-535.]
[19]	常冉, 杨来科, 钱志权. 区域价值链嵌入有利于降低我国境内增加值碳排放成本吗? 基于制造业数据实证分析[J]. 国际贸易问题, 2020, (5):117-131.
	[ Chang R, Yang L K, Qian Z Q. Can integration in regional value chain help China to reduce carbon emission cost? An empirical analysis based on data from manufacturing sectors[J]. Journal of International Trade, 2020, (5):117-131.]
[20]	胡剑波, 任香, 高鹏. 中国省际贸易、国际贸易与低碳贸易竞争力的测度研究[J]. 数量经济技术经济研究, 2019, 36(9):42-60.
	[ Hu J P, Ren X, Gao P. Research on the measurement of China’s inter-provincial trade, international trade and low carbon trade competitiveness[J]. The Journal of Quantitative & Technical Economics, 2019, 36(9):42-60.]
[21]	Yabe N. An analysis of CO₂ emissions of Japanese industries during the period between 1985 and 1995[J]. Energy Policy, 2004, 32(5):595-610. doi: 10.1016/S0301-4215(02)00312-9
[22]	Wang C, Chen J N, Zou J. Decomposition of energy-related CO₂ emission in China: 1957-2000[J]. Energy, 2005, 30(1):73-83. doi: 10.1016/j.energy.2004.04.002
[23]	戴小文, 漆雁斌, 唐宏. 1990-2010年中国农业隐含碳排放及其驱动因素研究[J]. 资源科学, 2015, 37(8):1668-1676.
	[ Dai X W, Qi Y B, Tang H. Embodied CO₂ emission calculation and influence factors decomposition in China’s agriculture sector [J]. Resources Science, 2015, 37(8):1668-1676.]
[24]	王若梅, 马海良, 王锦. 基于水-土要素匹配视角的农业碳排放时空分异及影响因素: 以长江经济带为例[J]. 资源科学, 2019, 41(8):1450-1461.
	[ Wang R M, Ma H L, Wang J. Spatial and temporal differences of agricultural carbon emissions and impact factors of the Yangtze River Economic Belt based on a water-land perspective[J]. Resources Science, 2019, 41(8):1450-1461.]
[25]	徐皓, 张祝利, 张建华, 等. 我国渔业节能减排研究与发展建议[J]. 水产学报, 2011, (3):472-480.
	[ Xu H, Zhang Z L, Zhang J H, et al. The research and development proposals on fishery energy saving and emission reduction in China[J]. Journal of Fisheries of China, 2011, (3):472-480.]
[26]	刘晃, 车轩. 中国水产养殖二氧化碳排放量估算的初步研究[J]. 南方水产科学, 2010, 6(4):77-80.
	[ Liu H, Che X. Elementary study on evaluation of CO₂ emissions from aquaculture in China [J]. South China Fisheries Science, 2010, 6(4):77-80.]
[27]	邵桂兰, 孔海峥, 李晨. 中国海水养殖的净碳汇及其与经济耦合关系[J]. 资源科学, 2019, 41(2):277-288.
	[ Shao G L, Kong H Z, Li C. Net amount of mariculture carbon sink and its coupling relationship with economics growth of China[J]. Resources Science, 2019, 41(2):277-288.]
[28]	徐敬俊, 张洁, 佘翠花. 海洋碳汇渔业绿色发展空间外溢效应评价研究[J]. 中国人口·资源与环境, 2020, 30(6):136-145.
	[ Xu J J, Zhang J, She C H. Evaluation of economic spillover effect of green development of marine carbon sink fishery[J]. China Population, Resources and Environment, 2020, 30(6):136-145.]
[29]	孙康, 崔茜茜, 苏子晓, 等. 中国海水养殖碳汇经济价值时空演化及影响因素分析[J]. 地理研究, 2020, 39(11):2508-2520. doi: 10.11821/dlyj020190731
	[ Sun K, Cui Q Q, Su Z X, et al. Spatio-temporal evolution and influencing factors of the economic value for mariculture carbon sinks in China[J]. Geographical Research, 2020, 39(11):2508-2520.]
[30]	史俊晖, 戴小文. 我国省域农业隐含碳排放及其驱动因素时空动态分析[J]. 中国农业资源与区划, 2020, 41(8):169-180.
	[ Shi J H, Dai X W. Spatial dynamics of agricultural embodied carbon emissions in provinces of China and the related driving factors[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2020, 41(8):169-180.]
[31]	闫云凤, 赵忠秀. 中国对外贸易隐含碳的测度研究: 基于碳排放责任界定的视角[J]. 国际贸易问题, 2012, (1):131-142.
	[ Yan Y F, Zhao Z X. CO₂ emissions embodied in China’s international trade: A perspective of allocating international responsibilities [J]. Journal of International Trade, 2012, (1):131-142.]

能源名称	平均低位发热量/(kJ/kg)	折标准煤系数/(kgce/kg)	单位热量含碳量/(t/TJ)	碳氧化率	碳排放系数/(kgCO₂/kg)
煤炭	20908	0.7143	26.37	0.94	1.9003
焦炭	28435	0.9714	29.50	0.93	2.8604
原油	41816	1.4286	20.10	0.98	3.0202
汽油	43070	1.4714	18.90	0.98	2.9251
煤油	43070	1.4714	19.50	0.98	3.0179
柴油	42652	1.4571	20.20	0.98	3.0959
燃料油	41816	1.4286	21.10	0.98	3.1705
天然气	38931	1.3300	15.30	0.99	2.1622

部门序号	产业分类	部门序号	产业分类
1	农林牧渔	15	金属制品业
2	煤炭开采和洗选业	16	通用、专用设备制造业
3	石油和天然气开采业	17	交通运输设备制造业
4	金属矿采选业	18	电气机械及器材制造业
5	非金属矿及其他矿采选业	19	通信设备、计算机及其他电子设备制造业
6	食品制造及烟草加工业	20	仪器仪表及文化办公用机械制造业
7	纺织业	21	工艺品及其他制造业
8	纺织服装鞋帽皮革羽绒及其制品业	22	电力、热力的生产和供应业
9	木材加工及家具制造业	23	燃气生产和供应业
10	造纸印刷及文教体育用品制造业	24	水的生产和供应业
11	石油加工、炼焦及核燃料加工业	25	建筑业
12	化学工业	26	交通运输、仓储和邮政业
13	非金属矿物制品业	27	批发、零售业和住宿、餐饮业
14	金属冶炼及压延加工业	28	其他服务行业

年份	渔业第一产业		渔业第二产业						渔业第三产业				合计
年份	养殖与捕捞	水产苗种	水产品加工	渔用机具制造	渔用饲料	渔用药物	渔业建筑业	其他工业和建筑业	水产流通	（仓储）运输	休闲渔业	其他流通和服务业	合计
2002	7454.65	402.05	2164.15	140.79	274.13	5.81	91.44	306.77	2323.69	142.54	127.92	227.51	13661.45
2005	8375.11	408.22	2775.87	168.48	305.50	13.12	109.71	232.53	2968.44	207.34	172.12	271.48	16007.93
2007	6975.75	372.70	2670.48	145.26	340.35	15.07	98.22	192.72	2717.09	187.59	227.62	200.52	14143.38
2010	7608.71	448.02	2814.45	164.92	361.00	15.10	128.21	202.10	3003.63	183.89	252.07	246.27	15428.38
2012	9439.51	567.17	3480.90	272.28	494.88	12.90	174.70	128.45	3818.72	243.57	329.41	193.13	19155.62
2015	9449.56	543.51	3423.07	300.25	471.71	13.87	198.92	87.72	4082.39	267.09	431.59	154.17	19423.84
2017	8678.39	507.88	3211.64	267.30	482.37	13.76	175.44	76.85	4061.27	277.95	570.26	149.04	18472.16

年份	渔业人口效应	渔业经济增长效应	渔业产业结构效应	渔业技术进步效应		渔业隐含碳排放总效应
年份	渔业人口效应	渔业经济增长效应	渔业产业结构效应	一般渔业技术进步效应	低碳渔业技术进步效应	逐期效应	累积效应
2002—2005	156.83	4193.59	0.00	3835.08	-5839.02	2189.65	2189.65
2005—2007	333.16	3621.82	160.95	757.31	-6423.80	-1883.72	305.93
2007—2010	-215.78	5523.44	-28.95	-362.49	-4421.54	710.46	1016.39
2010—2012	-53.52	5294.85	-5.74	5377.96	-6892.04	3780.76	4797.16
2012—2015	-66.45	5261.87	0.00	26.71	-4953.91	334.67	5131.83
2015—2017	-819.61	3237.70	0.00	4385.81	-7755.58	-132.08	4999.75
平均贡献	-110.89	4522.21	22.00	2336.73	-6047.65	13039.48
2002—2017	-935.14	45810.47	0.00	51870.18	-91934.79	190550.58