Volume 41 Issue 5
Sep.  2024
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LI Wenhan, LIU Feiyang, ZHANG Meng, GU Lei, ZHOU Guomo. Spatiotemporal evolution law and driving factors of carbon emissions in planting industry in Zhejiang Province[J]. Journal of Zhejiang A&F University, 2024, 41(5): 898-908. doi: 10.11833/j.issn.2095-0756.20240156
Citation: LI Wenhan, LIU Feiyang, ZHANG Meng, GU Lei, ZHOU Guomo. Spatiotemporal evolution law and driving factors of carbon emissions in planting industry in Zhejiang Province[J]. Journal of Zhejiang A&F University, 2024, 41(5): 898-908. doi: 10.11833/j.issn.2095-0756.20240156

Spatiotemporal evolution law and driving factors of carbon emissions in planting industry in Zhejiang Province

doi: 10.11833/j.issn.2095-0756.20240156
  • Received Date: 2024-01-30
  • Accepted Date: 2024-07-26
  • Rev Recd Date: 2024-07-12
  • Available Online: 2024-09-25
  • Publish Date: 2024-09-25
  •   Objective  The aim is to explore the spatiotemporal evolution law and influencing factors of carbon emissions in planting industry in Zhejiang Province, in order to develop a carbon sequestration and emission reduction plan and promote low-carbon and green transformation of planting industry in Zhejiang Province.  Method  Based on the energy input data of planting industry in Zhejiang Province, the carbon emission coefficient method was used to assess the spatiotemporal evolution of carbon emissions of planting industry in Zhejiang Province from 2006 to 2021. Combined with Logarithmic Mean Weighted Divisia Index(LMDI), the driving factors of carbon emissions changes in planting industry were analyzed, and the grey prediction model was used to predict the carbon emissions of planting industry from 2022 to 2040.  Result  (1) From 2006 to 2021, the overall carbon emissions from planting industry in Zhejiang Province showed an upward trend followed by a downward trend, with an annual variation rate of −1.80%, and a total cumulative carbon emission of 129 million tons. Between 2006 and 2012, carbon emissions slowly increased and then sharply decreased after reaching a peak in 2012, with an average annual decline of 2.94%. (2) The carbon emissions from planting industry in Zhejiang Province showed a distribution pattern of high in the central region and low in the north and south. Hangzhou, Jinhua and Taizhou in the central region were the main carbon emitting regions, accounting for 39.86% of the province’s carbon emissions. (3) The improvement of planting industry efficiency and the optimization of regional industrial structure played a promoting role in carbon reduction, and the improvement of economic development level, the increase of population size, and the optimization of agricultural production structure were the driving factors that caused the increase in carbon emissions, among which the level of economic development was the dominant factor affecting the changes in carbon emissions, accounting for 41.58% of the total carbon emissions change. (4) The grey prediction model prediction results showed that the carbon emissions from planting industry in Zhejiang Province would continue to decline from 2022 to 2040, and the carbon emissions in 2040 would decrease to 37.20% of those in 2021. Among them, Hangzhou, Jiaxing, Shaoxing and Jinhua would have the largest decline.   Conclusion  Planting industry in Zhejiang Province has achieved carbon peak. To ensure a continuous decline in carbon emissions in the future, we should focus on optimizing agricultural production technology and adjusting industrial structure, applying chemical fertilizers rationally, improving planting efficiency, reducing energy consumption, and speeding up modernization of green agriculture, so as to accelerate the achievement of carbon neutrality goal. [Ch, 4 fig. 2 tab. 37 ref.]
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  • [1]
    ZHOU Pengfei, GU Lei, PENG Weiliang, et al. A carbon footprint assessment and composition analysis of flattened bamboo chopping board[J]. Journal of Zhejiang A&F University, 2014, 31(6): 860 − 867.
    [2]
    SHE Wei, WU Yao, HUANG Huang, et al. Integrative analysis of carbon structure and carbon sink function for major crop production in China’s typical agriculture regions[J]. Journal of Cleaner Production, 2017, 162: 702 − 708.
    [3]
    FU Jun, DING Rui, ZHU Yuqi, et al. Analysis of the spatial-temporal evolution of green and low carbon utilization efficiency of agricultural land in China and its influencing factors under the goal of carbon neutralization [J/OL]. Environmental Research, 2023, 237 (1): 116881[2023-12-30]. doi. org/10.1016/j. envres. 2023.116881.
    [4]
    ZHENG Bofu, LIANG Han, WAN Wei, et al. Spatial-temporal pattern and influencing factors of agricultural carbon emissions at the county level in Jiangxi Province of China[J]. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38(23): 70 − 80.
    [5]
    LIU Yang, LIU Hongbin. Characteristics, influence factors, and prediction of agricultural carbon emissions in Shandong Province[J]. Chinese Journal of Eco-Agriculture, 2022, 30(4): 558 − 569.
    [6]
    FAN Zhenhao, XING Weiwei, BU Yuanqing, et al. Calculation and peak analysis of carbon emission from agricultural planting in Jiangsu Province[J]. Journal of Soil and Water Conservation, 2023, 37(5): 78 − 85.
    [7]
    YANG Binjian, TIAN Jingren, SUN Hongyu. Study on the factors affecting the carbon footprint of agricultural production based on the SVAR Model[J]. Ecological Economy, 2019, 35(8): 120 − 127.
    [8]
    ZHANG Huiyu. Research on agricultural development in Zhejiang Province based on factor analysis[J]. Rural Economy and Science-Technology, 2019, 30(18): 149 − 151.
    [9]
    WU Haoyue, ZHOU Lei, HE Yanqiu , et al. Peaking process and decoupling analysis of carbon emissions of crop production in China[J]. Chinese Journal of Eco-Agriculture,, 2023, 31(8): 1275 − 1286.
    [10]
    HU Wanling, ZHANG Jinxin, WANG Hongling. Spatiotemporal differentiation of carbon emissions from planting industry in China[J]. Statistics & Decision, 2020(15): 92 − 95.
    [11]
    LIU Yufeng, YUAN Zhihua, GUO Lingxia, et al. Carbon footprint of crop production in China from 1993 to 2013 and its spatial distribution[J]. Chinese Journal of Applied Ecology, 2017, 28(8): 2577 − 2587.
    [12]
    CHEN Wei, YIN Tianyuan, LI Hongbing. Spatiotemporal distribution characteristics of carbon emission from plant industry and the relationship with agriculture development in China from 1997 to 2015[J]. Journal of Arid Land Resources and Environment, 2019, 33(2): 37 − 44.
    [13]
    SHI Hongxu, CHANG Ming. How does agricultural industrial structure upgrading affect agricultural carbon emissions? Threshold effects analysis for China[J]. Environmental Science and Pollution Research, 2023, 30: 52943 − 52957.
    [14]
    DING Baogen, ZHAO Yu, DENG Junhong. Calculation, decoupling effects and driving factors of carbon emission from planting industry in China.[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2022, 43(5): 1 − 11.
    [15]
    TIAN Jianxiang, YANG Huilin, XIANG Pingan, et al. Drivers of agricultural carbon emissions in Hunan Province, China [J/OL]. Environmental Earth Sciences, 2016, 75 [2023-12-31]. doi: 10.1007/s12665-015-4777-9.
    [16]
    LI Bo, ZHANG Junbiao, LI Haipeng. Research on spatial-temporal characteristics and affecting factors decomposition of agricultural carbon emission in China[J]. China Population, Resources and Environment, 2011, 21(8): 80 − 86.
    [17]
    ZHANG Lu, HE Yufei, CHEN Tan, et al. The spatial and temporal pattern evolution of carbon footprint of farmland ecosystem in Fenwei Plain from 2011 to 2020[J]. Ecology and Environmental Sciences, 2023, 32(6): 1149 − 1162.
    [18]
    SHANG Jie, YANG Binjian. Estimation of carbon source and carbon sequestration in planting industry and dynamic analysis of influencing factors of net carbon sequestration: a case study of Shandong Province[J]. Reform, 2019(6): 123 − 34.
    [19]
    HUANG Heping, LI Zixia, HUANG Dian, et al. Research on the measurement, analysis and prediction of agricultural carbon emissions in Jiangxi Province under the “Dual Carbon” Goals[J]. Journal of Ecology and Rural Environment, 2024, 40(2): 179 − 190.
    [20]
    ZHANG Guangsheng, WANG Shanshan. China’s agricultural carbon emission: structure, efficiency and its determinants [J]. Issues in Agricultural Economy, 2014(7): 18 − 26, 110.
    [21]
    GAO Chenxi, LU Qiupping, OU Nianqing, et al. Research on influencing factors and prediction of agricultural carbon emission in Henan Province under the carbon Peaking and Carbon Neutrality goal[J]. Chinese Journal of Eco-Agriculture, 2022, 30(11): 1842 − 1851.
    [22]
    LIU Lihui, XU Jun. Analysis of influencing factors of agricultural carbon emission in Guangdong Province with the extended STIRPAT model[J]. Science and Technology Management Research, 2016, 36(6): 250 − 255.
    [23]
    ZHAO Ci, SONG Xiaocong, LIU Xiaoyu, et al. Prediction and analysis of peak carbon emissions in Zhejiang Province based on STIRPAT Model[J]. Ecological Economy, 2022, 38(6): 29 − 34.
    [24]
    LI Miande, ZHOU Dongmei, ZHU Xiaoyan, et al. Spatial-temporal characteristics of agricultural carbon emissions and influencing factors in the Hexi Corridor from 2000 to 2020[J]. Journal of Agricultural Resources and Environment, 2023, 40(4): 940 − 952.
    [25]
    QIAO Guotong, CHEN Fei, WANG Na, et al. Inter-annual variation patterns in the carbon footprint of farmland ecosystems in Guangdong Province, China [J/OL]. Scientific Reports, 2022, 12 : 14134[2023-12-31]. doi: 10.1038/s41598-022-18425-z.
    [26]
    CUI Yongfu, GAO Ce, WANG Junfeng, et al. Spatial evolution and countermeasures of county agricultural carbon emission in Hebei Province [J]. [J]. Journal of Chinese Agricultural Mechanization, 2023, 44(5): 241 − 248, 256.
    [27]
    WANG Liang, ZHAO Jie, CHEN Shouyue. Analysis of ecosystem carbon sources/sinks and carbon footprint in farmland ecosystem of Shandong Province[J]. Journal of China Agricultural University, 2016, 21(7): 133 − 141.
    [28]
    ZHU Qiaoxian, MEI Yun, CHEN Yinrong, et al. Regional differentiation characteristics and optimization of the structural efficiency of land use in Hubei Province based on the carbon emissions[J]. Economic Geography, 2015, 35(12): 176 − 184.
    [29]
    DUAN Huaping, ZHANG Yue, ZHAO Jianbo, et al. Carbon footprint analysis of farmland ecosystem in China[J]. Journal of Soil and Water Conservation, 2011, 25(5): 203 − 208.
    [30]
    TIAN Yun, WANG Mengchen. Research on spatial and temporal difference of agricultural carbon emission efficiency and its influencing factors in Hubei Province[J]. Scientia Agricultura Sinica, 2020, 53(24): 5063 − 5072.
    [31]
    WANG Shufen, GAO Guanlong, LI Wei et al. Carbon emissions from agricultural and animal husbandry in Shanxi Province: temporal and regional aspects, and trend forecast[J]. Journal of Agro-Environment Science, 2023, 42(8): 1882 − 1892.
    [32]
    DONG Gang, MAO Xianqiang, ZHOU Ji, et al. Carbon footprint accounting and dynamics and the driving forces of agricultural production in Zhejiang Province, China[J]. Ecological Economics, 2013, 91: 38 − 47.
    [33]
    LIU Zihan, WANG Bingfan, LI Zhaoyang, et al. Plastic film mulch combined with adding biochar improved soil carbon budget, carbon footprint, and maize yield in a rainfed region [J/OL]. Field Crops Research, 2022, 284 : 108574[2023-12-31]. doi: 10.1016/j.fcr.2022.108574.
    [34]
    LI Mingqi, LIU Shiliang, WU Xue, et al. Temporal and spatial dynamics in the carbon footprint and its influencing factors of farmland ecosystems in Yunnan Province[J]. Acta Ecologica Sinica, 2018, 38(24): 8822 − 8834.
    [35]
    FANG Miao, HE Yixiong, YU Xiaoyang. Agricultural carbon emissions: spatial pattern, decoupling effect and driving factors: taking Zhejiang Province as an example[J]. Resource Development & Market, 2022, 38(12): 1461 − 1467, 1528.
    [36]
    KONG Delei, JIANG Peikun. Approaches and policy recommendations for reducing emissions and increasing carbon sinks in crop industry under the background of carbon peak and carbon neutrality[J]. Journal of Zhejiang A&F University, 2023, 40(6): 1357 − 1365.
    [37]
    ZHANG Jing, FANG Yu, WEI Jinda, et al. Carbon footprint-based temporal and spatial analysis on carbon sources/sinks at farmlands in Anhui Province.[J]. Fujian Journal of Agricultural Sciences, 2021, 36(1): 78 − 90.
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Spatiotemporal evolution law and driving factors of carbon emissions in planting industry in Zhejiang Province

doi: 10.11833/j.issn.2095-0756.20240156

Abstract:   Objective  The aim is to explore the spatiotemporal evolution law and influencing factors of carbon emissions in planting industry in Zhejiang Province, in order to develop a carbon sequestration and emission reduction plan and promote low-carbon and green transformation of planting industry in Zhejiang Province.  Method  Based on the energy input data of planting industry in Zhejiang Province, the carbon emission coefficient method was used to assess the spatiotemporal evolution of carbon emissions of planting industry in Zhejiang Province from 2006 to 2021. Combined with Logarithmic Mean Weighted Divisia Index(LMDI), the driving factors of carbon emissions changes in planting industry were analyzed, and the grey prediction model was used to predict the carbon emissions of planting industry from 2022 to 2040.  Result  (1) From 2006 to 2021, the overall carbon emissions from planting industry in Zhejiang Province showed an upward trend followed by a downward trend, with an annual variation rate of −1.80%, and a total cumulative carbon emission of 129 million tons. Between 2006 and 2012, carbon emissions slowly increased and then sharply decreased after reaching a peak in 2012, with an average annual decline of 2.94%. (2) The carbon emissions from planting industry in Zhejiang Province showed a distribution pattern of high in the central region and low in the north and south. Hangzhou, Jinhua and Taizhou in the central region were the main carbon emitting regions, accounting for 39.86% of the province’s carbon emissions. (3) The improvement of planting industry efficiency and the optimization of regional industrial structure played a promoting role in carbon reduction, and the improvement of economic development level, the increase of population size, and the optimization of agricultural production structure were the driving factors that caused the increase in carbon emissions, among which the level of economic development was the dominant factor affecting the changes in carbon emissions, accounting for 41.58% of the total carbon emissions change. (4) The grey prediction model prediction results showed that the carbon emissions from planting industry in Zhejiang Province would continue to decline from 2022 to 2040, and the carbon emissions in 2040 would decrease to 37.20% of those in 2021. Among them, Hangzhou, Jiaxing, Shaoxing and Jinhua would have the largest decline.   Conclusion  Planting industry in Zhejiang Province has achieved carbon peak. To ensure a continuous decline in carbon emissions in the future, we should focus on optimizing agricultural production technology and adjusting industrial structure, applying chemical fertilizers rationally, improving planting efficiency, reducing energy consumption, and speeding up modernization of green agriculture, so as to accelerate the achievement of carbon neutrality goal. [Ch, 4 fig. 2 tab. 37 ref.]

LI Wenhan, LIU Feiyang, ZHANG Meng, GU Lei, ZHOU Guomo. Spatiotemporal evolution law and driving factors of carbon emissions in planting industry in Zhejiang Province[J]. Journal of Zhejiang A&F University, 2024, 41(5): 898-908. doi: 10.11833/j.issn.2095-0756.20240156
Citation: LI Wenhan, LIU Feiyang, ZHANG Meng, GU Lei, ZHOU Guomo. Spatiotemporal evolution law and driving factors of carbon emissions in planting industry in Zhejiang Province[J]. Journal of Zhejiang A&F University, 2024, 41(5): 898-908. doi: 10.11833/j.issn.2095-0756.20240156
  • 人类活动引发的碳排放是全球温室气体排放的主要来源[12]。为有效应对气候变化,2020年中国宣布将力争在2030年前实现碳达峰,2060年前实现碳中和[34]。农业是第二大温室气体排放源[57],种植业是整个农业的核心,包括粮食作物、经济作物、饲料作物和绿肥等的生产[78]。中国作为一个农业大国,2000—2020年全国种植业年均碳排放量为2.33 亿t[9],占中国温室气体排放总量的16%~17%[1013]。种植业在迅速发展的同时,碳排放量也在逐渐增加[1415],并呈现出不同的时空演变特征。2021年,为进一步减少农业碳排放,中国明确提出了改善农业生态系统、增强减排固碳能力的发展目标。种植业低碳化发展既是重要举措,又是潜力所在。在未来严峻的减排任务下,全面掌握种植业的碳排放现状、时空演变特征以及影响因素,对中国实现农业的低碳转型发展和农业现代化具有重要的意义。

    目前,与农业碳排放相关的研究集中在碳排放指标体系构建、测算及影响因素分析等方面。李波等[16]、张露等[17]、尚杰等[18]认为:种植业碳排放源主要包括农用物资如化肥、农药、农用薄膜使用过程中的碳排放,翻耕、灌溉等过程中柴油、电力等能源消耗产生的碳排放;黄和平等[19]将种植业碳排放分为农业物资投入、农田土壤利用、秸秆焚烧三大类。在测算方法方面,张广胜等[20]应用生命周期评价法(LCA)计算农业碳排放量;高晨曦等[21]采用排放因子法测算了河南省的农业碳排放量。在农业碳排放影响因素的研究上,丁宝根等[14]等运用对数平均权重迪氏指数模型(LMDI)探析中国种植业碳排放的主要驱动因素,得出农业生产结构、农业产出水平和农业劳动力规模是碳排放增加的重要驱动因素;刘丽辉等[22]基于STIRPAT 模型分析了广东农业碳排放强度的影响因素。这些研究为农业低碳发展提供了参考依据,但从研究区域看,已有研究大多集中在国家、省域层面。由于经济发展、自然禀赋差异,不同地区在农业减排措施上会有所不同;从研究对象看,多以山东、河南等农业大省为主,针对经济发达地区种植业碳排放的研究较少。作为农业的重要组成部分,种植业的碳减排研究尤为重要。

    浙江省是中国经济最活跃的省份之一,人口众多,耕地面积少,农产品产量的提高高度依赖化肥、农药等化学品的投入,环境污染严重,因此合理估算浙江省各市种植业碳排放量,对制定有效的减排措施具有重要意义,也可为评估种植业碳减排措施成效及碳达峰提供依据。此外,浙江省地形复杂多样,不同地区农业生产情况相差较大,各地区地理位置、功能定位、农业发展水平的不同导致了农产品结构和经营管理措施的多样性,从而在空间上呈现出种植业碳排放差异。本研究在估算2006—2021年浙江省种植业碳排放量的基础上,分析浙江省11个市种植业碳排放在时间上的变化和空间上的异质性,运用LMDI模型从经济、社会和人口的角度分析浙江省种植业碳排放变化的驱动因素,并使用GM(1, 1)灰色模型预测2022—2040年浙江省各市种植业碳排放的发展趋势,以期为浙江省种植业碳减排工作提供有益的参考。

    • 浙江省地处中国东南沿海长江三角洲南翼,陆域面积10.55 万km2,占全国的1.06%,耕地面积208.17 万hm2,占全国的1.63%。浙江省属于亚热带季风气候,雨量丰沛,年均降水量为1 600.00 mm。2022年,浙江省人口5 100.51万人,下辖杭州、宁波、嘉兴、湖州、绍兴、温州、台州、金华、衢州、丽水和舟山11个市。省内地域差异明显,种植生产条件复杂,地形土壤多样。粮食种植面积占农作物播种面积的一半左右,2021年浙江省粮食总产量为620.90 万t,占全国的0.91%,农作物播种面积为2.015 万hm2,占全国的1.19%。

      浙江省经济发达。2022年浙江省国内生产总值(GDP)列全国第4位,人均GDP列全国第1位。浙江省致力于发展低碳经济,其绿色产业规模位居全国前列,一直是国内绿色环保的排头兵[23]

    • 参考联合国政府间气候变化专门委员会 (IPCC)发布的碳排放系数[24]测算浙江省种植业碳排放量。浙江省种植业碳排放来自于多个排放源,包括农用薄膜、化肥、农药、农业机械总动力、农用柴油及灌溉[25],且不同排放源的碳排放量计算方式不同(表1)。碳排放量为所有排放源的碳排放量之和,计算公式如下:

      碳排放源 计算公式及碳排放系数 资料来源
      农用薄膜 Em = α1Gmα1 =5.180 0 kg∙kg−1 南京农业大学农业资源与生态环境研究所[26]
      化肥 Ef=α2Gfα2 =0.895 6 kg∙kg−1 美国橡树岭国家实验室[26]
      农药 Ep=α3Gpα3 =4.934 1 kg∙kg−1 美国橡树岭国家实验室[26]
      农业机械总动力 Ee=α4Se +α5Geα4 =16.47 kg∙hm−2α5 =0.180 0 kg∙kW−1 王梁等[27];朱巧娴等[28]
      农用柴油 Ed=α5Gdα5=0.592 7 kg∙kg−1 联合国气候变化政府间专家委员会[26]
      灌溉 Eu=α6 Siα6 =266.48 kg∙hm−2 段华平等[29]
        说明:Gm. 农用薄膜使用总量(kg);Gf. 化肥使用总量(kg);Gp. 农药使用总量(kg);Se. 农作物种植面积(hm2);Ge. 农业机械总动力(kW);Gd. 农用机械柴油消耗量(kg);Si. 农田的有效灌溉面积(hm2)。EmEfEpEe、Ed、Eu分别为农用薄膜、化肥、农药、农业机械总动力、农用柴油和灌溉的碳排放量。

      Table 1.  Carbon emission calculation formula and carbon emission factors for each carbon emission source

      式(1)中:E为种植业碳排放量;$ {E}_{i} $为第i种碳源的排放量;$ {T}_{i} $为第i 种碳源的使用量;$ {\delta }_{i} $为第i碳源的碳排放系数。

    • 为了量化不同地区的碳排放差异,采用单位面积的种植业碳排放量表示不同地区的碳排放强度。计算公式为:

      式(2)中:$ {{E}}_{{{\mathrm{c}}}{j}} $为第j个地区碳排放强度;$ {{E}}_{{j}} $为第j个地区种植业碳排放量;$ {{S}}_{{j}} $为第j个地区农作物种植面积。

    • 作为研究能源环境问题常用的方法之一,LMDI模型相较于其他方法操作性和适用性更强,可以有效分析总体指标,保持各个分解指标之间的高度一致性,因此本研究参考刘杨等[5]、丁宝根等[14]的方法,将浙江省种植业碳排放影响因素分解为种植业生产效率、农业产业结构、地区产业结构、经济发展水平和人口数量等5个因素,进行影响因素驱动分析。具体分解如下:

      式(3)~(7)中:C为浙江省种植业碳排放量(万t);GcGaGz分别为浙江省种植业产值、农业产值、生产总值(亿元);P为浙江省总人口数量(万人);CI表示种植业生产效率,用单位种植业产值上的碳排放来表示,这是一个反指标因素,该指标越大,生产效率就越低;AI表示农业生产结构,种植业是农业中能源消耗的主体产业,在农业产出规模既定的情况下,种植业所占比例越大,碳排放也会越大,本研究用种植业在农业产值中的比例来表示;GI表示地区产业结构,反映该地区产业内部结构的优化程度,用农业产值占全省GDP的比例表示;EI表示地区经济发展水平,以人均GDP表示,经济发展水平高的地区,农业生产会更倾向于资本密集型[30],对农药、化肥等高碳农资的依赖度更高;P表示人口因素,人口规模的扩大必然带来能源消费水平的增加,以浙江省人口数量表示。

    • 使用灰色预测模型GM(1, 1)预测2022—2040年浙江省的种植业碳排放量,其基本原理是用原始数据组成原始序列(0),再通过累加生成法生成序列(1),该步骤可以弱化原始数据的随机性,使其呈现更为明显的特征规律,对生成变换后的序列(1)建立1阶微分方程模型即GM(1, 1)模型[56]

    • 化肥施用量、农药用量、农用薄膜用量、农作物播种面积、农业机械总动力、有效灌溉面积、耕地面积、种植业产值、农业产值、全省生产总值及人口数据均来源于浙江省统计局发布的2007—2022年的《浙江统计年鉴》、浙江省各市统计局发布的统计年鉴以及自然资源统计公报。

    • 基于灌溉、农业机械、柴油、化肥、农药、农用薄膜等6种碳排放源核算的2006—2021年浙江省种植业碳排放量、碳排放强度测算结果见图1。2006—2021年浙江省种植业碳排放量从2006年的870.89 万t减少至2021年的619.66 万t,年均减排率为−1.80%,累计排放总量为12 868.10 万t,年均排放量为804.26 万t。2006—2012年碳排放量处于比较稳定的状态,在870 万t上下波动,于2012年达到峰值(877.56 万t);2012—2021年为持续下降阶段,且下降趋势较为明显,从2012年的877.56 万t下降至2021年的619.66 万t,年均减排率为−2.94%。2006—2014年浙江省种植业碳排放强度呈波动上升,至2014年达到峰值(4.21 t·hm−2),2014—2021年持续下降,平均每年下降3.36%,2021年下降至3.08 t·hm−2

      Figure 1.  Carbon emission and intensity of planting industry in Zhejiang Province from 2006 to 2021

      浙江省种植业碳排放量及强度的变化趋势与国家的农业政策密不可分。2004年,政府开始对粮食种子及农机进行购买补贴,2006年,全面取消传统农业税[31]。在一系列政策措施的激励下,农药、农用薄膜等农资投入不断增加,农用机械消耗能源持续加大,在作物产量上升的同时,碳排放量及强度也稳步提高[9]。2013年后,浙江省推广使用有机肥,实施化肥减量增效,并积极发展绿色无公害农业,在全国率先提出“肥药两制”改革,健全化肥农药源头管理管控机制,为全国碳减排提供“浙江方案”。2014年开始,浙江省种植业碳排放强度开始下降,种植业碳排放量得到有效控制。

      就碳排放结构而言,2006—2021年化肥始终是种植业碳排放的首要来源,平均占比达到45.32%,引起的碳排放量在2006—2013年呈缓慢下降趋势,2014—2021年下降幅度较大,下降了102.44万t,减少了26.15%。其次是农业机械总动力和柴油,平均占比分别为28.03%和14.43%。2006—2013年,农业机械总动力引起的碳排放量稳定,年均排放量维持在260 万t。期间浙江省耕地面积不断减少,农业机械化水平不断提高,耕作机械、拖拉机、脱粒机大量应用,在提高农业生产效率的同时消耗了大量能源,使这几年的碳排放量在耕地面积持续缩减的情况下依旧保持稳定[32];2014—2021年碳排放量呈迅速下降趋势,年均下降13.9 万t,占比从2013年的29.88%下降至21.79%。因其他碳排放源排放量的下降,柴油的碳排放量比例不断上升,2021年达16.85%。农药、农用薄膜、灌溉引起的碳排放量占比较小,分别为3.34%、4.06%、4.82%。农药引起的碳排放量逐年下降,由2006年的32.64 万t下降到2021年的17.04 万t,年平均下降率为3.2%。农用薄膜与之相反。农用薄膜的施用可以有效提高作物产量且污染较小[33],但很难回收处理,导致碳排放量由2006年的24.61 万t增长至2021年36.10 万t,年平均增长率为3.11%。灌溉引起的碳排放量由有效灌溉面积决定,变化趋于稳定。

    • 表2可以看出:2006—2021年,浙江省11个市种植业碳排放量年平均值呈中部高南北两侧低的分布格局,且各市碳排放量均呈下降趋势。台州、杭州、金华与宁波种植业碳排放量平均值超过90 万t·a−1,分别为112.89、98.64、95.08、93.71 万t·a−1,占全省的51.54%;嘉兴、绍兴与温州种植业碳排放量平均值为60~80 万t·a−1,占全省的28.31%;湖州、衢州与丽水为30~60 万t·a−1,占全省的18.86%;舟山为10.03 万t·a−1,占全省的1.29%。2021与2006年相比,温州、杭州、嘉兴与湖州种植业碳排放量下降幅度最大,超过了25.00%,其次是丽水、衢州、台州与宁波,为15.00%~25.00%,绍兴、金华与舟山下降幅度最小,为10.00%~15.00%。

      地区 碳排放量平均值/
      (万t·a−1)
      碳排放量
      变化率/%
      碳排放强度/
      (t·hm−2)
      地区 碳排放量平均值/
      (万t·a−1)
      碳排放量
      变化率/%
      碳排放强度/
      (t·hm−2)
      台州 112.89 −17.17 4.92 温州 69.57 −33.68 3.00
      杭州 98.64 −28.13 3.05 衢州 55.87 −17.77 2.69
      金华 95.08 −14.40 4.01 湖州 55.11 −27.98 2.99
      宁波 93.71 −16.13 3.42 丽水 35.46 −24.30 2.16
      嘉兴 76.98 −29.60 2.73 舟山 10.03 −13.35 5.76
      绍兴 73.31 −10.53 2.59

      Table 2.  Average value, rate of change and carbon intensity of carbon emissions from planting industry in Zhejiang Province in 2006-2021

      浙江省各市面积差异较大,用碳排放强度可以更加直观地反映各市间种植业碳排放的差异[34]。由表2可以看出:舟山、台州与金华碳排放强度最大,分别为5.76、4.92、4.01 t·hm−2,超过全省的平均碳排放强度(3.69 t·hm−2),其次是宁波、杭州与温州,分别为3.42、3.05、3.00 t·hm−2,湖州、嘉兴、衢州与绍兴等市为2.59~2.99 t·hm−2,丽水碳排放强度最低,为2.16 t·hm−2

      浙江省地形复杂,各市的地理条件、经营措施、主要种植作物都存在着差异,导致各地区碳排放量的不同。台州位于浙江省东部沿海地区,碳排放量与碳排放强度居全省前列。台州辖区内的温黄平原富含肥沃且保肥能力强的中碱性土壤,是浙江省重要的商品粮生产基地。碳排放量较高的水稻Oryza sativa等粮食作物是台州市主要种植的农作物,农用薄膜、化肥等高碳物资用量大,要降低碳排放量首先要调整种植业生产结构,并减少化肥、农药等物资的投入。舟山群岛虽然耕地面积小,但柴油消耗导致的碳排放量较高;可能与当地的种植特点有关。杭州、嘉兴、湖州与绍兴位于杭嘉湖水网平原、宁绍平原地区,是浙江省重要的农业区,农田精耕细作,低碳农业发展迅速,农业生产方式先进,现代化机械及技术应用较多,农业人才聚集,种植业碳排放强度较低,碳排放量下降快。丽水、衢州、金华位于浙江省西南部,多丘陵盆地,耕地质量差别大且面积少。这些地区农药、化肥、柴油使用量相较于沿海地区都较低,种植业较其他地区发展滞后,对种植业的人才、资金、政策等扶持力度较弱,导致种植业碳排放强度低。

    • 根据LMDI模型从种植业生产效率、农业生产结构、地区产业结构、经济发展水平以及人口因素共5个方面对浙江省种植业碳排放进行影响因素分解分析,结果见图2。测算结果中各影响因素变化所引起的碳排放变化量为正值,表示该因素对碳排放的增加起促进作用;反之,各影响因素变化引起的碳排放变化量为负值,表示该因素对碳排放的增加起抑制作用。

      Figure 2.  Contribution value of carbon emission driving factors in planting industry of Zhejiang Province

      与2006年相比,2021年浙江省种植业实现碳减排251.23 万t。提升种植业生产效率以科技创新为基础,提高化肥、农用薄膜及农药等农产品要素的投入使用率,降低能源消耗,促使种植业在科技带动下向绿色低碳转型。由图2可知:种植业生产效率、地区产业结构的贡献值为负值,是浙江省种植业碳减排的主要因素。种植业生产效率是促进碳减排的首要因素,平均每年贡献碳减排量为64.49 万t。地区产业结构平均每年贡献碳减排量34.19 万t,农业产值占全省生产总值比例由2006年的5.97%下降至2021年3.00%,表明浙江省正在积极推进地区产业结构升级。经计算,第一产业产值占全省生产总值比例每下降1.00%,碳排放量就会减少184.66 万t。地区经济发展水平、人口数量、农业生产结构的贡献值为正值,对浙江省种植业碳排放量的增加起促进作用,其中地区经济发展水平是主要决定性因素。研究期间,由经济发展累计增加的碳排放量达1 208.47 万t,占增加碳排放量的91.03%,人口数量变化引起的碳排放量比较稳定,占比较小。农业生产结构对碳排放的贡献值虽然总体是正值,但在2011、2016年种植业比例下降时是负值。可见,降低种植业在农业中的比例,可以起到抑制碳排放量增加的效果,且潜力巨大。因此,促进产业结构升级是保证浙江省未来种植业碳排放量持续下降的关键。

    • 上述研究发现:2014年后,浙江省种植业碳排放总量及强度开始下降。为了更精准地预测浙江省11个市未来种植业的碳排放量,分别以2006—2021、2014—2021年的种植业碳排放量数据作为样本,构建灰色预测模型GM(1, 1),预测2022—2040年种植业碳排放量。由图3可知:2种预测结果均显示2022年后浙江省种植业碳排放量呈下降趋势,其中以2014—2021年种植业碳排放量数据为样本的预测值曲线与原始值的曲线拟合度更高,置信区间更加接近,即预测结果更加精确,原因可能是种植业的发展与国家的政策密切相关。预测值显示,2040年较2021年下降62.80%,年均下降20.28 万t。

      Figure 3.  Projected carbon emissions from planting industry in Zhejiang Province

      基于上述分析,以浙江省11个市2014—2021年种植业碳排放量为依据,构建灰色预测模型GM(1, 1),预测2022—2040年种植业碳排放量。由图4可知:2022—2040年浙江省各市种植业碳排放量呈现持续下降趋势,不同地区下降幅度并不相同,其中杭州、嘉兴、绍兴、金华未来减排潜力很大,2040年碳排放量较2021年分别下降67.55%、64.69%、68.07%、66.33%,占全省的44.85%。舟山、台州、温州碳排放量降幅较小,2040年较2021年分别下降46.60%、52.41%、50.95%。究其原因,可能是舟山地区农业机械与种植技术比较落后,清洁型农业能源推广效果甚微,台州、温州地区是浙江省重要的粮仓,为了保证粮食产量,投入的化肥、柴油、农用薄膜消耗较大。因此,各地区应因地制宜,根据实际情况充分发挥地区特色,制定相应的减排目标,促进各地区种植业低碳发展。

      Figure 4.  Projected carbon emissions from planting industry in 11 cities of Zhejiang Province

    • 种植业碳排放量受多重因素的影响,计算结果可能会与实际碳排放有一定误差。一方面,本研究数据来源于统计年鉴,存在省级与市级及统计年份不一致的现象,出现冲突时以省级年鉴及最新年份数据为准;另一方面,本研究采用的碳排放因子数据来源于各类数据库及期刊,由于核算边界、地域等的差异,可能会与浙江省的实际情况有所出入。这些还有待进一步探究。另外,从研究结果看,化肥、柴油、农用机械总动力3种碳排放源的碳排放量列前3位,与方苗等[35]的研究相似,但在总碳排放量上存在差异,可能是农业和种植业核算对象的不同导致。

    • 低碳化种植,提升能源利用率。化肥与农用机械总动力是浙江省种植业最大的碳排放源,因此首先要采取措施控制化肥的使用强度,积极推广有机肥、水肥一体化,提升作物对肥料的利用率[12]。其次,提高种植业生产环节的智能化、精准化水平[36],减少使用高能耗农机设备与技术,推广应用绿色环保型农业机械,使用清洁能源,减少化石能源的消耗。此外,推广使用可降解地膜,减少农业废弃物造成的碳排放。

      全面统筹,协调发展。对浙江各市的农业低碳发展应因地制宜,实施不同的策略,制定不同的固碳减排目标[37]。浙江省各市碳排放存在显著差异,碳排放量下降幅度领先的地区可作为其他下降趋势较弱地区的发展示范[9],以强带弱,带动碳排放强度较大,农业发展比较落后的地区,缩小地方差距。建议投入更多的资金、人才到碳排放高的地区,展开跨区域农业合作,进行技术交流,集中力量解决薄弱地区的发展问题,降低高强度碳排放地区的碳排放量。

      优化农业产业结构,促进产业升级。调整种植业产值在农业产值中的比例,注重环境生态效益,提高农产品附加值,鼓励农户选择绿色、有机等生态农业模式,改变传统农业粗放型碳排放发展方式,向智慧农业转型,注重长远规划,将资金投入到低碳农业技术的开发上。

    • 浙江省种植业碳排放量及强度总体呈下降趋势。碳排放量变化分为2个阶段:2006—2012年缓慢上升阶段,2012—2021年持续下降阶段。从结构来看,化肥与农用机械总动力是最大的种植业碳排放源,化肥引起的碳排放量占总排放量的45.32%,各种碳排放源导致的碳排放量从大到小依次为化肥、农业机械总动力、柴油、灌溉、农用薄膜、农药。碳排放强度的变化趋势与碳排放量相似,2006—2014年处于上升时期,2014—2021年处于下降时期。随着农业科学技术的持续发展,以及绿色经济、绿色消费理念的提出与实践,浙江省种植业逐渐向绿色低碳转型,并起到了一定的效果。

      浙江省种植业碳排放存在明显的地区差距,呈现中部高南北两侧低的格局。从碳排放量看,金华、杭州、台州、宁波较高,舟山、丽水农业欠发达地区较小;温州的碳排放量变化幅度最大,从2006年到2021年减少了27.66 万t,下降了33.68%;绍兴的碳排放量变化幅度最小,减少了6.92 万t,下降了10.53%。各地区间碳排放量差距在逐渐减少。从碳排放强度看,东部沿海地区明显高于西南内陆。

      运用LMDI模型分析浙江省种植业碳排放驱动因素可知,促进浙江省种植业碳排放量增加的因素从高到低依次是经济发展水平、人口数量、农业生产结构,其中,经济发展水平是最主要的促进因素,种植业生产效率提高,地区产业结构调整是抑制碳排放量增加的主要因素。各因素综合作用下,2021年浙江省种植业实现碳减排251.23 万t。

      利用灰色预测模型预测浙江省种植业2022—2040年的碳排放量,发现模型精度随年份增多而降低,以2014—2021年样本预测的碳排放量更加精确。预测结果表明:2022—2040年浙江省种植业碳排放量继续保持下降趋势,2040年下降至228.29 万t,并且11个市均保持下降趋势,平均下降率为56.78%。

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