广东省农业碳氮磷排放的时空演变特征与驱动力

doi:10.12052/gdutxb.230152

Abstract

Abstract: Climate change caused by greenhouse gas emissions and aquatic eutrophication formed by nitrogen and phosphorus enrichment are the key issues that need to be solved urgently in the field of ecological environment in China. The agricultural sector is one of the main sources of carbon, nitrogen, and phosphorus emissions, and analyzing their characteristics and driving forces is of great significance to the implementation of China's pollution reduction and carbon reduction strategy. Based on the cross-sectional data of the agricultural sector in Guangdong province, the carbon emission model, nitrogen and phosphorus runoff model, and the LMDI driver model of agricultural sources in Guangdong province are constructed to analyze the evolution characteristics and driving factors of carbon, nitrogen and phosphorus emissions in each agricultural source sector at the provincial and county levels. The results showed that: (1) the carbon, nitrogen and phosphorus emissions from county agricultural sources in Guangdong province had significant spatial differences, and had the characteristics of concentration, correlation and homology in space; (2) From 1990 to 2021, the carbon and nitrogen emissions from agricultural sources showed heterogeneous changes, carbon emissions increased, nitrogen and phosphorus emissions decreased, and the nitrogen to phosphorus ratio showed an upward trend; (3) Per capita primary industry added value and unit primary industry added value emissions were the largest driving forces affecting the rise and decrease of agricultural carbon, nitrogen and phosphorus emissions in Guangdong province, respectively, and the ranking was different among different elements, indicating that there were differences in the driving factors controlling the change of agricultural source carbon and nitrogen emissions. The results of this study will provide decision-making support for the identification and collaborative regulation of key source areas of agricultural source carbon, nitrogen and phosphorus emissions in Guangdong province.

Key words: agricultural source, carbon emissions, non-point source, driving forces, emission inventory, logarithmic mean divisia index (LMDI)

CLC Number:

X592

Gao Wei, Zhang Xiang, Chen Jun, Du Qing-ping, Zhang Yuan. Spatiotemporal Evolution and Driving Forces of Agricultural Carbon, Nitrogen, and Phosphorus Emissions in Guangdong Province[J].Journal of Guangdong University of Technology, 2023, 40(06): 147-154.

References

[1] 项颂, 吴越, 吕兴菊, 等. 洱海流域农业面源污染空间分布特征及分类控制策略[J]. 环境科学研究, 2020, 33(11): 2474-2483.
XIANG S, WU Y, LYU X J, et al. Characteristics and spatial distribution of agricultural non-point source pollution in Erhai lake basin and its classified control strategy [J]. Research of Environmental Sciences, 2020, 33(11): 2474-2483.
[2] 郑田甜, 赵筱青, 卢飞飞, 等. 云南星云湖流域种植业面源污染驱动力分析[J]. 生态与农村环境学报, 2019, 35(6): 730-737.
ZHENG T T, ZHAO X Q, LU F F, et al. Analysis on the driving force of daming non-point source pollution in the Xingyun lake basin of Yunan province [J]. Journal of Ecology and Rural Environment, 2019, 35(6): 730-737.
[3] 邹凯波, 张玉虎, 刘晓伟, 等. 气候变化下乌伦古河流域农业面源污染负荷响应[J]. 干旱区研究, 2022, 39(2): 625-637.
ZOU K B, ZHANG Y H, LIU X W, et al. Response of agricultural nonpoint source pollution load in the Ulungur river basin under climate change [J]. Arid Zone Research, 2022, 39(2): 625-637.
[4] 毛中明, 杨豪毅, 刘陈思雨, 等. 基于LMDI方法的恩施州化肥施用驱动因素分析[J]. 农业工程学报, 2021, 37(13): 111-120.
MAO Z M, YANG H Y, LIU C S Y, et al. Analysis of driving factors of chemical fertilizer application by LMDI method at Enshi of Hubei province in China [J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2021, 37(13): 111-120.
[5] TAN S, XIE D, NI J, et al. Output characteristics and driving factors of non-point source nitrogen (N) and phosphorus (P) in the three gorges reservoir area (TGRA) based on migration process: 1995~2020 [J]. Science of the Total Environment, 2023, 875: 162543.
[6] 尹岩, 郗凤明, 邴龙飞, 等. 我国设施农业碳排放核算及碳减排路径[J]. 应用生态学报, 2021, 32(11): 3856-3864.
YIN Y, XI F M, BING L F, et al. Accounting and reduction path of carbon emission from facility agriculture in China [J]. Chinese Journal of Applied Ecology, 2021, 32(11): 3856-3864.
[7] DAI X W, SUN Z L, MULLER D. Driving factors of direct greenhouse gas emissions from China's pig industry from 1976 to 2016 [J]. Journal Of Integrative Agriculture, 2021, 20(1): 319-329.
[8] MAKUTENIENE D, PERKUMIENE D, MAKUTENAS V. Logarithmic mean divisia index decomposition based on Kaya identity of GHG emissions from agricultural sector in baltic states [J]. Energies, 2022, 15(3): 1195.
[9] 姜钰卿, 唐旭, 任凯鹏, 等. 基于双层嵌套SDA的中国减污降碳驱动因素研究[J]. 系统工程理论与实践, 2022, 42(12): 3294-3304.
JIANG Y Q, TANG X, REN K P, et al. Research on the drivers of pollution and carbon reduction in China based on double-layer nested SDA [J]. Systems Engineering-Theory & Practice, 2022, 42(12): 3294-3304.
[10] 刘茂辉, 刘胜楠, 李婧, 等. 天津市减污降碳协同效应评估与预测[J]. 中国环境科学, 2022, 42(8): 3940-3949.
LIU M H, LIU S N, LI J, et al. Evaluation and prediction of the synergistic effect of pollution reduction and carbon reduction in Tianjin [J]. China Environmental Science, 2022, 42(8): 3940-3949.
[11] 王家一, 孙亭亭, 沙润钰, 等. 富营养化湖泊蓝藻打捞减污降碳效果模拟研究[J]. 生态环境学报, 2023, 32(6): 1108-1114.
WANG J Y, SUN T T, SHA R Y, et al. Study on the synergic effect of algae salvage on pollution control and carbon emission reduction in eutrophic lakes [J]. Ecology and Environmental Sciences, 2023, 32(6): 1108-1114.
[12] 广东省生态环境厅. 广东省市县(区) 温室气体清单编制指南(试行) [EB/OL]. (2020-06-16)[2023-09-27]. http://gdee.gd.gov.cn/.
[13] 生态环境部. 农业污染源产排污系数手册[EB/OL]. (2021-06-11)[2023-09-27]. http://www.mee.gov.cn.
[14] 高伟, 陈岩, 严长安, 等. 长江经济带氮淋溶流失空间格局及其水环境影响评估[J]. 环境科学学报, 2020, 40(1): 325-333.
GAO W, CHEN Y, YAN C A, et al. Evaluating spatial features of nitrogen loss in Yangtze river economic belt and its impact on water environment [J]. Acta Scientiae Circumstantiae, 2020, 40(1): 325-333.
[15] KOVEN C D, RILEY W J, SUBIN Z M, et al. The effect of vertically resolved soil biogeochemistry and alternate soil C and N models on C dynamics of CLM4 [J]. Biogeosciences, 2013, 10(11): 7109-7131.
[16] FRANKE N A B. Grey water footprint accounting: tier 1 supporting guidelines [J]. Unesco-IHE Institute dor Water Education, 2013, 65: 20-21.
[17] YU F, DONG H, GENG Y, et al. Uncovering the differences of household carbon footprints and driving forces between China and Japan [J]. Energy Policy, 2022, 165: 112990.
[18] 张惠芳, 刘欢, 苏辉东, 等. 1995~2014年拉萨河流域水环境变化及其驱动力[J]. 生态学报, 2019, 39(3): 770-778.
ZHANG H F, LIU H, SU H D, et al. Analysis on trend and driving force for water environmental change of the Lhasa river basin (1995~2014) [J]. Acta Ecologica Sinica, 2019, 39(3): 770-778.
[19] 刘晓永, 李书田. 中国秸秆养分资源及还田的时空分布特征[J]. 农业工程学报, 2017, 33(21): 1-19.
LIU X Y, LI S T. Temporal and spatial distribution characteristics of crop straw nutrient resources and returning to farmland in China [J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2017, 33(21): 1-19.
[20] 张卫峰, 李亮科, 陈新平, 等. 我国复合肥发展现状及存在的问题[J]. 磷肥与复肥, 2009, 24(2): 14-16.
ZHANG W F, LI L K, CHEN X P, et al. The present status and existing problems in China's compound fertilizer development [J]. Phosphate & Compound Fertilizer, 2009, 24(2): 14-16.

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Spatiotemporal Evolution and Driving Forces of Agricultural Carbon, Nitrogen, and Phosphorus Emissions in Guangdong Province

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