Spatial Differentiation and Dynamic Evolution of Carbon Emission Efficiency in Grain Production in North China
Affiliation:

Shandong academy of Macroeconomic Research

Clc Number:

TP79

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    Abstract:

    [Objective] Under the background of “double carbon”, exploring the carbon emission efficiency of grain production in North China and quantifying its spatial differentiation and dynamic evolution, and analyzing the current situation of carbon emission efficiency of grain production in North China are conducive to promoting green and low-carbon grain production in North China. [Method] The carbon emission coefficient method and the three-stage super-efficiency SBM model were used to measure the carbon emission and carbon emission efficiency of grain production in North China from 2011 to 2020, and then the Theil index and kernel density estimation were used to explore the regional differences and dynamic evolution of carbon emission efficiency of grain production. [Result] ①During the study period, the carbon emissions of grain production in North China showed an “M” type fluctuation downward trend, but the decline was slow. Among them, the use of chemical fertilizers is the main cause of grain carbon emissions. ②The carbon emission efficiency of grain production in North China showed an evolution trend of decreasing first and then increasing. The average efficiency of the first stage was 0.59. Excluding the influence of environmental variables and random errors on the efficiency value, the average efficiency of the third stage was 0.48, which was 18.6 % lower than the efficiency value of the first stage. Chuzhou, Zhumadian, Dezhou and other places have higher efficiency values, while Huangshan, Weihai and other places have lower efficiency values. ③The spatial difference of carbon emission efficiency of grain production is on the rise, and the regional difference is the main factor affecting the overall difference, among which the difference among cities in Henan Province is the most significant. ④During the investigation period of the sample, the kernel density function. changed from “single peak” to “double peak”, the main peak showed a fluctuating rise and a slight right shift trend, and the side peak uplift was small. [Conclusion] The overall level of carbon emission efficiency of grain production in North China is low, and there are obvious spatial differentiation characteristics. In the future, all regions should reduce the redundancy of material input such as chemical fertilizer, and adopt the strategy of “counterpart assistance ” to promote the benign interaction of grain production technology, so as to narrow the regional differences in carbon emission efficiency of grain production among regions.

    Reference
    [1] Maciel V, Zortea R, GrilloR I et al Greenhouse gases assessment of soybean cultivation steps in southern Brazil[J], Journal of Cleaner Production, 2016, 131: 747-753.
    [2] 田云,吴海涛. 产业结构视角下的中国粮食主产区农业碳排放公平性研究[J]. 农业技术经济,2020,297(01):45-55.
    [3] 张军伟,张锦华,吴方卫. 我国粮食生产的碳排放及减排路径分析[J]. 统计与决策,2018,34(14):168-172.
    [4] 蒋岩,韦陈华,董振杰,等. 江苏省粮食生产碳排放时序特征及脱钩弹性分析[J]. 江苏农业科学,2022,50(16):239-245.
    [5] 王海娜. 我国玉米生产碳排放效率研究[D]. 吉林:吉林大学,2018.
    [6] 鲁庆尧,王树进,孟祥海. 基于SBM模型的我国粮食生产生态效率测度与PS收敛检验[J]. 农村经济,2020,458(12):24-32.
    [7] 鲁庆尧,张旭青,孟祥海. 我国粮食种植生态效率的空间相关性及影响因素研究[J]. 经济问题,2021,504(08):82-88+94.
    [8] 史琛,金涛,李在军,等. 我国粳稻生态效率的演变与区域差异研究[J]. 中国农业资源与区划,2022,43(05):93-101.
    [9] 张源. 我国粮食主产区的粮食生产生态效率:区域差异、动态演进及影响因素[D]. 东北财经大学,2022.
    [10] 肖红波, 王济民. 新世纪以来我国粮食综合技术效率和全要素生产率分析. 农业技术经济, 2012, (1): 36-46
    [11] Robaina-alves M,Moutinho V,Macedo P.A new frontier approach to model the eco-efficiency in European countries[J].Journal of Cleaner Production,2015,103: 562-573.
    [12] 宋海风,刘应宗. 粮食主产区小麦生态效率及降污潜力研究——基于藏粮于田的视角[J]. 干旱区资源与环境,2017,31(07): 97-101.
    [13] [3] 田云,王梦晨. 湖北省农业碳排放效率时空差异及驱动因素[J]. 中国农业科学,2020,53(24): 5063-5072.
    [14] Fried H, Lovell C, Schmidt S et al. Accounting for Environmental Effects and Statistical Noise in Data Envelopment Analysis[J]. Journal of Productivity Analysis, 2002, 17(1-2): 157-174.
    [15] 杨骞,司祥慧,王珏. 减排增汇目标下中国粮食生产效率的测度及分布动态演进[J]. 自然资源学报,2022,37(03): 600-615.
    [16] 李雪,顾莉丽,李瑞. 我国粮食主产区粮食生产生态效率评价研究[J]. 中国农机化学报, 2022, 43(02): 205-213.
    [17] Wang R, Feng Y. Research on China''s agricultural carbon emission efficiency evaluation and regional differentiation based on DEA and Theil models[J]. International journal of Environmental Science and Technology, 2020, 18(6): 1-12.
    [18] Qin Q, Yan H, Liu J et al. China’s agricultural GHG emission efficiency: regional disparity and spatial dynamic evolution[J]. Environ Geochem Health, 2020, 44(9): 2863-2879.
    [19] 李倩娜,姚娟,唐洪松. 新疆棉花低碳生产率、区域差异与动态演进[J]. 干旱区资源与环境,2022,36(07): 1-8.
    [20] 曹慧,赵凯. 粮食主产区粮食生产技术效率时空特征分析[J]. 华东经济管理, 2017, 31(12): 82-90.
    [21] 袁培,周颖. 黄河流域农业生态效率的时空演变及改善路径研究[J].生态经济,2021,37(11): 98-105.
    [22] 李波,张俊飚,李海鹏. 中国农业碳排放时空特征及驱动因素分解[J]. 中国人口.资源与环境,2011,21(08): 80-86.
    [23] 田云,张俊飚,李波. 中国农业碳排放研究:测算、时空比较及脱钩效应[J]. 资源科学,2012,34(11): 2097-2105.
    [24] West T, Maryland G. A Synthesis of Carbon Sequestration, Carbone Missions, and Net Carbon Flux in Agriculture: Comparing Tillage Practices in the United States[J]. Agriculture Ecosystems and Environment, 2002: 91.
    [25] 智静,高吉喜. 中国城乡居民食品消费碳排放对比分析[J]. 地理科学进展,2009,28(03): 429-434.
    [26] 李波,张俊飚. 基于投入视角的我国农业碳排放与经济发展脱钩研究[J]. 经济经纬,2012,149(04): 27-31.
    [27] IPCC. Climate Change 2007: The Physical Science Basis: Working Group I Contribution to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change[M]. New York: Cambridge University Press, 2007.
    [28] 伍芬琳,李琳,张海林,等. 保护性耕作对农田生态系统净碳释放量的影响[J]. 生态学杂志,2007,26(12): 2035-2039.
    [29] Dubey A, Lal R. Carbon Footprint and Sustainability of Agricultural Production Systems in Punjab, India, and Ohio, USA[J]. CropImprovement, 2009.
    [30] 闫星,罗义,赵芹,等. 基于SBM—DEA的陕西省制造业高质量发展效率评价及对策研究[J],科技管理研究,2022,42(01): 44-50.
    [31] Tone K. A slacks-based measure of efficiency in data envelopment analysis[J]. European Journal of Operational Research, 2001, 130(3): 498-509.
    [32] Tone K. A slacks-based measure of super-efficiency in data envelopment analysis[J]. European Journal of Operational Research, 2002, 143(1): 32-41.
    [33] Li N, Jiang Y , Yu Z , et al. Analysis of Agriculture Total-Factor Energy Efficiency in China Based on DEA and Malmquist indices[J]. Energy Procedia, 2017, 142: 2397-2402.
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History
  • Received:May 18,2023
  • Revised:August 09,2023
  • Adopted:August 10,2023
  • Online: June 28,2024