中国农业碳排放绩效评价及随机性收敛研究——基于SBM-Undesirable模型与面板单位根检验

Assessment of agricultural carbon emission performance and stochastic con-vergence in China using SBM-Undesirable model and panel unit root test

  • 摘要: 当前,全局层面及工业视角下的碳排放绩效评价和收敛性分析已趋于成熟,但农业碳排放方面的研究尚十分薄弱。为补充相关研究,对区域农业碳排放总量、绩效及两者的收敛趋势有更清晰的认识,本文在测算2000-2014年我国30个省市农业碳排放总量的基础上,运用SBM-Undesirable模型计算农业碳排放绩效,并通过面板单位根检验方法对全国和各区域的农业碳排放总量及绩效进行了随机性收敛检验。结果显示:1)2000-2014年间,全国农业碳排放量整体呈递增趋势,但各区域排放量差异明显。比较中部与全国、东部和西部各年总量均值,发现2000年的差值分别为3.357 4×106 t,3.965 0×106 t和5.904 7×106 t,到2014年,差距扩大至5.244 8×106 t,7.351 2×106 t和7.681 0×106 t,对应增长比例分别为56.2%、85.4%和30.0%。2)各区域农业碳排放绩效存在显著差异。绩效均值折线图显示,东部平均绩效较高,15年来基本稳定在0.8左右;西部和中部绩效均值较低,绝大多数年份处于0.3~0.5,但西部不断改善,中部则持续下降。3)对总量进行收敛性检验,发现全国、西部、中部呈现明显的随机性分异,仅东部出现随机性趋同;在绩效的收敛性检验中,全国范围内不存在随机性收敛,但东部、中部、西部各自呈俱乐部式随机性收敛态势。随机性收敛检验结果表明,中国农业碳排放总量和绩效不会自动降低到稳态水平,有必要进行相关政策干预,以缩小各省市间的差距。本研究为制定区域间差异化和区域内统一性农业减排政策奠定了基础。

     

    Abstract: On overall scale and industrial perspective, researches on the evaluation of carbon emissions and the test of convergence in China have matured. However, the study has remained relatively weak in agricultural carbon emissions. To complement not only existing research but also for better understanding of the carbon emissions, performance and convergence in different regions, the paper used SBM-Undesirable model to assess the performance of agricultural carbon emissions based on the estimation of agricultural carbon emissions in 30 provinces in 2000-2014. Then three panel unit root tests were selected to determine stochastic convergence test for the investigated districts. The main conclusions were as follows:1) for the period 2000-2014, the overall trend in national agricultural carbon emissions increased, but the quantities of the emissions in different regions were significantly different. The mean agricultural carbon emission in the middle region was much larger than that in the whole nation, the eastern region and western region. The gaps in agricultural carbon emissions between the middle region and the whole nation, the eastern and western region were 3.357 4×106 t, 3.965 0×106 t and 5.904 7×106 t respectively in 2000, whereas this gaps widened to 5.244 8×106 t, 7.351 2×106 t and 7.681 0×106 t in 2014, corresponding respectively to growth rates of 56.2%, 85.4% and 30.0%. 2) The performance of agricultural carbon emissions in different regions turned out to differ apparently from distinct to district. A line graph of the average performance suggested that the performance was better for the eastern region, which was stable at 0.8 for 15 years. On the contrary, the average performance was relatively low for the west and middle regions, which was for most of the time within 0.3-0.5. The performance improved for the western region. However, the trend for the middle region was apparently the reverse. 3) In terms of convergence test of quantity, the examination of simulated convergence confirmed that stochastic convergence occurred only for the eastern region. There was no sign that stochastic convergence existed for the whole country, western region or even middle region. In the test of performance, there was no stochastic convergence for the whole country, while three regions exhibited relatively obviousness in the trend in club convergence. The results suggested that neither the quantity nor the performance of the whole country was automatically reducible to steady-state level. Thus it was necessary to make effective policy intervention to narrow the gap among the regions. Finally, this paper provided a further data-driven reference base for developing reasonable policies for the reductions of regional and national carbon emissions.

     

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