摘要:研究农业碳排放空间格局及影响因素对中国制定农业分区碳减排政策意义重大。为弥补以往研究中静态分析法难以考察动态影响的缺陷，将动态灰色关联法和回归模型结合，应用2001-2016年统计数据，从分析农业碳排放空间格局入手，深入探讨省际农业碳排放空间格局成因和影响因素与空间差异的数量关系。研究发现：中国农业碳排放强度省际差异大，中部排放等级有所降低，西部排放等级有所升高，农业碳排放省际差异随农业经济水平、农业机械化、农业产业结构和农业人力资本等差异扩大而增加；大部分排放等级上升的省市农业碳排放的长期主导因素为农地利用和农业生产技术（机械），且种植业和畜牧业双发展；大部分排放等级下降的省市农业碳排放的长期主导因素为反刍动物饲养和农业生产技术（人力），且着重发展优势产业。因此，中国未来较长时间内仍应重点关注农地利用减排，进一步推动反刍动物饲养减排技术发展和充分发挥农业产业结构调整对减排的抑制作用等建议。
关键词:农业碳排放/
主导因素/
空间格局演变/
动态灰色关联法/
非线性分析/
分区减排
Abstract:The reduction of carbon emission in agricultural lands is not only important in sustainable agriculture, but also inevitable for China to achieve an overall emission reduction targets and carbon emission control. It is also of great significance to conduct research into the spatial distribution and the driving factors of carbon emissions in agricultural lands in China. Studies that have focused on regional distributions of carbon emissions in agricultural lands adopted different measurement methods and used inconsistent driving indicators, therefore had reached different conclusions in different researches. Moreover, in order to compensate for the deficiency with static analysis methods in dealing with dynamic effects, we combined dynamic grey correlation method with regression model. It not only analyzed the non-linear impacts of carbon emission in agricultural lands and the influencing factors, but also analyzed the dynamic impacts of carbon emission in agricultural lands and the influencing factors. Based on the preceding researches, our study started by analyzing the spatial distribution of carbon emissions in agricultural lands in China, including total amount of carbon emission, carbon emission intensity, carbon emission structure and carbon emission level in agricultural lands. It then discussed in detail the causes of the spatial patterns of inter-provincial carbon emissions in agricultural lands and quantitative relationship between the influencing factors and spatial distribution. The study was a critical source of reference on zonal carbon emission reduction that could be useful in formulating carbon emission policies in China. The main conclusions of this paper were as follows:inter-provincial differences in carbon emission intensities in agricultural lands had increased with time. There was no significant reduction in structural differences in carbon emission among provinces or cities. The polarization of carbon emission level in agricultural lands was ever more severe. The level of carbon emissions in agricultural lands fell in the central region, but increased in the west. In contrast, farmland utilization and mechanization of agricultural production were more important factors driving carbon emission in agricultural lands. Some achievements were made in reducing ruminant emissions, with a widening gap among provinces or cities due to differences in agro-economic level, agricultural mechanization, agricultural structure and agro-human capital. The differences in inter-provincial carbon emissions of agricultural lands increased. Agriculture and animal husbandry, farmland utilization and mechanization of agricultural production technology were the leading factors driving the improvement in carbon emission in agricultural lands in most of the provinces and cities with more attention on agricultural development. In these regions, the development of superior industries, ruminant feeding and agricultural production techniques (human capital) were the dominant factors reducing carbon emissions. Finally, we forwarded three recommendations:First, there was need to focus on long-term carbon emission reduction in farmlands. Specifically, major grain-producing areas were to strengthen innovation of emission reduction technology and push forward with progress in emission reduction projects. Second, there was need for further attention on promoting technology of emission reduction in feeding ruminants and in exploring agricultural development models that combined farming with breeding, especially in pastoral areas. Third, there was need to fully exert the role of agro-economic structure in reducing carbon emission. The eastern, central and western regions were to adjust industrial structure in accordance with the level of development.
Key words:Carbon emission in agricultural land/
Dominant factor/
Spatial evolution pattern/
Dynamic Gray Correlation Method/
Nonlinear analysis/
Zonal carbon emission reduction
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