摘要:减少化肥面源污染的同时保持农业产值持续增长是实现农业产业生态化和农业高质量发展的必然要求。中国各省均制定并实施了化肥零增长行动计划，但进展和成效并不一致，并可能相互影响。论文运用化肥流失系数法对中国1997—2018年31个省（市、自治区）化肥面源污染排放强度进行核算，再运用空间自相关和热点分析对其进行时空格局演变分析，揭示化肥面源污染的时空演变规律，探讨区域间的相互影响。根据时空格局特征将全国分为热点区、冷点区和非热（冷）点区，在考虑相邻省份间空间异质性和相关性的条件下，分组模拟和预测化肥面源污染排放强度与人均农业产值间的环境库兹涅茨曲线（EKC）时间路径。结果表明：1）化肥面源污染排放强度省际差异较大，表现为空间正自相关，呈集聚模式。热点分析显示，化肥面源污染时空格局相对稳定，热点区主要集中在中南部，长江中下游地区尤其显著，黄淮海地区近年热点程度下降较明显；冷点区主要集中在西部地区和黑龙江。2）基于时空格局分组的环境库兹涅茨曲线（EKC）趋势模拟表明，各组均存在显著的非线性EKC关系但趋势和拐点差异明显，热点区为“倒U型”，冷点区和非热（冷）点区为“倒N型”，多数省份正处于曲线上升阶段且距拐点较远。3）产业结构的调整和转移促使区域间存在化肥面源污染空间溢出效应，要从整体上把握区域间的协同治理。根据研究结果，提出热点区应研发推广适用施肥设备，提高化肥利用率；冷点区应保护性耕作，增施有机肥；非热（冷）点区应合理调整农业产业结构，注重种养循环。区域间则应当通过生态补偿、排污权交易等方式实现协同治理。
关键词:化肥面源污染/
时空格局演变/
空间相关性/
环境库兹涅茨曲线(EKC)/
分组预测
Abstract:Reducing pollution caused by chemical fertilizers while continuously increasing the output value of agriculture at the same time is an inevitable requirement to improve high quality development of agriculture. Actions aimed at achieving zero growth in chemical fertilizer use have been formulated and implemented in provinces across China. However, inconsistencies in progress and effectiveness among provinces may affect each other. The fertilizer loss coefficient method was used to calculate the emission intensity of non-point source pollution in 31 provinces across the Chinese mainland from 1997 to 2018. Spatial autocorrelation and hotspot analysis methods, based on the calculated emission intensity, were used to reveal the emission intensity temporal and spatial characteristics, and to analyze the interaction effect on neighboring provinces. According to these characteristics, the country was grouped into three regions: a hot spot region, a cold spot region, and a non-hot (cold) spot region. Then, the Environmental Kuznets Curve (EKC) trend between the non-point source pollution emission intensity of fertilizers and the per capita agricultural output value in each group was simulated and predicted under spatial heterogeneity and spatial correlation conditions. The results showed that the emission intensity varied considerably among provinces across the country. The emission intensity was spatially positively autocorrelated across the country with a cluster mode. A hot spot analysis showed that the spatiotemporal pattern for non-point source pollution caused by fertilizers was relatively stable. The hot spot region was mainly concentrated in the central and southern parts of China, especially in the middle and lower reaches of the Yangtze River. However, in recent years, the number of hotspots in the Huanghuaihai region had significantly decreased. The cold spot region was mainly concentrated in the western region and Heilongjiang Province. The EKC trend simulation, based on temporal and spatial pattern feature grouping, showed that there was a significant nonlinear EKC relationship between agricultural economic growth and chemical fertilizer non-point source pollution in each group. However, the trend and the inflection point of each curve were clearly different. The hot spot region appeared as an "inverted U-type" curve, but both the cold spot region and the non-hot (cold) spot region had "inverted N-type" curves. Most provinces in each region were in the ascending phase of the curves. Industrial structure adjustment and industrial transfer promoted the spatial spillover effect of fertilizer derived non-point source pollution among the regions. Therefore, coordinated governance among regions needs to be introduced. Based on the above results, we propose that there should be corresponding policy implications, and that suitable fertilization equipment should be developed and promoted to increase fertilizer utilization in the hot spot region. Conservation farming should be applied and the use of organic fertilizer promoted in the cold spot region. The non-hot (cold) spot region should reasonably adjust the structure of its agricultural industry and focus on the crop-livestock and poultry farming cycle. Ecological compensation and emissions trading should be implemented to achieve collaborative governance.
Key words:Fertilizer non-point source pollution/
Spatio-temporal pattern evolution/
Spatial correlation/
Environmental Kuznets Curve (EKC)/
Group prediction
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