中国服务业碳生产率的空间收敛性研究

在经济服务化与低碳化的双重背景下,研究中国服务业碳生产率的演变轨迹,区域差异及空间收敛特征,对于推动中国服务业高质量发展具有重要的意义。采用2004—2016年中国30个省(区、市)的面板数据,对服务业碳生产率进行测度,并对其演变轨迹与区域差异进行分析,在此基础上,系统考察中国服务业碳生产率的空间收敛性。研究表明:①中国服务业碳生产率在整体上呈逐年增长态势,且呈现从东到西逐次递减的格局。②中国服务业碳生产率存在正向空间自相关关系,并且空间自相关程度表现出波动上升趋势。③全国与西部服务业碳生产率均不存在σ收敛,即服务业碳生产率的地区差异并未随着时间的推移而缩小;东、中部服务业碳生产率则在整体上呈现σ收敛特征。全国以及东、中、西部服务业碳生产率均存在绝对β收敛和条件β收敛,即服务业碳生产率落后地区对发达地区存在"追赶效应",各地区服务业碳生产率最终向自身的稳态水平趋同;空间效应加快了全国与东部服务业碳生产率的β收敛速度。④人均服务业增加值增长速度、部门结构以及能源消费结构对服务业碳生产率具有显著的负向效应,人力资本则对服务业碳生产率产生了显著的正向影响。据此,本文的政策启示如下:①重视服务业碳排放问题,构建服务业低碳绩效评估体系。②探索建立定期会商制度,加强服务业低碳技术的跨区域交流合作。③完善基础设施,促进要素空间互动和优化重组。④调整服务业能源结构与部门结构,推进人力资本积累和服务业稳增长。     

海洋碳汇渔业绿色发展空间外溢效应评价研究

海洋碳汇渔业绿色发展空间关联性及其外溢效应对于海水养殖业的有效协调和区域海洋环境的有效保护具有重要意义,科学估算沿海各省(自治区)海水养殖渔业碳汇量并探讨其空间相关性特征是制定差异化渔业碳汇发展政策的重要基础。根据2006—2016年中国大陆沿海9个省(自治区)的碳汇渔业资源清查数据,在检验和比较省域空间渔业碳汇总量相关性特征的基础上,运用空间计量模型分析了渔业碳汇的外溢效应及其影响因素。结果表明:①中国海水养殖渔业碳汇量整体上呈现上升趋势,但各省渔业碳汇量也存在明显差异。②研究期内的Moran's I指数整体呈现为"V"型的波动变化特征,渔业碳汇在省域空间分布上的差异性并不是随机的,而是具备显著的空间相关性。③海水养殖渔业碳汇存在明显的空间外溢效应,通过随机效应的杜宾模型分解后得出渔业产值、劳动力投入的直接效应为正,而渔业受灾面积和科研项目经费投入的直接效应为负;从间接效应来看,渔业产值在各省域间存在竞争与依存关系,海水养殖业劳动力投入和渔业技术推广的项目经费投入在各省域间存在互补关系。因此,中国沿海各省份在发挥海洋水产养殖业生态功能时,应当考虑省域区位因素,合理制定兼具差异化和协调性的海洋碳汇渔业发展政策。     

Spatial Patterns and Rankings of Contaminant Concentrations in Herring Gull Eggs from 15 Sites in the Great Lakes and Connecting Channels, 1998–2002

Mean values of eight contaminants in Herring Gull (Larus argentatus) eggs were calculated for 15 Great Lakes sites for the 5 year period 1998–2002. The sites were ranked according to the concentrations of each of seven compounds relative to fish flesh criteria for the protection of piscivorous wildlife, and a single overall rank of contamination was calculated for each site. Based on this weighted ranking scheme, we found that sum PCBs, dioxin and DDE contributed the most (60.2, 30.5% and 8.5%, respectively) to the overall rankings. The weighted ranking scheme showed that eggs from Channel-Shelter Island (Saginaw Bay, Lake Huron), Strachan Island (St. Lawrence River) and Gull Island (northern Lake Michigan) ranked as the three most contaminated sites, while Agawa Rocks (eastern Lake Superior), Chantry Island (southern Lake Huron) and Port Colborne (eastern Lake Erie) ranked as the three least contaminated sites. Two of the three most contaminated sites are Areas of Concern; none of the three least contaminated sites are Areas of Concern.     

Spatial Characterization and Prioritization of Heavy Metal Contaminated Soil-Water Resources in Peri-Urban Areas of National Capital Territory (NCT), Delhi

Due to rapid industrialization and urbanization during last two decades, contamination of soils by heavy metals is on an increase globally. Lands under peri-urban agriculture are the worst affected. In NCT, Delhi about 14.4% of land area is chemically degraded. In order to take care of this problem, recently the Supreme Court of India ordered to shift various non-confirming (about 39,000 units) industries to regions outside NCT, Delhi. However in spite of this, there have been several reports and parliamentary debates on the phyto-toxicity and extensive accumulation of heavy metals in the region. Literature review revealed that the basis of these debates is a few studies on some point locations in/around Delhi. It was further observed that information on the distribution and extent of heavy metal pollution problem in the region was completely missing. The present study was thus basically aimed at assessing the spatial distribution/extent and type of heavy metal pollution in the study area, for enabling future designing of appropriate site-specific management measures by the decision makers.For this, detailed spatial information on bio-available heavy metal concentrations in the soils and surface/sub-surface waters of NCT (Delhi) was generated through actual soil/water surveys, standard laboratory methods and GIS techniques. The study showed that concentration of all micronutrients (viz. Zn: 0.05–0.18 ppm; Cu: in traces; Fe: 0–0.5 ppm; and Mn: 0–1.2 ppm) and most heavy metals (viz. Ni: 0–0.7 ppm; Pb: 0–0.15 ppm and Cd: in traces) in the surface/sub-surface irrigation waters were well within permissible limits. However Cr concentrations in irrigation waters of Alipur and Shahdara blocks were far above their maximum permissible limit of 1 ppm. It was further observed that Ni and Cr concentrations in the drinking waters of almost entire test area were far above maximum permissible levels of 0.02 and 0.01 ppm, respectively. Bio-available concentrations of several heavy metals (viz. Pb: 0.1–2 ppm; Cd: traces; Ni: 0.05–2 ppm and Cr: 0–0.4 ppm) in the study area soils were also observed to be well within the maximum permissible limits. However there were point Cu contaminations (5–10 ppm) in the sewage-sludge amended soils of vegetable growing areas near south Shahdara block. This was attributed to increased Cu availability due to oxidized acidic conditions generated by over-irrigation of agricultural lands. Available Mn concentrations in Kanjhawala, western Najafgarh and Alipur soils were also observed to be above maximum permissible limit of 10 ppm. This was observed to be mainly due to the geology (i.e. presence of Mn rich sedimentary rocks) and prevalence of reduced acidic conditions, due to paddy cultivation, in these areas. It was further observed that there is acute zinc (Zn) deficiency (< 0.6 ppm) in paddy growing soils of north Kanjhawala, Alipur and some parts of Najafgarh and Shahdara blocks due to extensive leaching of available Zn fractions to lower soil horizons. Similar available Zn deficiencies in high pH (8.5) soils of areas around Bamnoli village in E-Najafgarh block were also observed.     

典型产业承接区土壤砷含量的空间分布特征及影响因素

本文以揭阳市为研究区域,采集了1330个表层土壤(0-20 cm)样品和331个深层土壤(150-200 cm)样品,利用富集因子法分析了表层土壤中As的污染状况,通过GIS空间分析技术以及单因素分析法探究了土壤As的空间分布特征以及影响因素.结果表明,研究区表层土壤As含量的均值为6.03 mg?kg-1,低于该区土...     

宁东能源化工基地核心区表层土壤酚类污染物的污染评价、空间分布及来源解析

采集了宁东能源化工基地核心区的146个表层土壤样品,检测了6种优控酚类污染物.运用单因子污染指数、内梅罗综合污染指数和致癌风险指数评价了酚类污染物的污染程度及健康风险.借助主成分分析和绝对主成分分数-多元线性回归受体模型,结合地统计学方法,对酚类污染物的空间分布及来源进行了分析.结果显示:2,4-二氯酚和2,4,6-三...     

冬季动态槽式堆肥温度的空间变异

以鲜鸡粪、蘑菇渣和污泥按照体积比3∶1∶1混合进行动态堆肥模拟试验.堆肥槽沿物料前进方向分为7个部分,对每个部分按等间距分别做5个水平方向上切分和5个垂直方向上切分,在形成的125个交叉点上进行温度监测.研究结果表明,第1天的混合物料温度在同一层中变异很小,不同层之间略有差异.随着动态过程的进行,同一层温度变异逐渐增大,从第一天相差1～3℃,增加到相差30～40℃,靠近墙体的堆料温度较低,远离墙体的温度较高.随着堆肥时间延长,差异增大.机械翻堆能起到通风的作用,同时使每一个堆方的堆料在纵向方向上上下混合,但达不到横向混匀,因此,靠近墙体两侧的堆料始终处于较低的温度,只有中部能达到较高的温度,以堆肥温度50℃作为无害化指标,自墙体向中心方向的1m为没有达到无害化厚度,无害化体积占堆肥总体积50%.整个动态堆肥过程符合二级动力学方程.     

基于GIS的北京市妫水河流域水土流失的初步分析

基于ArcGIS空间分析平台和ERDAS空间建模平台,利用水土流失综合指数法,分别以坡度、植被覆盖度和土地利用类型为主要背景因子,分析了北京市妫水河流域水土流失的空间背景特征.结果表明,妫水河流域水土流失的空间背景特征是缓坡、低植被覆盖度和疏林灌草地,而这些特征正是妫水河流域山前洪冲积扇地区的主要特点,缓坡、低植被覆盖度(不考虑城市建成区)和疏林灌草地也主要集中在流域内山前洪冲积扇地区.并提出防治妫水河流域水土流失的有效措施.      

重庆市大气污染物的空间统计分析

利用空间统计分析方法(spatial statistics analysis),借助GIS,分析了重庆市3种主要大气污染物TSP,SO₂和NO_x质量浓度在空间上的分布特点.研究表明,在"九五"期间和2002年2个时期,重庆市大气污染物在空间上均呈不均衡分布.整体上,SO₂的空间自相关性很强,属集中分布模式;TSP的空间自相关性较弱,属于随机分布模式;NO_x处于二者之间.局部相关性分析表明,大部分区县的大气污染物之间相关性较弱.此外,利用缓冲区和内插浓度表面法分析了2个时期的污染物质量浓度随离行政中心距离变化的趋势,发现ρ(TSP)和ρ(SO₂)在各地带内均有明显削减,而ρ(NO_x)削减不显著;ρ(SO₂)和ρ(NO_x)随距行政中心距离增大下降,但ρ(TSP)随距离增大而增加.3种污染物质量浓度都在离行政中心50～60 km的地带发生明显变化,由此得出目前重庆市大气污染物空间分布的特征距离为50～60km.      

Determination of Frequency for Remeasuring Ground and Vegetation Cover Factor Needed for Soil Erosion Modeling

Determining a remeasurement frequency of variables over time is required in monitoring environmental systems. This article demonstrates methods based on regression modeling and spatio-temporal variability to determine the time interval to remeasure the ground and vegetation cover factor on permanent plots for monitoring a soil erosion system. The spatio-temporal variability methods include use of historical data to predict semivariograms, modeling average temporal variability, and temporal interpolation by two-step kriging. The results show that for the cover factor, the relative errors of the prediction increase with an increased length of time interval between remeasurements when using the regression and semivariogram models. Given precision or accuracy requirements, appropriate time intervals can be determined. However, the remeasurement frequency also varies depending on the prediction interval time. As an alternative method, the range parameter of a semivariogram model can be used to quantify average temporal variability that approximates the maximum time interval between remeasurements. This method is simpler than regression and semivariogram modeling, but it requires a long-term dataset based on permanent plots. In addition, the temporal interpolation by two-step kriging is also used to determine the time interval. This method is applicable when remeasurements in time are not sufficient. If spatial and temporal remeasurements are sufficient, it can be expanded and applied to design spatial and temporal sampling simultaneously.