Impact of Human Activities on Carbon Dioxide (CO<Subscript>2</Subscript>) Emissions: A Statistical Analysis

Summary The balance of evidence suggests a perceptible human influence on global ecosystems. Human activities are affecting the global ecosystem, some directly and some indirectly. If researchers could clarify the extent to which specific human activities affect global ecosystems, they would be in a much better position to suggest strategies for mitigating against the worst disturbances. Sophisticated statistical analysis can help in interpreting the influence of specific human activities on global ecosystems more carefully. This study aims at identifying significant or influential human activities (i.e. factors) on CO₂ emissions using statistical analyses. The study was conducted for two cases: (i) developed countries and (ii) developing countries. In developed countries, this study identified three influential human activities for CO₂ emissions: (i) combustion of fossil fuels, (ii) population pressure on natural and terrestrial ecosystems, and (iii) land use change. In developing countries, the significant human activities causing an upsurge of CO₂ emissions are: (i) combustion of fossil fuels, (ii) terrestrial ecosystem strength and (iii) land use change. Among these factors, combustion of fossil fuels is the most influential human activity for CO₂ emissions both in developed and developing countries. Regression analysis based on the factor scores indicated that combustion of fossil fuels has significant positive influence on CO₂ emissions in both developed and developing countries. Terrestrial ecosystem strength has a significant negative influence on CO₂ emissions. Land use change and CO₂ emissions are positively related, although regression analysis showed that the influence of land use change on CO₂ emissions was still insignificant. It is anticipated, from the findings of this study, that CO₂ emissions can be reduced by reducing fossil-fuel consumption and switching to alternative energy sources, preserving exiting forests, planting trees on abandoned and degraded forest lands, or by planting trees by social/agroforestry on agricultural lands.     

Quantifying forest fragmentation spatial process in the developing State of Selangor,peninsular Malaysia

Forest fragmentation has several phases; thus, the ecological significance of each phase during a particular period of time must be interpreted. To interpret, this study quantifies the magnitude of forest loss and the changes in the temporal pattern of fragmentation in the State of Selangor, peninsular Malaysia. Using the decision tree model of land transformation, five phases of forest fragmentation were identified: perforation, dissection, dissipation, shrinkage and attrition. This analysis showed that the magnitude of forest loss was the highest during the dissipation phase. The patchiness analysis showed that dissipation contributes to the highest environmental uncertainty found for the forest patches. This study can be considered a first step in the exploration of the properties and the behavioural pattern shown by the spatial process of forest fragmentation.     

Effectiveness of conservation easements in agricultural regions

Conservation easements are a standard technique for preventing habitat loss, particularly in agricultural regions with extensive cropland cultivation, yet little is known about their effectiveness. I developed a spatial econometric approach to propensity‐score matching and used the approach to estimate the amount of habitat loss prevented by a grassland conservation easement program of the U.S. federal government. I used a spatial autoregressive probit model to predict tract enrollment in the easement program as of 2001 based on tract agricultural suitability, habitat quality, and spatial interactions among neighboring tracts. Using the predicted values from the model, I matched enrolled tracts with similar unenrolled tracts to form a treatment group and a control group. To measure the program's impact on subsequent grassland loss, I estimated cropland cultivation rates for both groups in 2014 with a second spatial probit model. Between 2001 and 2014, approximately 14.9% of control tracts were cultivated and 0.3% of treated tracts were cultivated. Therefore, approximately 14.6% of the protected land would have been cultivated in the absence of the program. My results demonstrate that conservation easements can significantly reduce habitat loss in agricultural regions; however, the enrollment of tracts with low cropland suitability may constrain the amount of habitat loss they prevent. My results also show that spatial econometric models can improve the validity of control groups and thereby strengthen causal inferences about program effectiveness in situations when spatial interactions influence conservation decisions.     

The Quagga Mussel Crisis at Lake Mead National Recreation Area,Nevada (U.S.A.)

Abstract: Parks are cornerstones of conservation; and non‐native invasive species drive extensive changes to biological diversity in parks. Knowing this, national park staff at Lake Mead National Recreation Area in the southwestern United States had a program in place for early detection of the non‐native, invasive quagga mussel (Dreissena rostriformis bugensis). Upon finding the mussel in January 2007, managers moved quickly to access funding and the best available science to implement a response. Managers considered four options—doing nothing, closing the park, restricting movement on the lakes, and educating and enforcing park visitors—and decided to focus on education and enforcing existing laws. Nonetheless, quagga spread throughout the park and soon began to appear throughout the western United States. I examined why efforts to control the expansion failed and determined the general lessons to be learned from this case. Concentrating human visitation on the lakes through land‐use zoning opened a pathway for invasion, reduced management options, and led to the rapid spread of quagga. To reconcile competing mandates to protect nature and provide recreation, zoning in parks has become a common practice worldwide. It reduces stress on some areas of a park by restricting and thus concentrating human activity in particular areas. Concentrating the human activity in one area does three things: cements pathways that repeatedly import and export vectors of non‐native invasive species; creates the disturbed area necessary to enable non‐native invasive species to gain a foothold; and, establishes a source of invasions that, without appropriate controls, can quickly spread to a park's wilderness areas.     

AHP-SWOT法在城市圈建设中耕地保护策略分析中的应用——以湖北赤壁为例

在实地调研湖北赤壁的基础上,探讨AHP-SWOT法在城市圈建设过程中的耕地保护策略分析中的应用.结果表明,耕地异地占补平衡是主要优势,耕地数量持续减少是主要劣势,新一轮土地利用总体规划是主要机遇,城市圈建设占用耕地是主要威胁,建议赤壁市在城市圈建设过程中应发挥耕地异地占补平衡优势,回避城市圈建设占用耕地的威胁.     

中国城市热岛时空特征及其影响因子的分析

全球气候变暖背景下,城市热岛效应会加重城市地区的热胁迫,对人类健康和生存发展提出严峻挑战.近年来我国雾-霾污染情况严重,但雾-霾对城市热岛影响的认识仍较匮乏.本研究基于MODIS遥感卫星地表温度数据,明确了我国2003~2013年白天、夜间以及四季城市热岛的空间变化,并从生物物理学和生物化学角度定量分析其控制机制.结果表明,影响我国白天城市热岛强度的主要因素为人口、农田灌溉和植被活动.纬度、降水量、反照率以及气溶胶浓度是夜间城市热岛强度的主控因子.从对比城乡粗糙度、反照率等生物物理学属性的角度,揭示了乡村背景环境对城市热岛分析的重要影响.结果表明,雾-霾治理可以缓解我国夜间城市热岛现象和热胁迫,有利于缓解区域甚至全球气候变化.     

不同国家基于健康风险的土壤环境基准比较研究与启示

20世纪90年代以来,许多发达国家颁布了基于健康风险的土壤环境标准及基准背景技术文件,为发展中国家启动标准制订和开展基准研究提供了重要的方法学参考.然而,由于各国在环境立法框架、基准推导过程、环境气候特征、土壤类型、人群生活方式与习惯等方面的不同,导致其基准名称、基准功能、保护受体以及基准取值等存在较大的差异.本文在综述基于健康风险的土壤环境基准科学内涵与基本特征的基础上,对不同国家场地土壤环境基准的名称、功能及基准值的差异进行了比较研究,从土地利用类型划分、暴露情景和暴露途径设定、致癌物可接受风险水平选择以及人体暴露参数确定等4个方面,对不同国家制定场地土壤环境基准的关键技术和影响因素进行了总结分析,探讨了借鉴国外经验考虑区域差异制定我国土壤环境基准的对策与方略,并指出了当前我国制定场地土壤环境基准和标准面临的困难和挑战.     

梁平县境内高滩河水质的空间分布及其对土地利用方式的响应

以梁平县境内高滩河流域为研究对象,综合应用GIS空间分析、Pearson相关分析和RDA冗余分析方法,研究在子流域尺度上COD、BOD5、氨氮和总磷4种水质要素对土地利用方式的生态响应。结果表明:高滩河流域土地利用类型对水质要素有着重要影响,林地能够改善河道水环境,旱地和城镇村和工矿用地是COD、BOD5和氨氮的主要污染来源,园地、交通运输用地以及水域对污染物的影响较弱,说明农业面源污染和农村居民生活污染是高滩河水质恶化的主要原因。     

基于GIS和LUR模型的西安市PM2.5浓度空间分布模拟研究

基于地理信息系统GIS和土地利用回归LUR模型,模拟西安市PM2.5浓度空间动态分布,结果表明:与PM2.5浓度相关性最高的分别为缓冲区为2 km的水域面积、人口密度和距离水域距离,R 2分别为0.501,0.393和0.280;与PM2.5浓度相关性最低的分别为缓冲区为4 km的水域面积、未利用地面积和耕地面积,R 2分别为0.039、0.021和0.017.未考虑风速建立的LUR模型多元回归的相关系数为0.856,R 2为0.733,考虑风速的相关系数为0.892,R 2为0.796,表明风速对于污染物的分布影响较大,LUR模型模拟效果较好.模拟的PM2.5年均浓度高风险区分布于中部,中风险区分布于中西部,低风险区分布于东南部和西部.