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.     

土地利用生态服务价值指标体系评估结果比较研究

生态系统服务价值评估越来越受到学术界关注。针对当前生态服务价值评估方法、指标体系和评估效果较多的实践,通过文献归纳,对国内外土地利用生态服务价值评价方法、指标体系及其系数进行了梳理、分类和评价,将指标体系分为Costanza和谢高地两大类,并将谢高地指标体系分为价值当量、价值系数和区域修正。以辽宁省2005、2010年土地利用数据为案例,运用Costanza价值系数、谢高地两个版本价值当量、两个版本价值系数的区域修正,分别核算了案例区生态系统服务价值及变化,并比较评价结果。研究得出Costanza价值系数应用于中国实践时评估结果显得偏低,谢高地2002版的价值当量和2002版的价值系数区域修正结果最接近平均值,相对较为可取,2007版的价值系数区域修正评估结果也较为接近均值但略低,2007版的价值当量评估结果总体偏高。研究结果有利于指导土地利用生态服务价值评估指标体系选取。     

The Relative Importance of Landscape Amenity and Health Impacts in the Wind Farm Debate in Australia

In recent years, Australia has experienced public debate around the growth in wind farms as part of government and community strategies to move from fossil fuels to renewable energy. Many of the arguments put forward by opponents and supporters are similar to those that have been evident in North America and Western Europe, including possible environmental impacts on wildlife, noise levels and, perhaps most prominently adverse changes in ‘landscape amenity’. A concern of increasing prominence is that of adverse human health impacts from proximity to operating turbines. Using analysis of submissions to public inquiries and a small number of detailed interviews, we consider the increased focus in Australia on the health impacts of wind farms. We note that health impacts, as elsewhere in the world, are often part of a suite of objections to wind farm developments but conclude that for some people near such developments, these are the main concern and not a cover or proxy for other concerns, such as changes in landscape amenity and aesthetics.     

基于改进粒子群算法和元胞自动机的城市扩张模拟——以南京为例

为合理利用多智能体算法解决城市扩张动态模拟问题,基于地理学理论和社会学规律对粒子群算法进行有针对性的改进,提出分段式粒子群算法(SPSO),并结合元胞自动机模拟复杂时空过程的能力,构建出适用于城市扩张模拟的地理元胞自动机SPSO-CA。在SPSO-CA中我们利用多时像的土地利用数据、交通路网数据和地形数据,挖掘出1995~2000年南京城市扩张的土地转换规则。再由此规则实现1995~2008年的南京市城市扩张过程的动态模拟。最后对比SPSO-CA、PSOCA及NULL模型结果得:SPSO-CA总精度86.3%,Kappa系数为0.792,Moran’s I为0.078,PSO-CA总精度83.6%,Kappa系数为0.755,Moran’s I为0.054,NULL模型总精度81.9%,Kappa系数为0.741,真实的Moran’s I为0.072。这表明无论是总精度还是空间一致性,SPSO-CA都优于PSO-CA和NULL模型,即用SPSO-CA模拟城市扩张是可行的。     

湖北省土地利用减碳增效系统仿真及结构优化研究

土地利用变化是引起碳排放的重要原因之一,土地经济效益是利用土地的目标之一,如何通过优化土地利用结构实现土地利用减碳增效是值得研究的重要问题。基于系统结构与功能相互作用的视角,梳理复杂系统内变量间的反馈关系,运用系统动力学(SD)进行建模,将约束条件纳入到多目标规划(MOP)中,实现MOP与SD模型整合,进行系统仿真并得出优化后2020年湖北省土地利用结构。结果显示,利用SD-MOP模型能够实现减碳增效目标下土地利用结构优化,与2008年真实值相比,耕地、林地、牧草地及建设用地分别增加了0.33×10~4、30.17×10~4、0.08×10~4和16.37×10~4 hm~2,其他农用地及未利用地分别减少7.23×10~4、33.15×10~4 hm~2;与无约束SD单模型仿真相比,土地利用碳排放量减少了58×10~4 t,经济效益年增长率维持在3.58%,优化方案具有可行性。SD-MOP模型优化的土地利用结构符合区域可持续发展要求,兼顾了土地利用碳减排和经济效益增长的双重目标,能为区域土地资源优化配置提供参考。     

基于安徽省土地利用变化的地形梯度效应分析

为揭示地形因素与土地利用空间格局演变的关系,以安徽省2000年、2005年以及2010年Landsat TM影像和DEM数据为数据源,利用坡向、地形起伏度、坡度变率和地形位指数多种地形因子,并结合地学信息图谱分析理论从综合角度系统研究土地利用结构时空分异格局在地形梯度上的变化特点及其规律。结果表明:1)研究区主要的土地利用类型是耕地和林地,其次是建设用地和未利用土地,且建设用地面积逐期增加趋势显著。2)2000~2010年研究区不同土地利用类型在不同地形梯度区间的分异规律存在明显差异。低地形梯度范围是耕地、水域和建设用地的优势分布区;中地形梯度范围是草地的集中分布区;林地的优势分布区集中在高地形梯度范围。3)2000~2010年研究区土地利用图谱以稳定型和后期变化型为主,低地形梯度和高地形梯度是稳定型图谱的集中分布区,中低、中高和高地形梯度范围是后期变化型图谱的优势分布区。2000~2005年林地的优势分布区有向较低地形位梯度扩张的趋势,其中,耕地是主要的转入来源。2005~2010年耕地的优势分布区有向较高地形位梯度扩张的趋势,变化模式主要为:"林地–耕地"、"未利用地–耕地"和"草地–耕地"。     

Accounting for monogastric livestock as a driver in global land use and cover change assessments

Land systems are described based on various characteristics, including land cover composition and agricultural production. However, it is uncertain to what extent livestock, particularly monogastric livestock, determines land systems. We included monogastrics in a land system classification, and statistically analyzed the land cover composition and agricultural production of otherwise similar land systems with and without monogastric livestock. The results indicate that land systems with monogastrics are statistically different from their counterparts in the classification without monogastrics in terms of grassland area and crop yields, but are less different in terms of tree area, crop area, and ruminant livestock production. We then used a land systems map that includes monogastrics in the classification and a similar map that does not include monogastrics to project future changes in a novel manner that integrates livestock as a determinant of land systems. The results show that including monogastrics in otherwise similar projections yields less cropland intensification and more cropland expansion in several world regions, including Northern Africa and the Middle East. Other regions, such as Europe and Australia, were characterized by less decrease or more increase in tree area in the application with monogastrics, mainly due to the occurrence of open forests with monogastrics. This study prompts a call for improved characterization of land systems for land use and cover change (LUCC) assessments in order to better represent LUCC driven by monogastric livestock.     

环境约束下大都市城郊土地利用结构评价——基于信息熵和非期望产出模型分析

土地利用过程中会产生期望和非期望产出,针对以往土地利用结构评价中忽略环境产生的非期望产出而影响土地利用结构效率真实性问题,以大都市城郊的上海青浦区为例,宏观上,运用信息熵模型对该区2005~2014年土地利用结构有序性进行评价;微观上,运用非期望产出模型测算青浦区研究期间土地利用结构效率,为寻求区域土地利用结构效率损失的原因及改善途径提供参考。结果表明:(1)2005~2014年青浦区土地利用结构信息熵值时序上呈现"M"形变化趋势,空间上由中部向东西两翼递减;(2)2005~2014年青浦区土地利用结构效率变化趋势在时间序列上呈现"W"形变化趋势,空间上由中部的中心城区向东西两翼递减格局;(3)将环境的负产出纳入非期望产出模型进行土地利用结构效率评价,能更加准确的反映土地利用结构效率的真实性;(4)影响青浦区土地利用结构效率水平变化的主要因素为纯技术效率的变化,资源过度消耗和环境污染物排放过量是制约青浦区土地利用结构效率提升的主要原因。针对土地利用结构效率损失的原因及区域差异提出效率提升途径。研究结果可为青浦区土地利用结构优化和可持续发展提供科学借鉴。     

长湖流域水质时空分布特征及影响因子

利用2009—2014年长湖5个水质监测点数据,采用时间序列法分析了长湖水质的时间变化规律,采用相关性分析法,分析了流域水污染的影响因子。结果表明:在时间上,长湖水污染物质量浓度季节变化明显,COD、TN、NH3-N均为7、9月较低,1、3月较高,丰水期水质好于枯水期。入湖地区TP质量浓度7月达最高值,且7月份入湖地区的桥河口、关沮口的NH3-N、TN含量稍高于5月。空间上,西北部入湖地区水质劣于湖心及东南部出湖地区。工业、生活等点源污水,以耕地为主的农业非点源以及天然降水量和径流量是影响水质的主要因素,入湖排污量、降水量和径流量与长湖水质呈显著相关关系(P0.05)。     

Parks versus PES: Evaluating direct and incentive-based land conservation in Mexico

Protected areas (PAs) and payments for ecosystem services (PES) are the top two mechanisms available for countries to achieve international REDD agreements, yet there are few empirical comparisons of their effects. We estimate the impacts of PAs and PES on forest conservation, poverty reduction, and population change at the locality level in Mexico in the 2000s. Both policies conserved forest, generating an approximately 20–25% reduction in expected forest cover loss. PES created statistically significant but small poverty alleviation while PAs had overall neutral impacts on livelihoods. Estimates by individual policy type for the same level of deforestation risk indicate that biosphere reserves and PES balanced conservation and livelihood goals better than strict protected areas or mixed-use areas. This suggests that both direct and incentive-based instruments can be effective, and that policies combining sustainable financing, flexible zoning, and recognition of local economic goals are more likely to achieve conservation without harming livelihoods.