Quantitative Appraisement on Ecological Carrying Capacity in Coastal Rapid Developing Region of Hainan Province

The ecological carrying capacity, an important indicator to evaluate the sustainable development of the ecosystem, means the potential ability of the natural ecosystem to carry socioeconomic development while the ecosystem is healthy. It is limited by the carrying capacity of natural resources and environment and the elasticity of the ecosystem. It will be greatly significant to study the ecological carrying capacity of Hainan Province, the first ecological province admitted by the State Environmental Protection Administration in China. Not only is the natural ecosystem reflected, but also the effects of human activities are emphasized by integrating the ecosystem health analysis into the ecological carrying capacity research. The research results, using the Factor Analysis tools of software SPSS, indicate that the ecological carrying capacity of Hainan Province fluctuated obviously from 1996 to 2005. The level of the ecological carrying capacity of Hainan Province was relatively high in 1996, and reached into trough from 1997 to 1999. It has steadily ascended to be above the middle level since the 21st century. The results also show that policy factors, especially the implementation of the ＇Ecological Province＇ strategy, were important driving forces to influence the ecological carrying capacity. With the population rapidly increasing, the land and water resources per capita have decreased quickly. The amount of the ecological carrying capacity was promoted remarkably by socioeconomic development especially economic growth and technology applications. All of these will provide useful suggestions to establish and enact regional development policies, especially for protecting and reconstructing the ecology and environment of Hainan Province.     

Ranking Canadian universities: a quantitative approach for sustainability assessment using uD-SiM

This paper introduces a model that enables a comparison between universities based on sustainability indicators related to environmental, economic, social and educational aspects. The proposed model is based on a driving force-pressure-state-exposure-effect-action (DPSEEA) framework and is called uncertainty-based DPSEEA-Sustainability index Model (uD-SiM). The uD-SiM applies the concept of causality and develops sustainability index (SI), which is an outcome of nonlinear relationships of sustainability indicators in different stages of DPSEEA. In this paper, this fuzzy-based multi-criteria decision-making model is used to evaluate the sustainability of five Canadian universities, namely the University of British Columbia, the University of Toronto, the University of Alberta, the McGill University and the Memorial University. The final ranking results are compared with the Green report card ranking for 2010 through SI. The application of various actions and strategies that can be applied to different stages of the framework to improve sustainability in higher education institutions is also discussed.     

若尔盖湿地沙漠化成因分析及对策探讨

本文旨在对若尔盖湿地退化、草原沙漠化的驱动因素进行分析。找出导致湿地退化、草原沙漠化的主导因素，并寻求合理对策。在前人已有研究和野外实地考察的基础上。对获得的数据资料进行对比分析，分析结果显示近几十年来若尔盖地区人口激增、挖沟排水、过度放牧等人为因素对湿地退化、草原沙漠化的影响明显．而气温升高、降雨减少等自然因素影响较弱。分析表明．若尔盖湿地沙漠化在于人类活动干扰和自然因素的双重作用。自然因素是湿地沙漠化发生的大环境，是大空间、大跨度造成湿地沙漠化的自然环境背景．为生态环境恶化、湿地沙漠化提供一种可能性；而近几十年来人类强烈的破坏性活动才是该地区生态环境恶化、湿地沙漠化的主导因素。在遵循自然客观规律的基础上．有针对性地制定切实有效的保护和恢复措施很有必要。     

Socio-economic driving forces of land-use change in Kunshan, the Yangtze River Delta economic area of China

This paper analyzes characteristics, major driving forces and alternative management measures of land-use change in Kunshan, Jiangsu province, China. The study used remote sensing (RS) maps and socio-economic data. Based on RS-derived maps, two change matrices were constructed for detecting land-use change between 1987 and 1994, and between 1994 and 2000 through pixel-to-pixel comparisons. The outcomes indicated that paddy fields, dryland, and forested land moderately decreased by 8.2%, 29% and 2.6% from 1987 to 1994, and by 4.1%, 7.6% and 8% from 1994 to 2000, respectively. In contrast, the following increased greatly from 1987 to 1994: artificial ponds by 48%, urban settlements by 87.6%, rural settlements by 41.1%, and construction land by 511.8%. From 1994 to 2000, these land covers increased by 3.6%, 28.1%, 23.4% and 47.1%, respectively. For the whole area, fragmentation of land cover was very significant. In addition, socio-economic data were used to analyze major driving forces triggering land-use change through bivariate analysis. The results indicated that industrialization, urbanization, population growth, and China's economic reform measures are four major driving forces contributing to land-use change in Kunshan. Finally, we introduced some possible management measures such as urban growth boundary (UGB) and incentive-based policies. We pointed out that, given the rapidity of the observed changes, it is critical that additional studies be undertaken to evaluate these suggested policies, focusing on what their effects might be in this region, and how these might be implemented.     

游客食物浪费行为驱动因素及监测评估

游客食物浪费是中国食物浪费的“重灾区”,给旅游地和区域可持续发展带来严峻挑战。本文运用文献分析法,探讨了游客食物浪费行为驱动因素及监测评估技术。结果表明：（1）消费性与文化性、复杂性与冲动性、必然性与象征性、习惯性与探奇性是游客食物消费行为的主要特征。（2）全球化、工业化、城镇化、传统文化及政策的推动作用,旅游企业运营管理及上下游产业链利益主体的促进作用,游客食物消费的情境性及年龄、收入等因素的引致作用是驱动游客食物浪费行为的重要因素。（3）游客食物浪费量的测度方法有食物日记法、直接测量法、质量平衡法、废物成分分析法、调查统计方法和树形结构模型,其行为评估主要运用问卷、访谈等定性分析,监控方式有采购和库存表、车载车辆称重设备、食品垃圾自动跟踪系统专利技术、智能菜单等。研究结论对推动《中华人民共和国反食品浪费法》的落地实施、提高相关部门减少游客食物浪费决策的精准性具有借鉴和参考价值。     

时空交互视角下淮海经济区城乡融合发展水平演化

以淮海经济区为研究对象,界定城乡融合内涵,并构建多维指标体系,测度城乡融合发展水平,并探索时空演化特征及驱动机理。（1）在空间集聚方面,淮海经济区城乡融合发展水平集聚较为明显,并且集聚性整体呈现增强趋势。（2）在时空联动方面,具有相对动态的局部空间结构和相对稳定的空间依赖方向,同时空间格局演化方面具有较强的整合性;城市自身因素对城乡融合发展水平空间结构的改变影响较大,受相邻城市城乡融合发展水平的溢出效应的影响较小。（3）要素驱动模式方面,低分位驱动和高分位驱动城市的城乡融合发展水平变化受经济发展—城镇化驱动,低分位制约类型城市的城乡融合发展水平变化受城镇就业—政府行为制约,高分位制约类型城市的城乡融合发展水平变化受产业—政府行为制约。     

2000—2015年青藏高原植被NPP时空变化格局及其对气候变化的响应

全球变化背景下,青藏高原作为我国乃至全球气候变化的“天然实验室”,植被生态系统发生了深刻变化。引入重心模型等方法分析和探讨2000—2015年青藏高原植被NPP时空变化格局及其驱动机理,并定量区分NPP变化过程中气候变化和人类活动的相对作用。研究发现：（1）2000—2015年,青藏高原植被NPP年均值总体上呈现从东南向西北递减的趋势。在年际变化方面,近16年植被NPP呈现波动上升趋势,其中在2005年出现上升陡坡,并在2005—2015年表现为高位波动的态势。（2）青藏高原植被NPP增加区（变化率>10%）主要集中于三江源地区、横断山区北部、雅鲁藏布江中下游以及那曲地区的中东部,而植被NPP减小区（变化率<-10%）则主要分布于雅鲁藏布江上游和阿里高原。（3）近16年青藏高原植被NPP重心总体向西南方向移动,表明西南部植被NPP在增量和增速上大于东北部。（4）青藏高原植被NPP与气候因子相关性的地区差异显著,其中植被NPP与降水显著相关的区域主要位于青藏高原中部、青藏高原东南部及雅鲁藏布江流域中下游,而植被NPP与气温显著相关的区域主要位于藏南地区、横断山区北部、青藏高原中部和北部。（5）气候变化和人类活动在青藏高原植被NPP变化过程中的相对作用存在显著的时空差异性,在空间上呈现“四线—五区”的格局。研究成果能够为揭示青藏高原区域生态系统对全球变化的响应机制提供理论和方法支撑。     

基于贝叶斯网络的生态用地流失机制研究

从中微观尺度对城市滨江地区生态用地流失进行驱动因素分析,尤其将政策规划等具有重要影响力的因子进行量化分析,可为生态用地开发与保护政策制定提供参考。利用贝叶斯网络模型综合考虑导致生态用地流失的邻域因子、自然因子和政策规划因子,以良好的图形描述方法较为清晰地展示出生态用地变化及其驱动因子之间的过程关系。以南京滨江地区作为典型案例区,研究结果表明：（1）2005—2018年间,南京滨江地区约11.0%的生态用地发生流失,维持稳定的生态用地占比89.0%;（2）政策规划保护力是影响南京滨江地区生态用地流失的主要驱动因素,城镇扩张惯性力次之,二者对生态用地流失的敏感性分别为9.37%和2.53%,而开发阻力的敏感性仅为0.21%;（3）在政策规划因子中,沿江岸线规划对维持生态用地稳定性的作用效果高于土地利用规划,表明要使生态空间得以长期存在,应制定基于生态用地功能目标的保护政策。     

1990~2015年东北地区草地变化遥感监测研究

基于1990年和2015年东北地区草地数据集,采用草地动态度模型、草地变化程度综合指数、草地相对变化率、质心模型和景观指数等作为参考指标对草地时空演变特征及其驱动因素进行分析.结果表明：25a间,东北地区草地面积减少了9755.94km²,其中,内蒙古自治区东部减少量最大;黑龙江省草地动态度、变化程度综合指数和相对变化率最高,分别为-30.67%、21.68%和5.55,草地变化最剧烈、稳定性最差;内蒙古自治区东部相应三个指标最低,分别为-3.76%、2.66%和0.68,年均变化强度最高,为0.0193%,相对其他省草地更稳定,但其减少规模最大;草地质心向西偏南方向移动11.28km,是由东部黑龙江省和西部内蒙古自治区东部草地动态强度巨大差异所致;东北地区草地破碎化程度降低、景观类型稳定性上升、斑块形状趋于规则化,主要是小斑块、破碎斑块转化为其他土地类型的结果.气候变暖、变干是草地退化的自然驱动因素,人口增加和经济增长间接影响了草地变化,牲畜大幅度增长是重要因素.     

陕西省PM2.5时空分布规律及其影响因素

PM_2.5是导致中国多省市发生灰霾的罪魁祸首,明确其时空分布规律,厘清其影响因素对灰霾的综合治理意义深远.基于陕西省2015年50个监测站点的PM_2.5浓度数据,采用空间数据统计方法、克里金插值法以及Morlet小波分析法对陕西省PM_2.5浓度的时空分布规律进行研究,并运用灰色关联模型来探讨PM_2.5浓度的影响因素.结果显示:①陕西省PM_2.5浓度整体呈"冬高夏低、春秋居中"的季节性变化规律,"U型"起伏的月变化规律,周期性脉冲波动型的日变化规律以及"W型"起伏的时变化规律;②陕西省PM_2.5浓度呈"北部低,中南部高"的空间分布特征,并且空间集聚性显著.不同季节的高值区均集聚于海拔相对较低的关中盆地内部城市.这与盆地内部空气不易扩散,静稳天气出现频率较高,易出现逆温现象密切相关;③影响陕西省PM_2.5浓度最大的指标层是PM_2.5污染来源（权重值为0.49）,其次是城市化与土地利用（权重值为0.37）,气象与地形因子影响最小（权重值为0.15）.不同城市各指标层的综合关联度差异较大.④各指标因子与PM_2.5浓度均为强度关联.降水量、机动车保有量、二氧化硫排放量、烟粉（尘）排放量、建成区面积、人口密度和人均GDP是影响陕西省PM_2.5浓度的主要因子,影响各城市PM_2.5浓度的主要因子具有一定的空间差异性.研究显示,人类活动对陕西省PM_2.5的影响显著,尤其是城市化的快速推进,相关指标（如人口、机动车、能耗、工业总产值等）持续增长,将进一步加大PM_2.5来源的多样性以及相关污染物的排放量.