良好的健康和人类福祉是联合国提出的可持续发展目标之一,提高人口预期寿命是迈向此目标的重要一步.由于中国城市在自然环境和社会发展方面有所差异,因此理解不同城市居民的预期寿命主要受何种因素的影响是制定城市公共卫生策略的关键.本研究基于2015年中国286个城市的有效数据,利用探索性回归、普通最小二乘回归、地理加权回归筛选与预期寿命最相关的影响因素并探索其空间差异,再通过二阶聚类将城市分类,以针对性地提出每类城市政策建议.结果 显示:①经济发展,教育条件和医疗设施条件对预期寿命有显著的积极影响,平均海拔和环境污染则具有负面影响;②东南地区的经济发展对当地居民的预期寿命影响程度更大;东北和西南地区的医疗设施条件对其居民预期寿命促进程度更高;北部地区的教育条件对当地居民预期寿命影响比其他地区更高;平均海拔对西部地区居民预期寿命的影响最大;西北地区居民的预期寿命则更易受到环境污染带来的负面影响;③根据空间差异将城市分为3类,其居民预期寿命关键影响因素依次是经济发展和环境污染、教育条件、医疗设施,每类城市的城市管理者应重点关注不同因素来提升居民的预期寿命.

生态空间现状数量配比与模拟是新时期地理信息服务于国土空间优化的重要应用.以常州市武进区为研究区,利用InVEST模型与多类随机种子CA模型(CARS),对2015年研究区生态空间进行分区并模拟了2025年的生态空间分布演变情况.结果表明:2015年生态评价极重要区主要分布于滆湖(西太湖)湿地公园及其周边地区、雪堰镇竺山周边生态开敞空间和横林镇、武进高新区的一些留白用地区域;2025年的预测生态用地分布多围绕已有生态斑块呈现边缘式增长;综合现状评价与未来模拟结果得出,生态冲突区面积约为5992hm2,占区域总面积的5.62％.研究结果可为快速城镇化地区缓解生态保护与城市发展矛盾提供参考,为区域生态环境优化布局决策提供科学依据.

Global warming and climate change have become a serious environmental problem and China's carbon emissions are currently the highest in the world. Cities are the main sources of carbon emissions and the key to solving these problems. Therefore, research on reducing carbon dioxide emissions is a matter of concern. In this study, a spatial autocorrelation analysis was performed to understand the spatial characteristics of carbon dioxide emissions in 171 Chinese cities. Then, stepwise and geographically weighted regressions were used to explore the processes that drive carbon dioxide emissions in Chinese cities. A two-step cluster was used to classify Chinese cities into different categories based on the degree of impact of each driver. The results showed that there is a spatial aggregation relationship between urban carbon dioxide emissions. High-high clusters mainly occur in the Beijing-Tianjin-Hebei and Yangtze River Delta urban agglomerations, while low-low clusters occur in the central, western, and southwestern cities. Among all variables, freight volume, per capita gross domestic product, population density, and the proportion of secondary industries correlate positively with carbon dioxide emissions, whereas the number of buses per 10,000 people correlates negatively with carbon dioxide emissions. The geographically weighted regression model provided more detailed results and revealed the spatial heterogeneity of the effects of the different drivers. The impact of population, economic factors, and industrial factors in the eastern region is significantly greater than that in the central and western regions. Freight volume and public transport have the most significant impact in the northeast region. The clustering results showed that cities can be divided into four types. These findings provide a reference and policy suggestions for how cities in different regions should reduce carbon dioxide emissions.