开展旅游景区非使用价值评估是景区合理开发利用的前提.运用条件价值法(CVM),基于实地问卷调查数据,运用SPSS 21.0软件,以山东省济南市趵突泉景区为例,开展旅游资源非使用价值评估,并对游客的社会经济特征与支付意愿进行Pearson相关分析.结果表明,2018年趵突泉景区的非使用价值为5.350×107元,其中,存在价值为3.09×107元、遗产价值为1.469×107元、选择价值为7.91×106元.存在价值最大,表明人们对趵突泉景区资源环境保护的关注度、责任感及成效的认可度在不断提升;趵突泉景区游客的支付意愿受年龄、收入和文化程度的影响显著.提出科学评估旅游景区资源非使用价值的相关建议.

China became the country with the largest global carbon emissions in 2007. Cities are regional population and economic centers and are the main sources of carbon emissions. However, factors influencing carbon emissions from cities can vary with geographic location and the development history of the cities, rendering it difficult to explicitly quantify the influence of individual factors on carbon emissions. In this study, random forest (RF) machine learning algorithms were applied to analyze the relationships between factors and carbon emissions in cities using real-world data from Chinese cities. Seventy-three cities in three urban agglomerations within the Yangtze River Economic Belt were evaluated with respect to urban carbon emissions using data from regional energy balance tables for the years 2000, 2007, 2012, and 2017. The RF algorithm was then used to select 16 prototypical cities based on 10 influencing factors that affect urban carbon emissions while considering five primary factors: population, industry, technology levels, consumption, and openness to the outside world. Subsequently, 18 consecutive years of data from 2000 to 2017 were used to construct RFs to investigate the temporal predictability of carbon emission variation in the 16 cities based on regional differences. Results indicated that the RF approach is a practical tool to study the connection between various influencing factors and carbon emissions in the Yangtze River Economic Belt from different perspectives. Furthermore, regional differences among the primary carbon emission influencing factors for each city were clearly observed and were related to urban population characteristics, urbanization level, industrial structures, and degree of openness to the outside world. These factors variably affected different cities, but the results indicate that regional emission reductions have achieved positive results, with overall simulation trends shifting from underestimation to overestimation of emissions.

Regional resilience refers to the resilience of a country or region against the ecological environment, social economy, and other internal and external natural factors and human factors in the process of development. When this resilience is lower than a certain critical threshold, the country or region will be in a fragile state. The comprehensive embodiment of ecological resilience, social resilience, and economic resilience of a country or region is regional resilience. Due to the wide range of countries along “the Belt and Road”, differences in natural background conditions and stages of economic and social development among different countries lead to different degrees of vulnerability, and the improvement of resilience is conducive to reducing vulnerability. At the same time, the research on the measurement and differentiation characteristics of regional resilience is of considerable significance to solve the weak foundation of environmental management and the lack of ability to deal with climate change of “the Belt and Road” countries. In this study, by using entropy weighting method and multi-index comprehensive evaluation method, 24 specific indicators are selected from three different dimensions: ecology, economy, and society, to construct a comprehensive evaluation index system about “the Belt and Road” countries resilience, and to evaluate the comprehensive resilience and spatial heterogeneity characteristics of China and 64 countries along “the Belt and Road”, and use multiple linear regression analysis to identify the main influencing factors of comprehensive resilience and analyze its influencing mechanism. According to the research, the overall resilience level of “the Belt and Road” countries shows prominent differentiation characteristics of “extreme difference”, the countries with low and low recovery status account for the vast majority; and the spatial differentiation characteristics of the levels of ecological resilience, economic resilience, and social resilience of countries along “the Belt and Road” are quite different. In countries with high levels of economic development, their comprehensive resilience is significantly higher than that of countries with low levels of economic development. There is no inevitable connection between a country’s economic growth rate and its comprehensive resilience level. At the same time, the relationship between resource richness and comprehensive resilience of countries is not apparent, but for those countries that are over-dependent on resources, the level of resilience is generally below. There is a certain degree of correspondence between urbanization rate and comprehensive resilience, that is, the comprehensive resilience will increase with the increase of urbanization rate. When the urbanization rate rises to a certain level, the level of comprehensive resilience does not change much. In this study, it provides scientific guidance for enriching regional resilience and national sustainable development theory, solving the fragile ecological environment foundation of “the Belt and Road” countries, speeding up the transformation of economic growth mode and dealing with a series of social problems.

充分利用大数据开展城市用地功能识别,有助于把握城市空间结构,推动城市内部空间合理布局.POI数据是大数据时代一种较易获得且极具代表性的空间点状数据,能够有效地确定城市用地的实际功能.以济南市内五区的185126条POI数据为基础,对所得数据进行去重、纠偏、重分类,构建城市用地功能分类体系,运用频数密度、类型比例及核密度估计,识别济南市内五区城市用地功能并利用误差矩阵对识别结果进行检验.结果 表明:①混合功能用地与单一功能用地呈现圈层化地域分布特征,“核心-外围”分异明显;②由内向外单一功能用地集聚趋势减弱,混合功能用地多样性降低,不同用地表现出不同的空间分布模式;③通过误差矩阵及与用地规划图中规划用地及电子地图的实际用地对比,识别总体精度为75.67％,识别结果较为准确.

以黄河流域673个国家级传统村落为研究对象,借助ArcGIS等工具,运用最邻近指数、不平衡指数、核密度估计值、地理探测器等方法探讨黄河流域传统村落的空间分布特征及其影响因素.结果 表明:1)黄河流域传统村落空间分布为凝聚型,省际分布不均衡,总体呈现“大集聚、小分散”的分布格局,主要形成以青海省、山西省为主的两大高密度区;2)自然地理条件是影响黄河流域传统村落空间分布的首位因素,其次是社会经济因素.其中,河网密度、年降水量、公路密度、人均GDP是影响黄河流域传统村落空间分布的主要因素.

通过建立耕地资源利用的投入产出指标体系,选取2005—2017年相关数据,运用数据包络分析方法,基于CCR与BCC模型计算山东省耕地利用效率;基于自然断裂点分级法对山东省17地市的耕地利用效率进行空间分析.结果表明:2005—2017年山东省耕地资源利用效率较高,农业生产技术要素投入较大;综合效率值与纯技术效率值走向基本一致,纯技术效率值始终高于规模效率值,且纯技术效率值贡献较大;山东省耕地资源利用效率空间差异较为显著.