天津市经济增长与环境污染水平关系

摘要：以经济增长与环境污染水平计量模型——环境库兹涅茨曲线为理论基础,分析天津市经济增长与环境污染之间的关系,其环境库兹涅茨曲线与传统的环境库兹涅茨曲线形态不同,出现波动性特征,呈“U型+倒U型”。其中第一阶段为1992年前,工业结构以轻工业为主,随着工业废水治理力度的提高,污染恶化趋势有所遏制,1992-1993年为“U”型环境库兹涅茨曲线的低谷;第二阶段为1992年-1998年,工业结构以重工业为主,固体废弃物与气体污染程度逐渐增加,环境污染水平有所提高,1997-1998年出现“倒U”型曲线的高峰;第三阶段为1998年后,随着综合治理措施的不断加强,城市环境质量总体处于好转态势,曲线开始出现下降趋势。分析表明,引起上述变化的原因与产业结构的变化、环境政策取向、环境保护投资力度等因素有明显关系。     

基于复杂性科学的绿洲城镇化演进理论探讨

基于复杂性科学,引入涌现、协同、“吸引盆”等概念和模型,从涌现生成、协同维生、临界相变三个维度构建绿洲城镇化演进的理论框架。对绿洲城镇化的生成、演化和转型的过程与机理进行系统剖析,并据此对诺瑟姆曲线进行新的解读。研究表明：① 绿洲城镇是以人为核心的绿洲地域系统在多时空维度上的结构和功能的整体涌现现象,涌现形成的条件包括生成主体、自组织与受限生成机制、环境策略;② 竞争与协同机制维持了绿洲城镇化系统的生命活力与进化稳定,是系统演化的根本动力,系统不同层级具有不同的协同维生机制,根据协同学和耗散结构理论构建的绿洲城镇化演进的协同度和有序度可以识别系统演化方向;③ 绿洲城镇化过程实质上也是系统的自组织临界相变,该过程是一个渐变与突变、有序与混沌、稳态与非稳态的辩证统一;④ 时空间的正负反馈机制使绿洲城镇化复杂性与有序性不断提高,且不断向自组织临界态靠近,城镇化系统外在表现为活力与多样性增强。     

Multi-hazard susceptibility mapping based on Convolutional Neural Networks

Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations based on Google Earth images, extensive field surveys, topography, hydrology, and environmental data sets to train and validate the proposed CNN method. Next, the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria, i.e., coefficient of determination, overall accuracy, mean absolute error and the root mean square error. Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods, debris flows and landslides. Finally, the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map. It can be observed from the map that 62.43% of the study area are prone to hazards, while 37.57% of the study area are harmless. In hazard-prone areas, 16.14%, 4.94% and 30.66% of the study area are susceptible to flash floods, debris flows and landslides, respectively. In terms of concurrent hazards, 0.28%, 7.11% and 3.13% of the study area are susceptible to the joint occurrence of flash floods and debris flow, debris flow and landslides, and flash floods and landslides, respectively, whereas, 0.18% of the study area is subject to all the three hazards. The results of this study can benefit engineers, disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.