安全风险分级防控在油气开发领域的探索与应用

为推动油气开发领域安全风险分级防控有效落地,提高风险预控能力,本文基于风险防控现状评估结果,探索性地研究安全风险分级防控思路,构建了油气开发领域安全风险分级防控体系推进模型,提出了逆向与正向危害因素识别方法,明确了风险评价关键隐性过程原则,构建了风险-后果-能资分级防控决断模型。选取典型试点单位进行实践,结果证明,油气开发领域安全风险分级防控体系推进模型是科学合理的,其他领域也可借鉴该模型开展安全风险分级防控工作。     

锂电池检测的安全标准及安全防护研究

为评估并提高锂电池在使用、储存和运输过程中的安全,本文归纳总结了国内外锂电池安全检测的相关标准,对标准中测试项目异同作了重点分析和对比,并讨论了锂电池的安全风险及风险分级情况。同时,鉴于锂电池检测过程中可能出现的起火、爆炸等危险,探讨了电池检测过程的安全防护技术,并从样品区和试验区两方面提出了安全防护要求和建议。     

基于安全度与运营格局的油库选址优先级评价方法研究

从安全性和运营角度对油库选址的影响因素及评价指标分析对油库的安全稳定及有效运营具有重要的意义。该文选取与安全和运营有关的人口企业数量、安全管理能力、消防设施规划、地形地质条件、铁路公路数量、市场投资收益、政策支持情况等影响因子进行重要性及合理程度分析,综合运用安全度与运营格局影响因素多元耦合关系,构建了油库区安全与运营评价体系,改进了原有的模糊层次法评价模型,并对某油库区安全度与运营格局进行了评估验证。结果表明:利用改进模糊层次法能够较好地评价不同地址的优先级,评价模型具有一定应用性,能够对不同地址的特点及评价权重进行有效分析,可见油库选址优先级评价方法是实用且有效的。该研究方法及结果可以为油库区地址的优选提供一定参考与建议。     

建筑安全风险因素分级排序的风险矩阵法

为减少风险分级排序时产生的风险结,在引入施工安全事故频率的基础上,采用单元格细化和Borda序值法改进原有的风险矩阵法。改进后的方法使得风险等级数量增加,风险结数量减少。将该方法应用于脚手架工程的风险因素的分级与排序,风险等级分为6级,且实现了12个因素的明确排序。     

双重预防机制在家具制造业的应用与实践

为了减少安全事故的发生,进一步加强和规范企业生产现场的安全管理,探索双重预防机制在企业安全管理中的应用,结合家具制造企业安全生产现状,运用风险分级管控与隐患排查的相关理论,详细论述了企业构建双重预防体系的要点及方法,并提出了双重预防体系的监督与考核机制。实践结果证明,双重预防机制是一个统一的有机结合体,只有将风险分级管控体系和隐患排查治理体系有机结合,才能将导致事故发生的根源性因素进行有效识别和管控,双重预防机制建设也是一个动态管理的过程,需要及时进行监督、考核和调整。     

自愿减排项目碳泄漏：内涵、类别及应对

自愿减排项目是碳排放交易市场抵消机制的运行载体,灵活的自愿减排抵消机制是碳排放交易市场的补充,对应对全球气候变化问题具有重要意义。碳泄漏,作为严重影响抵消项目减排效率的热点问题值得关注。目前,中国已形成较大规模的自愿减排市场,截至2018年底,国家发展改革委共签发了约7200万tCO_2当量减排量,我国在建立健全自愿减排抵消市场机制的过程中,需要把握项目碳泄漏的负外部性这一实质,厘清其运行机理并积极应对。本文旨在系统阐述该问题,并为进一步实现抵消项目减排潜力提供思路和方向:从自愿减排项目碳泄漏的内涵与定义出发,首先对项目碳泄漏的分类进行梳理,并依据产生途径的不同重点分析了活动转移排放、生命周期、市场路径、生态以及技术泄漏的作用机制、影响因素及其相应的研究方法;其次从具体项目应用、系统项目应用和宏观应用三个层面讨论了应对自愿减排项目碳泄漏问题的管理对策;最后结合我国自愿减排抵消市场的实际情况,建议未来抵消项目碳泄漏的应对和管理应在分门别类对其概念进行精准定义的基础上将解决措施的成本效益考虑在内,此外,在评估泄漏量时需要同时测算不同类型的碳泄漏,探究其协同效应以免高估其泄漏程度。     

生活垃圾强制分类形势下的多源废荧光灯管回收管理机制建议

废荧光灯管的来源广泛,包括工业来源和社会来源,社会来源又包括居民来源,企业事业单位、公共场所等机构来源,具有多来源的特性。废荧光灯管中含有汞,若不经过妥善处理,会造成环境污染,对人的身体健康造成危害。随着国内生活垃圾分类的开展,废荧光灯管作为生活垃圾中有害垃圾的典型类别被强制分类,但仍存在很多问题。本文梳理了我国废荧光灯管的产生情况、LED灯替代情况,并对美国、欧盟、日本和我国国内典型城市的废荧光管理情况进行了总结。针对我国废荧光灯管的回收现状,从责任主体、责任划分、收集渠道、资金机制等角度提出了相关的管理建议,为推动我国废荧光灯管的管理提供借鉴。     

Indigenous Systems of Forest Classification: Understanding Land Use Patterns and the Role of NTFPs in Shifting Cultivators’ Subsistence Economies

This article discusses the system of classification of forest types used by the Pwo Karen in Thung Yai Naresuan Wildlife Sanctuary in western Thailand and the role of nontimber forest products (NTFPs), focusing on wild food plants, in Karen livelihoods. The article argues that the Pwo Karen have two methods of forest classification, closely related to their swidden farming practices. The first is used for forest land that has been, or can be, swiddened, and classifies forest types according to growth conditions. The second system is used for land that is not suitable for cultivation and looks at soil properties and slope. The article estimates the relative importance of each forest type in what concerns the collection of wild food plants. A total of 134 wild food plant species were recorded in December 2004. They account for some 80–90% of the amount of edible plants consumed by the Pwo Karen, and have a base value of Baht 11,505 per year, comparable to the cash incomes of many households. The article argues that the Pwo Karen reliance on NTFPs has influenced their land-use and forest management practices. However, by restricting the length of the fallow period, the Thai government has caused ecological changes that are challenging the ability of the Karen to remain subsistence oriented. By ignoring shifting cultivators’ dependence on such products, the involvement of governments in forest management, especially through restrictions imposed on swidden farming practices, is likely to have a considerable impact on the livelihood strategies of these communities.     

Synergistic Techniques for Better Understanding and Classifying the Environmental Structure of Landscapes

The desire to capture natural regions in the landscape has been a goal of geographic and environmental classification and ecological land classification (ELC) for decades. Since the increased adoption of data-centric, multivariate, computational methods, the search for natural regions has become the search for the best classification that optimally trades off classification complexity for class homogeneity. In this study, three techniques are investigated for their ability to find the best classification of the physical environments of the Mt. Lofty Ranges in South Australia: AutoClass-C (a Bayesian classifier), a Kohonen Self-Organising Map neural network, and a k-means classifier with homogeneity analysis. AutoClass-C is specifically designed to find the classification that optimally trades off classification complexity for class homogeneity. However, AutoClass analysis was not found to be assumption-free because it was very sensitive to the user-specified level of relative error of input data. The AutoClass results suggest that there may be no way of finding the best classification without making critical assumptions as to the level of class heterogeneity acceptable in the classification when using continuous environmental data. Therefore, rather than relying on adjusting abstract parameters to arrive at a classification of suitable complexity, it is better to quantify and visualize the data structure and the relationship between classification complexity and class homogeneity. Individually and when integrated, the Self-Organizing Map and k-means classification with homogeneity analysis techniques also used in this study facilitate this and provide information upon which the decision of the scale of classification can be made. It is argued that instead of searching for the elusive classification of natural regions in the landscape, it is much better to understand and visualize the environmental structure of the landscape and to use this knowledge to select the best ELC at the required scale of analysis.     

Assessing Impacts of Typhoons and the Chi-Chi Earthquake on Chenyulan Watershed Landscape Pattern in Central Taiwan Using Landscape Metrics

The Chi-Chi earthquake (M_L = 7.3) occurred in the central part of Taiwan on September 21, 1999. After the earthquake, typhoons Xangsane and Toraji produced heavy rainfall that fell across the eastern and central parts of Taiwan on November 2000 and July 2001. This study uses remote sensing data, landscape metrics, multivariate statistical analysis, and spatial autocorrelation to assess how earthquake and typhoons affect landscape patterns. It addresses variations of the Chenyulan watershed in Nantou County, near the earthquake’s epicenter and crossed by Typhoon Toraji. The subsequent disturbances have gradually changed landscape of the Chenyulan watershed. Disturbances of various types, sizes, and intensities, following various tracks, have various effects on the landscape patterns and variations of the Chenyulan watershed. The landscape metrics that are obtained by multivariate statistical analyses showed that the disturbances produced variously fragmented patches, interspersed with other patches and isolated from patches of the same type across the entire Chenyulan watershed. The disturbances also affected the isolation, size, and shape-complexity of patches at the landscape and class levels. The disturbances at the class level more strongly affected spatial variations in the landscape as well as patterns of grasslands and bare land, than variations in the watershed farmland and forest. Moreover, the earthquake with high magnitude was a starter to create these landscape variations in space in the Chenyulan watershed. The cumulative impacts of the disturbances on the watershed landscape pattern had existed, especially landslides and grassland in the study area, but were not always evident in space and time in landscape and other class levels.