实施OSHMS对企业职业安全健康管理状况影响分析

为了研究实施职业安全健康管理体系(OSHMS)对企业职业安全健康管理状况的影响,对百余家已实施与未实施OSHMS的企业进行了调研,在对调研结果分析研究的基础上,采用双对照分析的方法,对已通过OSHMS认证的企业在建立OSHMS前后的职业安全健康管理状况进行对照比较,以及已建立OSHMS与未建立OSHMS的企业的职业安全健康管理状况进行对照比较,研究结果表明,实施OSHMS有利于提高企业职业安全健康管理的整体水平,改善企业的职业安全健康管理状况,使职业安全健康管理更加科学化和规范化。     

云天化3#循环流化床锅炉项目技术经济分析

云天化股份公司在2004年建造了3#循环流化床锅炉，以煤代气。它可以缓解公司原料天然气的紧张状况，并能带来很大的经济效益。从技术经济的角度对该项目的技术先进性、可靠性及内部收益率、投资回收期、净现值等技术指标进行了分析。     

县级地质灾害防治管理评价指标体系探讨

通过建立评价指标体系,尽可能定量地评价地质灾害防治管理工作,这是地质灾害防治管理工作科学化、规范化的有益补充.在分析建立县级地质灾害防治工作评价指标体系必要性、可行性的基础上,应用工作分析法和经验总结法征求了指标,运用AHP方法建立了评价指标体系结构,采用专家咨询法确定了权重,提出了一套由16个指标(含40项分指标)构成的较为完整的县级地质灾害防治管理工作评价体系.最后,应用所建立的评价指标体系对部分县进行了评价,验证了评价指标的准确性.     

中国水灾社会脆弱性评估方法的改进与应用--以长沙地区为例

中国是洪水灾害发生频繁的国家之一,研究水灾脆弱性对中国的灾害风险管理有重要的意义.但是,合理评估脆弱性尤其是社会脆弱性却面临着极大的挑战.论文对评估社会脆弱性指数的研究现状进行了分析,指出了传统评估方法存在的问题,并尝试改进Hoovering评估模式.选择湘江流域的长沙地区为研究区,应用改进模型对研究区进行了社会脆弱性指数的评估.结果表明,长沙地区社会脆弱性从1980年至2000年基本处于下降趋势,但是在2002年和2003年增长迅速.2003年,长沙5区4县中社会脆弱性指数最大的是开福区,其次是长沙县,而宁乡县的社会脆弱性指数最低.     

作物秸秆资源的利用及其影响因素--以山西省夏县为例

作物秸秆资源的利用对提高食物安全,减轻环境污染,促进农业可持续发展具有重要意义.通过对农户的调查,分析了山西省夏县的秸秆利用途径,对该县秸秆利用现状进行了评价,并分析了造成这种现状的原因.结果表明,夏县作为典型的"小麦-玉米"二熟农作区,秸秆利用效率较高,其中小麦秸秆主要用于工业原料、饲料和还田,玉米秸秆则主要用于饲料和弃置堆放.这种利用方式与造纸业发展、传统的牲畜饲喂方式、农业机械化发展和气候特点有关.最后提出了进一步高效利用秸秆资源的建议.     

惠州市陈江镇土地定级暨基准地价评估与成果分析

2002年,陈江镇划归惠州市城区管辖,行政区划的改变给该镇的土地价格带来了明显变化.介绍了陈江镇土地定级与估价过程中资料的收集、评估参数的确定、估价技术方法的选择和土地定级的估价.在对比前后工业、商业、住宅用地的价格后,重点从级别范围的变动、地价体系的变化和地价水平三个方面详细分析了陈江镇的基准地价在行政区域变化前后土地价格变化的原因,并对基准地价与原惠州城区地价体系的衔接作了具体分析.     

企业可持续发展评价指标体系的选定原则及构建框架

为了制定企业可持续发展的战略决策,须制定可持续发展的指标体系,并依据这一体系对数据和资料进行收集、整理和分析,从而确定可靠而有效的政策方针。文中论述了可持续发展指标体系的定义、选定标准、原则和可持续发展的评价指标体系,提出了压力-状态-响应框架模型以及不同层次的指标体系框架。该指标体系分为总体层、系统层、状态层、变量层和要素层五个等级。对于持续发展的表征指标及对目前发展的可持续性评估,还需要长时间和切合实际的探讨。     

Impact of Human Activities on Carbon Dioxide (CO<Subscript>2</Subscript>) Emissions: A Statistical Analysis

Summary The balance of evidence suggests a perceptible human influence on global ecosystems. Human activities are affecting the global ecosystem, some directly and some indirectly. If researchers could clarify the extent to which specific human activities affect global ecosystems, they would be in a much better position to suggest strategies for mitigating against the worst disturbances. Sophisticated statistical analysis can help in interpreting the influence of specific human activities on global ecosystems more carefully. This study aims at identifying significant or influential human activities (i.e. factors) on CO₂ emissions using statistical analyses. The study was conducted for two cases: (i) developed countries and (ii) developing countries. In developed countries, this study identified three influential human activities for CO₂ emissions: (i) combustion of fossil fuels, (ii) population pressure on natural and terrestrial ecosystems, and (iii) land use change. In developing countries, the significant human activities causing an upsurge of CO₂ emissions are: (i) combustion of fossil fuels, (ii) terrestrial ecosystem strength and (iii) land use change. Among these factors, combustion of fossil fuels is the most influential human activity for CO₂ emissions both in developed and developing countries. Regression analysis based on the factor scores indicated that combustion of fossil fuels has significant positive influence on CO₂ emissions in both developed and developing countries. Terrestrial ecosystem strength has a significant negative influence on CO₂ emissions. Land use change and CO₂ emissions are positively related, although regression analysis showed that the influence of land use change on CO₂ emissions was still insignificant. It is anticipated, from the findings of this study, that CO₂ emissions can be reduced by reducing fossil-fuel consumption and switching to alternative energy sources, preserving exiting forests, planting trees on abandoned and degraded forest lands, or by planting trees by social/agroforestry on agricultural lands.     

Quantifying and Evaluating Ecosystem Health: A Case Study from Moreton Bay, Australia

As part of the program monitoring the ecosystem health of Moreton Bay, Queensland, Australia, we developed a means for assessing ecosystem health that allows quantitative evaluation and spatial representations of the assessments. The management objectives for achieving ecosystem health were grouped into ecosystem objectives, water quality objectives, and human health objectives. For the first two groups, aspects of the ecosystem (e.g., trophic status) were identified, and an indicator was chosen for each aspect. Reference values for each indicator were derived from management objectives and compared with the mapped survey values. Subregions for which the indicator statistic was equal to or better than the assigned reference value are referred to as “compliant zones.” High-resolution surface maps were created from spatial predictions on a fine hexagonal grid for each of the indicators. Eight reporting subregions were established based on the depth and predicted residence times of the water. Within each reporting subregion, the proportion that was compliant was calculated. These results then were averaged to create an integrated ecosystem health index. The ratings by a team of ecosystem experts and the calculated ecosystem health indices had good correspondence, providing assurance that the approach was internally consistent, and that the management objectives covered the relevant biologic issues for the region. This method of calculating and mapping ecosystem health, relating it directly to management objectives, may have widespread applicability for ecosystem assessment.