工业园区低碳发展模式评估与规划分析

通过对工业园区低碳发展模式的评估,分析其低碳发展的能源特征、经济特征与人力特征,并确定工业园区二氧化碳减排的潜力能源。基于能源-碳减排(EC)、经济-能源-碳减排(EEC)和人力-经济-能源-碳减排(HEEC)三种不同情景,在未来发展的一定规划期内,对工业园区具有二氧化碳减排潜力的能源进行规划分析,确定其年递减率,从而实现工业园区不同情景下的二氧化碳减排。通过实际案例研究,证明了评估与规划分析模型的可行性。     

基于贝叶斯网络气化炉供料系统风险分析

为解决当前气化炉供料系统风险分析不完善的状况,提出1种基于贝叶斯网络和HAZOP的风险分析模型。以某单日投煤量3 000 t级气化炉煤化工企业实际运行情况为研究对象,应用HAZOP法对其进行风险分析,并将HAZOP分析结果中各偏差产生原因转化为贝叶斯网络节点;考虑到先验知识的缺乏问题,引入Leaky Noisy OR模型,通过文献资料和相关领域专家经验知识获得先验概率,并利用贝叶斯网络进行风险分析,找出系统运行的薄弱环节。结果表明:未知因素影响会使各节点的后验概率值差异性降低,更加贴合实际;在引入未知因素影响后,系统运行薄弱环节并未发生改变。     

秦皇岛市工业领域“十二五”节能潜力分析

结合总体节能目标,对秦皇岛市“十二五”期间的能源需求进行了预测,并对三大产业能源利用形式及能源利用比例进行了分析。结果表明,第二产业的能源消耗比重最大,将成为节能总量控制的重点,而重中之重则是其中的工业领域节能。测算并分析了工业领域的节能潜力,范围涵盖玻璃制造、装备制造、粮油食品制造、金属压延业、建材、电力、化工7个主要行业。     

新能源汽车在我国发展的可行性分析

本文从我国石油能源短缺和汽车尾气污染环境加剧的背景出发，详细分析了目前主要的各种新能源汽车的特点及在我国发展的前景，最后得出乙醇汽车应是我国发展新能源汽车的最佳选择的结论。     

REGIONAL FLOOD EQUATIONS FOR THE PROVINCES OF ONTARIO AND QUEBEC1

ABSTRACT: When nonparametric frequency analysis was performed on 183 stations from Ontario and Quebec, unimodal and multimodal maximum annual flood density functions were discovered. In order to determine generating mechanisms, a monthly partitioning of the annual maximum floods was undertaken. The timing of the floods revealed that the unimodal distributions reflected a single flood generating mechanism while the multi-modal densities reflected two or more mechanisms. Based on the division of the flood series by mechanisms, nine homogeneous regions were delineated. L-moment distributional homogeneity tests along with smaller standard errors for the regional equations supported the delineation.     

发展中国西部的生态旅游业

生态旅游作为“保护生态”和“发展经济”双赢理念的新兴旅游类型,被西部各省(区、市)所关注并将其定为本地区发展的主导产业.通过对西部地区的生态旅游资源优缺点、市场定位、产品转化及空间布局的分析,初步勾画了西部生态旅游业的发展前景,并提出相应地发展策略.     

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.