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北京住宅建设的环境影响:1949~2003年——从生命周期角度评价建筑材料的环境影响 总被引:3,自引:0,他引:3
针对北京建国后尤其是最近20多年中住宅快速发展的状况,本文将住宅的环境影响考察从直接、本地的环境影响扩展到建筑材料的生产阶段,从生命周期角度阐述了北京住宅快速发展给整个生态环境所带来的影响,以及这种影响的大小、类型和来源等。通过分析表明,环境影响的增长速度要高于住宅建筑面积的增长,这主要是由于住宅不同建筑结构所占比例不同造成的。通过对六种建筑材料的分析,得到钢材的环境影响远远大于其它几种建筑材料。 相似文献
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基于绿色铸造设计的实施与评价 总被引:1,自引:0,他引:1
建立了绿色铸造的实施体系,对铸造过程中材料选择关键技术提出了相关原则,详细的分析了绿色铸造LCA过程评价指标和评价方案. 相似文献
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针对北京建国后尤其是最近20多年中住宅快速发展的状况,本文将住宅的环境影响考察从直接、本地的环境影响扩展到建筑材料的生产阶段,从生命周期角度阐述了北京住宅快速发展给整个生态环境所带来的影响,以及这种影响的大小、类型和来源等。通过分析表明,环境影响的增长速度要高于住宅建筑面积的增长,这主要是由于住宅不同建筑结构所占比例不同造成的。通过对六种建筑材料的分析,得到钢材的环境影响远远大于其它几种建筑材料。 相似文献
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A person-oriented approach to understanding dimensions of parenting in low-income mothers 总被引:1,自引:0,他引:1
The purpose of this study was to examine parenting types in a low-income sample from a person-oriented approach. Data were used from a public use data set from the Early Head Start Research and Evaluation Project (EHSREP) along with new extant ratings of parenting behavior from the EHSREP archive of videotaped parent–child observations. Parenting behavior indicators were examined using latent class analysis as a grouping strategy across three time points to characterize this sample of 2631 Early Head Start mothers. Three latent classes of mothers were identified at 14, 24, and 36 months: developmentally supportive (the largest group in this sample), unsupportive, and negative. Predictors of parenting types were also examined and parenting types were linked to child outcomes. The results of these analyses show common characteristics of these distinct types of parents likely to be in Early Head Start programs and may help programs identify which families would most benefit from services to help them increase behaviors to promote their young children's early development. 相似文献
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In this paper, the Life Cycle of Urban Development was firstly analyzed, and the phases of Life Cycle Assessment applied to Urban Development (ULCA) were described. As a case study, ULCA was applied in the environmental impact assessment of the land readjustment project of Hyogo District of Saga, Japan. In addition, mitigation proposals for reducing CO2 were also presented and the relevant environmental effects were simulated. 相似文献
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The aims of the present study were to identify discrete classes of adolescents based on their reporting of emotional abuse, physical abuse, sexual abuse, emotional neglect, and physical neglect of several levels of severity using a person-centered analytic approach (i.e., latent class analysis), and to compare the latent classes on 17 dimensions of personality pathology. It was hypothesized that based on types of maltreatment and severity levels within each type there would be discrete latent classes, and that classes of adolescents exposed to a larger number of maltreatment types with higher severity (i.e., moderate–severe) would report higher levels of personality pathology than adolescents in classes exposed to less types with less severity, after controlling for age and gender. Participants were 702 adolescents from Jammu, India (13–17 years, 41.5% females). The latent classes were based on three levels of severity for each type of maltreatment assessed via the Childhood Trauma Questionnaire (Bernstein et al., 2003). Four distinct classes of adolescents, namely, Moderate–severe abuse and physical neglect (Class 1), Low to moderate–severe abuse (Class 2), Moderate–severe neglect (Class 3), and Minimal abuse or neglect (Class 4) were found. Classes with higher percentages of adolescents reporting abuse and neglect with higher severity (Classes 1 and 2) reported higher levels of personality pathology than the other classes. There are distinct classes of adolescents’ identifiable based on levels of severity and types of abuse and neglect, which are differentially associated with specific dimensions of personality pathology. Implications for research and practice are discussed. 相似文献
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For some time, there have been differing recommendations about how and when to include covariates in the mixture model building process. Some have advocated the inclusion of covariates after enumeration, whereas others recommend including them early on in the modeling process. These conflicting recommendations have led to inconsistent practices and unease in trusting modeling results. In an attempt to resolve this discord, we conducted a Monte Carlo simulation to examine the impact of covariate exclusion and misspecification of covariate effects on the enumeration process. We considered population and analysis models with both direct and indirect paths from the covariates to the latent class indicators. As expected, misspecified covariate effects most commonly led to the overextraction of classes. Findings suggest that the number of classes could be reliably determined using the unconditional latent class model, thus our recommendation is that class enumeration be done prior to the inclusion of covariates. 相似文献
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