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From social impact subcategories to human health: an application of multivariate analysis on S-LCA
The International Journal of Life Cycle Assessment ( IF 4.8 ) Pub Date : 2021-06-16 , DOI: 10.1007/s11367-021-01935-9
Jaylton Bonacina de Araujo , José Roberto Frega , Cássia Maria Lie Ugaya

Purpose

This research aims to propose the SMiLe method (Social Metric for Life Cycle), developed to identify potential impact pathways from correlations between the subcategories and their effects on the human health endpoint. We used multivariate data analysis in the S-LCA context to obtain the impact pathways and obtain the characterization model.

Method

The proposed method was developed in three stages: (I) area of protection (AoP) definition; (II) estimation technique selection; (III) development of the cause-effect chain and characterization model. For stage I, AoP was well-being and the endpoint category, human health, indicated by the life expectancy at birth (LEX). As estimation techniques, the exploratory factor analysis (EFA), covariance-based SEM (CB-SEM), and partial least squares SEM (PLS-SEM) were respectively used in the exploratory step (analysis of the relationships between indicators representing the subcategories), confirmatory stage (validation of impact pathways), and predictive step (obtaining the characterization model). The three estimation techniques used socioeconomic indicators from 189 countries to represent the subcategories.

Results and discussion

As a result of the data collection, it was possible to develop a database of 21 indicators, representing 15 subcategories related to four stakeholders, including data from various international sources, such as the World Bank and the International Labour Organization. The EFA and CB-SEM results showed that it was possible to identify and confirm that the subcategories used in this study were organized in two factors (social dimensions) related to “Economy and competitiveness” and “Access to water, sanitation and conflict prevention”. Finally, through the PLS-SEM, there was a strong correlation between these social dimensions and the life expectancy at birth.

Conclusions

Using social indicators related to subcategories and multivariate techniques, such as EFA and SEM, it was possible to identify and estimate two impact pathways, Economy and competitiveness and Access to water, sanitation and conflict prevention related to the endpoint human health. Moreover, the results of the PLS-SEM presented in this study can be used as a characterization model, allowing to obtain the effects of each impact pathway on the life expectancy at birth. Future advances include the possibility of identifying new impact pathways from the subcategories, with the use of exploratory techniques and methodological advances in already identified pathways.



中文翻译:

从社会影响子类别到人类健康:多变量分析在 S-LCA 中的应用

目的

本研究旨在提出 SMiLe 方法(生命周期的社会指标),该方法旨在从子类别之间的相关性及其对人类健康终点的影响中确定潜在的影响途径。我们在 S-LCA 上下文中使用多变量数据分析来获得影响路径并获得表征模型。

方法

所提出的方法分三个阶段开发: (I) 保护区域 (AoP) 定义;(二)估算技术选择;(III) 因果链和表征模型的发展。对于第一阶段,AoP 是幸福感,终点类别是人类健康,由出生时预期寿命 (LEX) 表示。作为估计技术,探索性因素分析(EFA)、基于协方差的SEM(CB-SEM)和偏最小二乘SEM(PLS-SEM)分别用于探索步骤(分析代表子类别的指标之间的关系) 、确认阶段(影响路径的验证)和预测步骤(获得表征模型)。这三种估算技术使用来自 189 个国家的社会经济指标来代表子类别。

结果和讨论

数据收集的结果是,可以开发一个包含 21 个指标的数据库,代表与四个利益相关者相关的 15 个子类别,包括来自世界银行和国际劳工组织等各种国际来源的数据。EFA 和 CB-SEM 结果表明,可以确定并确认本研究中使用的子类别按照与“经济和竞争力”和“获得水、卫生和冲突预防”相关的两个因素(社会维度)进行组织. 最后,通过 PLS-SEM,这些社会维度与出生时的预期寿命之间存在很强的相关性。

结论

使用与子类别相关的社会指标和多元技术(例如全民教育和 SEM),可以确定和估计两个影响途径,即经济和竞争力以及与人类健康终点相关的水、卫生和冲突预防。此外,本研究中提出的 PLS-SEM 的结果可用作表征模型,从而获得每种影响途径对出生时预期寿命的影响。未来的进展包括在已经确定的路径中使用探索性技术和方法进步,从子类别中确定新的影响路径的可能性。

更新日期:2021-06-17
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