An analytic framework for social life cycle impact assessment

• Main Authors: Sheng-Wen Wang, 王聖文
• Other Authors: Chia-Wei Hsu
• Format: Others
• Language: en_US
• Online Access: http://ndltd.ncl.edu.tw/handle/3skv2t

博士 === 國立臺北科技大學 === 工程學院工程科技博士班 === 105 === The purpose of this study is to construct a new social life cycle impact assessment (SLCIA) method for Taiwan industry using the guidelines proposed by UNEP / SETAC as a model for assessing the social impact of industry. Through the review of the literature, the difference of the methods of impact assessment proposed by the UNEP / SETAC guidelines was analyzed to identify the stakeholders, subcategories and indicators applicable to Taiwans electronics industry. In considering the availability of data and the development of social impact, workers became the main stakeholders of the study, whereby seven subcategories and 19 indicators were identified. In the construction of impact assessment method, the indicators are divided into quantitative and quantitative indicators. The degree of impact of the quantitative indicators depends on the ratio of the performance data of the company to the statistical data of industry or country level. The semi-quantitative indicators are obtained from policy, communication, measure, record and response to the five-oriented assessment. In consideration of the importance of different indicators to the impact assessment, the Consistent Fuzzy Preference Relation (CFPR) is applied to the decision of each indicator weight, the integration weight and the indicator score, and the score of social impact. Finally, the SLCIA methodology proposed in this study was used by an IC packaging semiconductor company to assess the impact of labor at three factories as a basis for hotspots to identify significant labor impact issues across the plants and to implement continual improvement. In addition, the analysis of the three factories showed that labor risk was mainly due to the lack of trade union composition, lack of employment of mentally and physically disabled staff, overtime work and large number of staffing.