Integration of working environment into life cycle assessment framework

Life cycle assessment (LCA) has been considered one of the tools for supporting decision-making related to the environmental aspects of a product system. It has mainly been used to evaluate the potential impacts associated with relevant inputs and outputs to/from a given product system throughout its life cycle. In most cases, LCA has not considered the impacts on the internal environment, i.e. working environment, but only the external environment. Recently, it has been recognized that the consideration of the impacts on the working environment as well as on the external environment, is needed in order to assess all aspects of the effects on human well-being. To this end, this study has developed a total environmental assessment methodology which enables one to integrate both the working environment and the external environment into the conventional LCA framework.

In general, the characteristics of the impacts on the external environment are different from those on the working environment. In order to properly integrate the two types into total environmental impacts, it is necessary to define identical system boundaries and select impact category indicators at the same level. In order to define the identical system boundary and reduce the uncertainties of LCI results, the hybrid IOA (input–output analysis) method, which integrates the advantages between conventional LCI method and IOA method, is introduced to collect input and output data throughout the entire life cycle of a given product. For the impact category indicators at the endpoint level, LWD (Lost Work Days) is employed to evaluate the damage to human health and safety in the working environment, while DALY (disability-adjusted life years) and PAF (Potentially Affected Fraction) are selected to evaluate the damage to human health and eco-system quality in the external environment, respectively.

The environmental intervention factors (EIFs) are developed not only for the data categories of resource use, air emissions, and water emissions, but also for occupational health and safety to complete a life cycle inventory table. For the development of the EIFs on occupational health and safety, in particular, the number of workers affected by i hazardous items and the number of workers affected at the i magnitude of disability are collected. For the characterization of the impact categories in the working environment, such as occupational health and safety, the exposure factors, effect factors, and damage factors are developed to calculate the LWD of each category. For normalization, the normalization reference is defined as the total LWD divided by the total number of workers. A case study is presented to illustrate the applicability of the proposed method for the integration of the working environment into the conventional LCA framework.

This study is intended to develop a methodology which enables one to integrate the working environmental module into the conventional LCA framework. The hybrid IOA method is utilized to extend the system boundary of both the working environment module and the external environment module to the entire life cycle of a product system. In this study, characterization models and category indicators for occupational health and safety are proposed, respectively, while the methodology of Eco-indicator 99 is used for the external environment. In addition to aid further understanding on the results of this method, this study introduced and developed the category indicators such as DALY, and LWD, which can be expressed as a function of time, and introduced PAF, which can be expressed as a probability.

The consideration of the impacts not only on the external environment, but also on the working environment, is very important, because the best solution for the external environment may not necessarily be the best solution for the working environment. It is expected that the integration of occupational health and safety matters into the conventional LCA framework can bring many benefits to individuals, as well as industrial companies, by avoiding duplicated measures and false optimization.