Searching for the ‘Triple Bottom Line’; Is Social Life Cycle Assessment ready to play its part?

Market observation shows that consumers
have a preference and higher
triple bottom line vennwillingness to pay for products that can distinguish their environmental credentials through eco-labelling. However, while the environmental credentials of a product are now often visible, pause to consider how much you know about the social impacts of your consumption. Do the products you buy involve child labour? Have they been produced in unsafe working conditions? Have the workers received fair pay? Quantifying this has become the focus of the emergent process of Social Life Cycle Assessment (S-LCA).

In the mid-1990s, John Elkington pioneered the sustainability accountancy framework through the triple bottom line, which went beyond the traditional measures of profits, return on investment, and shareholder value to include environmental and social dimensions. Since then, socially and environmentally responsible investment and operation has become an increasingly important aspect of conducting business, being driven in concert by internal company motivations and regulatory requirements. Consequently, the accurate recognition and quantification of social and environmental achievements has emerged as a critical means of both meeting regulatory requirements and branding product.

The United Nations Environmental Programme (UNEP), responsible for many international environmental schemes and initiatives state the realisation of the ‘triple bottom line’ as a key mission statement. A visual description of the linkages between Environment, Economy and Society is given below, with the Triple Bottom Line illustrated as the full accountability across all three areas of economy, society and environment.

triple bottom line vennSocial Life Cycle Assessment is the method emerging to explore and account for the social impacts of a products production, consumption and disposal. With available modelling software and online information databases, the technique is reaching market readiness but the concerns over data representativeness, granularity and accuracy, as well as agreement over methodology persist
It should be noted at this point that Environmental Life Cycle Assessment (E-LCA), on which the S-LCA methodology is largely based, often utilises a range of generalised data on production systems based on geographic area or production technology. For example, decades of data collection and research has provided practitioners with detailed emissions profiling for each kWh of electricity generated in any given location across the world. This provides a crucial cost reduction solution in achieving the required representativeness and granularity, which has greatly assisted the popularity of E-LCA.

In comparison to E-LCA, S-LCA variables tend to be far more binary in their nature. Unlike environmental impacts, which can generally be reduced to a functional impact measure such as kgCO2 equivalents, the variety of impact measures required by S-LCA and their comparison is far less developed.

For the purpose of demonstration, consider child labour exploitation. While a risk profile (being the likelihood of child labour occurring) for a geographic area can be developed, the actual answer is likely to be firstly binary (i.e. Yes/No), often making geographically stratified risk estimates largely redundant in product specific analyses. Acknowledging this, perhaps it needs to be noted that S-LCA studies using currently available market available data sets may only be useful for scoping level assessments.

The implications of this simple thought experiment hint on the difficulty the S-LCA methodology needs to address before it can be used to underpin for example consumer information. My research will explore the accuracy with which aggregated database information are able to replace real information through a comparative analysis of a single product, firstly using actual observed inputs, and secondly using generalised database metrics based on geographic area etc.   This will provide a basis for comparison of the accuracy of these database tools and also a practical example with which to discuss their achievements, shortcomings or improvements.

A further difficulty facing the emergent S-LCA is its UNEP mandate to explore the ‘actual and potential positive as well as negative impacts along the life cycle’. While attributional E-LCA picks up on this, accounting for reductions to carbon footprint during production (e.g. growing trees), the treatment of positive social impacts are less easily quantified and their potential to offset different negative impacts implies implicit value judgements – variations to which, one would expect to significantly effect the outcome of a study.

The realisation of the ‘triple bottom line’ in production systems lies tantalisingly close, but is implicitly dependant on the success of the S-LCA methodology, the data it requires and an effective means of marketable communication. Logically, it is a rigorous and thorough means of identifying and conveying the social impacts of a products life cycle that remains the seminal scientific step in this process. The question I’ll be answering is just how close is S-LCA to achieving this.

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