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Purpose <p>This study developed and applied a pathway-based extension for Social Life Cycle Assessment (S-LCA) that links reference-scale child labour risk indicators to life-year impacts and human capital (HC) losses. This framework was used to assess upstream raw material extraction in lithium-ion battery supply chains for electric vehicles.</p> Methods <p>The pathway starts from the reference-scale probabilities of child labour and school dropout from the Social Hotspots Database, combined with labour hours per FU derived from a multi-regional input–output model. Midpoint child labour risk hours were converted into the expected numbers of affected children and cumulative activity life-years (CAYLs)—a metric representing deviations from a counterfactual life course trajectory—for both education and work. The endpoint indicators capture years of schooling lost (YSLL) and net years of work life gained (YWLG). An optional valuation layer converts these life-year changes into monetary HC losses (ΔHC) using a lifetime income framework consistent with inclusive wealth accounting, domain-specific HC weights, and constant shadow prices. The approach was applied to the upstream mining and raw material processing segments of a 53-kWh traction battery pack, focusing on nine raw material supply chains.</p> Results and discussion <p>The results indicate that several upstream countries–material combinations in cobalt, graphite, and lithium supply chains are associated with substantial losses of schooling years per 53 kWh battery pack, while gains in working years per affected child are relatively small in terms of welfare. Aggregated per FU, education-domain losses dominate work-domain gains for almost all suppliers, yielding a uniformly negative ΔHC. In some segments, absolute human capital losses are of the same order of magnitude as or even exceed, local value added. A sensitivity analysis showed that the magnitude of ΔHC was most affected by uncertainty in the reference-scale prevalence, whereas the results were less sensitive to the displacement parameter and discount rate. The hotspot ranking was robust across the tested parameter variants.</p> Conclusions <p>The proposed pathway offers a transparent, modular way to translate child labour risk indicators into life-year and human capital metrics within S-LCA. For upstream battery supply chains, the findings highlight forgone schooling as a central driver of long-term welfare loss and point to a small set of raw material segments as social hotspots. The framework can support the integration of education-related welfare indicators into life cycle sustainability assessments and responsible sourcing policies while remaining compatible with inclusive wealth and human capital accounting practices.</p>

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A pathway extension for S-LCA to quantify long-term impacts of child labour

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月3日

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  • Shuning Chen,
  • Sayaka Kakiuchi,
  • Norihiro Itsubo,
  • Shunsuke Managi

摘要

Purpose

This study developed and applied a pathway-based extension for Social Life Cycle Assessment (S-LCA) that links reference-scale child labour risk indicators to life-year impacts and human capital (HC) losses. This framework was used to assess upstream raw material extraction in lithium-ion battery supply chains for electric vehicles.

Methods

The pathway starts from the reference-scale probabilities of child labour and school dropout from the Social Hotspots Database, combined with labour hours per FU derived from a multi-regional input–output model. Midpoint child labour risk hours were converted into the expected numbers of affected children and cumulative activity life-years (CAYLs)—a metric representing deviations from a counterfactual life course trajectory—for both education and work. The endpoint indicators capture years of schooling lost (YSLL) and net years of work life gained (YWLG). An optional valuation layer converts these life-year changes into monetary HC losses (ΔHC) using a lifetime income framework consistent with inclusive wealth accounting, domain-specific HC weights, and constant shadow prices. The approach was applied to the upstream mining and raw material processing segments of a 53-kWh traction battery pack, focusing on nine raw material supply chains.

Results and discussion

The results indicate that several upstream countries–material combinations in cobalt, graphite, and lithium supply chains are associated with substantial losses of schooling years per 53 kWh battery pack, while gains in working years per affected child are relatively small in terms of welfare. Aggregated per FU, education-domain losses dominate work-domain gains for almost all suppliers, yielding a uniformly negative ΔHC. In some segments, absolute human capital losses are of the same order of magnitude as or even exceed, local value added. A sensitivity analysis showed that the magnitude of ΔHC was most affected by uncertainty in the reference-scale prevalence, whereas the results were less sensitive to the displacement parameter and discount rate. The hotspot ranking was robust across the tested parameter variants.

Conclusions

The proposed pathway offers a transparent, modular way to translate child labour risk indicators into life-year and human capital metrics within S-LCA. For upstream battery supply chains, the findings highlight forgone schooling as a central driver of long-term welfare loss and point to a small set of raw material segments as social hotspots. The framework can support the integration of education-related welfare indicators into life cycle sustainability assessments and responsible sourcing policies while remaining compatible with inclusive wealth and human capital accounting practices.