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Reliable Life Cycle Assessment (LCA) depends on high-quality, fit-for-purpose data. However, data may be unsuitable due to poor quality (a) or inappropriate user choices (b), especially in decarbonization contexts. Existing standards and initiatives lack clear data quality definitions, and new data concepts risk misuse—while relatively harmless in academia may have financial and legal implications in industry. This study analyzes various data concepts and key risks: reliance on statistical vs. specific data, poor regional/technological modeling, greenwashing via gap closing, and issues with AI-generated or outdated data. To mitigate risks under “(a),” data must be practically relevant and continuously confirmed; under “(b),” users must understand its background and suitability. Findings show not all data concepts meet industrial and regulatory LCA needs and concluding recommendations are given.

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How Unsuitable Data and Data Choices Can Negatively Influence Reliability and Costs of Decarbonization Efforts, Supplier Choices, and Sustainability Approaches in Companies

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Martin Baitz,
  • Tim Becker,
  • Lionel Thellier Mercier,
  • Cecilia Makishi Colodel,
  • Matthias Rudolf,
  • Magnus Piotrowski

摘要

Reliable Life Cycle Assessment (LCA) depends on high-quality, fit-for-purpose data. However, data may be unsuitable due to poor quality (a) or inappropriate user choices (b), especially in decarbonization contexts. Existing standards and initiatives lack clear data quality definitions, and new data concepts risk misuse—while relatively harmless in academia may have financial and legal implications in industry. This study analyzes various data concepts and key risks: reliance on statistical vs. specific data, poor regional/technological modeling, greenwashing via gap closing, and issues with AI-generated or outdated data. To mitigate risks under “(a),” data must be practically relevant and continuously confirmed; under “(b),” users must understand its background and suitability. Findings show not all data concepts meet industrial and regulatory LCA needs and concluding recommendations are given.