ArticleData in brief2025
Plastics in the German building and infrastructure sector: A high-resolution dataset on historical flows, stocks, and legacy substance contamination.
Article in Data in brief, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of plastics in buildings and infrastructure has been steadily increasing in the past. Due to the longevity of building components, in the upcoming decades a dramatical growth in waste quantities is expected as a delayed response to the increased consumption, posing a major challenge for waste management and recycling. However, comprehensive high-resolution data on plastic flows in the building and infrastructure sector in Germany have so far not been available. This dataset addresses this gap by presenting simulated data on historical (1950-2023) plastic flows and stocks in the German building and infrastructure sector including their contamination with selected legacy substances based on a dynamic material flow analysis. The focus is on major plastic applications in buildings and infrastructure, namely profiles, flooring, pipes, insulation material, cable insulation, and films. Input data for the dynamic material flow model were compiled from various sources, including scientific literature, reports, statistics, databases, and personal communication with industry stakeholders. The linked data repository contains simulated data, input data, and model files, enabling model reproduction, adjustment, or expansion. This dataset forms a basis for quantitatively assessing current and future plastic cycles in buildings and infrastructure and potential challenges for recycling arising through legacy contamination. The dataset can be reused in different contexts as it bridges various disciplines such as environmental science, industrial ecology, material science, construction engineering, and toxicology, providing valuable insights for research in sustainability, waste management, and pollution mitigation.
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Registered trials
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