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The digital sharing economy is commonly seen as a promising circular consumption model that could potentially deliver environmental benefits through more efficient use of existing product stocks. Yet whether sharing is indeed more environmentally benign than prevalent consumption models and what features shape platforms’ sustainability remains unclear. To address this knowledge gap, we conduct a systematic literature review of empirical peer reviewed and conference proceeding publications. We screen over 2200 papers and compile a dataset of 155 empirical papers, and consolidate reported results on the environmental impacts of the sharing economy. We find that sharing is not inherently better from an environmental perspective. The type of resource shared, logistic operations, and the ways in which sharing influences users’ consumption more broadly affect environmental outcomes. Sharing goods is generally associated with better environmental outcomes compared to shared accommodations or mobility. Within mobility, shared scooters and ride-hailing emerge as particularly prone to negative environmental outcomes. Contrary to previous suggestions, peer-to-peer sharing (vs. centralized ownership) does not seem to be a good proxy for environmental performance. As sharing becomes intertwined with urbanization, efforts to steer digital sharing towards environmental sustainability should consider system levels effects and take into account platform operations as well as potential changes in consumer behavior.
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This content will become publicly available on January 10, 2026
Ten simple rules for good model-sharing practices
Computational models are complex scientific constructs that have become essential for us to better understand the world. Many models are valuable for peers within and beyond disciplinary boundaries. However, there are no widely agreed-upon standards for sharing models. This paper suggests 10 simple rules for you to both (i) ensure you share models in a way that is at least “good enough,” and (ii) enable others to lead the change towards better model-sharing practices.
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- Award ID(s):
- 2231406
- PAR ID:
- 10627900
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Editor(s):
- Schwartz, Russell
- Publisher / Repository:
- PLOS Computational Biology
- Date Published:
- Journal Name:
- PLOS Computational Biology
- Volume:
- 21
- Issue:
- 1
- ISSN:
- 1553-7358
- Page Range / eLocation ID:
- e1012702
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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