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Poudyal, Mahesh, Kraft, Franziska 
ORCID: https://orcid.org/0009-0001-3956-2233, Wells, Geoff, Das, Anamika, Attiwilli, Suman, Schreckenberg, Kate, Lele, Sharachchandra, Daw, Tim, Torres-Vitolas, Carlos, Setty, Siddappa, Adams, Helen, Ahmad, Sate, Ryan, Casey, Fisher, Janet, Robinson, Brian, Jones, Julia P. G., Homewood, Katherine, Bluwstein, Jevgeniy, Keane, Aidan, Macamo, Celia and Mugi, Lilian Mwihaki
(2024)
Nature’s contribution to poverty alleviation, human wellbeing and the SDGs.
Scientific Data, 11 (1).
ISSN 2052-4463
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Abstract
Millions of households globally rely on uncultivated ecosystems for their livelihoods. However, much of the understanding about the broader contribution of uncultivated ecosystems to human wellbeing is still based on a series of small-scale studies due to limited availability of large-scale datasets. We pooled together 11 comparable datasets comprising 232 settlements and 10,971 households in ten low-and middle-income countries, representing forest, savanna and coastal ecosystems to analyse how uncultivated nature contributes to multi-dimensional wellbeing and how benefits from nature are distributed between households. The resulting dataset integrates secondary data on rural livelihoods, multidimensional human wellbeing, household demographics, resource tenure and social-ecological context, primarily drawing on nine existing household surveys and their associated contextual information together with selected variables, such as travel time to cities, population density, local area GDP and land use and land cover from existing global datasets. This integrated dataset has been archived with ReShare (UK Data Service) and will be useful for further analyses on nature-wellbeing relationships on its own or in combination with similar datasets.
| Item Type: | Article |
|---|---|
| Additional Information: | Code availability: The dataset was generated collaboratively by a team of researchers, working in the R or STATA platforms depending on their institutional and disciplinary preference. The final data generation code in R also draws upon a range of raw quantitative and qualitative datasets all with different owners, many of which contain confidential information that can be used to identify the names, demographics, opinions, and locations of individual respondents. Hence, not all code can be made publicly available. However, we have made all non-sensitive data generation code public through a public GitHub repository (https://github.com/mpoudyal/n4sdatagen), with clear explanation of those not in the public domain and how they fit within the data generation process in the README file within the repository. More importantly, we want to make clear that no custom code is necessary to utilise the collated dataset, and that the Data Description document on the data archive contains detailed written descriptions of all data generation processes including detailed description of variables developed in the process. Finally, parts of the codes containing sensitive information are available upon request to authors and after permission from the owners of the original datasets. |
| Uncontrolled Keywords: | sdg 1 - no poverty,sdg 15 - life on land ,/dk/atira/pure/sustainabledevelopmentgoals/no_poverty |
| Faculty \ School: | Faculty of Science > School of Environmental Sciences |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 08 Oct 2026 10:11 |
| Last Modified: | 08 Oct 2026 12:02 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/104751 |
| DOI: | 10.1038/s41597-024-02967-0 |
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