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Elisa Bruni

Biographic Data

ID7993253
NAMEElisa Bruni
GIVEN NAMESElisa
FAMILY NAMEBruni
SIGNATUREBRUNI E
AFFILIATIONSCentre National de la Recherche Scientifique
ORCID0000-0001-8074-0516
VERIFIEDYes
TOTAL WORKS3
TOTAL CITATIONS0
WORKS AS AUTHOR3
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2012
LATEST PUBLICATION YEAR2025
H-INDEX0
  • Mining global soil carbon datasets

    Open Access•Shoji Hashimoto, Elisa Bruni et al.•ARTICLE•Environmental Research Letters•2025

    The future of terrestrial soil carbon stocks plays a crucial role in climate change prediction. Modern machine learning techniques are now widely applied in soil science to predict the spatial distribution of soil properties from observational data. Beyond prediction, the use of machine learning as a data-mining tool offers a promising pathway for improving soil carbon modelling and refining projections of climate–carbon feedbacks. In this paper,…

  • Evaluation of cities’ smartness by means of indicators for small and medium cities and communities

    Open Access•Giuliano Dall’O, Elisa Bruni et al.•ARTICLE•Sustainable Cities and Society•2017

  • The Green Energy Audit, a new procedure for the sustainable auditing of existing buildings integrated with the Leed Protocols

    Open Access•Giuliano Dall’O, Alessandro Speccher et al.•ARTICLE•Sustainable Cities and Society•2012

No prominent works on this page.

  • The Green Energy Audit, a new procedure for the sustainable auditing of existing buildings integrated with the Leed Protocols

    Open Access•Giuliano Dall’O, Alessandro Speccher et al.•ARTICLE•Sustainable Cities and Society•2012

  • Evaluation of cities’ smartness by means of indicators for small and medium cities and communities

    Open Access•Giuliano Dall’O, Elisa Bruni et al.•ARTICLE•Sustainable Cities and Society•2017

  • Mining global soil carbon datasets

    Open Access•Shoji Hashimoto, Elisa Bruni et al.•ARTICLE•Environmental Research Letters•2025

    The future of terrestrial soil carbon stocks plays a crucial role in climate change prediction. Modern machine learning techniques are now widely applied in soil science to predict the spatial distribution of soil properties from observational data. Beyond prediction, the use of machine learning as a data-mining tool offers a promising pathway for improving soil carbon modelling and refining projections of climate–carbon feedbacks. In this paper,…

Business (2 works) · Economics (2 works) · Environmental economics (2 works) · Environmental Science (2 works) · Sustainability (2 works) · Sustainable development (2 works) · Accounting (1 work) · Action plan (1 work) · Architectural engineering (1 work) · Artificial Intelligence (1 work)

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