Evidence map›Paper›PMID 38816434›Full record

ArticleScientific data2024

Global high-resolution growth projections dataset for rooftop area consistent with the shared socioeconomic pathways, 2020-2050.

Siddharth Joshi, Behnam Zakeri, Shivika Mittal, Alessio Mastrucci, Paul Holloway, Volker Krey, Priyadarshi Ramprasad Shukla, Brian O'Gallachoir, James Glynn

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In one paragraph

Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Siddharth JoshiSFI MaREI Centre for Energy Climate and Marine, Cork, Ireland. joshis@iiasa.ac.at.ORCID http://orcid.org/0000-0002-5746-3079
Behnam ZakeriEnergy, Climate, and Environment Program, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria.ORCID http://orcid.org/0000-0001-9647-2878
Shivika MittalGrantham Institute - Climate Change and the Environment, Imperial College London, London, UK.ORCID http://orcid.org/0000-0003-4718-0064
Alessio MastrucciEnergy, Climate, and Environment Program, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria.ORCID http://orcid.org/0000-0002-5611-7780
Paul HollowayEnvironmental Research Institute, University College Cork, Cork, Ireland.ORCID http://orcid.org/0000-0002-9773-4531
Volker KreyEnergy, Climate, and Environment Program, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria.ORCID http://orcid.org/0000-0003-0307-3515
Priyadarshi Ramprasad ShuklaGlobal Centre for Environment and Energy, Ahmedabad University, Ahmedabad, India.
Brian O'GallachoirSFI MaREI Centre for Energy Climate and Marine, Cork, Ireland.
James GlynnSFI MaREI Centre for Energy Climate and Marine, Cork, Ireland.

Funding

EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020) 101056810EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020) 101081179Science Foundation Ireland (SFI) 17/NSFC/5181
6 · The paper itself

Abstract

Assessment of current and future growth in the global rooftop area is important for understanding and planning for a robust and sustainable decentralised energy system. These estimates are also important for urban planning studies and designing sustainable cities thereby forwarding the ethos of the Sustainable Development Goals 7 (clean energy), 11 (sustainable cities), 13 (climate action) and 15 (life on land). Here, we develop a machine learning framework that trains on big data containing ~700 million open-source building footprints, global land cover, road, and population datasets to generate globally harmonised estimates of growth in rooftop area for five different future growth narratives covered by Shared Socioeconomic Pathways. The dataset provides estimates for ~3.5 million fishnet tiles of 1/8 degree spatial resolution with data on gross rooftop area for five growth narratives covering years 2020-2050 in decadal time steps. This single harmonised global dataset can be used for climate change, energy transition, biodiversity, urban planning, and disaster risk management studies covering continental to conurbation geospatial levels.

Identifiers

PMID38816434
PMCPMC11139859

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.