Course
AI for Climate & NatureTech Training
Learn how to apply artificial intelligence, remote sensing, and spatial intelligence to solve real-world environmental challenges.
Scientific Paper · source-verified
Global Database of Cement Production Assets and Upstream Suppliers
Tkachenko, Nataliya; Tang, Kevin; McCarten, Matthew; Reece, Steven; Kampmann, David; Hickey, Conor; Bayaraa, Maral; Foster, Peter; Layman, Courtney; Rossi, Cristian; Scott, Kimberly; Yoken, Dave; Christiaen, Christophe; Caldecott, Ben · 2023-10-13
A globally consolidated asset-level dataset for cement production, incorporating plant age and raw material sourcing, enhances emissions tracking, revealing inefficiencies in supply chains while leveraging geospatial computer vision and Large Language Models for comprehensive industry analysis.
Show citation
Tkachenko, Nataliya; Tang, Kevin; McCarten, Matthew; Reece, Steven; Kampmann, David; Hickey, Conor; Bayaraa, Maral; Foster, Peter; Layman, Courtney; Rossi, Cristian; Scott, Kimberly; Yoken, Dave; Christiaen, Christophe; Caldecott, Ben. 2023-10-13. Global Database of Cement Production Assets and Upstream Suppliers. https://www.nature.com/articles/s41597-023-02599-w (AI & Environment Resource Hub; record paper-053; collection snapshot 2026-09-15).
Classification and context
Included in the original Scientific Paper collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
topic
lifecycle
orientation
Available source metadata
- Author or creator
- Tkachenko, Nataliya; Tang, Kevin; McCarten, Matthew; Reece, Steven; Kampmann, David; Hickey, Conor; Bayaraa, Maral; Foster, Peter; Layman, Courtney; Rossi, Cristian; Scott, Kimberly; Yoken, Dave; Christiaen, Christophe; Caldecott, Ben
- Publisher
- Scientific Data
- Publication date
- 2023-10-13
- Date precision
- day
- Geographic scope
- Unknown
- Language
- unknown
- Peer review
- unknown
- Source status
- verified
- Last verified
- 2026-09-06T18:15:17.785Z
- Snapshot import
- 2026-09-06
- Legacy domain
- Governance, Justice & Society
- Legacy subdomain
- Using AI in Cement Production Ecosystem Mapping
- journal
- Scientific Data
Provenance and review
Source sheet: Scientific Paper · Row 57 · Original ID: paper-053.
A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source link reachable · checked 2026-09-15. Source identity and required metadata verified. The import date is not the original date added.
Suggest a correction for this record →Related through shared environmental topics
Ordered by the number of shared provisional topic labels. This indicates a browsing connection, not agreement between sources.
Shared topics: Governance and society; Research tools and geospatial methods; Climate and greenhouse gases; Biodiversity and ecosystems
Shared topics: Governance and society; Research tools and geospatial methods; Climate and greenhouse gases; Biodiversity and ecosystems
Tool
EarthMap
Earth Map is an innovative, free and open-source tool developed by the Food and Agriculture Organization of the United Nations (FAO) in the framework of the FAO - Google partnership. It was created to support countries, research institutes, farmers and members of the general public with internet access to monitor their land in an easy, integrated and multi-temporal manner. It allows everyone to visualize, process and analyze satellite imagery and global datasets on climate, vegetation, fires, biodiversity, geo-social and other topics. Users need no prior knowledge of remote sensing or Geographical Information Systems (GIS).
Shared topics: Governance and society; Climate and greenhouse gases; Materials and critical minerals; Biodiversity and ecosystems
Policy Document · 2025-01-07
How to Include Environmental Sustainability Criteria in National AI Funding Schemes?
Artificial intelligence (AI) is in the media spotlight for its potential to transform the economic and research sectors, among others. This drives funding bodies to support AI-based innovation, with for example the Horizon Europe and Digital Europe programmes run by the European Union, or France’s investment strategy France 2030 (national strategy for AI). On the other hand, the environmental impacts of AI are now better understood, and we cannot ignore the role of AI on electricity and water usage, mineral resource depletion, and greenhouse gas emissions1,2. To bring together innovation and sustainability, the French Department for the Environment (Ministère en charge de la Transition Écologique) has decided to require the use of the Green Algorithms tool for funding applications on the topic of AI and climate change. Applicants now have to include estimates of the carbon footprint and energy usage of the different development phases of the proposed AI solution. This was tested on a first funding call “Demonstrators of frugal AI for sustainable development of local communities”. The first applications were received in December 2023, with positive feedback from the different stakeholders. Applicants in particular approved of this new criterion, as they understood its necessity, found the tool easy to use, and did not consider this to slow down innovation. Following this successful implementation in a first funding call, it was decided to include the Green Algorithms tool more systematically in the application guidelines of other AI-related funding calls run by the Department. The goal of this piece is to reflect on the inclusion of environmental criteria in AI funding calls and share the lessons learned with other funding bodies internationally to promote similar initiatives across the AI ecosystem.
Shared topics: Governance and society; Research tools and geospatial methods; Biodiversity and ecosystems
Scientific Paper · 2025-07-18
A Call for Transdisciplinary Trust in the AI Era
Trust is a cornerstone and enabler of human civilization, determining the very nature of how people interact with each other. The swift integration of artificial intelligence (AI) into daily life poses grand societal challenges and necessitates a reevaluation of trust. Our bibliometric literature review calls for scientists and stakeholders to cross traditional academic boundaries to address emerging and evolving societal challenges arising from AI. We propose a transdisciplinary research framework to understand and bolster trust in AI and address grand challenges in domains as diverse and urgent as misinformation, discrimination, and warfare.