ML can potentially reduce industrial emissions by helping to streamline supply chains, invent cleaner materials and chemicals, design for fewer materials, improve production quality, predict machine breakdowns, optimize heating and cooling systems, and prioritize the use of clean electricity over fossil fuels.
ML can have a substantial positive climate impact on the industrial sector under the following circumstances:
- When there is enough accessible, high-quality data around specific processes or transport routes.
- When firms have an incentive to share their proprietary data and/or algorithms with researchers and other firms.
- When aspects of production or shipping can be readily fine-tuned or adjusted, and there are clear objective functions.
- When firms’ incentives align with reducing emissions (for example, through efficiency gains, regulatory compliance, or high GHG prices).
Yet greater efficiency may increase the production of goods and thus GHG emissions (via the Jevons paradox) unless industrial actors have sufficient incentives to reduce overall emissions.
Machine Learning Application Areas
Optimizing supply chains
- Optimizing supply chains and logistics
- Carbon-conscious sourcing
Developing cleaner products and materials
- Accelerated science for clean energy technologies: Designing new materials is important for many applications, including energy storage via batteries or solar/chemical fuels. ML can help suggest promising materials to try, thereby speeding up the materials discovery process.
- Minimizing material use through intelligent design
- Designing for carbon-conscious products/processes
Optimizing factory operations
- Optimizing energy usage
- Minimizing wastage
- Predictive Maintenance
- Rebitzer, G. et al. Intro to life-cycle analysis (2004)
- Gao, J. et al. Google’s white paper on ML for data center optimization
- Gustavsson, J. et al. Intro to food waste (2011)
- Wikipedia - Intro to Industry 4.0
- Carbon180 - Building a New Carbon Economy (substantial overlap with carbon sequestration and agriculture)
Online Courses and Course Materials
Autodesk Sustainability Workshop: "free online learning resources that teach the principles and practice of sustainability in engineering and design. This site was created to help students, educators and professionals learn more about sustainability in engineering and architecture professions." Practical videos on sustainable product design, materials selection, and engineering available here.
Conferences, Journals, and Professional Organizations
Major professional organizations
IEEE Power & Energy Society: "[T]he world's largest forum for sharing the latest in technological developments in the electric power industry, for developing standards that guide the development and construction of equipment and systems, and for educating members of the industry and the general public." Website here.
- Carbon180 New Carbon Economy Consortium is focused on carbon capture and sequestration, including agriculture and forestry
- Ellen MacArthur Foundation -- consortium around the circular economy and sustainable supply chains/materials development
- Helps (mostly large) manufacturers consider the entire life cycle of their products
- Sustainability Accounting Standards Board (SASB) -- popular global standards board for corporate sustainability efforts
- The BlueGreen Alliance's Clean Economy Manufacturing Center (USA) helps American manufacturers and construction firms improve efficiency and sustainability
Past and upcoming events
Libraries and Tools
- The Materials Project
- Inorganic Crystal Structure Database
- SciFinder (paid)
- USA Industrial Assessment Centers: database of solutions from university collaborations with SMEs to save energy and reduce costs
See the subpages listed above for application-specific datasets.
- Davis, Steven J.; Lewis, Nathan S.; Shaner, Matthew; Aggarwal, Sonia; Arent, Doug; Azevedo, Inês L.; Benson, Sally M.; Bradley, Thomas; Brouwer, Jack; Chiang, Yet-Ming; Clack, Christopher T. M. (2018-06-29). "Net-zero emissions energy systems". Science. 360 (6396): eaas9793. doi:10.1126/science.aas9793. ISSN 0036-8075.
- Mike Gualtieri, Noel Yuhanna, Holger Kisker, Rowan Curran, Brandon Purcell, Sophia Christakis, Shreyas Warrier, and Matthew Izzi. The Forrester Wave: Big data streaming analytics, Q1 2016. Forrester.com, January 2016.
- Kazi, Rubaiat Habib; Grossman, Tovi; Cheong, Hyunmin; Hashemi, Ali; Fitzmaurice, George (2017-10-20). "DreamSketch: Early stage 3D design explorations with sketching and generative design". Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology. New York, NY, USA: ACM. doi:10.1145/3126594.3126662. ISBN 978-1-4503-4981-9.
- Richard Evans and Jim Gao. DeepMind AI reduces Google data centre cooling bill by 40%. DeepMind blog, 20, 2016.
- Zhang, Xiao; Hug, Gabriela; Kolter, J. Zico; Harjunkoski, Iiro (2016-07). "Model predictive control of industrial loads and energy storage for demand response". 2016 IEEE Power and Energy Society General Meeting (PESGM). IEEE. doi:10.1109/pesgm.2016.7741228. ISBN 978-1-5090-4168-8. Check date values in:
- Berral, Josep Ll.; Goiri, Íñigo; Nou, Ramón; Julià, Ferran; Guitart, Jordi; Gavaldà, Ricard; Torres, Jordi (2010). "Towards energy-aware scheduling in data centers using machine learning". Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking - e-Energy '10. New York, New York, USA: ACM Press. doi:10.1145/1791314.1791349. ISBN 978-1-4503-0042-1.
- Sorrell, Steve (2009-04). "Jevons' Paradox revisited: The evidence for backfire from improved energy efficiency". Energy Policy. 37 (4): 1456–1469. doi:10.1016/j.enpol.2008.12.003. ISSN 0301-4215. Check date values in: