Industry: Difference between revisions

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* When firms’ incentives align with reducing emissions (for example, through efficiency gains, regulatory compliance, or high GHG prices).

In particular, ML can potentially reduce global emissions by helping to streamline supply chains, improve production quality, predict machine breakdowns, optimize heating and cooling systems, and prioritize the use of clean electricity over fossil fuels ~~[360–363]~~. However, it is worth noting that 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 ~~[364]~~.

In particular, ML can potentially reduce global emissions by helping to streamline supply chains, improve production quality, predict machine breakdowns, optimize heating and cooling systems, and prioritize the use of clean electricity over fossil fuels<ref>{{Cite journal|last=Kazi|first=Rubaiat Habib|last2=Grossman|first2=Tovi|last3=Cheong|first3=Hyunmin|last4=Hashemi|first4=Ali|last5=Fitzmaurice|first5=George|date=2017-10-20|title=DreamSketch|url=http://dx.doi.org/10.1145/3126594.3126662|journal=Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology|location=New York, NY, USA|publisher=ACM|doi=10.1145/3126594.3126662|isbn=978-1-4503-4981-9}}</ref><ref>Richard Evans and Jim Gao. DeepMind AI reduces Google data centre cooling bill by 40%. DeepMind blog, 20, 2016.</ref><ref>{{Cite journal|last=Zhang|first=Xiao|last2=Hug|first2=Gabriela|last3=Kolter|first3=J. Zico|last4=Harjunkoski|first4=Iiro|date=2016-07|title=Model predictive control of industrial loads and energy storage for demand response|url=http://dx.doi.org/10.1109/pesgm.2016.7741228|journal=2016 IEEE Power and Energy Society General Meeting (PESGM)|publisher=IEEE|doi=10.1109/pesgm.2016.7741228|isbn=978-1-5090-4168-8}}</ref><ref>{{Cite journal|last=Berral|first=Josep Ll.|last2=Goiri|first2=Íñigo|last3=Nou|first3=Ramón|last4=Julià|first4=Ferran|last5=Guitart|first5=Jordi|last6=Gavaldà|first6=Ricard|last7=Torres|first7=Jordi|date=2010|title=Towards energy-aware scheduling in data centers using machine learning|url=http://dx.doi.org/10.1145/1791314.1791349|journal=Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking - e-Energy '10|location=New York, New York, USA|publisher=ACM Press|doi=10.1145/1791314.1791349|isbn=978-1-4503-0042-1}}</ref>. However, it is worth noting that 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<ref>{{Cite journal|last=Sorrell|first=Steve|date=2009-04|title=Jevons’ Paradox revisited: The evidence for backfire from improved energy efficiency|url=http://dx.doi.org/10.1016/j.enpol.2008.12.003|journal=Energy Policy|volume=37|issue=4|pages=1456–1469|doi=10.1016/j.enpol.2008.12.003|issn=0301-4215}}</ref>.

== Machine Learning Application Areas ==