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[[File:Industry.png|thumb|500x500px|Selected opportunities to use machine learning to reduce greenhouse gas emissions in industry.|alt=]]Industrial production, logistics, and building materials are leading causes of difficult-to-eliminate GHG emissions [155]. Fortunately for ML researchers, the global industrial sector spends billions of dollars annually gathering data on factories and supply chains [359] – aided by improvements in the cost and accessibility of sensors and other data-gathering mechanisms (such as QR codes and image recognition). The availability of large quantities of data, combined with affordable cloud-based storage and computing, indicates that industry may be an excellent place for ML to make a positive climate impact. ML demonstrates considerable potential for reducing industrial GHG emissions 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). In particular, ML can potentially reduce global emissions (Fig. 4) 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].
[[File:Industry.png|thumb|500x500px|Selected opportunities to use machine learning to reduce greenhouse gas emissions in industry.|alt=]]Industrial production, logistics, and building materials are leading causes of difficult-to-eliminate GHG emissions<ref>{{Cite journal|last=Davis|first=Steven J.|last2=Lewis|first2=Nathan S.|last3=Shaner|first3=Matthew|last4=Aggarwal|first4=Sonia|last5=Arent|first5=Doug|last6=Azevedo|first6=Inês L.|last7=Benson|first7=Sally M.|last8=Bradley|first8=Thomas|last9=Brouwer|first9=Jack|last10=Chiang|first10=Yet-Ming|last11=Clack|first11=Christopher T. M.|date=2018-06-29|title=Net-zero emissions energy systems|url=https://www.sciencemag.org/lookup/doi/10.1126/science.aas9793|journal=Science|language=en|volume=360|issue=6396|pages=eaas9793|doi=10.1126/science.aas9793|issn=0036-8075}}</ref>. Fortunately for ML researchers, the global industrial sector spends billions of dollars annually gathering data on factories and supply chains<ref>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.</ref> – aided by improvements in the cost and accessibility of sensors and other data-gathering mechanisms (such as QR codes and image recognition). The availability of large quantities of data, combined with affordable cloud-based storage and computing, indicates that industry may be an excellent place for ML to make a positive climate impact. ML demonstrates considerable potential for reducing industrial GHG emissions 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).

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].


== Machine Learning Application Areas ==
== Machine Learning Application Areas ==

* Optimizing supply chains
* Improving materials
* Optimizing energy usage


== Background Readings ==
== Background Readings ==

Revision as of 18:51, 31 August 2020

Selected opportunities to use machine learning to reduce greenhouse gas emissions in industry.

Industrial production, logistics, and building materials are leading causes of difficult-to-eliminate GHG emissions[1]. Fortunately for ML researchers, the global industrial sector spends billions of dollars annually gathering data on factories and supply chains[2] – aided by improvements in the cost and accessibility of sensors and other data-gathering mechanisms (such as QR codes and image recognition). The availability of large quantities of data, combined with affordable cloud-based storage and computing, indicates that industry may be an excellent place for ML to make a positive climate impact. ML demonstrates considerable potential for reducing industrial GHG emissions 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).

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].

Machine Learning Application Areas

  • Optimizing supply chains
  • Improving materials
  • Optimizing energy usage

Background Readings

Online Courses and Course Materials

Community

Journals and conferences

Societies and organizations

Past and upcoming events

Libraries and Tools

Data

References

  1. ↑ 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.
  2. ↑ 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.
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