Industry

This page is about the intersection of industrial systems and machine learning in the context of climate change mitigation. For an overview of industry as a whole, please see the Wikipedia page on this topic.

[todo redo intro]

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. Fortunately for ML researchers, the global industrial sector spends billions of dollars annually gathering data on factories and supply chains – 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. 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.

Machine Learning Application Areas

• Optimizing supply chains and logistics
• [Accelerated science]
• Designing for carbon-conscious products/processes
• Optimizing energy usage
• Minimizing wastage

Background Readings

• 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)

Community

Journals and conferences

• Journal of Cleaner Production
• Industrial Management Journal
• Energy and Buildings

Societies and organizations

• Carbon180 New Carbon Economy Consortium -- carbon sequestration initiative
• Ellen MacArthur Foundation -- consortium around the circular economy and sustainable supply chains/materials development
• Sustainability Accounting Standards Board (SASB) -- popular global standards board for corporate sustainability efforts
  • List of specific SASB standards

Libraries and Tools

• The Materials Project
• Inorganic Crystal Structure Database
• SciFinder (paid)

Data

• UCI Machine Learning Repository datasets, e.g. “Concrete Compressive Strength”