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.''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 cloud-based storage and computing, as well as sensors and data-gathering mechanisms such as QR codes and image recognition (vaguely referred to as "Industry 4.0" and "Smart/Connected Factories").

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 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)

Journals and conferences

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

Societies and organizations

 * 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
 * List of specific SASB standards
 * The BlueGreen Alliance's Clean Economy Manufacturing Center (USA) helps American manufacturers and construction firms improve efficiency and sustainability

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

Data

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