TITLE:

Climate Smart Computing: A Perspective

PRESENTER:

Shashi Shekhar : Biography ( 100 words, 400 words ), Picture ( 1 , 2 )

AFFILIATION:

Computer Science and Engineering Department, University of Minnesota.

Webpages:

official , personal , wikipedia entry

VIDEOS:

SLIDES:

ABSTRACT:

Climate change is a societal grand challenge and many nations have signed the Paris Agreement (2015) aiming for net-zero emission (a.k.a. Carbon neutrality) around 2050. The computing community can make many contributions. We can help improve climate resilience, lower emissions in other economic sectors (e.g., energy, transportation, agriculture) and accelerate absorption of greenhouse gasses (GHG) in nature. Also, we can reduce computing emissions (about 2% of total in 2020 from IoT, pervasive devices, networks, data centers, etc.). Further, we can provide new tools for advancing scientific understanding and for GHG monitoring, reporting and verification.

However, traditional computing methods face major challenges. First, computational models are approximations of the natural world, and it is important to reduce approximation error due to the high cost of errors besides speeding up computations. In addition, there are significant interactions and optimizing for a goal (e.g., mitigation) or a subsystem (e.g., food) may have unintended consequences for other goals (e.g., adaptation) or subsystems (e.g., water). Moreover, there are significant stochastic and systematic uncertainties in future projections. Further, machine learning is overwhelmed due to non-stationarity (e.g., climate change), data paucity (e.g., rare climate events), high cost of ground truth collection, and the need to observe natural laws (e.g., conservation of mass). Furthermore, addressing climate often requires deep and sustained interdisciplinary and multi-sector collaboration.

This talk shares a perspective on the climate-smart computing challenges and opportunities based on multi-decade scholarly activities such as the recent AI-CLIMATE, the National AI Research Institute for Climate-Land Interactions, Mitigation, Adaptation, Tradeoffs and Economy.

KEYWORDS:

ACKNOWLEDGMENTS: This work is supported by AFRI Competitive Grant No. 2023-67021-39829 / Project No. MINW-2023-03616 from the USDA National Institute of Food and Agriculture as well as the National AI Research Institute program of the National Science Foundation.

RESOURCES

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  26. Related Webpages: