AGENDA - PAW CLIMATE 2023

October 4-5, 2023 – Livestream Direct to You

In recent years, we have seen an increase in power outages resulting in brownouts, blackouts and deadly wildfires, which will only increase if we don’t change how we use our energy resources. Manik will discuss the opportunity businesses have to leverage ML paired with sensors & smart plugs to use cooling – refrigeration and air conditioning – dynamically to reduce energy consumption, carbon emissions and operating costs. Manik will share how companies like 7/11, McDonald’s, Marriott Hotels & Vail Resorts are using this technology to shift energy usage to cleaner times of day, relieving grid stress while reducing energy costs without affecting the quality of food and guest experience.

In any major climate-linked crisis, access to geo-spatial-temporal datasets, mapping, modeling, and analytical tools are critical to aid recovery efforts and protect communities. Many expert researchers, especially scientists and institutions in low-income countries, lack the tools to access and analyze relevant data, to inform local decision makers on how to act rapidly and effectively. Currently, there is no mechanism to harmonize global access to urgently needed datasets and high-performance computing tools. This table discussion, led by the International Science Reserve, will bring together climate tech and corporate leaders to discuss how to bridge the data analytics divide.

KoBold is accelerating the clean energy future by finding new battery metal deposits. This requires effectively leveraging various types of geoscience data and applying ML and physical computational techniques to guide exploration decisions.

We will walk through challenges we faced and lessons learned from developing and scaling scientific computation and ML techniques (FFT, derivative raster products, tree based ML models) to rasters, which requires a different set of considerations from scaling computation on tabular data. We will discuss key differences between computation of geospatial rasters vs. tabular data, and generalize our learnings to other climate change problem domains.

Recycling is one of the primary levers we can use to fight our society’s impact on our climate, and the industry has tremendous potential to improve its operations and drive real impact in our fight against climate change.

Glacier is deploying cutting-edge ML to perform classification and detection work that is revolutionizing the circular economy. Our challenges are unique and complex – distinguishing plastic resins, characterizing truckloads of trash, automatically detecting contamination. We’re using ML to extract value out of the products we consume and throw away, to ensure that businesses in the space have the technology they need to thrive, and to make an immediate impact on our climate.

The present era is marked by the imminent reality of climate change, a phenomenon that carries undeniable scientific evidence regarding its profound impact on society. Nevertheless, we possess the means to confront this challenge through a multi-faceted approach. This deep dive delves into various strategies tailored to data scientists, environmental researchers, and corporations seeking to analyze corporate climate mitigation initiatives. The discourse entails a comparative analysis between corporate environmental and sustainability disclosures and the Intergovernmental Panel on Climate Change (IPCC) mitigation report, leveraging the power of Natural Language Processing (NLP) and Topic Modeling. By delving deep into the subject matter, the talk elucidates the capacity of the topic model to extract latent themes from the IPCC report and subsequently rank climate investments in relation to the corporate disclosures.

Electricity Maps maintains a comprehensive hourly dataset for electricity generation and exchanges. Our most recent work leveraged this dataset to predict the best hour to use electricity to minimize carbon footprint. We will present how we broke down electricity generation into granular time series production events which can be forecasted with simple Lasso models and the infrastructure we built to support it. We will discuss challenges we encountered along the way and how we used those predictions to deploy a carbon aware scheduler for running our computational workloads. Finally, we will present our evaluation of avoided emissions using this scheduler.

How will we meet the nutritional needs of a growing population using fewer hectares per ton of food, with lower labor availability? Conventional agriculture will not get us there. With AI and machine-learning integrated into modern agricultural solutions, we can leapfrog to precision agriculture and encourage soil regenerative practices, driven by actionable data. For example, sensor-supported precision agriculture for efficient water (80-90% reduction) and nutrient delivery (80% less fertilizer use) can cap input costs while significantly improving production output through data-driven harvesting and planting schedules.

In this session, we will explore the transformative power of machine learning in optimizing sustainable energy production from agricultural waste. Specifically, we will examine how predictive analytics can be employed to forecast spatial biomass distribution, efficiently allocate resources, and design a cost-effective bioenergy supply chain. Using real-world examples, we’ll demonstrate how these technologies are vital in transitioning to a lower-carbon future, benefitting both the environment and farmers. This discussion promises to provide insightful, practical knowledge on employing machine learning for sustainability challenges in energy sector.

In this session we will look at several examples of use of AI and predictive modeling applied to the natural world – and enabling trust and collaboration within the value chain that supports our food industry. We’ll look at how TheoryMesh is enabling data analysis from crop farming through food processing, and on to consumers, and at other use cases of the use of AI in anticipating climate change for woodland creation and conservation, and soil protection, leveraging LIDAR and other public data.

The end goal is to find the balance between current insights and future trends – both social and natural – to increase and preserve quality and yields, drive circularity and better serve our changing needs. Soils and forests evolve slowly, which is in itself a challenge when it comes to future predictions, as we are building on data lacking long enough time series. However, the analysis can allow short term decision making in terms of sourcing, use and optimization of resources, land management and stakeholder engagement.

The impact on future generations as well as on short term business cases is well worth the work.

Air pollution in cities has to stay below certain limits for health reasons. Reliable forecasts of air quality offer the opportunity to control air pollution, e.g. by managing peak traffic flows.  This talk shows an approach to forecast air pollution within the city of Berlin for the next few days using xgboost. The talk will focus on the model and the challenges we faced during the modeling process. We will also have a glance at the tech stack we use (e.g., Clickhouse, kubernetes, Docker, Redash, FastAPI).

Harnessing the potential of predictive analytics, we revolutionize supply chains for a greener tomorrow. By leveraging data-driven insights, we optimize inventor, and distribution processes, minimizing waste and reducing carbon emissions. Predictive models empower us to foresee demand patterns, enhance resource allocation, and lowering energy consumption. This strategic approach not only enhances efficiency but also significantly mitigates the supply chain’s environmental footprint. The synergy of predictive analytics and sustainability transforms industries, showcasing how innovation aligns with responsible practices, ultimately fostering a positive impact on the climate.

In an era of heightened accountability for environmental and social impacts, comprehensive sustainability reporting is crucial. Currently, existing reporting methods are unreliable and a hindering process. Discover how our framework transforms the document querying, providing a quantifiable process with natural language processing and dynamic search term generation. Gain insights into the step-by-step process and uncover the benefits of enhanced document querying in driving meaningful change. Don’t miss this chance to shape a data-driven, sustainable future.