This study shows the impact of black carbon (BC) aerosol atmospheric rivers (AAR) on the Antarctic Sea ice retreat. We detect that a higher number of BC AARs arrived in the Antarctic region due to increased anthropogenic wildfire activities in 2019 in the Amazon compared to 2018. Our analyses suggest that the BC AARs led to a reduction in the sea ice albedo, increased the amount of sunlight absorbed at the surface, and a significant reduction of sea ice over the Weddell, Ross Sea (Ross), and Indian Ocean (IO) regions in 2019. The Weddell region experienced the largest amount of sea ice retreat ($ \sim \mathrm{33,000} $ km2) during the presence of BC AARs as compared to $ \sim \mathrm{13,000} $ km2 during non-BC days. We used a suite of data science techniques, including random forest, elastic net regression, matrix profile, canonical correlations, and causal discovery analyses, to discover the effects and validate them. Random forest, elastic net regression, and causal discovery analyses show that the shortwave upward radiative flux or the reflected sunlight, temperature, and longwave upward energy from the earth are the most important features that affect sea ice extent. Canonical correlation analysis confirms that aerosol optical depth is negatively correlated with albedo, positively correlated with shortwave energy absorbed at the surface, and negatively correlated with Sea Ice Extent. The relationship is stronger in 2019 than in 2018. This study also employs the matrix profile and convolution operation of the Convolution Neural Network (CNN) to detect anomalous events in sea ice loss. These methods show that a higher amount of anomalous melting events were detected over the Weddell and Ross regions.

Eight major supply chains contribute to more than 50% of the global greenhouse gas emissions (GHG). These supply chains range from raw materials to end-product manufacturing. Hence, it is critical to accurately estimate the carbon footprint of these supply chains, identify GHG hotspots, explain the factors that create the hotspots, and carry out what-if analysis to reduce the carbon footprint of supply chains. Towards this, we propose an enterprise decarbonization accelerator framework with a modular structure that automates carbon footprint estimation, identification of hotspots, explainability, and what-if analysis to recommend measures to reduce the carbon footprint of supply chains. To illustrate the working of the framework, we apply it to the cradle-to-gate extent of the palm oil supply chain of a leading palm oil producer. The framework identified that the farming stage is the hotspot in the considered supply chain. As the next level of analysis, the framework identified the hotspots in the farming stage and provided explainability on factors that created hotspots. We discuss the what-if scenarios and the recommendations generated by the framework to reduce the carbon footprint of the hotspots and the resulting impact on palm oil tree yield.

Edge AI is the fusion of edge computing and artificial intelligence (AI). It promises responsiveness, privacy preservation, and fault tolerance by moving parts of the AI workflow from centralized cloud data centers to geographically dispersed edge servers, which are located at the source of the data. The scale of edge AI can vary from simple data preprocessing tasks to the whole machine learning stack. However, most edge AI implementations so far are limited to urban areas, where the infrastructure is highly dependable. This work instead focuses on a class of applications involved in environmental monitoring in remote, rural areas such as forests and rivers. Such applications have additional challenges, including failure proneness and access to the electricity grid and communication networks. We propose neuromorphic computing as a promising solution to the energy, communication, and computation constraints in such scenarios and identify directions for future research in neuromorphic edge AI for rural environmental monitoring. Proposed directions are distributed model synchronization, edge-only learning, aerial networks, spiking neural networks, and sensor integration.

Sustainability practices of a company reflect its commitments to the environment, societal good, and good governance. Institutional investors take these into account for decision-making purposes, since these factors are known to affect public opinion and thereby the stock indices of companies. Though sustainability score is usually derived from information available in self-published reports, News articles published by regulatory agencies and social media posts also contain critical information that may affect the image of a company. Language technologies have a critical role to play in the analytics process. In this paper, we present an event detection model for detecting sustainability-related incidents and violations from reports published by various monitoring and regulatory agencies. The proposed model uses a multi-tasking sequence labeling architecture that works with transformer-based document embeddings. We have created a large annotated corpus containing relevant articles published over three years (2015–2018) for training and evaluating the model. Knowledge about sustainability practices and reporting incidents using the Global Reporting Initiative (GRI) standards have been used for the above task. The proposed event detection model achieves high accuracy in detecting sustainability incidents and violations reported about an organization, as measured using cross-validation techniques. The model is thereafter applied to articles published from 2019 to 2022, and insights obtained through aggregated analysis of incidents identified from them are also presented in the paper. The proposed model is envisaged to play a significant role in sustainability monitoring by detecting organizational violations as soon as they are reported by regulatory agencies and thereby supplement the Environmental, Social, and Governance (ESG) scores issued by third-party agencies.