Corporate carbon accounting: Current practices and opportunities for research

This article reviews current practices in corporate carbon accounting and highlights opportunities for future research. The common framework for determining and reporting corporate greenhouse gas (GHG) emissions today is the GHG Protocol. Like financial accounting stan- dards, this framework includes overarching objectives, principles for conceptual guidance,and procedures for determining key outcome variables. Their design and implementation, however, often result in disclosures that obscure firms’ actual emissions and decarbonization progress. Recognizing the growing demand for transparency, standard-setters worldwide have recently introduced regulations for carbon accounting and reporting. These regulations require companies to disclose decision-useful information on their emissions. Yet, they have also largely adopted the GHG Protocol for how companies should determine and report their emissions. Accounting scholars now have the opportunity to develop solutions that will make corporate carbon accounting an effective tool in combating climate change

Decision trees that remember: Gradient-based learning of recurrent decision trees with memory

Neural architectures such as Recurrent Neural Networks (RNNs), Transformers, and State-Space Models have shown great success in handling sequential data by learning temporal dependencies. Decision Trees (DTs), on the other hand, remain a widely used class of models for structured tabular data but are typically not designed to capture sequential patterns directly. Instead, DT-based approaches for time-series data often rely on feature engineering, such as manually incorporating lag features, which can be suboptimal for capturing complex temporal dependencies. To address this limitation, we introduce ReMeDe Trees, a novel recurrent decision tree architecture that integrates an internal memory mechanism, similar to RNNs, to learn long-term dependencies in sequential data. Our model learns hard, axis-aligned decision rules for both output generation and state updates, optimizing them efficiently via gradient descent. We provide a proof-of-concept study on synthetic benchmarks to demonstrate the effectiveness of our approach

Preventing harmful data practices by using participatory input to navigate the machine learning multiverse

In light of inherent trade-offs regarding fairness, privacy, interpretability and performance, as well as normative questions, the machine learning (ML) pipeline needs to be made accessible for public input, critical reflection and engagement of diverse stakeholders.In this work, we introduce a participatory approach to gather input from the general public on the design of an ML pipeline. We show how people’s input can be used to navigate and constrain the multiverse of decisions during both model development and evaluation. We highlight that central design decisions should be democratized rather than “optimized” to acknowledge their critical impact on the system’s output downstream. We describe the iterative development of our approach and its exemplary implementation on a citizen science platform. Our results demonstrate how public participation can inform critical design decisions along the model-building pipeline and combat widespread lazy data practices