相关论文: Rethinking Digitalization and Climate: Don't Predi…

Projecting climate change is a generalization problem: we extrapolate the recent past using physical models across past, present, and future climates. Current climate models require representations of processes that occur at scales smaller…

Environmental sustainability, particularly in relation to climate change, is a key concern for consumers, producers, and policymakers. The carbon footprint, based on greenhouse gas emissions, is a standard metric for quantifying the…

Data centers are carbon-intensive enterprises due to their massive energy consumption, and it is estimated that data center industry will account for 8\% of global carbon emissions by 2030. However, both technological and policy instruments…

Integrated Assessment Models (IAMs) of the climate and economy aim to analyze the impact and efficacy of policies that aim to control climate change, such as carbon taxes and subsidies. A major characteristic of IAMs is that their…

Context: Digitalization brings new opportunities and also challenges to software companies. Objective: Software companies have mostly focused on the technical aspects of handing changes and mostly ignoring the business model changes and…

The digitalization of societies raises questions about its sustainability and the socio-technical impacts it generates. Ecological redirection applied to organizations is a field of research aiming for achieving sustainability as a…

Climate change has intensified the urgency of effective carbon sink solutions, yet the integration of Information and Communications Technologies (ICT) in these systems remains fragmented despite its transformative potential. This paper…

Data-driven machine learning models for weather forecasting have made transformational progress in the last 1-2 years, with state-of-the-art ones now outperforming the best physics-based models for a wide range of skill scores. Given the…

The goal of climate change governance is to stabilize greenhouse gas concentrations. This requires the reduction of anthropogenic global net emissions. In the pursuit of such a reduction, knowledge of greenhouse gas sources and sinks is…

Ecological forecasts are model-based statements about currently unknown ecosystem states in time or space. For a model forecast to be useful to inform decision makers, model validation and verification determine adequateness. The measure of…

Earlier meta-analyses of the economic impact of climate change are updated with more data, with three new results: (1) The central estimate of the economic impact of global warming is always negative. (2) The confidence interval about the…

The latest trends in the adoption of cloud, edge, and distributed computing, as well as a rise in applying AI/ML workloads, have created a need to measure, monitor, and reduce the carbon emissions of these compute-intensive workloads and…

The versatility of digital technologies relies on a capacity to represent and subsequently manipulate algorithmically selected physical processes, objects or qualities in a domain. Organizationally real digital representations are those…

Cloud-related parameterizations remain a leading source of uncertainty in climate projections. Although machine learning holds promise for Earth system models (ESMs), many data-driven parameterizations lack interpretability, physical…

Accurate reporting of energy and carbon usage is essential for understanding the potential climate impacts of machine learning research. We introduce a framework that makes this easier by providing a simple interface for tracking realtime…

Accurately forecasting the weather is a key requirement for climate change mitigation. Data-driven methods offer the ability to make more accurate forecasts, but lack interpretability and can be expensive to train and deploy if models are…

Climate change, which is now considered one of the biggest threats to humanity, is also the reason behind various other environmental concerns. Continued negligence might lead us to an irreparably damaged environment. After the partial…

Different advertising messages work for different people. Machine learning can be an effective way to personalise climate communications. In this paper we use machine learning to reanalyse findings from a recent study, showing that online…

Catastrophic transitions, where a system shifts abruptly between alternate steady states, are a generic feature of many nonlinear systems. Recently these regime shift were suggested as the mechanism underlies many ecological catastrophes,…

Probabilistic projections of baseline (with no additional mitigation policies) future carbon emissions are important for sound climate risk assessments. Deep uncertainty surrounds many drivers of projected emissions. Here we use a simple…