Call for Papers

Sustainability and Climate Change

Submissions start date: Friday, 10 January 2025
Submission deadline: Monday, 31 March 2025

With the increasing availability of new data streams, developments in statistical and AI methods, and access to increasing computational power, we now have an exciting and timely opportunity to use these combined technologies to rigorously and efficiently enhance the study of challenges related to climate change and sustainability. Such developments could inform, for example, the exploration of strategies for adaptation and mitigation of biodiversity loss, declining water quality and availability, or real-time prediction of hazards and extremes. Environmetrics is calling for submissions of original papers related to developments in environmetrics specifically related to sustainability and/or climate change that have arisen as an output or related output to The International Environmetrics Society (TIES) conference 2024. These could include contributions in statistics and/or data science and/or AI and relate to any aspects of sustainability and climate change. Papers should have novelty in the methodological contribution with a substantive application and/or present a novel, timely, and substantive case study using cutting-edge environmetrics approaches to advance knowledge in sustainability and/or climate change.

The special issue topics include novel statistical, data science, and AI methodological contributions for applications related to sustainability and climate change. Examples are given below, but submissions are not limited to these topics, and the methodological and application areas below are also interchangeable.

  • Advances in spatiotemporal statistics for predicting natural hazards, including multivariate spacetime approaches, computational developments in geostatistics, and spatial modeling
  • Bayesian hierarchical models for biodiversity – including advances in uncertainty quantification and computational developments for, e.g. efficiency, presence/absence data, nondetect data
  • Explainable AI for water quality monitoring – including automatic tools for early warning, anomaly detection, and event description
  • Deep learning innovations for renewable energy – including prediction of energy use and future energy demand, and related software tools
  • Prediction and forecasting of climate extremes – including advances in extreme value theory, quantile regression, and probabilistic forecasting
  • Functional data approaches for climate adaptation and resilience planning – including new approaches for high-dimensional functional data
  • Data fusion and integration for hydrology – including new methodology for the fusion and linkage of data from multiple data sources (including satellite, sensor data) with uncertainty quantification.

Guest Editors:

Prof. Claire Miller
University of Glasgow
United Kingdom

Prof. John Boland
University of South Australia
Australia

Prof. Belinda Chiera
University of South Australia
Australia


Keywords: Adaptation and resilience; Biodiversity; Data fusion and integration; Deep learning and explainable AI; Monitoring design; Natural hazards; Probabilistic forecasting; Renewable energy; Spatiotemporal statistics; Water quality and hydrology


Submission Guidelines/Instructions

Please refer to the Author Guidelines to prepare your manuscript. When submitting your manuscript, please answer the question: "Is this submission for a special issue?" by selecting the special issue title from the drop-down list.

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