CICA 2026

Call for Book Chapters

Computational Intelligence for Climate Action: Scaling, Ethics and Governance

BookTitle: Computational Intelligence for Climate Action: Scaling, Ethics and Governance.

An Official Volume in the UNU-Macau & Springer Nature Series:

"Artificial Intelligence and Sustainable Development."

No-Publications Charges.

Publisher: Springer Nature

Indexing: Scopus, Web of Science, SpringerLink

CMT ACKNOWLEDGEMENT

About the Book:

This book offers a rigorous and sophisticated analysis of the boundaries and frontiers of climate research, extending beyond policy summaries to address fundamental computational issues. It systematically bridges the gap between traditional High-Performance Computing (HPC) limitations in EarthSystem Models (ESMs) and the revolutionary potential of specialized Artificial Intelligence (AI) architectures. The text is one of the first to rigorously confront HPC scaling limitations, the core bottleneck in modern climate forecasting, by introducing novel, mathematically sound solutions. It provides in-depth examinations of sophisticated computational methodologies, including Physics-Informed Neural Networks (PINNs) and Graph Neural Networks (GNNs), demonstrating their application in critical areas such as data assimilation, effective parameterization, and the creation of Digital Twins of the Earth system.

The topic encompasses the architecture of climate simulation (HPC/Cloud), sophisticated AI approaches (Deep Learning, PINNs), the administration of extensive climate data, and the ethical implementation of AI for policy-relevant Uncertainty Quantification (UQ). This is an essential resource for researchers, graduate students, and advanced professionals in Computational Science, Climate Modeling, HPC, and AI/Deep Learning who require a technical, academic guide to the future of high-fidelity climate science.

Keywords: High-Performance Computing (HPC), Computational Limits, Digital Twins, Physics-Informed Neural Networks (PINNs), Graph Neural Networks (GNNs), Big Data Handling, Bias Rectification, Climate Action, Financial Risk Modeling, Sustainable Development Goals (SDGs).

Key Themes: The book focuses on three pillars:

Scaling: Transferring computational intelligence (CI) from theoretical models to practical climate change solutions.
Ethics: An investigation of the manner in which AI manages bias, objectivity, algorithmic accountability and transparency.
Governance: ensuring computational frameworks align with the United Nations' Sustainable Development Goals (SDGs) and global climate policy.

Track List

Scientific Background: Climate basics and computer constraints.
Architecture at the Advanced Level: GenAI, LLM, and ML is all part of this group.
Data science: dealing with issues including bias, noise, and discrepancies in weather records.
Forecasting the weather: using predictive modeling and simulation to predict bad weather.
Farming and renewable energy: AI for managing resources.
Disaster preparedness: Implementation of real-time response and early warning systems.
City planning and strengthening: Smart Infrastructure.
Biodiversity: CI will safeguard the ocean, vegetation, and animals.
Assessment of risk and green finance: Financial Analysis.
Green Artificial Intelligence: the conversion of energy-intensive AI processes into more efficient ones.
Equity and Strategy: The role of global governance and social justice in the struggle against climate change.

Chapters(But Not limited)

Chapter 1: Scientific Context and Computational Limits.
Chapter 2: Advanced ML Architecture Methods for Making Artificial Intelligence (AI)
Chapter 3: Big Data Handling, consistency, and bias in climate change.
Chapter 4: Computational Forecasting, Modeling, and Simulation
Chapter 5: AI to control resources
Chapter 6: Disaster Response and Readiness
Chapter 7: Urban areas and their Infrastructure
Chapter 8: Ecosystems and Biodiversity
Chapter 9: Financial Risk and Analysis
Chapter 10: AI's Energy Footprint
Chapter 11: Strategies, Equity, and Regulations

Important Dates

1-Page Abstract Submission Closes on: 25th April 2026
Abstract Acceptance/Rejection Notification: 10th June 2026
Full-Chapter Submission: May 31st, 2026
First-Review Notification: June 30th, 2026
Revised Article Submission: July 15th, 2026
Acceptance/Rejection Notification: July 30th, 2026
Camera-Ready Submission: 31st August 2026

Submission Procedure

Researchers, Industrial Professionals, and practitioners are invited to submit their chapter Proposal/Abstract for inclusion in the upcoming book, CICA 2026. The Abstract should be 300-500 words, outlining the chapter's scope, Objectives, and Key contributions. Upon acceptance, the chapters will be published in Springer Nature.

Authors must ensure that their work is original and free from plagiarism. Submissions should be made via the given submission link, and queries can be directed to the provided email address.

Please provide the following points in your proposals/abstracts:

1) Title of the contribution.

2) Name of author, co-authors, institution, email address.

3) Content/Abstract of the proposed chapter.

Submission Instructions:

All contributions must be submitted via the Microsoft CMT portal to guarantee a stringent double-blind peer-review procedure.

Click Here to Access the CICA-2026 CMT Portal (Link to: https://sites.google.com/view/cica2025/home)
Create an account and select "New Submission."
AI Disclosure: During submission, authors must disclose the use of Generative AI tools in the preparation of the manuscript, in accordance with our AI Policy: “https://group.springernature.com/gp/group/ai”.

4. Manuscript Preparation (Springer Nature Guidelines)

All final chapters must comply with the Springer Nature Manuscript Guidelines to guarantee high-quality production and indexing (Scopus, WoS):

Formatting: Chapters should be prepared in either LaTeX or Microsoft Word using Springer’s standard templates.
Structure: Each chapter must include an abstract (150–250 words), keywords (3–10), and a clearly defined introduction and conclusion.
Permissions: Authors are responsible for obtaining permissions for any third-party material (images, charts, or datasets) before the final handover.
References: Use a consistent citation style (preferably Springer Basic or Vancouver) as outlined by clicking this link: “https://www.springernature.com/gp/authors/publish-a-book/manuscript-guidelines”.

5. Publisher:

This edited book is scheduled for publication by Springer. This publication is anticipated to be released in 2026.

6. Editorial Standards, Ethics, and AI Policy.

To maintain the high academic standards of Springer Nature and the UN University, every manuscript is carefully reviewed by two independent experts.
Policymaking on Artificial Intelligence and Authorship:
In accordance with Springer Nature's dedication to doing research in an ethical manner, CICA-2026 has adopted the following AI policy:

Authorship: Generative AI technologies and Large Language Models (LLMs) do not fulfill the criteria for authorship. Every author on the list must be genuine.
Integrity: Writers should check their work for errors and plagiarism. Always be sure to include in the "Acknowledgements" or "Methods" section if any content was created using AI.
license: By submitting a paper, authors acknowledge their acceptance of Springer Nature's responsible AI license, which protects their research and facilitates its accessibility through ethical AI indexing.
Objective of the Final Manuscript: The final manuscript will be essential for individuals working in the disciplines of study, climate science, and technological strategy worldwide.

"Inclusion in the volume is based upon agreement with these standards."