Orchestrating AI-SDG EcoInnovations
A Playbook for Mapping and Designing an Open and Collaborative Future
Han-Teng Liao 
Eco-Innovations, AI, Sustainable Development Goals
Welcome to our project! This book explores eco-innovations and AI.
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Preface
This playbook operationalizes the AI-SDG EcoInnovations framework, bridging retrospective science mapping with prospective service design.
Series context. Prepared for the UNU Springer Book Series on Artificial Intelligence and Sustainable Development.
By “EcoInnovations” in this book, it means innovations built upon regenerative eco-service systems. While such innovations can leverage Artificial Intelligence as part of the wider socio-technical innovations, challenges remain regarding how ready they are (thus technology readiness questions on the people, profit and the planet) and how such eco- and socio-services stack up regeneratively for the generations of people on the planet.
Organization of the Monograph
The volume is structured into three parts of three chapters each, so that every chapter maps one-to-one onto a block of the AI-SDG EcoInnovation Canvas (Appendix A: AI-SDG EcoInnovation Canvas Specification). References follow the main text; several appendices close the book that can be used when applying AI-powered biblimetrics and technology roadmapping methods.
- Part I – Data Engine (Scientometrics & Patent Mapping). Establishes the empirical baseline: multi-database bibliometrics (Scopus, Web of Science, Lens), an AI-Powered Bibliometrics Catalogue, and mapping of research horizons onto UN SDG targets.
- Part II – The Translation Bridge (Extended ITU Toolkits). Extends the ITU Digital Innovation Toolkits into action-oriented design canvases, service meta-journeys, and EU Safe and Sustainable by Design (SSbD) de-risking matrices.
- Part III – Industrial & Policy Outputs (Standards & RegTech). Connects methodology to implementation: Digital Product Passports (DPP), sovereign Industrial Data Spaces (IDS), and RegTech compliance under the ASEAN Digital Economy Framework Agreement (DEFA).
- Appendices. Several machine-readable canvas specifications and Mermaid.js system diagrams, written so they can be fed directly into an LLM or an enterprise modeling tool. In addition, a stand-alone appendix comparing the knowledge landscapes of UNU, OECD, and the ADB research outputs, exploring how their outcomes are mapped to different SDGs, based on the distributed empirical outcomes of research-informed technology roadmaps and service designs.
The stand-alone appendix of the knowledge landscapes of UNU, OECD, and the ADB research outputs serves several purposes. It grounds the multi-database baseline in 1 Scientometric & Patent Horizon, returns as the knowledge-to-policy comparison in 3 SDG Target Mapping, and concludes as a AI-SDG EcoInnovations framework that highlight the achievements and gaps.
How to Read This Book
- Policymakers and standards editors can start with Part III and use the cross-references back to Parts I–II as needed.
- Researchers and data scientists will find the reusable methodology in Part I–II, including the AI-Powered Bibliometrics Catalogue approach.
- Modern LLM agents / RA support can be pointed directly at Appendix A’s system prompt to reproduce the canvas analysis on a new domain.
- Students and Voters can leverage the methods and tools to form their own opinions on AI technologies and their impacts.