Environmental Intelligence: Data Driven Insights for Stakeholders on the Environmental Sustainable Development Goals

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a reproducible bibliometric framework and topic-technology map for analyzing EI literature, useful for scholars in environmental informatics and SDG research.

🏢実務担当者:Offers a structured overview of EI technologies and applications (e.g., carbon accounting, pollution tracking) that can inform corporate sustainability strategy and reporting.

🏛政策担当者:Highlights cross-SDG linkages and emerging EI technologies, guiding R&D and policy integration for environmental intelligence infrastructure.

Environmental intelligence (EI) leverages AI and big data to garner critical insights for advancing the United Nations Sustainable Development Goals (SDGs) related to environment - SDG 13 (Climate), SDG 14 (Oceans), and SDG 15 (Land). However, investigations that target synthesis of its scope, methods, and contributions for proactive environmental and STI policymaking are scanty. So do frameworks for carrying out such investigations. This paper introduces a macro-meso-micro assessment framework that integrates machine learning-based BERTopic modeling, keyword co-occurrence mapping, and targeted content analysis to examine EI publications indexed in the Dimensions database (up to June 21, 2025) related to SDGs 13, 14 and 15. The novel top-down scientometric framework is aligned with the pilot version of guidance list for bibliometric analyses (GLOBAL). The macro–meso–micro assessments identify 1) five core EI topics, 2) sixteen technological areas within the five topics, and 3) key specific developments and activities concentrated on climate risk assessment, ecosystem and biodiversity monitoring, carbon accounting, and pollution tracking, with emerging attention to explainable AI, Ecorobotics, and green buildings, etc. Micro assessment synthesizes evidence from 29 publications on key technologies and 12 publications on cross-goal interactions to characterize technical advances, typical data/analytics pipelines, and decision-support use cases. Cross-SDG analysis reveals bidirectional links between environmental SDGs and allied goals—SDG 6 (Water), SDG 7 (Energy), SDG 9 (Industry), SDG 11 (Cities), and SDG 12 (Consumption)—particularly via water quality sensing, renewable energy siting, industrial emission management, urban heat/flood analytics, and supply chain traceability. This study contributes 1) a delineated EI landscape tied to SDGs, 2) a topic–technology map which highlights promising emerging technologies, and 3) a reproducible, GLOBAL-protocol compliant framework for bibliometrics. The implications of this paper are threefold: it aligns standards and data access to enable EI deployment; it prioritizes multi-SDG R&D and capacity building; and it directs integration of EI with STI intelligence and epidemiological intelligence to enable co-development of EI toolchains across policy, academia, and industry to advance pursuit of environmental and allied SDGs.

• semanticscholar https://doi.org/10.1109/access.2026.3679800first seen 2026-05-05 22:28:42