SCHOLARLY SHOWCASE 12 SCHOLARLY SHOWCASE Masaru Yarime. “Data Governance for Artificial Intelligence in Addressing Climate Change.” Field Actions Science Reports. The journal of field actions Special Report (2026): 68-71. Focus of Study This paper discusses the integration of artificial intelligence and climate action, centering on data governance. It analyzes AI’s wide applications in climate change mitigation and adaptation, and points out that data fragmentation, uneven data distribution, algorithmic bias, AI’s carbon footprint and imperfect institutional mechanisms restrict the effective use of climate AI. It also summarizes existing solutions including open data platforms, data trusts and public-private partnerships, and emphasizes that sound data governance is the core to unleash AI’s potential for addressing climate change. Policy Recommendation Policymakers should unify global data standards and break data silos, optimize technical infrastructure to narrow regional data gaps and digital divide, build inclusive and fair AI models to avoid algorithmic discrimination, establish multi-stakeholder governance mechanisms such as climate data trusts, deepen public-private cooperation, and balance AI’s climate benefits and energy consumption to promote responsible application of climate AI. Asatani, Kimitaka, Yurie Iwata, Yuta Tomokiyo, Basil Mahfouz, Masaru Yarime, and Ichiro Sakata. “Structure of Scientific Knowledge Flows to Intergovernmental Organizations.” Proceedings of the National Academy of Sciences - PNAS (United States) 123, no. 17 (April 2026): e2514861123. Focus of Study This study examines scientific knowledge flows from academia to intergovernmental organizations (IGOs) using 230,737 IGO-cited papers (2015–2023) across 23 research fields. It identifies “Highly IGO-Cited Scientists (HIC-Sci)” — merely 0.7%–4.4% of authors producing 30% of IGO-referenced research. The paper unpacks three drivers of this extreme concentration: dense transnational co-authorship networks overlapping IGO advisory bodies like IPCC, faster IGO citation uptake, and standardized policy-oriented vocabulary. Mature fields (climate modeling) show far tighter concentration than emerging areas (AI & data science). HIC-Sci networks follow a Western Europe core-periphery geographic structure, and cross-IGO co-citation amplifies elite dominance, even amid IGO diversity reforms. Policy Recommendation The research proposes targeted institutional reforms to balance scientific credibility and procedural legitimacy for IGOs. First, IGOs should implement transparent author-level citation tracking to expose uneven knowledge sourcing, complementing existing conflict-of-interest and expert selection rules. Second, emerging policy domains such as AI governance need formal inclusive monitoring mechanisms before epistemic consensus solidifies to avoid entrenched elite bias. Third, IGOs must mitigate core-periphery geographic imbalance by systematically integrating observational, non-mainstream research from underrepresented regions. Finally, regular long-term evaluations of evidence synthesis outcomes are required, as procedural diversity adjustments fail to offset persistent citation concentration driven by cumulative advantage and network lock-in. Julien de TROULLIOUD de LANVERSIN, Christopher Fichtlscherer, and Moritz Kütt. “Verification of very low-yield nuclear tests with post-experiment gamma spectroscopy.” Physical Review Applied 25.4 (2026): 044008. Focus of Study Against the background of the Comprehensive Nuclear-Test-Ban Treaty (CTBT) zero-yield standard and widespread low-yield nuclear experiment disputes among nuclear-armed states, this research distinguishes supercritical nuclear tests that produce self-sustaining fission chain reactions from non-prohibited subcritical experiments. Relying on 6,000 sets of neutronics, isotope evolution and photon transport numerical simulations, the paper develops a post-test gamma spectroscopy onsite verification method based on fission product and plutonium-239 gamma ray intensity discriminant ratios. It systematically analyzes how core variables including initial fission quantity per kilogram of plutonium, inspection interval after test, and pre-test assembly criticality affect detection accuracy. Simulation results confirm that the proposed technique can reliably identify all supercritical nuclear tests with explosive yields above 38 grams TNT equivalent, even with minimal prior information of test parameters. This study fills the technical research gap of onsite post-experiment verification for ultra-low- yield nuclear tests, and supplements technical evidence for international nuclear disarmament and treaty compliance monitoring theories. Policy Recommendation International nuclear non-proliferation and treaty monitoring authorities should take post-test gamma spectrum discriminant ratio detection as a core onsite verification tool to enforce the CTBT zero-yield standard, with existing remote seismic and radionuclide monitoring systems as auxiliary detection means. Stable international inspection frameworks are required to clarify standardized sampling, measurement and data analysis procedures for low-yield test sites, and additional technical R&D funding should be allocated to optimize portable high-purity germanium detector devices for field inspection. Relevant intergovernmental organizations need to establish unified isotope chronology calibration standards (based on Fe-59/Mn-54 isotopic ratios) to narrow the uncertainty of test time estimation. It is critical to abandon ambiguous blanket monitoring rules, balance inspection rigor and state confidentiality protection, and adopt composite technical verification instruments to eliminate loopholes in identifying covert ultra-low-yield supercritical tests, so as to reduce nuclear tension and advance the full entry-into-force of the Comprehensive Nuclear-Test-Ban Treaty 11
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