A Predictive Analytics Framework for Data-Driven Sustainability in Reducing Energy Consumption and Carbon Footprint Across Urban Infrastructure
データ駆動型持続可能性のための予測分析フレームワーク：都市インフラにおけるエネルギー消費とカーボンフットプリント削減 (AI 翻訳)
Evha Rozario, Shuchita Shahnaz, Foysal Mahmud, Shuchona Malek Orthi, Gazi Touhidul Alam, Ashraful Islam, Subha Shamarukh
🤖 gxceed AI 要約
日本語
本論文は、都市インフラにおけるエネルギー消費とカーボンフットプリント削減のための統合的予測分析フレームワークを提案する。6層から成るフレームワークは、データ取得から予測モデリング、説明可能性、最適化、フィードバックループまでを包含し、解釈可能性を組織的な受容の前提条件と位置付け、持続可能な開発目標（SDGs）に整合する。概念的枠組みを提供し、今後の実証的検証のための設計も提示する。
English
This paper proposes an integrative predictive analytics framework for data-driven sustainability in urban infrastructure, focusing on reducing energy consumption and carbon footprint. The framework consists of six interdependent layers including data acquisition, predictive modeling, explainability, optimization, and a continuous feedback loop. It treats interpretability as a precondition for institutional uptake, embeds carbon accounting, and aligns with SDGs. The paper provides a theoretical scaffolding and a validation design for empirical testing.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の都市でもエネルギー効率向上やカーボンニュートラル実現が急務であり、本フレームワークはスマートシティやSSBJ開示におけるデータ活用の指針となる可能性がある。特に、説明可能性を重視する点は、投資家向け開示や統合報告書でのデータ透明性要求に合致する。
In the global GX context
Globally, the framework addresses key challenges in urban decarbonization by linking predictive analytics to governance and decision-making. It aligns with TCFD/ISSB recommendations for data quality and transparency, and offers a structured approach for cities to operationalize climate action within the broader sustainability reporting landscape.
👥 読者別の含意
🔬研究者:Provides a coherent theoretical framework for integrating AI/ML into urban sustainability, with a validation design for empirical testing.
🏢実務担当者:Offers a structured approach for urban planners and sustainability teams to leverage predictive analytics for energy and carbon reduction.
🏛政策担当者:Highlights the need for interpretable analytics in policy-making and provides a model for aligning analytical outputs with SDGs and climate targets.
📄 Abstract（原文）
Rapid urbanization has intensified the energy demand and carbon intensity of the built environment, positioning cities at the center of global decarbonization efforts. Although predictive analytics, machine learning, and ubiquitous sensing now generate unprecedented volumes of urban energy data, their translation into measurable reductions in consumption and emissions remain fragmented, opaque, and weakly connected to decision-making. This paper develops an integrative predictive analytics framework that operationalizes data-driven sustainability across urban infrastructure. Grounded in a structured synthesis of recent scholarship on urban building energy modelling, smart-grid analytics, digital twins, and explainable artificial intelligence, the framework is organized as six interdependent layers: data acquisition and sensing, data integration and governance, predictive modelling, explainability and trust, optimization and decision support, and a continuous feedback loop linking analytical outputs to policy and operational action. The framework treats interpretability not as an optional refinement but as a precondition for institutional uptake, embedded carbon accounting within the analytical pipeline rather than appending it downstream and explicitly aligns analytical objectives with the Sustainable Development Goals, particularly those concerning affordable clean energy, sustainable cities, resilient infrastructure, and climate action. A proposed validation design is specified, comprising candidate data sources, a comparative modelling pipeline, an evaluation protocol, and a staged deployment with outcome feedback, so that the conceptual contribution can be empirically tested without recourse to fabricated results. The paper contributes a coherent theoretical scaffolding that connects technical prediction to governance, clarifies persistent barriers related to data quality, interoperability, privacy, and trust, and articulates a research agenda for evidence-based, transparent, and equitable urban decarbonization.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.70917/ijcisim-2026-2680first seen 2026-07-15 05:11:52
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