特定排出係数と空間自己相関分析の統合による住宅・廃棄物部門の都市炭素排出評価：インドネシア・モジョケルト市のケーススタディ
Integrating Specific Emission Factors and Spatial Autocorrelation Analysis to Evaluate Urban Carbon Emissions in Residential and Waste Sectors: A CaseStudy of Mojokerto City, Indonesia (原題)
Taufiq Hidayat, Imam Santoso, Maharani Pertiwi Koentjoro
🤖 gxceed AI 要約
日本語
インドネシア・モジョケルト市を対象に、IPCC Tier2準拠の特定排出係数（SEF）法と空間自己相関分析（Moran's I、LISA、Getis-Ord Gi*）を組み合わせ、地区別の炭素排出を定量化・地図化した。住宅部門が主因で年間13,439.87トンCO₂、Moran's I=0.24で有意な空間クラスタリングを確認。排出ホットスポットとコールドスポットを特定し、空間的に標的化した緩和策の必要性を示した。
English
Using IPCC Tier 2 Specific Emission Factors combined with spatial autocorrelation analysis (Moran's I, LISA, Getis-Ord Gi*), this study quantifies and maps sub-district carbon emissions in Mojokerto City, Indonesia. Residential emissions dominate at 13,439.87 tCO2/year, with significant spatial clustering (Moran's I=0.24, p=0.0127) and identified hotspots/coldspots. It argues for spatially targeted, context-sensitive low-carbon urban planning.
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
日本では市町村レベルの排出量推計は環境省の自治体排出量カルテ等で整備が進むが、地区単位の空間クラスタリング分析は限定的。自治体の地域脱炭素計画や住宅・廃棄物部門のきめ細かい対策立案に示唆を与える。
In the global GX context
While global disclosure frameworks (TCFD/ISSB) focus on corporate reporting, this paper addresses the sub-national spatial dimension of emissions accounting relevant to city-level climate action and GHG inventories under the Paris Agreement's bottom-up pledges. It offers a replicable method for medium-sized cities in emerging economies.
👥 読者別の含意
🔬研究者:都市スケールの排出量推計に空間統計を組み合わせる手法の実証例として参考になる。
🏢実務担当者:自治体・地域企業が地区別の排出特性を把握し、住宅・廃棄物対策の優先地域を特定する際に活用可能。
🏛政策担当者:地方自治体の低炭素計画において、空間的に標的化した緩和策の設計根拠を提供する。
📄 Abstract（原文）
The rise of urban carbon emissions is being seen as an important global climate change mitigation target, especially for medium-sized cities during rapid urbanization including Mojokerto in Indonesia. Yet almost all the existing studies rely on the basis of macro-scale inventories and overlook micro-spatial variations between urban villages, which cause very little insights into how residential and waste activities drive localized emissions patterns. This study fills this gap through a synergy of the Specific Emission Factor (SEF) method combined with spatial autocorrelation analysis to quantify and map carbon emissions sub-district wise. Empirically generated SEF indicators using the IPCC (2006) Tier 2 method demonstrate 0.278 tons CO₂ /household/year for residential emissions and 0.000579 tons CO₂-eq/person/year for waste emissions. This, was associated with the use of spatial statistical methods (Global Moran’s I, Local Moran’s I (LISA), and Getis–Ord Gi*) to characterize clusters and hotspots. The findings reveal total annual emissions amounting to 13,439.87 tons CO₂, primarily the former related to the residential sector. A Moran’s I value of 0.24 (p = 0.0127) indicated that there was a strong positive spatial autocorrelation, Wates and Kedundung were indicated the emission hotspots and Purwotengah and Sentanan were the cold spots. Results show that Mojokerto carbon emissions are spatially clustered and geographically concentrated, which is indicative of population density and household energy use intensity differences. The findings emphasise the potential of spatially specific mitigation approaches, suggesting that multisectoral and spatio-temporal analyses are necessary to enhance adaptive, context sensitive low carbon urban planning.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.29244/jpsl.16.3.396first seen 2026-07-31 05:19:46 · last seen 2026-07-31 05:20:46
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