ユニット対数対数分位混合モデルを用いた南アジア諸国のGHG排出量のモデリング
Modeling GHG Emissions Across Selected South Asian Countries Using a Unit Log‐Log Quantile Mixed Model (原題)
Nirajan Bam
🤖 gxceed AI 要約
日本語
本研究は南アジア諸国のGHG排出量（対世界比と総量）を、人口、GDP、都市化、再生可能エネルギー比率などの要因と関連付けて分析する。新たなベイズ型Unit Log-Log Quantile Mixed Modelを提案し、モンテカルロシミュレーションで性能を評価した。結果、時間・人口・都市化は排出比率と正の関連、再生可能エネルギー比率とGDPは負の関連を示し、再生可能エネルギーの拡大が排出削減に有効であることを示唆する。
English
This study analyzes GHG emissions in South Asian countries, linking their proportion and total to factors like population, GDP, urbanization, and renewable energy share. A new Bayesian Unit Log-Log Quantile Mixed Model is proposed and validated via Monte Carlo simulation. Results show time, population, and urbanization positively correlate with emission proportion, while renewable energy share and GDP negatively correlate, highlighting renewable expansion as a key mitigation lever.
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、アジア地域の排出動向は国際的なサプライチェーン排出（Scope 3）や海外事業のリスク評価に直結する。本研究成果は、南アジアでの再生可能エネルギー導入の効果を定量的に示すもので、日本企業の海外投資判断や気候関連リスク管理に参考となる。
In the global GX context
For global GX scholarship, this paper provides empirical evidence on demographic and economic drivers of emissions in South Asia, reinforcing the role of renewable energy in mitigation. It offers a novel statistical approach (ULLQMM) applicable to bounded proportional data, relevant for climate modeling and policy analysis in developing regions.
👥 読者別の含意
🔬研究者:Provides a new Bayesian quantile model for bounded proportional data and empirical insights into South Asian emission drivers.
🏢実務担当者:Offers quantitative support for renewable energy investments in South Asia, useful for regional sustainability strategy.
🏛政策担当者:Highlights the importance of renewable energy expansion and demographic factors in shaping national emission trajectories.
📄 Abstract（原文）
ABSTRACT This study examines greenhouse gas (GHG) emissions in South Asian countries by analyzing both their proportion relative to global emissions and total emissions, along with key country‐level determinants. To address the bounded (0‐1) and correlated nature of the GHG proportion, this study proposes a new Bayesian Unit Log‐Log Quantile Mixed Model (ULLQMM), and its performance was evaluated through Monte Carlo simulation. The ULLQMM is applied to investigate the associations between GHG proportion and time, population size, log(GDP), urban population growth, and renewable energy share in selected South Asian countries. Results show that time, population size, and urban population growth are positively associated with the proportion of GHG emissions, whereas renewable energy share and log(GDP) are negatively associated with the proportion of GHG emissions. Additionally, the association between total GHG emissions and key determinants: time, population size, log(GDP), urban population growth, and renewable energy share was analyzed using a Bayesian general linear mixed model. The results show that time, population size, and log(GDP) are positively associated with total GHG emissions, whereas renewable energy share and urban population growth are negatively associated. These findings underscore the critical role of demographic, economic, and energy‐related factors in shaping GHG emissions and demonstrate the potential of renewable energy expansion to reduce GHG emissions in South Asia.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1002/env.70132first seen 2026-09-01 05:07:28
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