BRICSにおける物質フットプリント、制度の質、炭素排出：脱物質化プロキシを用いた分布的分析
Material Footprint, Institutional Quality, and Carbon Emissions in BRICS: Distributional Evidence Using Dematerialization Proxies (原題)
Fortune Ganda, Lelethu Mantangayi
🤖 gxceed AI 要約
日本語
BRICS5カ国（1980-2022年）を対象に、物質フットプリント・経済成長・選挙民主主義・政治腐敗と一人当たり炭素排出の関係をMMQRで推定。物質フットプリントは全分位で排出と正に関連し、高分位ほど係数が増大。成長と民主主義は高排出分位で負に関連するが、制度変数の符号は手法依存で頑健性に欠ける。
English
Using MMQR on five BRICS economies (1980–2022), the study links material footprint, growth, electoral democracy, and corruption to per-capita carbon emissions. Material footprint is positively associated with emissions across quantiles, rising at the upper tail, while growth and democracy show negative associations mainly at high quantiles. Institutional signs are specification-dependent and not robust across DCCE and DML checks.
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
日本企業のGX戦略に直接の示唆は乏しいが、新興国における資源消費と排出の結びつきは、Scope 3やサプライチェーン排出管理、移行リスク評価の文脈で参考になる。
In the global GX context
Adds distributional evidence on how resource throughput and governance shape emissions in major emerging economies, relevant to global transition finance and country-level climate risk assessment, though not directly tied to TCFD/ISSB disclosure frameworks.
👥 読者別の含意
🔬研究者:BRICSにおける排出と制度・物質フットプリントの分位点別関連を、MMQRと機械学習で検証した実証例として参考になる。
🏢実務担当者:新興国サプライチェーンにおける資源集約度と排出リスクの把握に役立つ可能性がある。
🏛政策担当者:新興国での排出削減には資源効率と制度設計の両面が関与しうることを示唆するが、因果解釈には注意が必要。
📄 Abstract（原文）
The BRICS economies have expanded rapidly while remaining large contributors to global carbon dioxide emissions, which motivates a closer look at the structural covariates of their carbon trajectories. The growth–emissions nexus is well documented, but fewer studies jointly place material footprint and institutional quality in a distributional panel setting. This study estimates the heterogeneous associations of electoral democracy, political corruption, economic growth, and material footprint with per capita carbon emissions in the five BRICS countries from 1980 to 2022. The baseline estimator is the Method of Moments Quantile Regression (MMQR). The Dynamic Common Correlated Effects (DCCE) estimator is used as a mean-based check for persistence and cross-sectional dependence. Double/Debiased Machine Learning (DML), SHAP, and Quantile Regression Forests (QRF) are used as non-parametric checks of pattern consistency, not as independent causal identification. MMQR estimates show that material footprint is positively associated with emissions across quantiles, with the coefficient rising from 0.3859 at the 10th quantile to 0.6224 at the 90th quantile. Economic growth and electoral democracy are negatively associated with emissions mainly at higher emission quantiles; at the 90th quantile the democracy coefficient is −0.2988. The political corruption index also enters with a negative coefficient at the upper tail (−0.8468 at the 90th quantile). Because higher values of this index denote more, not less, corruption, that sign does not support a simple claim that corruption control reduces emissions, and it is not recovered in DCCE or DML. Dumitrescu–Hurlin tests indicate a unidirectional Granger-predictive link from material footprint to emissions and bidirectional Granger links between the institutional variables and emissions. These results are associations from a small-N panel (N = 5, T = 43). They suggest that resource throughput remains tightly linked to carbon outcomes at the upper tail, while several of the growth and governance associations are specification-dependent.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/su18189598first seen 2026-09-24 04:45:13
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