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Decoupling Economic Growth from CO2 Emissions in Honduras: A Longitudinal Machine-Learning and Econometric Assessment of Low-Carbon Development, 1990–2023

ホンジュラスにおける経済成長とCO2排出のデカップリング：1990〜2023年の低炭素開発に関する縦断的機械学習・計量経済学的評価 (AI 翻訳)

Dely Ramírez, Jonathan Muñoz Tabora, Ozy D. Melgar‐Dominguez

Sustainability📚 査読済 / ジャーナル2026-07-30#AI×ESG対象セクター: cross_sector
DOI: 10.3390/su18157726
原典: https://doi.org/10.3390/su18157726

🤖 gxceed AI 要約

日本語

本研究は1990〜2023年のホンジュラスを対象に、k-means、PELT、Tapio指数、EKCモデル、Granger因果、ランダムフォレストを統合し、経済成長とCO2排出のデカップリングを検証。EKCの逆U字関係が確認されたが、再エネ比率は排出削減に寄与しておらず、GDPが支配的要因であることを示す。

English

This study assesses GDP-CO2 decoupling in Honduras (1990-2023) using k-means, PELT, Tapio index, EKC modeling, Granger causality, and Random Forest. An inverted-U EKC is supported, but renewable share has negligible predictive power, suggesting Honduras has passed the turning point without renewable-driven decoupling.

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

日本企業が中米市場で気候関連リスクを評価する際、途上国のデカップリング実態は投資判断に有用。SSBJ対応でも新興国の排出動向理解は重要だが、直接的な政策含意は限定的。

In the global GX context

For global disclosure scholarship, this adds rare longitudinal evidence from Central America on the EKC and highlights that renewable share may not drive decoupling in low-income economies, challenging assumptions in transition finance and climate risk models.

👥 読者別の含意

🔬研究者:Methodological integration of ML and econometrics offers a replicable framework for decoupling analysis in data-scarce developing countries.

🏢実務担当者:Limited direct application, but useful for understanding climate risk in Honduran/Central American operations.

🏛政策担当者:Provides evidence that GDP-driven growth outpaces renewables; policy should focus on structural transformation rather than energy mix alone.

📄 Abstract（原文）

Decoupling economic growth from CO2 emissions is a key challenge for developing economies, with limited evidence for small Central American economies. This study evaluates Honduras during 1990–2023 using GDP per capita, CO2 emissions per capita, energy intensity, and renewable energy share from World Bank Indicators and the Global Carbon Project. The methodology integrates k-means clustering, PELT structural break detection, the Tapio decoupling index, Environmental Kuznets Curve (EKC) modelling, Granger causality, and Random Forest analysis. Since energy-intensity data are available only from 2000, a full GDP-CO2 series (1990–2023, n = 34) is distinguished from a complete four-variable panel (2000–2021, n = 22). Clustering identifies two structural regimes rather than three (silhouette 0.490 vs. 0.476). EKC results support an inverted-U relationship (β2 = −3.012, p < 0.001; adjusted R2 = 0.766), with an estimated turning point of USD 2388 (95% CI: USD 2156–2644, delta method), near the upper boundary of the estimation sample, which Honduras’ 2023 GDP per capita (USD 2527) marginally exceeds as an out-of-sample extrapolation. However, Granger tests find no significant temporal precedence between GDP, renewable share, and CO2 emissions (all p > 0.05), and Random Forest shows GDP per capita (%IncMSE = 31.47) vastly outweighs renewable share (%IncMSE = 0.16). Honduras has likely crossed the EKC threshold, but evidence does not support a renewable-driven decoupling.

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