Structural Determinants of Carbon Market Effectiveness: A Machine Learning Approach to Emissions Trading Gaps in Developed and Developing Economies
炭素市場の有効性の構造的要因：先進国と発展途上国における排出権取引ギャップへの機械学習アプローチ (AI 翻訳)
Ángeles Montserrat Govea Franco, Saúl Domínguez Casasola, Heriberto Salazar-Soto
🤖 gxceed AI 要約
日本語
本研究は、排出権取引制度（ETS）の有効性を機械学習で分析。58のETSを4つの類型に分類し、ANNによりCO2排出削減の要因を特定。再生可能エネルギーの普及が最も重要で、都市化や汚職が阻害要因となることを示した。
English
This study uses machine learning (k-prototypes clustering and ANN) to analyze the effectiveness of emissions trading systems (ETSs) across 53 countries. It classifies 58 ETSs into four archetypes and identifies renewable energy consumption and production as the strongest drivers of CO2 reduction, while urbanization and corruption exacerbate emissions.
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
日本はGXリーグ等の炭素価格制度を導入中だが、本論文は炭素価格単独では不十分で、再生可能エネルギー推進やガバナンス強化などの補完政策が不可欠であることを示唆する。日本の制度設計に示唆を与える。
In the global GX context
As ETSs expand globally under ISSB and transition finance frameworks, this paper provides evidence that carbon pricing effectiveness depends on structural and institutional factors, not just price levels. It offers a machine-learning-based classification that can inform policy design and disclosure requirements.
👥 読者別の含意
🔬研究者:The clustering approach and ANN analysis provide a novel methodological framework for evaluating ETS effectiveness across diverse institutional contexts.
🏢実務担当者:Companies operating in ETS jurisdictions should note that beyond carbon price, structural factors like renewable energy availability and institutional quality affect their compliance costs and market positioning.
🏛政策担当者:ETS design must be complemented with policies promoting renewables, strengthening institutions, and addressing urbanization and trade-related emissions pressures.
📄 Abstract（原文）
Emissions Trading Systems (ETSs) have become some of the most widely adopted market-based instruments for reducing greenhouse gas emissions. However, their environmental performance varies considerably across jurisdictions, suggesting that carbon pricing mechanisms operate under heterogeneous structural and institutional conditions. This study analyzes the factors influencing CO2 emissions performance in economies implementing ETSs. Grounded in Ecological Modernization Theory and Institutional Theory, the research combines a k-prototypes clustering model and an Artificial Neural Network (ANN). First, 58 ETSs across 53 countries were classified into four archetypes according to their institutional maturity, regulatory scope, and structural characteristics. Second, an ANN model was estimated using annual data from 2000–2021 to examine the influence of environmental, socio-demographic, economic, and development-related variables on CO2 emissions per capita. The results show that ETS performance depends not only on economic development levels but also on broader structural and institutional factors. Renewable energy consumption and renewable energy production emerge as the most influential drivers of lower CO2 emissions, particularly in developing economies. Conversely, urbanization, export-oriented activities, and governance weaknesses are associated with greater emissions pressures. Corruption also exhibits a stronger negative effect on environmental performance in emerging economies. Overall, the findings suggest that ETSs should not be viewed as standalone climate instruments; their effectiveness depends on complementary policies that promote renewable energy deployment, strengthen institutional quality, and address the pressures associated with trade and urbanization.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/economies14070287first seen 2026-07-22 04:56:04
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