Systemic Carbon Lock-In Dynamics and Optimal Sustainable Reduction Pathways for a Just Industrial Transition in South Africa
南アフリカの産業における体系的なカーボン・ロックインのダイナミクスと公正な移行のための最適な持続可能な削減経路 (AI 翻訳)
O. Inah, P. Sotenga, U. Akuru
🤖 gxceed AI 要約
日本語
本研究は、南アフリカの製造業セクターを対象に、Kaya-LMDI分解と遺伝的アルゴリズム最適化を統合した手法で、脱炭素化経路を分析。歴史的には残差炭素因子と経済活動が排出増加を牽引し、再生可能エネルギー比率向上が抑制効果を示した。シナリオ分析では、急速な再生可能エネルギー導入でも経済成長により排出が増加する一方、最適経路では90.8%削減可能だが、それは産業縮小を伴い社会的に持続不可能であることを示した。真の持続可能な経路には、産業プロセス熱の化石燃料基盤への直接的な対応が必要とされる。
English
This study analyzes decarbonization pathways for South Africa's manufacturing sector using an integrated Kaya-LMDI decomposition and genetic algorithm optimization. Historical analysis shows emissions growth driven by residual carbon factor and economic activity, partially offset by renewable share gains. Scenario projections reveal a trilemma: accelerated renewable deployment leads to 469% emissions increase due to economic growth, while the optimal pathway achieves 90.8% reduction but implies deindustrialization. The paper concludes that incremental decarbonization is insufficient and calls for directly addressing fossil fuel use in industrial process heat.
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、鉄鋼や化学等のエネルギー多消費産業の脱炭素化が課題であり、本論文のファジィな最適化手法やロックイン分析は参考になる。ただし、南アフリカの石炭依存やJETP文脈は日本と異なるため、直接適用には注意が必要。
In the global GX context
This paper contributes to the global debate on just transition and industrial decarbonization, particularly in the context of Just Energy Transition Partnerships (JETPs). It challenges the assumption that incremental renewable deployment suffices, highlighting the need for deeper structural change—a key theme for international climate policy and transition finance discussions under ISSB and CSRD frameworks.
👥 読者別の含意
🔬研究者:Carbon lock-in and LMDI decomposition methodology combined with genetic algorithm optimization offers a novel analytical framework for studying industrial decarbonization pathways.
🏢実務担当者:Corporate sustainability teams in heavy industry can use the scenario analysis to understand the limits of incremental decarbonization and the need for process heat electrification.
🏛政策担当者:The study provides evidence that rapid renewable deployment alone may be insufficient; policymakers must address industrial fossil fuel use directly and consider social impacts of structural contraction.
📄 Abstract（原文）
South Africa’s manufacturing sector, a driving force for sustainable development, faces a profound challenge in decarbonizing without deindustrializing. This study provides an optimized, scenario-based assessment of the sector explicitly aligned with its Just Energy Transition Partnership (JETP) objectives. A novel framework is applied, integrating an extended Kaya–Logarithmic Mean Divisia Index (Kaya–LMDI) decomposition with scenario forecasting and Genetic Algorithm (GA) optimization. The decomposition disaggregates a conventional carbon intensity (CI) driver to include Electrification Share (ELE), Renewable Share (REN), and a newly defined Residual Carbon Factor (RCF) that captures direct fossil fuel use for industrial process heat. Historical analysis (2002–2022) shows that emissions growth was primarily driven by the RCF (224.1 MtCO2, 160%) and Economic Activity (187.5 MtCO2, 134%), partly offset by gains in Energy Intensity (−141.8 MtCO2, 101.35%) and REN (−202.2 MtCO2, −144.53%). Carbon emissions projections to 2040 reveal a critical sustainability trilemma: the Just Transition accelerated scenario (JTAS), despite achieving rapid renewable deployment, increases emissions by 469% as economic growth overwhelms decarbonization efforts. Conversely, the mathematically optimal (GA) pathway achieves a 90.8% reduction but only through structural contraction that implies socially unsustainable deindustrialization. This tension exposes the systemic limits of incremental decarbonization and underscores that a truly sustainable pathway requires transcending this binary choice by directly addressing the fossil fuel substrate of industrial production.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/su18020956first seen 2026-05-05 23:42:51
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