Forecasting South Africa’s Coal-to-Clean Energy Transition: A Monte Carlo Simulation
南アフリカの石炭からクリーンエネルギーへの移行予測：モンテカルロシミュレーション (AI 翻訳)
Luyanda Majenge, Simiso Msomi, Sakhile Mpungose
🤖 gxceed AI 要約
日本語
本研究は、南アフリカの石炭からクリーンエネルギーへの移行を予測するため、構造変化分析、ベイズ推定、強化モンテカルロシミュレーションを統合したモデルを開発。2011年に構造変化を確認し、石炭比率が50%を下回る移行年を2053年と予測。政策シナリオ分析では、単独介入ではなく調整された政策ポートフォリオの重要性を示した。
English
This study develops an integrated model combining structural break analysis, Bayesian estimation, and an enhanced Monte Carlo simulation to forecast South Africa's coal-to-clean energy transition. It identifies a structural break in 2011 and forecasts that coal's share will fall below 50% by 2053. Policy scenario experiments highlight that accelerating the transition requires coordinated policy portfolios rather than isolated interventions.
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
日本も石炭依存からの脱却が課題だが、本論文の不確実性を明示した予測手法は、日本のエネルギー政策やSSBJ（サステナビリティ開示基準）における移行計画の基礎として応用可能。
In the global GX context
This paper provides a transparent, uncertainty-explicit forecast of a coal-dependent economy's transition, offering a methodological template for countries planning phase-out pathways. Its findings on policy synergies are relevant for global transition finance and ISSB-aligned scenario analysis.
👥 読者別の含意
🔬研究者:The integrated forecasting methodology combining structural breaks with Monte Carlo simulation offers a replicable framework for energy transition modeling.
🏢実務担当者:Energy planners and utilities can use the probabilistic transition timeline to inform asset planning and investment decisions.
🏛政策担当者:The paper provides evidence that coordinated policy portfolios, not single measures, are needed to accelerate coal phase-out, informing regulatory design.
📄 Abstract（原文）
South Africa remains one of the world’s most coal-dependent electricity systems, with coal accounting for 81.57% of generation in 2023. Despite policy interventions to diversify the energy mix, structural change is slow to emerge. This study provides the first integrated, empirically calibrated forecast of South Africa’s coal-to-clean-energy transition using a unified modelling architecture that combines structural break analysis, Bayesian estimation, and an enhanced Monte Carlo simulation with dynamic volatility (10,000 stochastic pathways). The findings confirm a permanent structural break in 2011 that coincided with the implementation of REIPPPP, following which coal began a statistically significant and sustained decline of approximately 0.7–0.75% points per year. The simulation produced a full probability distribution for the transition year (2053) when coal share falls below 50%. This demonstrated that long-term uncertainty rises faster than linearly and that, under current conditions, deep decarbonisation milestones are unattainable before mid-century. Policy scenario experiments also demonstrated that accelerating the annual decline rate necessitates coordinated, synergistic policy portfolios rather than isolated interventions. These findings provide a transparent, uncertainty-explicit forecast of South Africa’s transition trajectory, as well as a decision-relevant evidence base for planning, regulation, and equitable transition implementation.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/forecast8030047first seen 2026-07-02 05:14:36
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