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The Relationship Between Environmental Sustainability, Economic Growth, and the Creation of Green Jobs in Saudi Arabia

環境持続可能性、経済成長、およびサウジアラビアにおけるグリーンジョブ創出の関係 (AI 翻訳)

Houcine Benlaria, Naima Sadaoui, Badreldin Mohamed Ahmed Abdulrahman, Balsam Saeed Abdelrhman, Taha Khairy Taha Ibrahim, Abdullah A. Aljofi, Mohamed Djafar Henni

Sustainability📚 査読済 / ジャーナル2026-05-19#エネルギー転換
DOI: 10.3390/su18105133
原典: https://doi.org/10.3390/su18105133

🤖 gxceed AI 要約

日本語

1990~2024年のサウジアラビアを対象に、グリーン雇用の長短期的決定要因をARDLモデルで分析。再生可能エネルギー容量、都市化、Vision 2030政策が雇用促進に寄与する一方、環境クズネッツ仮説は支持されず、積極的な脱炭素政策の必要性を示唆。誤差修正項は均衡への回帰速度が約1年であることを示す。

English

This study analyzes the long- and short-run determinants of green employment in Saudi Arabia (1990-2024) using an ARDL bounds testing approach. Key findings: renewable energy capacity, urbanization, and the Vision 2030 policy regime are significant drivers, while the Environmental Kuznets Curve hypothesis is not supported, implying active decarbonization policy is needed. The error correction term indicates a half-life of about one year.

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

サウジアラビアのVision 2030は日本のグリーントランスフォーメーション政策と比較可能。資源依存国から脱却する政策の効果を実証しており、日本が中東諸国との連携を検討する際の参考となる。

In the global GX context

As a case study of a resource-dependent economy, this paper offers insights into how structural policies (e.g., Vision 2030) can drive green job creation. It adds to global literature on green transitions in fossil-fuel-based economies, though the specific results are context-bound.

👥 読者別の含意

🔬研究者:Provides an empirical framework for analyzing green employment determinants, especially the role of policy shifts and renewable capacity.

🏛政策担当者:Highlights the importance of active decarbonization policies over relying on income growth for environmental improvement, relevant for countries designing green job strategies.

📄 Abstract(原文)

This study examines the long- and short-run determinants of green employment in Saudi Arabia over the period 1990–2024 using an Autoregressive Distributed Lag (ARDL) bounds testing framework within an error-correction model. Six macroeconomic and structural variables are analyzed: renewable energy capacity, GDP growth, domestic credit, urbanization, foreign direct investment, and the Vision 2030 policy regime shift. Supplementary analyses test the Environmental Kuznets Curve (EKC) hypothesis and map causal relationships using pairwise Granger causality tests. The bounds test indicates long-run cointegration among the variables (F = 8.45, exceeding the 5% I(1) critical bound of 3.61). The model explains 89% of the variation in log green employment (R2 = 0.89) and passes standard diagnostic tests for serial correlation, heteroskedasticity, normality, and parameter stability. Three correlates of long-run green employment are identified. The post-2016 dummy used to capture the Vision 2030 regime shift is associated with the largest coefficient in the long-run equation (θ = 1.75, p = 0.008), although this estimate should be interpreted with caution because the dummy absorbs all post-2016 changes, including policy effects, the rapid expansion of renewable capacity, broader institutional reforms, and possibly changes in measurement practices. Renewable energy capacity is the primary continuously measurable driver (θ = 0.145, p = 0.018), with Toda–Yamamoto modified Wald tests indicating a bidirectional predictive relationship between investment and employment. Urbanization exerts a significant positive long-run effect (θ = 0.098, p = 0.001). The error correction term (δ = −0.520, p < 0.001) implies equilibrium reversion with a half-life of approximately one year. The EKC hypothesis is not supported in the Saudi context, suggesting that active decarbonization policy—rather than income-driven structural change alone—is needed for environmental improvement. The findings carry implications for Vision 2030 implementation and for other resource-dependent economies undertaking structural green transitions.

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