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Renewable Energy Credits Transforming Market Dynamics

Author

Listed:
  • Bankole I. Oladapo

    (School of Science and Engineering, University of Dundee, Dundee DD1 4HN, UK)

  • Mattew A. Olawumi

    (Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK)

  • Francis T. Omigbodun

    (Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU, UK)

Abstract

This research uses advanced statistical methods to examine climate change mitigation policies’ economic and environmental impacts. The primary objective is to assess the effectiveness of carbon pricing, renewable energy subsidies, emission trading schemes, and regulatory standards in reducing CO 2 emissions, fostering economic growth, and promoting employment. A mixed-methods approach was employed, combining regression analysis, cost–benefit analysis (CBA), and computable general equilibrium (CGE) models. Data were collected from national and global databases, and sensitivity analyses were conducted to ensure the robustness of the findings. Key findings revealed a statistically significant reduction in CO 2 emissions by 0.45% for each unit increase in carbon pricing ( p < 0.01). Renewable energy subsidies were positively correlated with a 3.5% increase in employment in the green sector ( p < 0.05). Emission trading schemes were projected to increase GDP by 1.2% over a decade ( p < 0.05). However, chi-square tests indicated that carbon pricing disproportionately affects low-income households ( p < 0.05), highlighting the need for compensatory policies. The study concluded that a balanced policy mix, tailored to national contexts, can optimise economic and environmental outcomes while addressing social equity concerns. Error margins in GDP projections remained below ±0.3%, confirming the models’ reliability.

Suggested Citation

  • Bankole I. Oladapo & Mattew A. Olawumi & Francis T. Omigbodun, 2024. "Renewable Energy Credits Transforming Market Dynamics," Sustainability, MDPI, vol. 16(19), pages 1-17, October.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:19:p:8602-:d:1491840
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    References listed on IDEAS

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    1. Bankole I. Oladapo & Mattew A. Olawumi & Francis T. Omigbodun, 2024. "AI-Driven Circular Economy of Enhancing Sustainability and Efficiency in Industrial Operations," Sustainability, MDPI, vol. 16(23), pages 1-17, November.

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