Faieq Hawta Tareq, Cek Kemal
Department of Accounting and Finance, Cyprus International University, Northern Cyprus, Mersin 10, Lefkosa, 99040, Turkey.
Department of Accounting, College of Administrative and Financial Sciences, Knowledge University, Iraq, Erbil, 44001.
Heliyon. 2024 Jun 8;10(12):e32725. doi: 10.1016/j.heliyon.2024.e32725. eCollection 2024 Jun 30.
The significance of accurate energy production prediction cannot be overstated, especially in the context of achieving carbon neutrality and balancing traditional and clean energy sources. Unlike conventional models with simplified assumptions or limited data inputs hindering energy usage optimization, waste reduction and efficient resource allocation, we introduced a novel structural equation modelling approach to eight manufacturing industries' sustainable waste management practices (SWMPs) in Iraq. This comprehensive analysis, conducted with Smart PLS software on 375 responses aims to enhance energy production predictions' accuracy and support sustainability goals contribute to achieving carbon neutrality goals and promote a balanced energy mix that supports sustainability and environmental stewardship. The findings reveal noteworthy insights: notably, chemical manufacturing companies exhibit a substantial advantage from green accounting practices, witnessing a 78.1 % and 45.8 % improvement in environmental auditing oversight and SWMPs, respectively, compared to other manufacturing sectors. Compared to conventional grey models, our model demonstrates that a 1-unit improvement in CSR enhances environmental auditing oversight effectiveness by 33.4 % and sustainable waste management by 56.9 % across industries. By leveraging these data-driven insights and innovative approaches, we can drive positive change towards a more sustainable and resilient energy future, collectively contributing to a more resilient, efficient, and sustainable energy ecosystem that benefits societies, economies, and the environment. The heightened accuracy of energy production prediction facilitated by our novel model empowers stakeholders at regional and global levels to make informed decisions, mitigate risks, support policy development, achieve sustainability goals, formulate effective policies and foster collaboration.
准确的能源生产预测的重要性再怎么强调都不为过，尤其是在实现碳中和以及平衡传统能源和清洁能源的背景下。与那些假设简化或数据输入有限从而阻碍能源使用优化、减少浪费和有效资源分配的传统模型不同，我们针对伊拉克八个制造业的可持续废物管理实践（SWMP）引入了一种新颖的结构方程建模方法。这项使用Smart PLS软件对375份回复进行的全面分析，旨在提高能源生产预测的准确性，并支持可持续发展目标，为实现碳中和目标做出贡献，促进支持可持续性和环境管理的平衡能源结构。研究结果揭示了值得注意的见解：特别是，化学制造公司从绿色会计实践中展现出显著优势，与其他制造业相比，其环境审计监督和可持续废物管理实践分别提高了78.1%和45.8%。与传统灰色模型相比，我们的模型表明，企业社会责任提高1个单位，各行业的环境审计监督有效性提高33.4%，可持续废物管理提高56.9%。通过利用这些数据驱动的见解和创新方法，我们可以推动积极变革，迈向更可持续、更具韧性的能源未来，共同为一个更具韧性、高效和可持续的能源生态系统做出贡献，造福社会、经济和环境。我们新颖的模型提高了能源生产预测的准确性，使区域和全球层面的利益相关者能够做出明智决策、降低风险、支持政策制定、实现可持续发展目标、制定有效政策并促进合作。
