The use of artificial intelligence engineering techniques and green cost-effective technologies in manufacturing and their role in reducing environmental costs

Authors

  • منال جبار سرور قسم المحاسبة ،كلية الادارة والاقتصاد ،جامعة التراث ،بغداد ،العراق
  • فراس فيصل غازي مركز تكنلوجيا المعلومات ،الجامعة المستنصرية ،بغداد ،العراق

This research aims to reduce the environmental costs of the economic unit by fostering a green culture in manufacturing engineering processes using artificial intelligence for value chain activities, transforming them into environmentally friendly activities. It also aims to shift from traditional to cleaner production to achieve production efficiency and manufacture environmentally friendly green products using green engineering equipment. This contributes to recycling production waste and byproducts, reducing environmental failure costs, optimizing resource and energy utilization, and achieving financial savings through recycling products, waste, and production emissions. These savings can then be redirected towards investments that generate significant revenue, creating a green image in the eyes of environmental monitoring units and customers. The conclusions are that using artificial intelligence and green cost-effective technologies leads to reduced emissions and waste, increased production efficiency, elimination of material waste, and a reduction in environmental costs

Keywords:

Artificial Intelligence, Green Cost-effective Technologies, Green Value Chain, Cleaner Production, Environmental Costs

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The use of artificial intelligence engineering techniques and green cost-effective technologies in manufacturing and their role in reducing environmental costs. (2026). Journal Port Science Research, 9(1), 189-211. https://doi.org/10.36371/port.2026.1.13

How to Cite

The use of artificial intelligence engineering techniques and green cost-effective technologies in manufacturing and their role in reducing environmental costs. (2026). Journal Port Science Research, 9(1), 189-211. https://doi.org/10.36371/port.2026.1.13