IoT Enabled Motor Drive Vehicle for the Early Fault Detection in New Energy Conservation
DOI:
https://doi.org/10.69996/s21tyd22Keywords:
Smart Cities, Machine Learning, Business Sustainability, Corporate Responsibility, Sustainability Strategies, Development GoalsAbstract
The key areas of interest in respect of which analyses are provided within the context of the paper are sustainable business practices that can help drive the change towards a low carbon economy. The study focuses on advanced techniques for managing businesses to advance the link between economic development and environmental conservation to optimize economic and environmental performance. Furthermore, based on the features, discussed in the paper, it is possible to identify the meaning of the concepts making the organizations introduce sustainability factors, as well as the possible effect these decisions may have on their future performance. This paper explores the role of machine learning (ML) and data-driven business strategies in promoting sustainable business development within smart cities. By integrating ML models across various sectors such as energy management, transportation, waste management, water usage, public safety, and urban planning, cities can optimize resources, reduce environmental impact, and enhance overall efficiency. Results from implementing these strategies in a smart city context show a 10% reduction in energy consumption (from 200,000 kWh to 180,000 kWh), a 50% increase in renewable energy share (from 20% to 30%), and a 40% decrease in CO2 emissions (from 500 tons to 300 tons) due to the use of autonomous electric vehicles. In transportation, average traffic speed improved by 16.67% (from 30 km/h to 35 km/h), and public transport efficiency increased by 21.43% (from 70% to 85%). In waste management, waste collection costs were reduced by 20% (from $50,000 to $40,000), and the recycling rate increased by 40% (from 25% to 35%). Water usage efficiency rose by 12.5% (from 80% to 90%), while water leak incidents decreased by 60% (from 50 to 20). Additionally, predictive policing reduced crime rates by 40% (from 5 to 3 per 1,000 residents), and emergency response times improved by 33.33% (from 15 minutes to 10 minutes). Finally, urban planning efficiency increased by 25% (from 60% to 75%) in land use, and green space area expanded by 40% (from 50 to 70 square kilometers).
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