Panacea for Exorbitant Trends of Air Pollutants: Creative Destruction Model of Electric Vehicles

IJEP 44(3): 224-234 : Vol. 44 Issue. 3 (March 2024)

Perini Praveena Sri1*, Vaddi Naga Padma Prasuna2, Shilpa Manjunath Nilugal3, R. Murugesan3 and K. Purushotham Prasad3

1. ICFAI Foundation for Higher Education, Department of Economics, ICFAI Faculty of Social Sciences, Hyderabad – 501 203, Telangana, India
2. Atria Institute of Technology, Department of Electronics and Communication Engineering, Bengaluru – 560 024, Karnataka, India
3. Narsimha Reddy Engineering College, Department of Electronics and Communication, Secunderabad – 500 100, Telangana, India


A robust air quality governance increasingly requires customarily prompt surveillance and analysis of air pollutants that are posing impounding risks of environmental hazards from the automobile and industrial industries. This research work portrays the variable trend and exorbitance analysis of varied harmful pollutants across multiple cities in India for five years (2015-2020). The interstate comparisons of 29531 observations for PM2.5, PM10, nitric oxide, nitrogen dioxide, ammonia, sulphur dioxide, carbon monoxide, ground level ozone, benzene, toluene and xylene air pollutants were assessed. The study by deploying a correlation matrix revealed that air quality index of PM2.5 and CO are moderately positively correlated and fall within the correlation size range of 0.50-0.70 and NOx and SO2 are positively associated at meager levels, falling within the range of 0.30-0.50. Industrial pollutants have less impact relatively, with vehicular pollution denoting less correlation. The movements in exceedance with fluctuating thresholds confirmed that the vehicular pollution of Delhi is more aggravated and Ahmedabad is recorded as a high polluted city in terms of industrial pollutants. The study, through its machine learning algorithms, such as linear, laso and ridge regression, principal component analysis found the model accuracy and good score of explained variance ratio with precision of 61% and 71-100% on its various principal components. The paper documents, using real-time data, exemplary illustrations of creative destruction technological models of electric vehicles powered by renewable energy.


Pollutants, Technology, Electric vehicles, Machine learning algorithms 


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