Application of the STIRPAT Model in unravelling Carbon Dioxide (CO2) emission patterns in the India and Global scales

K. Nirmal Ravi Kumar; M. S. Madhav; N. T. Krishna Kishore; K. Vijay Krishna Kumar; Adinan B. Shafiwu; Ishaque Mahama

doi:10.36005/jplm.v3i1.89

Authors

K. Nirmal Ravi Kumar Department of Agric. Economics, Agricultural College, Bapatla, Acharya NG Ranga Agricultural University (ANGRAU), Government of Andhra Pradesh, India
M. S. Madhav ICAR-Central Tobacco Research Institute, Rajahmahendravaram - 533 105. Andhra Pradesh
N. T. Krishna Kishore Institute of Agri-Business Management, S.V. Agricultural College, Tirupati, ANGRAU, Government of Andhra Pradesh, India
K. Vijay Krishna Kumar Department of Plant Pathology, Agricultural College, Rajahmahendravaram, ANGRAU, India
Adinan B. Shafiwu Department of Agricultural and Food Economics, University for Development Studies, Ghana
Ishaque Mahama Corresponding Author, Department of Applied Statistics, Simon Diedong Dombo University of Business and Integrated Development Studies, Wa, Ghana

DOI:

https://doi.org/10.36005/jplm.v3i1.89

Keywords:

Kaya’s equation, STIRPAT, Carbon dioxide emissions, Ridge Regression, Population

Abstract

This study explores the significance of Kaya’s Identity in understanding and addressing CO2-emissions (CO2-emi) in both India and globally, utilising FAOSTAT data from 1991 to 2021. Kaya's Identity breaks down CO2-emi into population, GDP per capita (GDP-PC), emissions intensity (EI), and CO2-Emissions Intensity (CO2-EI). The STIRPAT model was used to analyse these factors, with Ridge regression applied to address multicollinearity. The findings highlight that population growth is a major driver of emissions, with increases of 4.14% in India and 21.36% globally. India's GDP-PC growth of 7.69%, compared to 3.67% globally, also significantly contributes to emissions. Despite improvements in energy efficiency and transitions to renewable energy, CO2-emi rose by 6.46% in India and 2.29% globally. The study identifies positive associations between population growth and GDP-PC with CO2-emi, while EI and CO2-EI show negative associations. Forecasts suggest that in India, sustained GDP-PC growth initially curbed CO2-emi, but post-2080, rising population and energy demands accelerated emissions. Globally, consistent GDP-PC growth initially slowed emissions, but after 2000, population growth and increased energy consumption led to a significant surge, driven by slower economic expansion and higher fossil fuel use. The results also indicate a long-term cointegration relationship between CO2-emi and the selected variables at both the all-India and global levels. The significantly negative coefficient for CO2-emi lagged by one period (CO2emi(-1)) suggests a strong long-run adjustment mechanism both at the all-India and global levels. This study underscores the need for integrated strategies addressing population growth, GDP-PC, energy efficiency, and clean energy adoption to combat climate change sustainably. Policymakers should focus on emerging technologies such as carbon capture, understanding consumer behaviour's impact on emissions, analysing regional disparities, and developing long-term emissions scenarios. JEL classification: Q54, Q56, Q58.

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