first_page Download PDF settings Order Article Reprints Open AccessArticle The Driving Factors of Italy’s CO2 Emissions Based on the STIRPAT Model: ARDL, FMOLS, DOLS, and CCR Approaches by Dulal Chandra Pattak 1, Farian Tahrim 2, Mahdi Salehi 3 [ORCID] , Liton Chandra Voumik 2,* [ORCID] , Salma Akter 2, Mohammad Ridwan 2, Beata Sadowska 4 [ORCID] and Grzegorz Zimon 5,* [ORCID] 1 Department of Banking & Insurance, Faculty of Business Studies, University of Dhaka, Dhaka 1205, Bangladesh 2 Department of Economics, Noakhali Science and Technology University, Noakhali 3814, Bangladesh 3 Department of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran 4 Department of Accounting, Faculty of Economics, Finance and Management, University of Szczecin, 70-453 Szczecin, Poland 5 Faculty of Management, Rzeszow University of Technology, 35-959 Rzeszow, Poland * Authors to whom correspondence should be addressed. Energies 2023, 16(15), 5845; https://doi.org/10.3390/en16155845 Received: 6 July 2023 / Revised: 26 July 2023 / Accepted: 31 July 2023 / Published: 7 August 2023 (This article belongs to the Section B: Energy and Environment) Download keyboard_arrow_down Browse Figure Versions Notes Abstract As the sustainability of the environment is a very much concerning issue for developed countries, the drive of the paper is to reveal the effects of nuclear, environment-friendly, and non-friendly energy, population, and GDP on CO2 emission for Italy, a developed country. Using the extended Stochastic Regression on Population, Affluence, and Technology (STIRPAT) framework, the yearly data from 1972 to 2021 are analyzed in this paper through an Autoregressive Distributed Lag (ARDL) framework. The reliability of the study is also examined by employing Fully Modified Ordinary Least Square (FMOLS), Dynamic Ordinary Least Square (DOLS), and Canonical Cointegration Regression (CCR) estimators and also the Granger causality method which is used to see the directional relationship among the indicators. The investigation confirms the findings of previous studies by showing that in the longer period, rising Italian GDP and non-green energy by 1% can lead to higher CO2 emissions by 8.08% and 1.505%, respectively, while rising alternative and nuclear energy by 1% can lead to falling in CO2 emission by 0.624%. Although population and green energy adversely influence the upsurge of CO2, they seem insignificant. Robustness tests confirm these longer-period impacts. This analysis may be helpful in planning and developing strategies for future financial funding in the energy sector in Italy, which is essential if the country is to achieve its goals of sustainable development.

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