Exploring the LCC Hypothesis in the Nordic Region: The Role of AI Innovation, Environmental Taxes, and Financial Accessibility via Panel ARDL

Md Sibbir Hossain; Mohammad Ridwan; Afsana Akhter; Md Boktiar Nayeem; M Tazwar Hossain Choudhury; Md Asrafuzzaman; Shaharina Shoha; Shake Ibna Abir; Sumaira

doi:10.56556/gssr.v3i3.972

Authors

Md Sibbir Hossain Department of Computer Science, The City College of New York, Convent Ave, New York, NY 10031, USA https://orcid.org/0009-0002-0795-4512
Mohammad Ridwan Department of Economics, Noakhali Science and Technology University, Sonapur, Noakhali-3814, Bangladesh https://orcid.org/0000-0003-2239-2706
Afsana Akhter Department of Economics, Noakhali Science and Technology University, Sonapur, Noakhali-3814, Bangladesh https://orcid.org/0009-0001-1748-2654
Md Boktiar Nayeem College of Graduate and Professional Studies, Trine University, University Ave, Angola, IN 46703 https://orcid.org/0009-0007-3060-0276
M Tazwar Hossain Choudhury College of Graduate and Professional Studies, Trine University, University Ave, Angola, IN 46703
Md Asrafuzzaman Department of Management -Business Analytics, St Francis College, USA https://orcid.org/0009-0002-3231-1359
Shaharina Shoha Department of Mathematics, Western Kentucky University, Bowling Green, KY, USA https://orcid.org/0009-0008-8141-3566
Shake Ibna Abir Department of Mathematics, Western Kentucky University, Bowling Green, KY, USA https://orcid.org/0009-0004-0724-8700
Sumaira College of Economics and Management, Zhejiang Normal University, Zhejiang China https://orcid.org/0009-0004-2384-0210

DOI:

https://doi.org/10.56556/gssr.v3i3.972

Keywords:

Artificial Intelligence, Environmental Tax, Financial Accessibility, Load Capacity Factor, Nordic Region

Abstract

This study investigates the impact of artificial intelligence (AI) innovation on environmental sustainability in the Nordic region. Additionally, it tests the Load Capacity Curve (LCC) hypothesis by incorporating factors such as financial accessibility, environmental tax, and urbanization, using data spanning from 1990 to 2020. The methodology includes the Cross-Sectional Dependence test and the slope homogeneity test, revealing issues of heterogeneity and cross-sectional dependence. Furthermore, first and second-generation panel unit root assessments indicate that the variables are free from unit root problems. Panel Cointegration tests confirm that the variables are cointegrated in the long run. To analyze both short-run and long-run relationships, the study employs the Panel Autoregressive Distributed Lag (ARDL) model. The results from the Panel ARDL model support the LCC hypothesis in the Nordic region, showing a U-shaped relationship between income and load capacity factor. Moreover, AI innovation and environmental tax significantly and positively contribute to environmental sustainability in both the short and long run. In contrast, higher financial accessibility and urbanization degrade environmental sustainability over these timeframes. To validate the robustness of the Panel ARDL estimations, the study also uses Fully Modified OLS, Dynamic OLS, and Fixed Effects OLS approaches, all of which corroborate the ARDL findings. The study employs the D-H causality test to explore causal relationships among the variables. The test results reveal a unidirectional causal relationship between income and AI innovation to the load capacity factor and a bidirectional causal relationship between financial accessibility and the load capacity factor, as well as between urbanization and the load capacity factor. However, no causal relationship is found between environmental tax and the load capacity factor.