Energy Economy
Seyyed Mohammad Ghaem Zabihi; Fatemeh Akbari; Narges Salehnia
Abstract
The relationship between economic, financial, political risks and per capita carbon emission (CO2) is considered as one of the major global challenges. The effect of these three factors on carbon emissions is very important. Therefore, the current research seeks to investigate the role of economic, financial, ...
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The relationship between economic, financial, political risks and per capita carbon emission (CO2) is considered as one of the major global challenges. The effect of these three factors on carbon emissions is very important. Therefore, the current research seeks to investigate the role of economic, financial, and political risk in reducing per capita carbon emissions (CO2) by using the very new and fresh approach of quantile-on-quantile regression (QQR) modeling in the annual period from 1990 to 2018. The statistical relationship between the variables mentioned in Eviews12 and Matlab2022 software platform has been investigated for Iran. The results show that the economic risk variable in all quantiles (0.5 to 0.95) had a positive effect on carbon emissions per capita in all quantiles (0.5 to 0.95), and this positive relationship was relatively stronger in the quantiles (0.3 to 0.95), of the economic risk variable. the financial risk variable in all quantiles (0.5 to 0.95) had a positive effect on carbon emissions per capita in all quantiles (0.5 to 0.95), and this positive relationship was relatively weak in all quantiles (0.5 to 0.95) of the financial risk variable, as well as Politics risk has a positive effect on carbon emissions per capita in all quantiles (0.5 to 0.95) and this positive relationship is relatively weak in all quantiles (0.5 to 0.95) of the Politics risk variable. Thus, the need to pay attention to Iran's economic, financial, and political stability to improve the environment's quality and reduce carbon emission (CO2) is very important.1.IntroductionThe most significant global threat in the 21st century is climate change and global warming, primarily driven by carbon dioxide (CO2) emissions (Akadiri et al., 2021; Oladipopo et al., 2021). The rapid development of modern industrial societies worldwide in recent years has led to a gradual rise in the consumption of fossil fuels, including coal, oil, and natural gas. The increased consumption has resulted in the substantial emission of CO2 (Danish et al., 2019; Dong et al., 2018; Zhao et al., 2021). In recent years, there has been an enhanced awareness among governments and international organizations worldwide regarding the impact of climate change on the economy, society, and the environment (Gambier et al., 2022). The heightened awareness has prompted the adoption of environmental protection policies (Roncroni et al., 2021). However, the implementation of these policies requires significant expenditures. Consequently, the role of financial stability in addressing the risks associated with climate change and reducing greenhouse gas (GHG) emissions has gained increasing importance (Sun et al., 2022). Research indicates that a stable financial environment is conducive to stimulating production and investment, albeit with a potential increase in energy consumption and CO2 emissions (Solimana et al., 2017).Global warming and CO2 emissions are closely intertwined with economic and political risks (Adoms et al., 2018). Global uncertainties have increased the volatility of economic and political policies on a global scale. Any form of uncertainty, be it social, political, economic, or war-related, invariably impacts economic activities (Blatman & Miguel, 2010; Guidolin & La Ferrara, 2010). Economic (in)stability plays a crucial role in shaping the environment in which companies operate, influencing the decision-making processes of economic entities. Similarly, political instability can significantly impact investors’ decision-making. Moreover, political risk is on the rise in nearly all countries, exerting pressure on military budgets at the expense of construction budgets. This situation leads to a reduction in overall production within the country, and the decreased production results in a further decline in energy consumption, and ultimately leading to a decrease in carbon emissions (Ahmad et al., 2022). Employing a novel methodology known as quantile-on-quantile regression (QQR), the present research aimed to explore the impact of economic, financial, and political risks on per capita carbon emissions in Iran during 1990–2018. Regarding the methodology and the specific focus, no similar research has been conducted in Iran. Therefore, the current study stands out for its innovation in terms of subject matter, methodology, and the targeted context, potentially yielding significant findings.2. Materials and MethodsThe QQR approach is a novel method for analyzing bivariate equations. Introduced by Sim and Zhu (2015), it combines ordinary regression and nonparametric estimation, providing more comprehensive insights compared to traditional estimation methods. QQR examines the intricate relationship between the lower and upper quantiles of the data series, which yields a more realistic analytical perspective than conventional methods (Yu et al., 2022). This study used the QQR approach to investigate the relationship between economic, financial, and political risks and per capita carbon emissions. In this line, the econometric model was formulated as in Equation (1): (1) In Equation (1), CO2t denotes per capita carbon emissions in year t. ERt represents economic risk in year t. FRt is financial risk in year t. PRt indicates political risk in year t, and 𝜀𝑡 is a component of the model error.Several methods were used to analyze the data, including the descriptive analysis, assessment of variable reliability, the diagnostic test (esp., the disruption components autocorrelation test), the correlation test, Johansen’s co-accumulation, and finally the quantile-by-quantile model estimation.3. Results and DiscussionUtilizing the innovative econometric approach of quantile-on-quantile regression (QQR), the research explored the statistical relationship between economic, financial, and political risk variables and per capita carbon emissions in Iran during 1990–2018. The findings revealed that the economic risk variable had a positive effect on carbon emissions per capita across all quantiles (0.5 to 0.95), with this positive relationship being relatively stronger in the 0.3–0.95 quantiles of the economic risk variable. Similarly, the financial risk variable had a positive effect on carbon emissions per capita in all quantiles (0.5 to 0.95), although this positive relationship is relatively weak across all quantiles of the financial risk variable. Likewise, political risk positively influenced carbon emissions per capita in all quantiles (0.5 to 0.95), with this positive relationship being relatively weak across all quantiles of the political risk variable. The research results align with the findings of Zhang and Chiu (2020), Abbasi and Riaz (2016), Mehmet et al. (2018), and Zaidi et al. (2019).4.ConclusionThe present study aimed to examine the correlation between economic, financial, political risks, and per capita carbon emissions in Iran during 1990–2018. The findings emphasize the significance of maintaining economic, financial, and political stability in Iran as it is crucial for enhancing the quality of the environment and mitigating carbon emissions.
