Evaluating the effects of ECON-ESG on load capacity factor in G7 countries.

For the first time, this study introduces the ECON-ESG quadruple, developed by Isiksik et al. (2024a), by adding the economy (ECON) dimension to the classical ESG (environment, social, governance) triad. Based on this new concept, it explores the impact of ECON-ESG factors on the Load Capacity Factor (LCF) in G7. The impact of ECON-ESG factors on LCF is vital because sustainability through these factors plays a critical role in a sustainable environment with LCF. CS-ARDL model finds that while governance factors (GOVNF) positively affect LCF, economic factors (ECONF) have negative effects. Environmental factors (ENVF) and social factors (SOCF) do not affect LCF. These findings can be interpreted as follows: (i) Negative effects of ECONF on LCF can be interpreted as high productivity levels in G7 leading to high resource consumption, exceeding biocapacity. (ii) In G7 with high-income levels, increased consumption may lead to overconsumption of natural resources and exceeding biocapacity. (iii) High technological progress in G7 can sometimes paradoxically lead to greater resource consumption rather than encouraging more efficient resource use, increasing an ecological footprint. The positive effects of GOVNF on LCF can be interpreted as follows: (iv) High and quality governance practices and policies in G7 can increase biocapacity. (vi) Under good governance, governments and environmental organizations can positively impact LCF by raising public awareness of environmental issues and enabling society to use natural resources more sustainably. Therefore, policymakers should harmonize economic policies through ECONF and governance policies through social factors (GOVNF), which contradict each other in LCF. Additionally, the effect of the single composite form ECON-ESG introduced and proposed in this study on LCF is found to be negative. This requires policymakers and firms to re-evaluate their sustainability one more time from a holistic perspective, including economic factors, as done in this study.

The impacts of renewable energy and institutional quality in environmental sustainability in the context of the sustainable development goals: A novel approach with the inverted load capacity factor.

It is crucial to fulfill sustainable development goals in combating environmental pollution. Recently, there has been a growing literature on environmental pollution; however, while many proxies represent environmental pollution, few proxies represent environmental sustainability. In this paper, we examine the effects of institutional quality (SDG-16), economic growth (SDG-8), and renewable energy (SDG-7) on the inverted load capacity factor (SDG-13) in OECD countries from 1999 to 2018. The objective is to ensure environmental sustainability within the Sustainable Development Goals (SDGs) framework. In this respect, the study differs from the existing literature by approaching the sustainable environment literature from a broader perspective. Long-term empirical estimates from the PMG-ARDL technique have shown that institutional quality, reel income, and population increase the inverted load capacity factor, that is, decrease environmental sustainability. However, on the contrary, renewable energy decreases the inverted load capacity factor. Therefore, renewable energy consumption helps reach SDG-7 and SDG-13 in OECD countries. In addition, it is found that economic growth is significant both in the long run and in the short run, and the impact of economic growth on the environment is greater in the short run than in the long run. This result supports the environmental Kuznets curve (EKC) hypothesis for OECD countries. The panel causality test results find a bidirectional causality relationship from renewable energy and population to inverted load capacity factor and a unidirectional causality relationship from institutional quality to inverted load capacity factor. This study argues that policymakers should concentrate on deploying environmentally friendly technology to slow down environmental degradation, increase the usage of renewable energy sources, and promote sustainable development in line with the SDGs.

The nexus between government spending, economic growth, and tourism under climate change: testing the CEM model for the USA.

This study examines the impact of government spending, income, and tourism consumption on CO2 emissions in the 50 US states through a novel theoretical model derived from the Armey Curve model and the Environmental Kuznets Curve hypothesis. The findings of this research are essential for policymakers to develop effective strategies for mitigating environmental pollution. Utilizing panel cointegration analysis, the study provides valuable insights into whether continued increases in government spending contribute to higher pollution levels. By identifying the threshold point of spending as a percentage of GDP, policymakers can make informed decisions to avoid the trade-off between increased spending and environmental degradation. For instance, the analysis reveals that Hawaii's tipping point is 16.40%. The empirical results underscore the importance of adopting sustainable policies that foster economic growth while minimizing environmental harm. These findings will aid policymakers in formulating targeted and efficient approaches to tackle climate change and promote long-term environmental sustainability in the United States. Moreover, the impact of tourism development on CO2 emissions varies across states, with some US states experiencing a decrease while others see an increase.

Food-based bilateral trade balance performances between the United States and Canada under COVID-19.

The food industry has been greatly impacted by COVID-19, causing governments to restrict food exports to prevent shortages. A negative food trade balance reveals a country's dependence on imports and underscores the significance of a sound food policy. Hence, for the first time, this study examines the J-curve hypothesis for the U.S. with Canada at the state rather than country level and creates maps based on the findings. The approach of this study differs from all empirical studies using country-level J-curve analyses, because the U.S. may require a state level analysis since its states differ in terms of economic-population sizes, tax rates, and administrative structures. For this aim, this study employs the linear and nonlinear autoregressive distributed lag (ARDL) approaches. The results indicate that while only 8 out of 47 U.S. states support the food-based asymmetric J-curve hypothesis, 15 U.S. states support the asymmetric inverse J-curve hypothesis. Additionally, 9 U.S. states support the food-based symmetric J-curve hypothesis, and 2 U.S. states support the symmetric inverse J-curve hypothesis. Based on these results, policymakers of U.S. states where the J-curve hypothesis is not supported should review their food-based bilateral trade policies with Canada. Graphical abstract: These maps depict the U.S. states in green and red, indicating support for the J-curve and inverse J-curve hypotheses, respectively. The map on the left was generated using the linear model (symmetric approach), while the map on the right was generated using the nonlinear model (asymmetric approach). Supplementary Information: The online version contains supplementary material available at 10.1007/s00003-023-01436-x.

Renewable energy, economic freedom and economic policy uncertainty: New evidence from a dynamic panel threshold analysis for the G-7 and BRIC countries.

This study aims to demonstrate the impact of renewable energy consumption (REC) on environmental degradation using the EKC hypothesis testing for the BRIC and G-7 countries. Two EKC models were created and tested, with Model 2 including REC and other independent variables such as economic freedom (EF) and economic policy uncertainty (EPU), which affect the level of renewable energy consumption and CO2 emissions. Empirical findings indicate that the EKC hypothesis is verified faster in the REC-EF-EPU-based EKC model (Model 2) than in the EF-EPU-based EKC model (Model 1) for G-7 countries since the turning point takes place earlier in Model 2 than in Model 1 with REC. This suggests that renewable energy consumption accelerates the reduction of CO2 emissions. Moreover, this earlier turning point results in lower environmental cleaning costs, less time vesting, and saving resources and money for G-7 countries. However, the study found no evidence supporting the EKC hypothesis for the BRIC countries.

Are economic growth and environmental pollution a dilemma?

For the first time, this study introduces-proposes using the Armey curve hypothesis (ACH) for testing the environmental Kuznets curve hypothesis (EKCH) in the relevant literature. The rationale for this new proposed methodology is that both hypotheses are expected to have similar inverted U-shaped curves. Hence, we combine the aforementioned hypotheses to obtain a single composite model. This single model may allow us to calculate a maximum (optimum) level of government expenditures that will increase or decrease CO2 emissions for USMCA (the USA-Mexico-Canada Agreement) countries. To this end, our study employs an augmented mean group (AMG) estimator. The results demonstrate that the EKCH is verified by way of the AC model only for Mexico. Additionally, with the advantage of this approach, we calculated the optimal government spending level, which will increase both per capita real GDP (RGDPPC) and CO2 emissions in this country by around 26.4% of RGDPPC. This level of spending will be a kind of threshold point for the Mexican government's policymakers. Hence, they will know that if they continue to spend more than this level, both the RGDPPC and CO2 emissions will decrease, implying either a lower RGDPPC or a cleaner environment. The primary purpose of the proposed methodology in this study is to reveal the possible effects of the government's economic growth-oriented increased public expenditures on the environment in a single composite model. In other words, the relationship between economic growth and the environment is approached from the perspective of public spending, and it is reminded that governments should have harmonious and sustainable public spending policies for both economic growth and a cleaner environment.

The US State-Level Geographic J-curve Hypothesis Mapping with Canada.

This study creates a US state-level asymmetric J­curve hypothesis testing map with Canada. The map may visually present how a US state policymaker manages bilateral trade balances with Canada. Green-colored US states support the evidence of the asymmetric J­curve hypothesis, while red-colored and gray-colored states do not. The main empirical finding indicates that the asymmetric J­curve hypothesis is supported for only 15 US states and D.C., shown in green on the map. This suggests that policymakers of these US states may have more sustainable and manageable bilateral trade policies with Canada. If so, policymakers in red/grey US states should reevaluate their bilateral trade policy regulations, especially those related to taxation, budgetary frameworks, energy prices, and other relevant factors that can impact consumer-producer prices and thereby create competitive state-level real exchange rates. By doing so, they may achieve the anticipated positive outcomes of the J­curve effect to export more.

Pandemic-induced economic policy uncertainty and US stock exchanges.

This study investigates the impacts of pandemic-induced economic policy uncertainties (PIEPU) on the S&P500, Nasdaq-100, and Dow Jones indexes (stock returns). To this aim, for the first time, newly created IDEMV (the Infectious Disease Equity Market Volatility index (henceforth, PIEPU index) is used. The Autoregressive Distributed Lag (ARDL) model and the Toda and Yamamoto (Journal of Econometrics, 1995, 66, pp. 225-250) causality test are applied for the 2009-2020 period. Empirical findings indicate that rises in the PIEPU index lead to falls of only the S&P500 and Dow Jones indexes. Corporations in the tech-heavy Nasdaq100 index do not negatively respond to rises in the PIEPU index. Additionally, the negative impacts of the rises in the specifically COVID-19 based-constructed PIEPU (DCOVPIEPU) index on the S&P500 and Dow Jones indexes are higher than the negative impacts of the general PIEPU index. This can be interpreted to mean that the larger the magnitude and spread rate of a pandemic, the larger the negative impacts on stock returns. In the sample period of this study, COVID-19 is the largest and most destructive pandemic compared to H1N1 and Ebola.

Retesting the EKC hypothesis through transmission of the ARMEY curve model: an alternative composite model approach with theory and policy implications for NAFTA countries.

We investigate the validity of the environmental Kuznets curve (EKC) hypothesis for the NAFTA countries. In this investigation, we approach this hypothesis from a different methodology and propose employing the ARMEY curve hypothesis since the mathematical-functional propositions of both hypotheses were constructed on the same inverted U-shaped relationships. Thus, for the first time, it can be interpreted that the EKC hypothesis is empirically tested through a transmission mechanism of the ARMEY curve hypothesis in a single composite model. Therefore, this approach makes our study different from all empirical studies in the relevant literature. We apply the Augmented Mean Group (AMG) estimator to this aim. Empirical findings indicate that the ARMEY curve hypothesis was verified only for the USA. However, this new approach proposed in this study cannot test the EKC hypothesis through the ARMEY curve model for any NAFTA country since this approach requires verification of the ARMEY curve hypothesis and a significant composite model for the same NAFTA country. If our composite model was significant, it might make it possible to numerically determine a maximum real GDP per capita level that would minimize or maximize CO2 emission levels for the USA. Therefore, this study introduces-proposes this new methodology as an alternative way of testing the EKC hypothesis in the relevant literature for future empirical studies.

Reinvestigating the Environmental Kuznets Curve (EKC) hypothesis by a composite model constructed on the Armey curve hypothesis with government spending for the US States.

This study reinvestigates the EKC hypothesis for US states with a new methodology that differs from all previous empirical studies using traditional EKC models. To this aim, this methodology, for the first time, unifies two seemingly different but strongly interrelated hypotheses (models), namely the Armey curve (AC) and traditional EKC models, into one single composite model. The rationale for creating this composite model is twofold. First, the functional propositions of these two hypotheses are depicted with inverted U-shaped curves. Second, they also have economically interrelated-causal relationships. This means that rising government spending (through the AC hypothesis) increases real GDP per capita (RGDPPC) and, consequently, increases in RGDPPC (through the EKC hypothesis) increase CO2 emissions. The composite model created may also allow US state policymakers to determine a single maximum spending level that will maximize or minimize CO2 emissions. Empirical findings indicate that the composite model is capable of testing the EKC hypothesis for 7 US states. Additionally, for 7 US states, maximum spending level was calculated to be around 15% of their RGDPPCs. Hence, with this calculated spending level, policymakers of these states may be able to determine-adjust their golden spending levels so as not to cause environmental degradation and declines in GDP.

Convergence analysis of the ecological footprint: theory and empirical evidence from the USMCA countries.

The lower ecological footprint (EF) is the sine qua non condition of cleaner energy. The purpose of this study is to investigate the convergence of per capita ecological footprint for the USMCA (The North American Free Trade Agreement) countries, involving the USA, Canada, and Mexico, over the 1961 to 2016. To this aim, the TAR (threshold autoregressive) panel unit root test is applied. Empirical findings indicate that convergence of the EF exists in the second regime, which represents 48.08 percent of the sample, and divergence in the first. Canada is the transition country between two regimes. These results signify common environmental policies-actions among the USMCA countries to mitigate-stop their environmental degradation. Additionally, detected convergence and divergence also might help the policymakers of the USMCA countries to understand which strategies-policies-actions converge or diverge them in the case of EF.

The increases and decreases of the environment Kuznets curve (EKC) for 8 OECD countries.

In this paper, we investigate the validity of the environmental Kuznets curve (henceforth, EKC) hypothesis for 8 OECD countries. To this aim, we decompose the per capita GDP series into its increases and decreases and consider only increases by excluding decreases from the model. Therefore, this method may enable us to test the EKC hypothesis more accurately, in accordance with the original theory. Following decomposition, we apply the fixed-effect regression model with Driscoll-Kraay standard errors, and the common correlated effects mean group (CCEMG) estimator. Empirical findings indicate that while the undecomposed model with undecomposed per capita GDP series supports the EKC hypothesis for 4 out of 8 countries, the decomposed model with decomposed per capita GDP series does not do so for any country. Hence, these mixed results reveal a need to employ different alternative techniques, such as the data transformation/decomposition applied in this study, for testing the EKC hypothesis.

The economic growth/development and environmental degradation: evidence from the US state-level EKC hypothesis.

This study investigates the validity of the EKC (environmental Kuznets curve) hypothesis for the 50 US states and a Federal District (Washington, D.C.). To this aim, the common correlated effects (CCE) and the augmented mean group (AMG) estimation procedures are applied between 1980 and 2015. While the CCE estimation does not support EKC hypothesis, the AMG does. The empirical findings of the AMG estimation indicate that only 14 states verify the EKC hypothesis. Additionally, the expected negative impacts of fossil energy consumption on the environment (CO2 emissions) are strongly detected in all states except Texas. However, the expected positive impacts of renewable energy consumption on the CO2 emissions are detected only in 13 states. Furthermore, the expected negative impacts of the population are not detected in some mostly populated states like New York, Texas, and Ohio. The overall findings of this study may help the US state-level policy makers in two ways: first, to understand whether their economic growths are sustainable (eco-friendly); second, to see how their fossil and renewable energy consumptions affect their environments and to review their energy policies.

Testing the EKC hypothesis for ten US states: an application of heterogeneous panel estimation method.

This study aims to test the EKC (Environmental Kuznets Curve) hypothesis for the ten states, having the highest levels of carbon dioxide emissions in the USA, through the independent variables of real GDP, population, and renewable energy and fossil energy consumptions. To this aim, the panel estimation method with cross-sectional dependence is applied to data from 1980 to 2015. The empirical findings of the study indicate that the EKC (inverted U-shaped) hypothesis is valid only for Florida, Illinois, Michigan, New York, and Ohio. Interestingly, the negative impacts of fossil energy consumption on CO2 emission levels in Texas are not detected statistically although this state is the leading oil-producing state. Furthermore, the positive impacts of renewable energy consumption in Florida, officially known as "Sunshine State", are considerably low when compared with the other states.