This study examines the relationship between renewable and nuclear energy consumption, carbon dioxide emissions and economic growth by using the Granger causality and non-linear impulse response function in a business cycle in Spain. We estimate the threshold vector autoregression (TVAR) model on the basis of annual data from the period 1970–2018, which are disaggregated into quarterly data to obtain robust empirical results through avoiding a sample size problem. Our analysis reveals that economic growth and CO2 emissions are positively correlated during expansions but not during recessions. Moreover, we find that rising nuclear energy consumption leads to decreased CO2 emissions during expansions, while the impact of increasing renewable energy consumption on emissions is negative but insignificant. In addition, there is a positive feedback between nuclear energy consumption and economic growth, but unidirectional positive causality running from renewable energy consumption to economic growth in upturns. Our findings do indicate that both nuclear and renewable energy consumption contribute to a reduction in emissions; however, the rise in economic activity, leading to a greater increase in emissions, offsets this positive impact of green energy. Therefore, a decoupling of economic growth from CO2 emissions is not observed. These results demand some crucial changes in legislation targeted at reducing emissions, as green energy alone is insufficient to reach this goal.

This study explores the impact of clean energy and non-renewable energy consumption on CO2 emissions and economic growth within two phases (formative and expansion) of renewable energy diffusion for three selected countries (France, Spain, and Sweden). The vector autoregression (VAR) model is estimated on the basis of annual data disaggregated into quarterly data. The Granger causality results reveal distinctive differences in the causality patterns across countries and two phases of renewables diffusion. Clean energy consumption contributes to a decline of emissions more clearly in the expansion phase in France and Spain. However, this effect seems to be counteracted by the increases in emissions due to economic growth and non-renewable energy consumption. Therefore, clean energy consumption has not yet led to a decoupling of economic growth from emissions in France and Spain; in contrast, the findings for Sweden evidence such a decoupling due to the neutrality between economic growth and emissions. Generally, the findings show that despite the enormous growth of renewables and active mitigation policies, CO2 emissions have not substantially decreased in selected countries or globally. Focused and coordinated policy action, not only at the EU level but also globally, is urgently needed to overhaul existing fossil-fuel economies into low-carbon economies and ultimately meet the relevant climate targets.

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This paper applies the threshold cointegration technique developed by Enders and Siklos (2001) to investigate the impact of an oil price changes on changes in production and inflation in the presence of structural break in seven European Union countries. This technique will allow for a different speed of adjustment to the long-run equilibrium depending on whether production in selected economies is above or below the long-run relationship. Given the presence of asymmetric cointegration between oil prices, production and inflation, we estimate threshold error correction models to examine long-and short-run Granger causality. We found evidence for cointegration with asymmetric adjustment in the case of France, Denmark and the total EU.

Purpose: This paper aims to develop a corporate failure prediction model for construction companies in Poland that allow assessing their financial situation and credit risk. Design/Methodology/Approach: For this purpose, the following research methods have been used, descriptive and comparative analysis, subject literature review, and logit analysis. The Polish construction companies' financial data in this research come from the Emerging Markets Information Service (EMIS). To achieve the main goal of the research, the logit model was built. The significance test, error matrix, and ROC curve were used to assess the quality of the estimated binary logit model. Findings: Based on the research, we identify seven financial indicators that significantly impact the probability of poor financial condition. The following variables are current assets turnover, debt to assets ratio, operating profit to assets, gross profit to assets, operating profit plus amortization to short-term liabilities, current assets to assets ratio, and equity to assets ratio. The research results show that corporate failure prediction models are interesting and important tools to assess the financial situation. Based on the developed model, it has been found that the growth of debts increases the credit risk of construction companies. Moreover, the increase in the share of current assets in the total assets harms the financial condition. Also, the risk of insolvency decreases with growing profitability measured by the rate of return on assets. Practical Implications: The built logit model can be beneficial for investment loan providers, insurance companies, and entities selecting contractors in construction projects due to the possibility of the credit risk assessment. Originality/Value: The use of logit models to identify statistically significant corporate failure prediction factors for construction companies in Poland.