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.

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The Environmental Kuznets Curve in Poland – Evidence from Threshold Cointegration Analysis

Background: Glucose variability (GV) is a matter of interest for researches in recent years. It is connected with oxidative stress, which is crucial in the development of multiple complication of prematurity. However, glycemic variability in preterm infants was poorly investigated. This study aims to investigate glycemic variability obtained from a continuous glucose monitoring (CGM) system in a cohort of very low-birthweight (VLBW) infants. Methods: A prospective, single-center, open cohort study enrolled 74 VLBW infants with a mean birthweight of 1066 g and median gestational age of 28 weeks. A CGM system (Guardian Real-Time CGM ^® , Medtronic, Northridge, CA) was used to measure interstitial glucose concentration. The glycemic variability was calculated using EasyGV. Results: Most glycemic variability indices in VLBW infants showed log-normal distribution and for these, geometric mean ÷/ × geometric standard deviation (GSD) was calculated: M-value 2.28 (÷/ × 1.82), mean amplitude of glycemic excursions (MAGE) 1.89 (÷/ × 1.34), average daily risk ratio (ADRR) 2.22 (÷/ × 2.56), lability index 0.46 (÷/ × 1.71), J-index 0.46 (÷/ × 1.71), low blood glucose index 2.05 (÷/ × 1.66), high blood glucose index 1.11 (÷/ × 2.44), continuous overlapping net glycemic action (CONGA) 5.54 (÷/ × 1.16), mean of daily differences (MODD) 1.23 (÷/ × 1.38), and coefficient of variation 1.15 (÷/ × 1.31). Only SD of glucose concentration showed a normal distribution: arithmetic mean 1.24 (+/−0.37). ADRR, J-index, MODD, CONGA, and MAGE are moderately to strongly correlated with SD. Conclusions: In our cohort of VLBW infants, almost all glycemic variability indices showed skewed positive distribution. The natural central tendency measure for the log-normally distributed data is the geometric mean and for statistical variation is the GSD.