Quantiles Of Data Research Articles

Heterogeneity of the impact of energy production and consumption on national greenhouse gas emissions

This research investigates the relationship between greenhouse gas (GHG) emissions and energy production and consumption patterns in 122 countries over the period from 1990 to 2020. Particular attention is paid to the role of different types of energy, including renewable, hydro and nuclear energy, as well as oil, gas and coal, in shaping GHG emissions. Unlike previous studies, the paper analyzes the full range of energy resources and focuses on different quantiles of data using the quantile-quantile regression (QQR) method. The results of this approach are compared with traditional quantile regression (QR) to assess its validity. Among the findings of this study are: (1) different parts of the energy mix have a heterogeneous impact on GHG emissions in different quantiles; (2) energy consumption patterns play a more significant role in shaping GHG emissions than production patterns; (3) increasing production and consumption of fossil fuels does not necessarily increase GHG emissions; (4) similarly, the development of renewable energy sources is not always accompanied by a reduction in GHG emissions. Our general conclusion is that existing climate policy requires significant diversification to not be focused only on renewable energy, which impact on GHG emissions is not as homogeneous as it usually considered, and which cannot solve the problem of raising emissions solely. Instead of this, it could be more rational to focus on the searching the ways of hydrocarbon industries sustainable development and support them in this journey, especially in the field of Scope 2 and Scope 3 operations management.

Does pandemic uncertainty spur public health expenditures? Evidence from European Union economies.

This research intends to evaluate the asymmetric relationship between pandemic uncertainty and public health expenditures in selected European Union nations (Germany, France, Sweden, Belgium, Austria, Netherlands, Denmark, Spain, Finland, and Portugal). Earlier studies used panel data methodologies to get consistent results about the pandemic-health expenditures nexus, irrespective of the reality that numerous economies did not identify such a link independently. By contrast, the present research utilizes a unique technique, quantile-on-quantile, that explores time-series dependency in every nation by offering worldwide yet country-related insight into the linkage between the variables. Estimations reveal that pandemic uncertainty increases public health expenditures in most of the selected economies at specified quantiles of data. Additionally, the data indicate that the level of asymmetries among our variables varies by country, stressing the significance of policymakers paying special attention while executing policies concerning health expenditures and pandemic uncertainty.

How Does Health Uncertainty Impact Greenhouse Gas Emissions in European Union Economies? A Blessing in Disguise.

The global outbreak of COVID-19 caused serious threats to public health and economic growth all around the world, but on the other hand, the betterment of the environment took place. How pandemics' health uncertainty will affect environmental quality is a crucial matter to address. The paper investigates the asymmetric association between pandemics-related health uncertainty and greenhouse gas emissions (GHG) in the top emitter European Union economies (Italy, Germany, France, Poland, Netherlands, Spain, Czech Republic, Belgium, Romania, and Greece). Employing data from 1996 to 2019, a unique approach called 'Quantile-on-Quantile', is adopted to evaluate the influence of various quantiles of the health uncertainty on GHG emissions. According to estimates, health uncertainty enhances environmental quality by minimizing GHG in most of our chosen nations at certain quantiles of data, which makes pandemics a blessing in disguise for environmental quality. Additionally, the estimations indicate that the grades of asymmetry between our variables varies by locality, accentuating the requisite for authorities to give specific consideration while executing health uncertainty and environmental quality policies.

Asymmetric linkages between nuclear energy and environmental quality: Evidence from Top-10 nuclear energy consumer countries

To lay a solid basis for prosperity and competitiveness, countries should achieve balance in the three fundamental aspects: energy availability, energy affordability and ecological balance. Nuclear energy has attracted international interest as one of the most crucial environmental quality strategies. The objective of this study is to analyze the non-linear link between nuclear energy and environmental quality in the top-10 nuclear energy consumer countries (USA, China, Russia, France, Canada, Spain, Sweden, South Korea, Ukraine, and Germany). Earlier research employed panel data methodologies to examine the linkage between nuclear energy and the environment, despite the fact that many nations did not independently demonstrate such a correlation. On the alternative, this study uses a novel approach known as ‘Quantile-on-Quantile,’ which allows for the analysis of time-series dependence in each country by giving universal yet country-specific insights into the relationship between the variables. Estimates show that the consumption of nuclear energy improves environmental quality by lowering ecological footprint in the majority of the nations studied at certain quantiles of data. Moreover, the data demonstrate that the degree of asymmetries between our variables changes by nation, emphasizing the importance of policymakers exercising caution when adopting nuclear energy and environmental quality regulations.

A novel grey wave forecasting method for predicting metal prices

The evolution of metal prices shows severe fluctuations and irregular cycles which bring difficulties to accurate forecasting. This paper proposes a novel grey wave forecasting method with unequal-interval contour lines and contour time sequences filtrating to predict metal prices. In the proposed model unequal-interval contour lines are determined by the quantiles of data, which considers the intensity of data. Contour time sequences are filtrated based on autocorrelation characteristics of time series. Furthermore, we use monthly prices of two metals - aluminum and nickel-to assess the performance of our novel grey wave forecasting model with a multi-step-ahead prediction. The empirical analysis indicates the modified grey wave forecasting method is much better than basic grey wave forecasting method in terms of prediction accuracy and it can also achieve better forecasting results than ARMA and random walk.