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1

Selected determinants of the development of renewable energy sources in the member states of the European Union

Firlej Krzysztof Adam, Stanuch Marcin

Economics and Environment

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2023

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No. 3

89--113

EN

The objective of this study is to explore the relationship between selected indicators of SDG7, supplemented by the variables of GDP and carbon dioxide emission contract prices, and the consumption of energy from renewable sources in the European Union. The research problem of the study is whether it is possible to explain the consumption of energy from renewable sources in the European Union from 2010 to 2020 within the group of selected indicators for SDG 7 supplemented by GDP variable and variable CO2 emission futures contracts. Based on conducted econometric research, it was proved that there was a certain interdependence and causality of selected factors on the devel-opment of renewable energy sources, which varied depending on the EU Member State. By making a critical evaluation of the obtained models, it was found that only in 10 cases (countries) can they be considered correct.

PL

Celem badania było określenie zależności pomiędzy wybranymi wskaźnikami SDG7, uzupełnionymi o zmienne PKB i ceny kontraktowe emisji dwutlenku węgla, a zużyciem energii ze źródeł odnawialnych w Unii Europejskiej. Problemem badawczym opracowania było to, czy możliwe jest wyjaśnienie zużycia energii ze źródeł odnawialnych w Unii Europejskiej w latach 2010-2020 w grupie wybranych wskaźników dla SDG 7 uzupełnionych o kontrakty terminowe na emisję CO2, czy PKB. Na podstawie przeprowadzonych badań ekonometrycznych wykazano, że istnieje pewna współzależność i przyczynowość wybranych czynników rozwoju odnawialnych źródeł energii, która różni się w zależności od państwa członkowskiego UE. Dokonując krytycznej oceny uzyskanych modeli, stwierdzono, że jedynie w 10 przypadkach (krajach) można je uznać za prawidłowe.

2

Some Generalized Estimating Equations Models Based on Causality Tests for Investigation of the Economic Growth of the Country Groups

Yonar Harun, İyit Neslihan

Foundations of Computing and Decision Sciences

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2021

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Vol. 46, No. 3

297--315

EN

In this study, investigation of the economic growth of the Organization for Economic Cooperation and Development (OECD) countries and the countries in different income groups in the World Data Bank is conducted by using causality analyses and Generalized Estimating Equations (GEEs) which is an extension of Generalized Linear Models (GLMs). Eight different macro-economic, energy and environmental variables such as the gross domestic product (GDP) (current US$), CO2 emission (metric tons per capita), electric power consumption (kWh per capita), energy use (kg of oil equivalent per capita), imports of goods and services (% of GDP), exports of goods and services (% of GDP), foreign direct investment (FDI) and population growth rate (annual %) have been used. These countries have been categorized according to their OECD memberships and income groups. The causes of the economic growth of these countries belonging to their OECD memberships and income groups have been determined by using the Toda-Yamamoto causality test. Furthermore, various GEE models have been established for the economic growth of these countries belonging to their OECD membership and income groups in the aspect of the above variables. These various GEE models for the investigation of the economic growth of these countries have been compared to examine the contribution of the causality analyses to the statistical model establishment. As a result of this study, the highlight is found as the use of causally-related variables in the causality-based GEE models is much more appropriate than in the non-causality based GEE models for determining the economic growth profiles of these countries.

3

From experimental, structural probability distributions to the theoretical causality analysis of molecular changes

Daniluk P., Dziubiński M., Lesyng B., Hallay-Suszek M., Rakowski F., Walewski Ł.

Computer Assisted Methods in Engineering and Science

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2012

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Vol. 19, no. 3

257-276

EN

A brief overview of causality analysis (CA) methods applied to MD simulations data for model biomolecular systems is presented. A CausalMD application for postprocessing of MD data was designed and implemented. MD simulations of two model systems, porphycene (ab initio MD) and HIV-1 protease (coarse-grained MD) were carried out and analyzed. Granger’s causality methodology based on a Multivariate Autoregressive (MVAR) formalism, followed by the Directed Transfer Function (DTF) analysis was applied. A novel approach based on the descriptors of local structure was also presented and prelim- inary results were reported. Casuality analyses are required for a better understanding of biomolecular functioning mechanisms. In particular, such analyses can link physics-based structural dynamics with functions inferred from molecular evolution processes. Current limitations and future developments of the presented methodologies are indicated.