National Carbon Accounting—Analyzing the Impact of Urbanization and Energy-Related Factors upon CO2 Emissions in Central–Eastern European Countries by Using Machine Learning Algorithms and Panel Data Analysis

Abstract

The work at hand assesses several driving factors of carbon emissions in terms of urbanization and energy-related parameters on a panel of emerging European economies, between 1990 and 2015. The use of machine learning algorithms and panel data analysis offered the possibility to determine the importance of the input variables by applying three algorithms (Random forest, XGBoost, and AdaBoost) and then by modeling the urbanization and the impact of energy intensity on the carbon emissions. The empirical results confirm the relationship between urbanization and energy intensity on CO2 emissions. The findings emphasize that separate components of energy consumption affect carbon emissions and, therefore, a transition toward renewable sources for energy needs is desirable. The models from the current study confirm previous studies’ observations made for other countries and regions. Urbanization, as a process, has an influence on the carbon emissions more than the actual urban regions do, confirming that all the activities carried out as urbanization efforts are more harmful than the resulted urban area. It is proper to say that the urban areas tend to embrace modern, more green technologies but the road to achieve environmentally friendly urban areas is accompanied by less environmentally friendly industries (such as the cement industry) and a high consumption of nonrenewable energy.

Renewable energy strategy analysis in relation to environmental pollution for BRICS, G7, and EU countries by using a machine learning framework and panel data analysis

“…Thus, it is assumed that gas consumption appears as a consequence of increasing the industrial capacity and economic sustainability, respectively, of a certain region. However, since economic development remains a major objective for all world economies, many economies are first targeting to obtain economic growth and, after achieving a level of comfort, an increase in investments in green technologies is considered (Nuţă et al, 2021), a hypothesis which can confirm our findings. Compared with gas consumption, oil consumption within BRICS has an immediate impact on renewable energy production capacity (Figure 5B).…”

“…Therefore, as GDP per capita increases, renewable energy capacity will increase until achieving a maximum predicted point, followed by a constant evolution (Figure 5C). The GDP from the industry indicates that investments in sustainable energy sources decrease in the first stage of economic growth (Figure 5D) since this process is characterized by high energy consumption (Nuţă et al, 2021). However, after achieving an optimum level of economic growth, a positive correlation between economic growth and energy consumption from renewable sources is observed, a situation confirmed also in other studies (Eggoh et al, 2011;Zhixin and Xin, 2011;Tang et al, 2016;Gozgor et al, 2018;Shahbaz et al, 2018;Rahman and Velayutham, 2020).…”

Repowering Industrial Combined Heat and Power Units: a Contribution to Cleaner Energy Production

“…On the contrary to radical solutions such as rapid shut down of coal power plants which is associated with immense costs [12], rehabilitation and repowering of the existing units to prolong their service, increase their efficiency and decrease the pollutants emissions appears more feasible [13,14]. Many countries that relied heavily on coal power plants, such as Russia [15], Poland [16,17], Czechia and Slovakia [18,19] or Balkan countries [20,21], chose to follow this path. The power production sector and the associated renovation activities receive a lot of attention both from governments and researchers [22,23].…”

“…Thus, a more realistic assessment of possible carbon dioxide balance changes resulting from repowering is targeted. Overall carbon dioxide balance incorporating the external power production effect is assessed by Equation (21) with EF denoting the applied emission factor. This equation allows comparing carbon footprint of individual repowering options with the base case.…”

Analysis of the Factors Influencing the Spatial Distribution of PM2.5 Concentrations (SDG 11.6.2) at the Provincial Scale in China

“…Many models have been used to analysis these factors of PM2.5 concentrations, such as the correlation analysis method [12], machine learning method [20], geographical detector [21][22][23], spatial econometric model (SEM) [24], geographically weighted regression model [25][26][27], spatial regression model [28], land-use regression model [29], and other models [30]. This study focuses on the spatiotemporal differentiation characteristics of PM2.5 at provincial scale in China and investigates the spatial spillover effect intensity of potential socioeconomic factors affecting PM2.5 concentrations across different provincial units.…”