Tracking the change in Spanish greenhouse gas emissions through an LMDI decomposition model: A global and sectoral approach.

The reduction of GHG emissions to reverse the greenhouse effect is one of the main challenges in this century. In this paper we pursue two objectives. First, we analyze the evolution of GHG emissions in Spain in 2008-2018, at both the global and sectoral levels, with the variation in emissions decomposed into a set of determining factors. Second, we propose several actions specifically oriented to more tightly controlling the level of emissions. Our results showed a remarkable reduction (18.44%) in GHG emissions, mainly due to the intensity effect, but also to the production-per-capita effect. We detected somewhat different patterns among the various sectors analyzed. While the intensity effect was the most influential one in the agricultural, transport, and others sectors, the production-per-capita effect was predominant in the case of industry. The carbonization effect was revealed as crucial in the commerce sector. The above findings highlight the importance of the energy efficiency measures taken in recent years in the Spanish economy, also pointing to the need to deepen those strategies and to propose new measures that entail greater efficiency in emissions. Additional efforts in areas like innovation, R&D, diffusion of more eco-friendly technologies, and a greater use of greener energies all prove to be essential reduction actions to fight the greenhouse effect.

Stationarity and cycles in the energy consumption in the United States.

The purpose of this paper is twofold: analyzing stationarity of energy consumption by source in the United States and studying their cycles and pairwise synchronization. We study a panel of nine time series of monthly energy consumption for the period 1973-2022. Four of the series (namely coal, natural gas, petroleum, and nuclear electric power consumption) are non-renewables, whereas the remaining ones (hydroelectric power, geothermal, biomass, solar, and wind energy consumption) are renewable energy sources. We employ a nonparametric, panel stationarity testing approach. The results indicate that most of the series may be trend-stationarity, with nuclear and geothermal energy consumption being the only exceptions. Additionally, a study on potential cycles in the series of energy consumption by source is carried out, and subsequently we analyze pairwise concordance between states of different energy sources and between states of energy sources and the business cycle. Significant correlations are detected in the latter analysis, which are positive in the case of fossil fuel sources and negative for two renewable sources, namely geothermal and biomass energy consumption.

Nonparametric panel stationarity testing with an application to crude oil production.

A nonparametric panel stationarity test is proposed which offers the advantage of not requiring prior specification of the trend function for each of the series in the panel. A bootstrap implementation of the test is outlined and its finite sample performance is analyzed via Monte Carlo simulations. An application is also included where the proposed test is used to analyze the stochastic properties of monthly crude oil production for a panel of 20 -both OPEC and non-OPEC- countries from 1973 to 2015. Our analysis detects strong evidence of non-stationarity, both globally and group-wise. Results have implications for the effectiveness of government intervention and stabilization policies.

EU-28's progress toward the 2020 renewable energy share: a club convergence analysis.

This paper assesses the convergence of the EU-28 countries toward their common goal of 20% in the renewable energy share indicator by year 2020. The potential presence of clubs of convergence toward different steady-state equilibria is also analyzed from both the standpoints of global convergence to the 20% goal and specific convergence to the various targets assigned to Member States. Two clubs of convergence are detected in the former case, each corresponding to different renewable energy source targets. A probit model is also fitted with the aim of better understanding the determinants of club membership, which seemingly include real GDP per capita, expenditure on environmental protection, energy dependence, and nuclear capacity, with all of them having statistically significant effects. Finally, convergence is also analyzed separately for the transport, heating and cooling, and electricity sectors.

A note on model-free regression capabilities of fuzzy systems.

Nonparametric estimation capabilities of fuzzy systems in stochastic environments are analyzed in this paper. By using ideas from sieve estimation, increasing sequences of fuzzy rule-based systems capable of consistently estimating arbitrary regression surfaces are constructed. Results include least squares learning of a mapping perturbed by additive random noise in a static-regression context. L1 (i.e., least absolute deviation) estimation is also studied, and the consistency of fuzzy rule-based sieve estimators for L1-optimal regression surfaces is shown, thus giving additional theoretical support to the robust filtering capabilities of fuzzy systems and their adequacy for modeling, prediction, and control of systems affected by impulsive noise.