Revisiting global energy efficiency and CO2 emission nexus: fresh evidence from the panel quantile regression model.

Given the importance of energy efficiency in environmental degradation, the effects of energy efficiency and renewable and nonrenewable energy consumption on global environmental pollution were investigated. For this purpose, panel data from 107 countries from 1996 to 2014 were examined. In addition, the present study also tested the well-known environmental Kuznets curve (EKC). The long-run relations were estimated by applying a panel quantile regression (PQR) approach, which is useful for finding heterogeneous effects at lower- and upper-level quantiles of CO2 emissions. The empirical results indicated that energy efficiency had a significantly negative impact on CO2 emissions with low intensity at higher-level quantiles.Furthermore, the impact of renewable and nonrenewable energy consumption on environmental degradation was significantly negative and positive across all quantiles, respectively. The empirical results provide evidence supporting an inverted U-shaped nexus between GDP and CO2, whereby the EKC is found valid. Hence, energy efficiency improvement and renewable energy consumption policies must align with strategies to curb environmental degradation.

Prioritization of water erosion-prone sub-watersheds using three ensemble methods in Qareaghaj catchment, southern Iran.

Water-induced erosion poses severe harm to the sustainable development of land and water resources that is essential for attaining agricultural sustainability in Qareaghaj catchment of Fars Province, Iran. This study evaluates the topo-hydrological, morphometric, climatic, and environmental characteristics of Qareaghaj catchment for prioritizing the sub-watersheds that are susceptible to erosion caused by water. We tested and compared a novel ensemble multi-criteria decision-making (MCDM) model, namely the weighted aggregated sum product assessment-analytical hierarchy process (WASPAS-AHP) with prevailing benchmark ensemble MCDM models including VlseKriterijumska optimizacija I Kompromisno Resenje (VIKOR)-AHP and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)-AHP for ranking sub-watersheds and determining the most significant parameter that influences water erosion (WE) in Qareaghaj catchment. The outcome of weights using pairwise comparison matrix (PCM) of AHP reveals that normalized difference vegetation index (NDVI), mean annual rainfall (MAR), slope degree (SD), and slope length and steepness factor (LS) governs the WE in Qareaghaj catchment. The prioritization rankings of sub-watersheds obtained using the VIKOR-AHP, TOPSIS-AHP, and WASPAS-AHP models demonstrate that SW31, SW63, and SW94 had the highest priority rank with a score of 0.047, 0.69, and 0.477, respectively. The comparison of rankings from the models using Spearman's correlation coefficient tests (SCCT) and Kendall's tau correlation coefficient tests (KTCCT) revealed that WASPAS-AHP had a higher correlation with TOPSIS-AHP and VIKOR-AHP ensemble models. The outcome of MCDM models was validated based on the erosion potential method (EPM), which displayed that the VIKOR-AHP model was better for mapping the erosion susceptibility than TOPSIS-AHP and WASPAS-AHP models. Thus, the erosion susceptibility mapping based on the VIKOR-AHP ensemble model can be considered for developing new strategies and land use policies in order to control WE in Qareaghaj catchment.

The impact of age structure on carbon emission in the Middle East: the panel autoregressive distributed lag approach.

Rapid evolution in the population age structure of the Middle East countries has major economic, social, and environmental outcomes. Therefore, to fill the gap in the previous literatures, in this study, the effect of age structure on environmental degradation was investigated in the Middle East region. To achieve this goal, a panel data of 10 Middle East countries were examined over the period of 1990 to 2014. Moreover, the carbon dioxide emission per capita was used as an environmental pollution index in this study. According to the stationary property of the variables, small sample size data, and the assumptions of the model, the panel autoregressive distributed lag method of mean group, pooled mean group, and dynamic fixed effect estimators were investigated in this study. The empirical results implied that the pooled mean group model emerged as the most efficient among the three estimators. Also, results revealed that the age structure have a significant relationship with environmental pollution. Children and working age population have a positive elasticity, whereas elderly people have negative elasticity. Furthermore, the results showed that the working age population has the greatest explanatory power on the carbon emissions. Also, the relationship between per capita energy consumption and gross domestic product per capita with air pollution was positive. Overall, the empirical results showed that any attempt to decrease carbon dioxide emissions in the Middle East region should consider the population age structure.

Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020).

OBJECTIVES: Coronavirus disease 2019 (COVID-19) represents a major pandemic threat that has spread to more than 212 countries with more than 432,902 recorded deaths and 7,898,442 confirmed cases worldwide so far (on June 14, 2020). It is crucial to investigate the spatial drivers to prevent and control the epidemic of COVID-19. METHODS: This is the first comprehensive study of COVID-19 in Iran; and it carries out spatial modeling, risk mapping, change detection, and outbreak trend analysis of the disease spread. Four main steps were taken: comparison of Iranian coronavirus data with the global trends, prediction of mortality trends using regression modeling, spatial modeling, risk mapping, and change detection using the random forest (RF) machine learning technique (MLT), and validation of the modeled risk map. RESULTS: The results show that from February 19 to June 14, 2020, the average growth rates (GR) of COVID-19 deaths and the total number of COVID-19 cases in Iran were 1.08 and 1.10, respectively. Based on the World Health Organisation (WHO) data, Iran's fatality rate (deaths/0.1M pop) is 10.53. Other countries' fatality rates were, for comparison, Belgium - 83.32, UK - 61.39, Spain - 58.04, Italy - 56.73, Sweden - 48.28, France - 45.04, USA - 35.52, Canada - 21.49, Brazil - 20.10, Peru - 19.70, Chile - 16.20, Mexico- 12.80, and Germany - 10.58. The fatality rate for China is 0.32 (deaths/0.1M pop). Over time, the heatmap of the infected areas identified two critical time intervals for the COVID-19 outbreak in Iran. The provinces were classified in terms of disease and death rates into a large primary group and three provinces that had critical outbreaks were separate from the others. The heatmap of countries of the world shows that China and Italy were distinguished from other countries in terms of nine viral infection-related parameters. The regression models for death cases showed an increasing trend but with some evidence of turning. A polynomial relationship was identified between the coronavirus infection rate and the province population density. Also, a third-degree polynomial regression model for deaths showed an increasing trend recently, indicating that subsequent measures taken to cope with the outbreak have been insufficient and ineffective. The general trend of deaths in Iran is similar to the world's, but Iran's shows lower volatility. Change detection of COVID-19 risk maps with a random forest model for the period from March 11 to March 18 showed an increasing trend of COVID-19 in Iran's provinces. It is worth noting that using the LASSO MLT to evaluate variables' importance, indicated that the most important variables were the distance from bus stations, bakeries, hospitals, mosques, ATMs (automated teller machines), banks, and the minimum temperature of the coldest month. CONCLUSIONS: We believe that this study's risk maps are the primary, fundamental step to take for managing and controlling COVID-19 in Iran and its provinces.