RESUMEN
This research investigates the factors influencing carbon emission intensity in 94 countries during 2018 using two qualitative methods: necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA). The study covers variables related to economics, human geography, energy, and institutions, showing significant variations among them. The NCA model identifies economic complexity and fossil energy consumption as necessary conditions for high-carbon emission intensity. On the other hand, the fsQCA model reveals sufficient conditions for both high- and low-carbon emission intensity, presenting different causal combinations of variables. For high-carbon emission intensity, nine causal solutions are identified, emphasizing the roles of economic growth, urbanization, fossil energy consumption, and institutional quality. Reducing carbon emission intensity requires addressing economic complexity and reducing reliance on fossil energy consumption. Policymakers should focus on sustainable economic development, environmentally friendly urbanization, and transitioning to renewable energy sources. This research's originality lies in its qualitative approach, going beyond traditional regression methods to explore necessary and sufficient conditions for carbon emission intensity. It offers valuable insights into the complex interplay of variables, providing multiple causal configurations for both high- and low-carbon emission intensity.
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Carbono , Desarrollo Económico , Humanos , Geografía , Instituciones de Salud , LuzRESUMEN
Life expectancy is one of the crucial criteria for determining the quality of life in today's societies. As such, the study of factors affecting life expectancy is a key issue for policymakers. This study aims to investigate the impact of energy consumption, the democratic process, and government service delivery on life expectancy in 100 countries during 2000-2018, using panel quantile regression. The impact of these factors on life expectancy has been estimated in quantiles of 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 0.95. Also, the impact of GDP, CO2 emission, and Gini coefficient variables has been explored as controlling variables on life expectancy. The results show that the impact of CO2 emissions and the democratic process on life expectancy is negative in all quantiles, and the impact of GDP is negative in all quantiles except 0.95. Moreover, the relationship between hydroelectricity consumption and life expectancy in the 0.05, 0.1, 0.2, 0.8, and 0.9 quantiles is negative and significant. Accordingly, based on the results, the impact of petroleum and other liquids consumption, government service delivery, and Gini coefficient on life expectancy in all quantiles is positive and only the impact of the Gini coefficient on life expectancy in all quantiles is significant.
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Dióxido de Carbono , Desarrollo Económico , Gobierno , Esperanza de Vida , Calidad de VidaRESUMEN
Energy plays a vital role in every economy, and it can be considered as a driving force of economic growth. The interrelations of energy with the other variables are also significant. As many developing countries rely on energy consumption, attracting energy-intensive facilities and installations is being satisfied with foreign direct investment (FDI) which can affect environment negatively. Accordingly, FDI can stimulate economic growth and the use of energy through economic growth indirectly affects foreign direct investment. Therefore, the primary purpose of this research is a comparative study on the impact of fossil and alternative energy consumption on foreign direct investment and economic growth. Thereby, we figure out the knowledge and technology transferring via FDI and its effect on economic growth, in which direction should it be, and how it should be managed to cause less environmental pollution. So, this research consists of 14 selected developing countries for 1986-2016. The results, estimated through seemingly unrelated regression (SUR), show that alternative energy and fossil fuels have a positive effect on the GDP with the coefficient values of 0.10 and 0.02, respectively. Still oil rents do not affect economic growth. The same findings have been reached with the FDI, and energy resources amplified the foreign investment on the cost of CO2 emissions. Also, these empirical results can be considered by policymakers to help them in creating the right policies for economic growth, adopting a strategy to use more alternative energy and investing in infrastructure to reduce the burning of fossil fuel.
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Desarrollo Económico , Fósiles , Dióxido de Carbono , Internacionalidad , Inversiones en SaludRESUMEN
Nowadays, determining the factors influencing carbon dioxide emissions is a crucial issue for policymakers. So, this study examines Porter and pollution haven's hypothesis via foreign direct investment, financial development, and energy consumption in 14 countries of the MENA region during 2004-2016, using panel quantile regression that estimated the impact of these factors in quantiles of 0.1, 0.25, 0.5, 0.75, and 0.9. Also, the effect of population, trade openness, and economic growth variables has been investigated as controlling variables on CO2 emissions. The results of the research show that the impact of energy consumption, economic growth, and total population on all quantiles of carbon dioxide emission is positive and significant. Still, the effect of direct foreign investment on the amount of CO2 emissions is negative and only significant at 0.1, 0.5, and 0.75 quantiles, which supports Porter's hypothesis. Based on this hypothesis, the foreign direct investment entrance helps reduce the environmental pollution of the host country. Also, the effect of financial development on 0.25, 0.5, 0.75, and 0.9 quantile carbon dioxide emissions is negative and significant. Finally, the trade openness variable has a positive and significant effect on the quantiles of 0.1 and 0.9 CO2 emissions.
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Dióxido de Carbono/análisis , Desarrollo Económico , Contaminación Ambiental/análisis , Internacionalidad , Inversiones en SaludRESUMEN
Clustering algorithms are critical data mining techniques used to analyze a wide range of data. This study compares the utility of ant colony optimization (ACO), genetic algorithm (GA), and K-means methods to cluster climatic variables affecting the yield of rainfed wheat in northeast Iran from 1984 to 2010 (27 years). These variables included sunshine hours, wind speed, relative humidity, precipitation, maximum temperature, minimum temperature, and the number of wet days. Seven climatic factors with higher correlations with detrended rainfed wheat yield were selected based on Pearson correlation coefficient significance (P value < 0.1). Three variables (i.e., sunshine hours, wind, and average relative humidity) were excluded for clustering. In the next step based on Pearson correlation (P value < 0.05) between the yield, and the seven climate attributes, fitness function, and silhouette index, only four attributes with higher correlation in its cluster were selected for reclustering. Four climate attributes had an extensive association with yield, so we used four-dimensional clustering to describe the common characteristics of low-, medium-, and high-yielding years, and this is the significance of this research that we have done four-dimensional clustering. The silhouette index showed that the best number of clusters for each station was equal to three clusters. At the last step, reclustering was done through the best-selected method. The results yielded that GA was the best method.