Investigating the residential electricity consumption-income nexus in Morocco: a stochastic impacts by regression on population, affluence, and technology analysis

In a comprehensive LMDI-STIRPAT-ARDL framework, this research investigates the residential electricity consumption (REC)-income nexus in Morocco for the period 1990 to 2018. The logarithmic mean Divisia index (LMDI) results show that economic activity and electricity intensity are the leading drivers of Morocco’s REC, followed by population and residential structure. And then, the LMDI analysis was combined with stochastic impacts by regression on population, affluence, and technology (STIRPAT) analysis and the bounds testing approach to search for a long-run equilibrium relationship. The empirical results show that REC, economic growth, urbanization, and electricity intensity are cointegrated. The results further show that there exists a U-shaped relationship between per capita gross domestic product (GDP) and REC: an increase in per capita GDP reduces REC initially; but, after reaching a turning point (the GDPPC level of 17,145.22 Dh), further increases in per capita GDP increase REC. Regarding urbanization, the results reveal that it has no significant impact on Morocco’s REC. The stability parameters of the short and long-term coefficients of residential electricity demand function are tested. The results of these tests showed a stable pattern. Finally, based on the findings mentioned above, policy implications for guiding the country's development and electricity planning under energy and environmental constraints are given.

Greece in the Eurozone: An Evaluation of the First Two Decades

On the 31st of December 2021, the euro celebrated its two decades in circulation. Initially, twelve countries adopted the euro as their new national currency, Greece being one of them. Starting in 2020, euro is the official currency of nineteen European Union countries. This paper aims to examine three issues. Firstly, the paper investigates Greek people’s perception about the euro, using data from the recent issue of the Eurobarometer (December 2021). Secondly, the economic performance of Greece is briefly examined by comparing the Greek Gross Domestic Product (GDP) two decades before and two decades after the introduction of euro. Finally, the Greek participation to the eurozone has been a controversial, political issue. The political developments in Greece during the first two decades of the euro are also studied, emphasizing the dramatic political events after the double elections of 2012. The period of the two decades ends with the detrimental impact of COVID-19. This issue is also mentioned by reviewing some recent publications. Keywords: Eurozone, Greece, GDP, per capita GDP, Eurobarometer, euro, elections, politics

The dynamics of CO2 emissions and economic growth: A comparative analysis using symbolic time series

The aim of this paper is to analyze the dynamic relationship between economic growth and CO2 emissions for a set of 98 countries over the lengthy period from 1951 to 2014. We describe the topology and hierarchy of countries and introduce a different concept of economic performance based on the idea of dynamic regimes. These regimes are defined by the average levels of per-capita CO2 emissions and the growth rates of per-capita GDP. By presenting a nonparametric clustering technique, the paper identifies two main groups. One cluster can be identified as the group of developed countries, which presents a homogeneous structure and tends toward more similar dynamics over time. The other cluster, associated with developing countries, is homogeneous but the dynamics of the countries do not show convergence. The study also finds some, though little, mobility between the groups.

Efficiency Evaluation of Energy in Northeastern China Based on Data Envelopment Analysis

The development of the energy economy in Northeastern China is unbalanced. In this paper, some energy structures such as oil, coal, and electricity are chosen as input indexes, while per capita gross domestic product (GDP) and total retail sales of consumer goods are desirable output indexes. The main causes of haze are industrial waste gas emission, industrial wastewater discharge, and solid waste production, which are regarded as undesirable output indexes. Environmental variables include industrial structure, urbanization, per capita GDP, and government planning. The proportion of the tertiary industry’s output value to its total output value is one of the important indexes to measure economic development in one area. Therefore, industrial structure plays a vital role in a region’s economic development. The tertiary industry oriented by service sector is the main force to promote economic transformation. The development of the urban economy will change from high to low as the process of urbanization, which can be characterized by green coverage of the urban built-up area. The higher per capita GDP, the higher the economic development can be achieved. Government planning refers to the total number of construction projects and completed projects in the year the government takes measures to control pollution. It shows that the total factor productivity of energy green efficiency has been stable in the last decades but fluctuated wildly in special periods, in which technical progress has played a great role, as well as the increase of technical efficiency.

Growth, inequality and poverty: a robust relationship?

The consequences of poverty and inequality for growth have long preoccupied academics and policy-makers. This paper revisits the inequality-growth and poverty-growth links. Using a panel of 158 countries between 1960 and 2010, we find that the correlation of growth with poverty is consistently negative: A 10 p.p. decrease in the headcount poverty rate is associated with a subsequent increase in per capita GDP between 0.5 and 1.2% per year. In contrast, the correlation of growth with inequality is empirically fragile—it can be positive or negative, depending on the empirical specification and econometric approach employed. However, the indirect effect of inequality on growth through its correlation with poverty is robustly negative. Closer inspection shows that these results are driven by the sample observations featuring high poverty rates.

Population economic function model based on data mining algorithm

Population structure changes interact with economic development, moderate population and reasonable population structure are important guarantees for sustainable social and economic development. The research ignores the specific impact of the change of population age structure on economic growth, and proposes and establishes a population economic function model based on data mining algorithm. Based on the changes of population structure in Liaoning Province in the past 20 years, Grey correlation analysis method is selected. The analysis shows that there is a close relationship between population structure and economic growth. Based on this research, the econometric method is used to construct a multiple linear regression model to further analyze the specific impact of population structure changes on economic growth. The analysis results show that the total population of urban areas, the total number of employed people in the primary industry, the number of middle school students per 10,000 people, and the total number of employed people in the tertiary industry are the four most significant demographic indicators for the per capita GDP of the study area. There is a significant positive correlation between the total number of employed people in the tertiary industry and per capita GDP and there is a significant negative correlation between the total number of employed people in the primary industry and the number of middle school students per capita and per capita GDP. The impact of other indicators on per capita GDP is not significant. According to the conclusion, countermeasures and suggestions to ease population structure change and promote the coordinated development of population and economy in the study area are put forward.

Factors Influencing Vaccine Hesitancy in China: A Qualitative Study

Vaccine hesitancy has become a significant issue. We aimed to elucidate the factors influencing vaccine hesitation in Chinese residents and to analyze and recommend promotional strategies and measures. In total, 92 Chinese residents from 10 provinces were interviewed using semi-structured face-to-face interviews following a predetermined survey framework in this qualitative study. We found trust in vaccine safety, access to professional advice, and vaccine price and effectiveness to be the main factors influencing vaccine hesitation. Additionally, residents in areas with a higher per capita GDP tend to receive more social support, believe that vaccination is beneficial and can prevent diseases, pay more attention to whether the vaccine is safe and has undergone various clinical trials, and are more likely to seek advice from individuals with vaccination experience as opposed to their counterparts in areas with a lower per capita GDP. Notably, as per capita GDP rises, individuals become more concerned about the price of vaccines. Measures such as clarifying vaccine safety and effectiveness, reducing self-funded vaccine prices, offering free vaccination for special groups, strengthening the publicity role of medical staff, and taking advantage of network platforms are essential to reduce vaccine hesitancy among Chinese residents.

Associations of motor neuron disease research productivity and socioeconomic factors in Southeast Asia: a bibliometric analysis

ABSTRACT Background: Scientific productivity on motor neuron disease (MND) research has been hypothesized to be low in Southeast Asia (SEA). Objective: To investigate the scientific productivity of SEA countries on MND and the associations between research metric indices and various country-specific socioeconomic parameters. Methods: We searched electronic databases for relevant articles from SEA on MND from the earliest indexed record to June 30, 2020. We obtained the following research productivity indices: bibliometric (number of publications in journals with impact factor (IF) and Scopus citations) and altmetric indices (PlumX metrics). We also collected data from published literature and reliable sources on the following socioeconomic variables: population, gross domestic product (GDP), GDP per capita, %GDP allocated for research and development (R&D) and the number of neurologists per country. Results: We included 196 articles that satisfied our inclusion criteria. Amyotrophic lateral sclerosis studies comprised the majority of the articles (n = 112; 57.1%). The top three countries in terms of the numbers of publications in journals with IF and in PlumX metrics were Singapore (n = 129), Malaysia (n = 26), and Thailand (n = 18). GDP per capita, %GDP for R&D and number of neurologists per one million population had strong positive correlations with the bibliometric and altmetric indices. Conclusions: This study highlights that although the scientific productivity of MND research in SEA has been low, it is continuously growing. This also emphasizes the imperative to improve economic indices and the number of neurologists in SEA to enhance scientific output on MND.

Spatial Evaluation of Earthquake Disaster Based On Correlation Characteristics And BP Neural Network

Rapid spatial evaluation of disaster after earthquake occurrence is required in the emergency rescue management, due to its significant support for decreasing casualties and property losses. The earthquake-hit population is taken as an example of earthquake disaster to construct the evaluation model using the data from the 2013 Ms7.0 Lushan earthquake. Ten influencing factors are classified into environmental factors and seismic factors. The correlation analysis reveals characteristics that there is a nonlinear relationship between the earthquake-hit population and various factors, and per capita GDP and PGA factor have a stronger correlation with earthquake-hit population. Moreover, the spatial variability of influencing factors would affect the distribution of earthquake-hit population. The earthquake-hit population is evaluated using BP neural network with optimizing training samples based on the spatial characteristics of per capita GDP and PGA factors. Different number of sample points are generated in areas with different value intervals of influencing factors, instead of the random distribution of sample points. The minimum value of RMSE (Root Mean Square Error) from testing set is 18 people/km2, showing good accuracy in the spatial evaluation of earthquake-hit population. Meanwhile, the optimizing samples considering spatial characteristics could improve the convergence speed and generalization capability comparing to random samples. The trained network was generalized to the 2017 Ms7.0 Jiuzhaigou earthquake to verify the prediction accuracy. The evaluation results indicate that BP neural network considering the correlation characteristics of factors has the capability to evaluate the seismic disaster information in space, providing more detailed information for emergency service and rescue operation.

Anticorruption

The MIT Press Essential Knowledge series provides the reader with accessible, concise, yet interesting and completely up-to-date information. Each part was written by excellent experts on the subject, in a language understood by non-experts, too. In this way, the current research data and results in the field of each topic can be really used. Nowadays, it is not easy to find in the endless set of information obtainable on the World Wide Web those that essentially provide the fundamental knowledge on a particular topic. The MIT series fill a gap in this. The topic of the present volume of the series is the anticorruption, as a world phenomenon, its current development and situation. And the topicality of the current theme is perhaps duly justified by the following World Bank estimate: “Much of the globe is infected with corruption, sapping as much as 3 percent of annual per capita GDP in large swathes of Africa, Asia, and Latin America. Even North America is hardly immune. The World Bank says that $1 trillion or more is lost each year to corruption, globally.” (Rotberg, 2020).