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Knowledge about current size and structure of a country's population is needed for the formulation and implementation of policies and programs in almost all sectors of public life. The purpose of this paper is to provide population projection for Oman, using population census and family health survey data.

Component method is used for projecting future population of Oman. Population Census data of 2003 by sex and by five‐year age groups were used. The base life expectancy of Oman is assumed to be 73 years and the base total fertility considered as 5.1 children per woman.

Depending upon the achievement of replacement fertility by the year 2025 or 2030 or 2035 the population of Oman in 2050 will vary from 4.5 million to 5.0 million. The different scenarios of population projection indicate that the population of Oman will not be stabilized before 2100.

Population projection depends on assumptions about mortality, fertility, base life expectancy and migration. If these assumptions change then the projections will also change.

Because of high fertility in the past, women in the reproductive ages will increase for up to several decades. Thus, population growth will continue because of momentum effect, even if Oman achieves replacement fertility say in 2030. The age at marriage will increase.

The rapid socio‐economic development and increased women empowerment will create a new outlook and ideas about lifestyles, leading to a decline in fertility. The decline in fertility is strongly related to social, health, education, employment opportunities of women and economic development, which through a variety of mechanisms, reduces the fertility desired and increases the fertility regulation through contraception, birth spacing and increased age at marriage of females. Because of increase in life expectancy and falling birth rate, the absolute number of the elderly population will have enormous impact on health care needs and hospitalization.

This paper deals with the population projection of Oman. Timely and accurate information about population trends is crucial for the socio‐economic development of a country. Knowledge about current size and structure of a country's population is needed for the formulation and implementation of policies and programs in almost all sectors of public life such as health, education and employment.

The economic law of population distribution and migration has been studied chiefly based on the Chinese situation. The distribution and development of productive forces decide the distribution and migration of population, and in turn, the latter influences the former. The population distributions in three different stages of social development, namely agricultural, industrial and information society, are described. A new concept in population economics is introduced, i.e. population economic density, which is different from the concept of population density. The formula of population economic density is P(population)/R(resources). Many kinds of migration are analysed, and it is believed that the main efficient cause of migration is economy.

The purpose of this paper is to investigate the relationship between ownership type and population health initiatives adopted by hospitals using the 2015 American Hospital Association data.

Hospitals of various sizes, ownership structures and geographic locations are represented in the survey. The outcome variables of interest include measures of hospital population health activities.

Findings indicate that nonprofit hospitals are most likely to express commitment to population health and participate in population health activities, with for-profit hospitals being least likely. Implications for policy and practice are discussed.

This study demonstrates that discrepancies in population health approaches exist across ownership status – particularly, nonprofit hospitals appear to be the most likely to be involved in population health efforts.

As we continue to push for population health management in the hospital setting, grappling with the definition and purpose of population health management will be essential.

Overall, these results suggest that nonprofit hospitals are more likely to be implementing population health efforts than for-profit or government-owned hospitals.

Although there are several studies on population health in hospitals, this study is the first to investigate the relationship between ownership type and population health initiatives adopted by hospitals.

The paper seeks to provide a comprehensive review of the most important current and future demographic trends around the world and to discuss their implications for business schools. It aims to do so by going beyond the usual consideration of population size and also by focusing on the changing composition by age, sex and highest educational attainment.

Standard cohort‐component population projections are complemented with methods of multi‐state projections and probabilistic population projections.

The paper shows the likely end of world population growth together with massive anticipated population ageing in low fertility countries and continuing very high population growth in Sub‐Saharan Africa and parts of Western Asia. Because of past investments in education younger cohorts tend to be better educated. For business schools this presents challenges in terms of the composition of the student body and faculty as well as the content of teaching.

This is the first time that such multi‐state projections of population and human capital around the world are discussed in the context of challenges arising for higher education.

The purpose of this paper is to apply grey system theory to population system and project China's population.

The paper applies the GM(1,1) model to China's population projections. Two key aspects of the method are crucial for obtaining best accuracy of prediction. They are the choice of the length for the original data to be used in the model and the adoption of the GM(1,1) metabolic model in prediction. The former determines what initial data to be used while the latter describes an iteration process on how to proceed to predict.

The results show that in 2015 China's population will reach 1.37 billion and in 2050 it will be between 1.42 and 1.48 billion, which is in accordance with the latest projections from the UN. The findings show the GM(1,1) metabolic model is an effective mathematical means in population projections.

The paper suggests that GM(1,1) metabolic model can provide an effective simulation model for complicated systems with uncertainty and can be used in many fields.

The paper provides useful advice for the department of population.

Most population projections have been based on assumptions about fertility, mortality, and migration. The paper considers the population system as a grey system and introduces the GM(1,1) metabolic model to population projections.

The purpose of this paper is to discuss the issues in studying hard-to-reach or dispersed populations, with particular focus on methodologies used to collect data and to investigate dispersed migrant entrepreneurs, illustrating shortcomings, pitfalls and potentials of accessing and disseminating research to hard-to-reach populations of migrant entrepreneurs.

A mixed methodology is proposed to access hard-to-reach or dispersed populations, and this paper explores these using a sample of Brazilian migrants settled in different countries of the world.

This paper explores empirical challenges, illustrating shortcomings, pitfalls and potentials of accessing and disseminating research to hard-to-reach populations of migrant entrepreneurs. It provides insights by reporting research experiences developed over time by this group of researchers, reflecting a “mixing” of methods for accessing respondents, contrasting to a more rigid, a-priori, mixed methods approach.

The main contribution of this paper is to showcase experiences from, and suitability of, remote data collection, especially for projects that cannot accommodate the physical participation of researchers, either because of time or cost constraints. It reports on researching migrant entrepreneurship overseas. Remote digital tools and online data collection are highly relevant due to time- and cost-efficiency, but also represent solutions for researching dispersed populations. These approaches presented allow for overcoming several barriers to data collection and present instrumental characteristics for migrant research.

The Organization for Economic Cooperation and Development (OECD) countries are facing unprecedented challenges related to climate change and population aging. The purpose of the analysis is to explore the relationship between population aging and environmental degradation, accounting for human capital, using a sample of 19 OECD countries over the period 1980–2019.

On the empirical methodology, the analysis uses panel estimators with heterogenous coefficients and an error structure that takes into consideration cross-country heterogeneity and cross-sectional dependence for a panel of 19 OECD countries over the period 1980–2019. To examine the relationship between population aging and environmental degradation, the authors employ two alternative measures of environmental degradation that is energy consumption and CO2 emissions in metric tons per capita. Concerning the regressors, the authors account for two alternative aging indicators, namely the elderly population and the old-age dependency ratios to confirm robustness.

The analysis provides evidence that population aging and human capital development (IHC) lead to lower energy consumption in the OECD sample. Overall, the growing number of elderly people in the OECD seems to act as a mitigating factor for energy consumption. The authors view these results as conveying the message that the evolution of population aging along with channeling government expenditures towards human capital enhancement are important drivers of curbing energy consumption and ensuring environmental sustainability. The authors' research is of great significance for environmental policymakers by illuminating the favorable energy consumption patterns that population aging brings to advanced economies.

The main limitation of this study concerns data availability. Future research, and subject to greater data availability in the future, could dig deeper into understanding the dynamics of this complex nexus by incorporating additional control variables. Similarly, the authors focus on aggregate renewable energy consumption, and the authors do not explicitly model the sources of renewable energy (wind, hydropower, solar power, solid biofuels and other). Additional analysis of the breakdown of renewable energy sources would be insightful – subject to data availability – especially for meeting the recently agreed new target of 42.5% for European Union (EU) countries by 2030. A deep transformation of the European energy system is needed for the EU to meet the target. Finally, extending the model to include a range of non-OECD countries that are also experiencing demographic transformations is a promising avenue for future research.

To the best of the authors' knowledge, this study is the first to examine the effects of population aging and human capital on environmental degradation using a broad set of OECD countries and advanced spectrum estimation methods. Given cross-sectional dependencies and cross-country heterogeneity, the authors' empirical results underline the importance of cross-OECD policy spillovers and knowledge diffusions across the OECD countries. The new “energy culture” calls for concerted policy action even in an aging era.

China's population aging is gradually deepening and needs to be actively addressed. The purpose of this paper is to construct a novel model for analyzing the population aging.

To analyze the aging status of a region, this study has considered three major indicators: total population, aged population and the proportion of the aged population. Additionally, the authors have developed a novel grey population prediction model that incorporates the fractional-order accumulation operator and Gompertz model (GM). By combining these techniques, the authors' model provides a comprehensive and accurate prediction of population aging trends in Jiangsu Province. This research methodology has the potential to contribute to the development of effective policy solutions to address the challenges posed by the population aging.

The fractional-order discrete grey GM is suitable for predicting the aging population and has good performance. The population aging of Jiangsu Province will continue to deepen in the next few years.

The proposed model can be used to predict and analyze aging differences in Jiangsu Province. Based on the prediction and analysis results, identified some corresponding countermeasures are suggested to address the challenges of Jiangsu's future aging problem.

The fractional-order discrete grey GM is firstly proposed in this paper and this model is a novel grey population prediction model with good performance.

This study examined the roles of public spending and population moderating characteristic structure of selected African economies on bank-based financial development through credit to private sector.

The study sampled 37 selected African economies for the years 1991–2018, and it applied a pooled mean group (PMG) estimator to account for short-run and long-run causal effects, and confirmed short-run adjustments towards the long-run convergences between the variables. Specific suitable tests were also applied.

Evidence confirms positive impacts of both capital formation and final consumption expenditures on financial development in the short run and long run. The moderation of population structures on expenditure structures help to speed up convergences.

This work attests its innovation by accounting for the separate effects of the expenditure types, the moderation effects of young and mature populations for capital and final consumption expenditure on financial development among selected economies in Africa.

Metaheuristic algorithms based on biology, evolutionary theory and physical principles, have been widely developed for complex global optimization. This paper aims to present a new hybrid optimization algorithm that combines the characteristics of biogeography-based optimization (BBO), invasive weed optimization (IWO) and genetic algorithms (GAs).

The significant difference between the new algorithm and original optimizers is a periodic selection scheme for offspring. The selection criterion is a function of cyclic discharge and the fitness of populations. It differs from traditional optimization methods where the elite always gains advantages. With this method, fitter populations may still be rejected, while poorer ones might be likely retained. The selection scheme is applied to help escape from local optima and maintain solution diversity.

The efficiency of the proposed method is tested on 13 high-dimensional, nonlinear benchmark functions and a homogenous slope stability problem. The results of the benchmark function show that the new method performs well in terms of accuracy and solution diversity. The algorithm converges with a magnitude of 10-4, compared to 102 in BBO and 10-2 in IWO. In the slope stability problem, the safety factor acquired by the analogy of slope erosion (ASE) is closer to the recommended value.

This paper introduces a periodic selection strategy and constructs a hybrid optimizer, which enhances the global exploration capacity of metaheuristic algorithms.