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The changing population age structure has a significant influence on the economy, society, and numerous other aspects of a country. This paper has innovatively applied the method of compositional data forecasting for the prediction of population age changes of the young (aged 0–14), the middle-aged (aged 15–64), and the elderly (aged older than 65) in China, India, and Vietnam by 2030 based on data from 1960 to 2016. To select the best-suited forecasting model, an array of data transformation approaches and forecasting models have been extensively employed, and a large number of comparisons have been made between the aforementioned methods. The best-suited model for each country is identified considering the root mean squared error and mean absolute percent error values from the compositional data. As noted in this study, first and foremost, it is predicted that by the year 2030, China will witness the disappearance of population dividend and get mired in an aging problem far more severe than that of India or Vietnam. Second, Vietnam’s trend of change in population age structure resembles that of China, but the country will sustain its good health as a whole. Finally, the working population of India demonstrates a strong rising trend, indicating that the age structure of the Indian population still remains relatively “young”. Meanwhile, the continuous rise in the proportion of elderly population and the gradual leveling off growth of the young population have nevertheless become serious problems in the world. The present paper attempts to offer crucial insights into the Asian population size, labor market and urbanization, and, moreover, provides suggestions for a sustainable global demographic development. [ABSTRACT FROM AUTHOR]

This paper compares the population transition in China and India during 1950-2015 by decomposing population growth into the growth attributed to the changes in fertility and mortality (intrinsic growth), and the growth attributed to the change in population age composition (momentum growth). The analysis reveals similarities and differences in the population transition path followed by the two countries and suggests that India lags behind China by about 30 years in terms of population transition. The population transition in China has been rapid and inconsistent, while India’s population transition has been slow and consistent. The momentum for growth intrinsic to respective populations has been the main contributor to population growth in both countries. The demographic dividend resulting from population transition in India has been substantially smaller than that in China because of differences in population transition paths. Unlike in China, population transition appears to have contributed only marginally to economic growth in India. This paper emphasises the importance of focussing population policy on the relative contribution of different demographic drivers to population growth. [ABSTRACT FROM AUTHOR]

Abstract: In a world increasingly affected by global environmental changes, Low Income countries will play an ever more central role in determining the future health of the biosphere. While global use of the biosphere''s capacity has increased over the past 45 years, per capita demand for biocapacity, as measured by the Ecological Footprint, has only increased in high-income countries and has remained constant or fallen in middle- and low-income nations. Consumption has increased faster than population in high-income nations, while population growth has been the dominant factor in middle- and low- income countries. Although listed in the middle-income group of countries, China showed atypical trends in the past 45 years, with a rapid increase in per capita Ecological Footprint that outstrip its gains in income. Typical trends were instead noticed for India, whose per person Ecological Footprint has fallen slightly. The results of this paper show that decisions made in China and India will be of fundamental importance for future global sustainability. [Copyright &y& Elsevier]

Measures of the effects of population pressure on the landscape using traditional methods for classifying urban territory are inadequate. The crude scale at which population densities are calculated and dependence on country-specific administration divisions hinder their ability to address such questions as the environmental impacts of cities and suburbs and make cross-national comparisons particularly difficult. This paper examines comparative urbanization measures among three case studies: the Pearl River Delta in Guangdong Province of China, the Indian state of Kerala, and the southern part of Florida in the United States. It proposes a measure based on the distribution of local population densities, taking advantage of the detailed data on small area populations and land area available in modern censuses and model-derived population databases such as LandScan, and the increasing potential of spatial analysis using geographic information systems (GIS). Examined with a similar set of thresholds, the resulting density distributions offer the potential to show better the ecological effects of population than do traditional measures. [ABSTRACT FROM AUTHOR]