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In this present era, artificial intelligence (AI) is a buzz word. With the increase in hype for AI and its increasing rate of application worldwide, it has raised a debate on the impact of AI on increase in growth rate, productivity and unemployment. Having said that, studies have shown AI is still at an infant stage. With the increase in the ability to understand context and reply in human language, it has surely paved its way towards an impeccable journey of transforming an economy with the increase in productivity, gross domestic product (GDP) and economic growth. The advancement may cause some setbacks in terms of labour force participation with higher demand for labour with certain skill set to be able to work with AI and hence increasing the unemployment rate as well as creating a widening gap between developing and developed countries. In this chapter, we aim to determine the effect of AI on GDP growth of India for the year 2022 and have selected terms of trade (ToT), human development index (HDI) and per capita real GDP for the year 2022 to understand its overall impact using secondary data. The results of the analysis show that ToT, global artificial intelligence (GAI) index, and HDI as explanatory variables significantly explain the variation in per capita real GDP.
Butterflies serve as important pollinators and bioindicators of ecosystem health. However, urbanization threatens these interactions through habitat fragmentation and altered floral resource availability. Their survival and interactions largely depend on the availability and composition of floral resources, which shape the structure of plant–butterfly interaction networks. We compared plant–butterfly interaction networks across three habitat types along a rural–urban gradient to understand how urbanization affects network structure and species interactions. The study was conducted over one year across three different habitats: rural areas near agricultural fields, suburban grasslands, and urban green spaces. Three permanent 500 m transect lines were established at each study site. A transect-based interaction monitoring approach was used to collect butterfly data. For each habitat, connectance, nestedness, weighted NODF, generality, vulnerability, interaction evenness, interaction strength, and plant-pollinator (butterfly-nectaring plant) network were analyzed. Connectance (0.21), generality (3.37), Shannon diversity (4.51), and weighted NODF (50.05) were highest in rural areas compared with suburban and urban green spaces. Urban areas exhibited the lowest weighted NODF (37.11), due to fragmented resources and habitat isolation. In each habitat, plant-butterfly networks were specialized, indicating that butterflies were highly specific in selecting their nectaring plants. Our study revealed that Lantana camara L., Lippia alba (Mill.) N.E.Br. ex Britton P.Wilson, Sphagneticola trilobata (L.) Pruski, and Tridax procumbens L. were among the most preferred plant species for butterflies. These results underline the importance of considering plant selection and habitat context in sustaining butterfly–plant networks under increasing urbanization.
The persistently large income gap between the developed countries (DCs) of the North and relatively less developed and developing countries (LDDCs) of the South is one of the most notable features of the international community over the last few decades. Different research works in this field have indicated that the average annual growth rate of per capita income (PCI) in LDDCs has been faster compared to that in DCs particularly since early 1990s indicating a sign of convergence in the growth process. However, the absolute gap between the DCs and LDDCs in terms of per capita Gross national product (GNP) has widened over years. In this backdrop, this chapter is an attempt to enquire into the dynamics of the gap between the developed, developing and less developed parts of the world over the period from 1990 to 2023. The beta-convergence analysis, using dynamic panel data regression, leaves emphatic evidence of conditional convergence, rather than the absolute, implying a case of typical club convergence, where the countries sharing similar conditions in health, education and prevalence of absolute poverty tend to converge in terms of PCI. This chapter essentially points out that a mere convergence in the growth rate does not necessarily translate into the narrowing of income and development gap, based on the selected 150 countries across the globe, classified with respect to income level, high, middle and low and four broad regions, namely, Latin American Caribbean (LAC), Sub-Saharan Africa (SSA), Middle East and North Africa (MNA) and East Asia Pacific (EAP), epitomising the developing world.
Though economic growth sometimes gets prime importance in the development policy of some less developed and developing countries, it has been observed that inequality adversely affects economic growth from both the supply and the demand sides in such economies. On the supply side, inequalities of income and wealth (particularly the latter) create imperfections in the credit market. If an economy is demand constrained, an increase in inequality will make matters even worse as the richer section of the people has a lower propensity to consume than the poor. Though the Gini coefficient of income distribution has been declining in regions like Sub-Saharan Africa and South-East Asia during 1981–2023, it has increased in India and North America. This dualistic nature of development has further been overshadowed with polarisation of income and wealth in India with the characteristics of considerable intra-group homogeneity and inter-group heterogeneity. Sluggish growth in real wages, educated unemployment, and the possible adverse impacts of the presently emerging artificial intelligence (AI) can lead our economy towards bipolarity, and our economy is in danger of being divided into two parts with no interconnection working through labour reallocation from the backward to the advanced sector. Needless to say, that would be the end of the idea of development because a developing economy, by definition, means a dual economy with a shrinking backward sector.
Union dissolution, encompassing divorce and separation, is an integral aspect of social transformation, reflecting shifts in societal norms, gender roles, economic independence, and legal frameworks. In India, while marriage has traditionally been seen as a lifelong commitment, increasing urbanisation, economic independence of women, and legal reforms have contributed to shifting perceptions of marriage, leading to greater acceptance of dissolution. The incidence of divorce and separation in India has historically been low compared to Western societies. However, there has been a steady increase in marital dissolution as indicated in this exploratory study that impacts the women immensely. Women, particularly in middle age groups, are more vulnerable to dissolution, with separation rates significantly higher than divorce rates. Factors such as financial independence, domestic conflicts, changing social norms, and globalisation have influenced this trend. Union dissolution varies across regions, showing a distinct north–south divide, with southern states experiencing higher dissolution rates. Among religious groups, Christians have the highest dissolution rates, particularly in urban areas. Across all demographics, separation remains the dominant form of dissolution. While union dissolution is increasing in India, challenges such as social stigma, economic hardship, and legal battles persist. Addressing these concerns through legal reforms, social awareness, and economic empowerment is crucial for ensuring the well-being of individuals undergoing marital dissolution.
