Purpose Smart cities are an attempt to recognize the pioneering projects designed to make the cities livable, sustainable, functional and viable. The purpose of this paper is to evaluate funding released by the government city wise and sources available for finance for the development of the smart cities. The impact of fund released by the government for the development of smart cities (Chandigarh, Karnal, Faridabad, Pune, Chennai, Ahmedabad, Kanpur, Delhi, Lucknow and Agra) in India has been studied in detail. Urbanization is a continuous process, which is taking place throughout the globe, especially in developing countries like India. Design/methodology/approach The research is descriptive in nature. The sources of funding for smart cities in India have been taken into consideration, and χ2 test of independence has been employed to study the impact of fund released by the government for smart city development in India by using IBM SPSS. Findings The total investment, area-based projects, pan-city initiatives and O&M costs for smart cities ranged between Rs 133,368 and Rs 203,979 lakh crores, Rs 105,621 and Rs 163,138 lakh crores, Rs 26,141 and Rs 38,840 lakh crores, and Rs 1,604 and Rs 1,999 lakh crores, respectively, in the year 2016 (for 60 smart cities) to 2017 (for 99 smart cities), which shows an increasing trend. The investment in retrofitting projects, redevelopment projects, greenfield projects and area-based projects ranged between Rs 94,419 and Rs 131,003 lakh crores, Rs 8,247 and Rs 23,119 lakh crores, Rs 2,955 and Rs 8,986 lakh crores, and Rs 105,621 and Rs 163,138 lakh crores, respectively, in the year 2016 (60 smart cities) to 2017 (99 smart cities), which shows the division of projects funding for smart city development in India. The funding released for smart city development such as other sources, loans from the financial institution, private investment, convergence, state government share funding and Central Government Funding ranged between Rs 14,828 and Rs 15,930 lakh crores, Rs 7,775 and Rs 9,795 lakh crores, Rs 30,858 and Rs 43,622 lakh crores, Rs 25,726 and Rs 43,088 lakh crores, Rs 27,260 and Rs 45,695 lakh crores, and Rs 29,207 and Rs 47,858 lakh crores, respectively, in the year 2016 (60 smart cities) to 2017 (99 smart cities), which reflects the different sources of funding for the development of smart cities in India. The χ2 test of independence has been applied, which shows that there is no impact of fund released by the government on cities for smart city development in India as the p-values of Chandigarh (0.213), Karnal (0.199), Faridabad (0.213), Pune (0.199), Chennai (0.213), Ahmadabad (0.199), Kanpur (0.199), Delhi (0.199), Kolkata, Lucknow (0.213) and Agra (0.199) are greater than 0.05. Research limitations/implications For the Smart Cities Mission to be financially sustainable, the right policy and institutional framework should be implemented for modernization and aggregation of government landholding. Consolidation of all the landholdings under the smart city project should be properly implemented, and the role of private sectors should be encouraged for public‒private partnership projects to make Smart City Mission more successful. Practical implications The benefits of smart cities development will help provide affordable, cleaner and greener housing infrastructure for all, especially the inclusive group of developers belonging to the lower middle-income strata of India, and the benefits will be replicated when adopted on a smaller scale in the rural part of the country. Originality/value The research paper is original and χ2 test has been used to study the impact of fund released by the government for smart city development in India.
The Coronavirus has become a curse for all the sectors in India. The impact of this virus has also been seen in the real estate sector, which has already been the most flourishing sector for the growth of India. This virus has a diverse impact on global economic growth. The estimates show that the virus could affect the global economic growth by at least 0.5 % to 1.5% and the global trade will reduce by 13% to 32%, which depends on the downturn in the global economy due to the impact of Covid-19. The real estate sector is the second largest employer after the agriculture in India, but the outbreak of Covid-19 has adversely impacted the real estate sector in India. The research study enlightens the overview and impact of Coronavirus on Real Estate Sector Development in India. The real estate sector in India will not grow until the economy does not show any signs of improvement. The problems in real estate loans will rise in India. It also depends on the workers who have gone back to their villages, when they will come back. This results in the loss of demand for Real Estate sector development in India. This paper is an attempt to understand the impact of Covid-19 on real estate sector development and way forward to the recovery of real estate development in India.
Purpose The purpose of this paper is to examine the impact of residential and commercial loans on total real estate sector loans by using partial least square–structured equation modelling (PL–SEM) method. The residential loans as a mediator have been used to know the mediation effect between commercial and total real estate loans of banks in India. The residential loans as a mediator govern the relationship between commercial loans and total real estate loans in India. Real estate sector development is a lucrative opportunity for India. The real estate sector plays a major role in shaping economic conditions of the individuals, firms and family. Design/methodology/approach The research is descriptive in nature. The study on residential loans, commercial loans and total real estate loans has been taken into consideration, and on the other hand the measurement and structural model have been employed to the study the impact of residential loans and commercial loans on total real estate loans in India by using PL–SEM. The residential loans as a mediator have been taken to study the mediation effect of the relationship between commercial loans and total real estate loans in India. Findings The outcome of the structural model that is bootstrapping technique shows that there is an impact of residential and commercial loans by public and private sector banks on total real estate sector development in India. The residential loans show the full mediation effect between commercial loans and total real estate loans as the value of variation accounted for (VAF) is more than 1.93 which shows residential loans govern the nature of variable between commercial loans and total real estate loans. Practical implications The public and private sector banks are contributing to the real estate sector development in India which increases the economic growth of the country. The mediation analysis shows that residential loans are an important aspect between commercial and total real estate loans in India as the demand for residential housing is more in India. The increasing role of banks in the real estate sector strengthens the financial capability in the real estate sector market, and the property buyers will able to purchase more property which leads to increasing demand for real estate sector. Originality/value The research paper is original, and PL–SEM has been used to find the results.
Background: The coronavirus-19 (COVID-19) pandemic has affected millions of people across the world since early 2020. Besides the large number of case fatalities, this virus has produced significant health-related sequelae involving multiple systems of the body. As with previous coronavirus infections, this was also found to be associated with various neuropsychiatric symptoms. Psychosis has been uncommon, and the few reported cases across the world have forwarded association with either raised inflammatory markers or the consequences of social isolation. Materials and Methods: This is a retrospective descriptive study of 12 patients, who were admitted with COVID-19 infection and psychosis, between March 2020 and December 2020. Cases of head injury, any neurological or metabolic illnesses, and substance use disorders were excluded. Results: Cases with psychosis formed only 0.19% of all cases of COVID-19 admissions. All of them were young male and employed. Most of them had abrupt onset of psychosis with confusion, delusions, hallucinations, agitation, and sleep disturbances. Investigations including inflammatory markers (C-reactive protein) and computerized tomography scans were largely normal. Medications used were mainly benzodiazepines and antipsychotics. Most of the cases resolved within the second week, and follow-up after a month did not elicit any residual symptoms in majority. Diagnosis was acute and transient psychotic disorder (about 75%), bipolar affective disorder (2 cases), and schizophrenia (one). Conclusions: The major findings included nonreactive inflammatory markers, quick resolution of symptoms, requirement of low doses of antipsychotic drugs, and no long-term sequelae.
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