Debanam Priyam Priyadarshi Joshi

The shift in Global Climatic conditions is one of the most pressing issues, with far‐reaching consequences for ecosystems, agriculture, and human livelihoods. Cereal production in low‐income countries (LIC) and lower‐middle income countries (LMIC) is highly vulnerable to climatic variations, affecting food security and livelihoods. This paper focuses on the interactions between the rainfall, CO ₂ emissions, and temperature with cereal production in the low‐ and lower‐middle‐income nations. We have employed panel autoregressive distributed lag (Panel ARDL), Westerlund cointegration, and panel causality to examine the impact of climate change on food security, and robustness has been checked using panel‐corrected standard errors (PCSE). On the other hand, method of moments quantile regression (MMQR) complements Panel ARDL by showing that climate effects on cereal production vary across output levels in both panels. The long‐run outcomes show that CO ₂ emissions and precipitation have a strong positive effect on the production of cereals in both types of income groups, and the effects of temperature are not significant in lower‐middle‐income nations but have a positive, but mild impact in low‐income nations. Whereas, the shock in temperature has a negative influence on cereal production, and CO ₂ and precipitation have insignificant direct effects in the short run. The negative and significant error adjustment terms are confirmation of adaptation towards the long‐run equilibrium. Results point to the fact that lower‐middle‐income nations are more resilient to changing temperatures but vulnerable to other climatic shocks. The policy implications highlight that climate‐smart agricultural practices and resilient infrastructure must be prioritized to enhance sustainable food production and safeguard food security.

We undertake an extensive literature survey and segregate the methods mainly into two approaches: parametric and non-parametric. Further, we categorise the non-parametric techniques into the Growth Accounting Approach and the Deterministic Frontier Approach. The evaluation and development of methods used in the study of productivity growth measurement is a continuous process over the years, even if we found a longstanding critical debate from the classical school, popularly known as the Cambridge controversy. This article presents a holistic review of methods for documenting the growth debates in independent India, which helps understand the concepts for the new researcher in the field and the policymakers in the developing and less developed countries. JEL Codes: D24, E24, O47

Purpose: This paper examines India's socio-economic attributes and different financial dimensions of financial inclusion (FI).Design/methodology/approach: The paper uses a principal component analysis (PCA) to build indexes related to financial dimensions. It applies the logistics regression model and the Fairlie decomposition method to determine India's socio-economic and financial characteristics of FI.Findings: Based on the logistic regression, socio-economic factors like age, gender, marital status, level of education and religion have an impact on FI. The use of financial institutions has positively contributed to the probability of FI, while the low proximity of financial service providers retards the process of FI. Fairlie decomposition concludes regional disparity and gender disparity in FI; however, the rural–urban gap in FI is not captured by the variables included in the study. The main reasons for the discrepancy are lack of education, financial literacy, the proximity of financial service providers and lack of financial institutions.Originality/value: This paper makes two important contributions: first, it presents a micro-level analysis of FI across the socio-demographic strata of India, and second, it demonstrates the regional, rural–urban and gender disparity in FI in India.

The efficient planning, execution, and management of institutional frameworks for climate change adaptation are essential to sustainable development. India, in particular, is known to be disproportionately vulnerable to the consequences of climate change. This study examines the effects of environmental taxes, corruption, urbanization, economic growth, ecological risks, and renewable energy sources on CO₂ emissions in India from 1978 to 2018. Therefore, the ARDL model is used to draw inferences, and Pairwise Granger causality is also applied to demonstrate a cause-and-effect relationship. The empirical results show that corruption, environmental dangers, GDP, and urbanization positively influence India’s carbon emissions. However, the results of short-run elasticities show that carbon emissions reduce ecological sustainability. Environmental hazards and costs, like other countries, impact India’s carbon emissions. Therefore, decision-makers in India should set up strict environmental regulations and anti-corruption measures to combat unfair practice that distorts competition laws and policies. In addition, the government concentrates more on energy efficiency policies that diminish carbon emissions without hampering economic growth in the country.

The research uses data from a multi-stage survey performed in 2019 to evaluate short-term migrants’ characteristics and sectoral transitions in Kashmir. After locating in-migrants in the selected clusters, we randomly selected 253 samples in a 70:30 urban–rural ratio. The IV-Probit is used to identify the features of short-term migration to the valley. Results reveal that short-term migrants are primarily absorbed in construction. In addition, most in-migrants are unskilled and come from marginalised communities. This study contributes to the knowledge on migration in a developing nation like India, particularly in Kashmir. In addition to temporarily increasing the urban inflow, short-term labour migration may assist the family left behind by remitting revenue. Hence, these results are critical for policymaking regarding mobility, urban development and the expanding construction sector. JEL Codes: J6; C2

The present study uses both carbon dioxide emission and ecological footprints as proxies for environmental degradation to examine the environmental Kuznets curve hypothesis for the top three emitters from Asia, i.e., China, India, and Japan. To this end, the autoregressive distributed lag model for time series and panel estimation is used for a period spanning over 1980–2016. For carbon dioxide emission, China presents an inverted-U shape of the environmental Kuznets curve, while a U-shape relationship is found for India and Japan. Similarly, when the hypothesis is tested with the ecological footprint, Japan offers an inverted U-shape and U-shaped association is detected for China and India. The panel analysis indicates the existence of the environmental Kuznets curve with both proxies of environmental degradation. Besides, human capital and renewable energy promote environmental sustainability, while non-renewable energy use hinders environmental quality. The findings of this study suggest that in order to meet the combined goals of economic growth and environmental protection, the three economies, i.e., China, India, and Japan, should employ renewable energy–enabled technology. Graphical abstract

The study examined the dynamic impact of climate such as Carbon dioxide (CO₂) emissions, temperature, and rainfall on agricultural yield in India from 1980 to 2017 by applying the nonlinear autoregressive distributed lag (NARDL) model. The empirical evidence supports a long- and short-run asymmetry effect, implying that the positive impact of the climatic variables on agricultural yield is distinct from the adverse effects on both the long- and short-run equilibrium. In addition, CO₂ emissions and average atmospheric temperature have diverse impacts on agriculture yield.