Groundwater depletion in India is an alarming concern that threatens agricultural sustainability, especially in the Punjab region, where groundwater extraction is at its peak compared to other agricultural states in India. The present research studies the two districts of the Malwa region in Central Punjab: Sangrur, the most depleted, and Barnala, the least depleted. This present study assesses the level of groundwater depletion, irrigation water productivity, and groundwater efficiency in these regions. The study employs an approach that combines primary data collected through multi-stage random sampling of farmers with secondary data obtained from official statistics reports to assess irrigation water productivity, crop-specific returns, and efficiency levels for wheat and paddy cultivation (A guide to DEAP version 2.1: a data envelopment analysis (computer) program Centre Efficiency. Prod Anal Univ N Engl Austr. 96:08 1–49, 1996) [1]. Results from the study show that water irrigation efficiency is lower when farms' gross returns are higher. This indicates that the groundwater is being exploited in these areas. On the contrary, small and semi-medium-sized farms show higher groundwater efficiency, especially for wheat cultivation. The analysis shows a persistent gap between groundwater efficiency and technological efficiency, suggesting that water resources are not used optimally. Finally, the conclusion of the study states that to resolve the issue of Punjab’s groundwater crisis, the state requires policy interventions, such as promoting the purchase of less water-intensive crops, promoting water-efficient technologies like direct-seeded rice (DSR), building reservoirs, and improving access to canal irrigation. Hence, the region of Punjab requires the use of these techniques to achieve a balance between the sustainable management of water resources and agricultural profitability in the long run.

Odisha exhibits notable disparities in educational access and outcomes across various districts, including both KBK and non-KBK regions. It is crucial to assess how efficiently the study area utilises educational resources and influences policy development to close regional gaps in school performance. Therefore, to achieve this aim, the present study analyses elementary education in Odisha, focusing on dropout rates, gender disparities, and regional differences from 2008-09 to 2021-22. To estimate efficiency, the Directional Distance Function (DDF), conventional Data Envelopment Analysis (DEA) methods, and second-stage Tobit regression have been employed to capture efficiency over time. Therefore, the study’s findings reveal marked disparities between KBK and non-KBK districts, as well as between scheduled and non-scheduled regions. While most districts are relatively efficient, the KBK districts have an average efficiency of 0.958 compared to non-KBK districts with an average efficiency of 0.85. Additionally, the dropout rate in KBK is 5 per cent, while in non-KBK areas, it is 7 per cent, because dropout has been treated as an undesirable factor. Later, Tobit estimates that on-the-job training for teachers, teacher qualifications, and the representation of SC/ST teachers have significantly enhanced efficiency. Therefore, these results offer valuable insights for policymakers to address the distinct challenges faced by tribal communities in scheduled areas, encouraging policies that foster inclusivity and equitable education.

The global shift to renewable energy depends on the integration of financial, governmental, and environmental policies, but the combined impacts of these policies on renewable energy consumption (REC) have received less focus in the G20. The present article discusses how financial development, governance quality, and green economy influence REC in 17 selected G20 countries from 1996 to 2021. Methodologically, this paper used the Pooled Mean Group Autoregressive Distributed Lag (PMG-ARDL) model as the primary estimator. Robustness testing is carried out by employing Dynamic Ordinary Least Squares (DOLS) and Fully Modified Ordinary Least Squares (FMOLS). The composite indicators for financial development (FD) and governance are constructed via Principal Component Analysis (PCA). The empirical findings show that FD, governance quality, and green economy have positive effects on REC, with financial development increasing REC by 9–13

This study investigates the asymmetric and nonlinear relationship between fiscal deficit and economic growth in India over the period 1970 to 2022. The primary objective is to assess whether fiscal deficits act as a stimulus or a constraint on economic performance, accounting for the roles of foreign direct investment (FDI), foreign aid (AID), and human capital development (proxied by primary school enrolment, PSE). Using advanced econometric methodologies, including Autoregressive Distributed Lag (ARDL), Nonlinear ARDL (NARDL), and Multiple Threshold NARDL (MTNARDL) models, the study captures both long-run and short-run dynamics along with threshold and asymmetric effects. The findings reveal that positive shocks in fiscal deficit negatively impact long-run growth, whereas negative shocks (i.e., fiscal consolidation) enhance it. In the short run, however, moderate increases in deficit have an expansionary effect, especially when starting from low-deficit levels. Therefore, fiscal deficit constrains economic growth in the long run but stimulates it in the short term, but only when beginning deficit levels are low. Control variables such as FDI and AID exhibit positive long-term growth effects, while PSE contributes to growth primarily in the long run, highlighting the lagged effect of education investments. The study recommends a threshold-sensitive and growth-oriented fiscal policy, prioritizing capital expenditure and maintaining deficits within moderate, sustainable bounds. Policymakers should also foster FDI, improve aid effectiveness, and invest strategically in human capital to ensure long-term macroeconomic stability and inclusive growth.

Purpose In the 21st century, the agricultural and food industries have faced the critical challenge of meeting the needs of a rapidly growing global population. Food production is estimated to increase by 70% by 2050, thus supporting this growth. Limited farmland availability, unpredictable climate conditions and widespread food insecurity make this challenge even more difficult. This study examines how advanced digital technologies can help address challenges in the agricultural sector. Design/methodology/approach A comprehensive bibliometric analysis examines agricultural digitalisation trends from 2013 to 2022. Findings The study uncovers promising trend-developed regions, such as Europe, Germany, the UK and the United States, at the forefront of digital agricultural practices. The analysis underscores the transformative power of digitalization in agriculture, with technologies such as artificial intelligence, smart farming, big data reshaping strategies, enhancing connectivity and fostering transparency. This study underscores the need for responsible innovation, inclusive development and targeted policies to harness the full potential of digitalization, ensuring sustainability and equity in the agricultural sector. It also identifies emerging trends like Industry 4.0 and precision livestock farming as pivotal components of agriculture in the digital age. Research limitations/implications This study offers insights into the digitalization of agriculture through bibliometric analysis, but it has limitations. It relies solely on published literature and trends and lacks real-world data and empirical case studies. Future research should incorporate secondary data and case studies to determine the significance of adapting digital agriculture to help overcome various obstacles in agriculture. Originality/value Several researchers have examined the use of technologies, such as UAVs, IoT, machine learning and other advancements in agriculture. Recent studies have highlighted the digital agriculture revolution, emphasizing IoT, SSM, CSA, RS and AI in Agriculture 4.0. These studies offer a comprehensive roadmap for the digital agriculture revolution, covering performance analysis and scientific mapping.

The present study conceptualizes the probability of vector-borne disease occurrence in the context of demographic, socioeconomic, and microenvironmental determinants. This study uses NSSO data from the 76th round to predict vector-borne diseases. A probit model has been used to analyse the determinants. Ten percent of the surveyed individuals reported having suffered from vector-borne diseases, and 11.82 and 7.61

This study examines the intricate relationship between economic growth, carbon emissions, and institutional quality across the extended BRICS countries, comprising Brazil, Russia, India, China, South Africa, Iran, Egypt, Ethiopia, and the United Arab Emirates, from 2000 to 2023. To address endogeneity and omitted variable bias, the model incorporates Foreign Direct Investment (FDI) alongside institutional quality and emissions. Using panel unit root and cointegration tests, we confirm that the variables are non-stationary at levels but stationary after first differencing, and share a long-run equilibrium relationship. Long-run estimates are obtained through Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) to quantify the long-run impacts of explanatory variables on economic growth. The key results support the notion that institutional quality is a key determinant of the growth-environment relationship. Our results also confirm that carbon emissions have an adverse “significant” effect on economic growth. However, their adverse impact diminishes in more institutionalized countries. Moreover, the significant and positive interaction impact between institutional quality and CO2 emissions suggests that effective institutions can decouple growth from environmental degradation, reinforcing the hypothesis of moderated environmental management dynamics of the environmental Kuznets curve (EKC). In addition, the quality of institutions has a positive and independent impact on GDP growth, highlighting the need for a strong institutional foundation to perform well in the economy. FDI is found to boost growth, while its environmental impact depends on governance quality. Panel Granger causality tests also confirmed bidirectional causality between GDP and CO2 emissions, as well as unidirectional causality from CO2 emissions to institutional quality. These findings underscore the importance of institutional reforms for achieving inclusive growth and climate goals, aligning closely with the SDGs.

In the wake of the 2015 Paris Agreement, green finance has emerged as a pivotal mechanism for addressing environmental challenges and achieving sustainable development goals (SDGs). This study employs a combined meta-analysis and bibliometric analysis to assess the evolving research landscape of green finance with a comparative lens on developed and developing economies. Based on 51 studies from Scopus (1990–2024) for bibliometric analysis and 17 studies for meta-analysis, the findings highlight distinct research patterns. Developing economies, particularly China and India, exhibit a rising trend in green finance research, emphasizing practical environmental solutions, whereas developed economies, including the United States, Sweden, and South Africa, focus on long-term strategies and foundational policy frameworks. The meta-regression analysis indicates that integrating green finance with financial development and renewable energy investments significantly enhances carbon reduction efforts. Model 2 results show that fintech and renewable energy investments contribute to emission reductions by 84

This study examines the changing climate patterns in Punjab from 1981 to 2020, focusing on precipitation concentration, seasonality, and temperature variability. These factors are important for the region’s agriculture, particularly wheat and rice production. The analysis utilizes the Precipitation Concentration Index (PCI), Seasonality Index (SI), and Standardized Anomaly Index (SAI) to identify long-term trends. The results show that after 2000, there were no years with low or moderate precipitation (PCI < 10 or PCI 10–15), with 10 years consistently experiencing highly irregular precipitation (PCI > 20). The Seasonality Index reveals that seven years between 2001 and 2020 had rainfall concentrated within three months (SI 1.0–1.19), compared to six years in the previous period. Temperature analysis indicates that summer maximum temperature anomalies reached + 8.763 °C and spring minimum temperatures peaked at + 17.11 °C. Further, the VAR (5) model forecasts slight increases in rainfall, with maximum and minimum temperatures expected to rise by 0.5 °C to 1.2 °C and 1.1 °C to 2.6 °C, respectively, by mid-century. These findings highlight the urgent need for climate-resilient agricultural practices and improved water management strategies to protect wheat and rice production in Punjab, as both crops are susceptible to these climate shifts.

The rapid economic growth and escalating energy requirements in recent decades have resulted in environmental degradation, giving rise to global warming issues that are now escalating to the point of global boiling. To guide aligned policies for BRICS countries, the current research assesses the effects of economic growth, renewable energy utilization, financial globalization, green innovation and digitalization on consumption-based carbon emissions (CCO2) from 1990 to 2019. The study applies Common Correlated Effects Mean Group Estimator (CCEMG) and Augmented Mean Group Estimator (AMG) estimation techniques. Results demonstrate that a 1

This study investigates the pattern of household cooking fuel choices and health outcomes, and the impact of household cooking fuel choices on household health, with a focus on low-income states (LIS) in India. Using data from the India Human Development Survey (IHDS-I and II), the study employs percentage analysis, chi-square tests, and binary logistic regression to examine trends and risk factors. The study categorized health outcomes into two groups based on the nature of the disease: short-term morbidity (STM) and long-term morbidity (LTM). The findings reveal that households in LIS have a higher prevalence of both STM and LTM, which correlates with the continued use of traditional biomass fuels and poor indoor air quality. Although there has been a moderate shift toward modern fuels, traditional fuel usage remains dominant, especially in rural areas and among women. Logistic regression results revealed that traditional fuel usage significantly increases the odds of STM and LTM and specific long-term diseases, such as asthma, cardiovascular issues, and cataracts. LPG use significantly reduced the likelihood of STM, asthma, and cataract morbidity. The results also show that people who are poor, live in houses led by women, or stay in kutcha houses are more likely to face short- and long-term morbidity. The use of LPG cylinders from unauthorized distribution further exposes households to health hazards. Various socioeconomic conditions, such as high education and wealth levels, significantly reduce the likelihood of STM and LTM. Notably, the study shows that education and health awareness among households significantly reduce the risk of diseases such as cataracts and tuberculosis. This study highlights the importance of targeted public health intervention. The intervention should prioritize the promotion and awareness of the use of clean fuels, fuel subsidies, infrastructure strengthening for safe cooking conditions, and awareness campaigns based on education. Redressing structural inequities in fuel access and health awareness is crucial to redressing the disease burden in India’s backward areas.

Purpose - This study aims to examine the impact of oil consumption on carbon dioxide (CO2) emissions and total factor productivity (TFP) in highly oil-consuming countries of the world from 1995 to 2019. Design/methodology/approach - For this purpose, fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) are applied.Findings - FMOLS and DOLS models reveal that oil consumption, human capital, population, trade openness and nonrenewable energy have a significant positive effect on CO2 emissions. While information and communication technology (ICT), as proxied by mobile and natural resources, has a significant negative effect on CO2 emissions. In the case of TFP, oil consumption, ICT and natural resources have a significant positive effect on the TFP. On the other hand, trade openness, population, human capital and nonrenewable energy have a significant negative effect on TFP. The results of this study can help to provide policy recommendations to reduce CO2 emissions in studied highly oil-consuming countries of the world.Originality/value - Due to the threat to sustainable development, climate change has become a major topic for debate around the world. The influence of oil consumption on CO2 emission and TFP is less known in the available literature. Another significance of this study is that many researchers considered aggregate energy consumption to study this relationship, but the authors have studied the effect of energy consumption, particularly from oil in the top oil-consuming countries, which is a significant shortcoming of the present research.

The present article aims to examine the asymmetric effects of foreign direct investment (FDI) on economic growth in India during 1991–2019. Along with FDI, financial development, inflation and trade openness are used as control variables. To check the influence of these variables on economic growth, this study employed the non-linear autoregressive distributed lag (NARDL) model. The results indicate that a positive shock in FDI inflows positively influences India’s economic growth while negative FDI inflows have a negative influence. Also, the Wald test establishes the asymmetric effect of FDI on gross domestic product (GDP) growth both in the short-run and long run. Moreover, financial development and inflation rate significantly reduce the pace of economic growth in both the long run and the short run. However, trade openness boosts economic growth only in the long run. Based on these empirical findings, several policy implications are designed to increase the pace of economic growth.

We investigated the variations in the corporate financial performance (CFP) of firms that integrate ESG factors into their business practices, focusing on the mediating role of corporate efficiency (CE). Using 909 company-level data, we applied Data Envelopment Analysis (DEA) to measure CE. We examined how these efficiency scores and CFP viz., Return on Assets (ROA), market value, and profit after tax (PAT) are influenced at different levels of ESG. To provide variational and distributional aspects, we employed quantile regression to estimate the relationship between ESG, CE, and CFP across different quantiles. The findings indicated that the impact of ESG integration on efficiency and CFP positively varies across quantiles. Further, a non-linear U-shaped relationship is established between the overall ESG score, environmental score, and social score with the CE. The efficiency initially dips at a lower disclosure score and surges to its highest at a higher disclosure score. Finally, our results revealed that ESG integration brings CE, which in turn channeled into financial outcomes, suggesting that CE plays a crucial mediating role. These results contribute to the understanding of how ESG practices can be leveraged for better financial outcomes through CE. These findings provide companies and policymakers with vital direction, encouraging a focus on robust ESG disclosure in establishing the path toward long-term corporate sustainability and profitability, guided by improved CE.

This study examines the asymmetric effect of economic policy uncertainty (EPU) on life and non-life insurance consumption in India using monthly data from April 2004–October 2020. The paper has employed a nonlinear autoregressive distributed lag (NARDL) model with a structural break. The results reveal that there exists an asymmetric effect of EPU on life insurance as well as non-life life insurance consumption. A negative relationship is found between EPU and insurance consumption in both life and non-life insurance. Based on the findings, the study suggests the policymakers to consider the asymmetric effects of EPU while formulating insurance-related policies in India.

Historically, India has been a great investment opportunity for foreigners. However, in the last several decades, the country has experienced a substantial increase in its foreign direct investment (FDI) owing to several reforms made by the government. This study analyzes the determining factors behind Indian FDI inflows from its top source countries using the augmented gravity model. The sample period of this study ranges from 2000 to 2019, thus providing an updated analysis regarding FDI inflows. To attain the objective of this paper, we employ several econometric techniques such as Poisson pseudo maximum likelihood (PPML), feasible generalized least square (FGLS), and Newey-West standard error models. The findings show that the source country’s per capita GDP is a negative determinant of FDI inflows in India from selected countries of the world. Moreover, FDI openness, gross fixed capital formation, and exports are found as the positive determinants of FDI inflows in India. The results imply that more export-oriented sectors can be identified for the selected nations, encouraging inflows. The country should look forward to incorporating new elements in old bilateral investment treaties as per the new conditions of the world economy.

Development of mitigation strategies to combat climate change necessitates an advanced analysis of the historical connection between crops and climate. Such an analysis is lacking for the cotton (Gossypium hirsutum L.)climate research in Mississippi (MS). Hitherto, research has been confined to small-scale experimental settings, leaving an opportunity to explore large-scale inferences. Therefore, the present study aimed to compute MS climatic trends during the cotton growing period (CGP) from 1970 to 2020 using the Mann-Kendall and Sen slope methods. The impact of climate change on MS cotton yield was assessed using the autoregressive distributed lag (ARDL) model. The climatic variables considered were maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), precipitation (PR), and CO2 emissions (COE). A required series of statistical tests, including pre- and post-analysis, model robustness, and goodness-of-fit were performed, and data met all criteria. Results revealed that Tmin (79.6 %) contributed more than Tmax (20.4 %) to the MS-climate warming over CGP. From 1970 to 2020, the Tmax, Tmin, DTR, and PR changed by + 0.30 degrees C, +1.17 degrees C, -1.07 degrees C, and + 22.54 mm, respectively, exhibiting change rate per decade of + 0.06 degrees C, +0.23 degrees C, -0.21 degrees C, and + 4.42 mm, respectively. Precipitation had no effect on cotton yield in the long or short-term. However, cotton yield significantly decreased with a rise in Tmax, and increased with a rise in Tmin and COE in the longterm. Conclusively, a 1 degrees C increase in Tmax reduced cotton yield by 6.1 %, a 1 degrees C increase in Tmin improved it by 5.5 %, and a unit increase in COE increased it by 0.45 % over the long run. Overall, the crop-climate link in MS cotton marked a varied sensitivity towards short and long-term, indicating the need to reassess current mitigation strategies. Additionally, testing the best agronomic practices in a controlled environment at the actual rates of climate change identified by the current study could provide cotton stakeholders with more precise and valuable insights.

Climate change poses a significant threat to agriculture. However, climatic trends and their impact on Mississippi (MS) maize ( Zea mays L.) are unknown. The objectives were to: (i) analyze trends in climatic variables (1970 to 2020) using Mann–Kendall and Sen slope method, (ii) quantify the impact of climate change on maize yield in short and long run using the auto-regressive distributive lag (ARDL) model, and (iii) categorize the critical months for maize-climate link using Pearson’s correlation matrix. The climatic variables considered were maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), precipitation (PT), relative humidity (RH), and carbon emissions (CO 2 ). The pre-analysis, post-analysis, and model robustness statistical tests were verified, and all conditions were met. A significant upward trend in Tmax (0.13 °C/decade), Tmin (0.27 °C/decade), and CO 2 (5.1 units/decade), and a downward trend in DTR ( − 0.15 °C/decade) were noted. The PT and RH insignificantly increased by 4.32 mm and 0.11% per decade, respectively. The ARDL model explained 76.6% of the total variations in maize yield. Notably, the maize yield had a negative correlation with Tmax for June, and July, with PT in August, and with DTR for June, July, and August, whereas a positive correlation was noted with Tmin in June, July, and August. Overall, a unit change in Tmax reduced the maize yield by 7.39% and 26.33%, and a unit change in PT reduced it by 0.65% and 2.69% in the short and long run, respectively. However, a unit change in Tmin, and CO 2 emissions increased maize yield by 20.68% and 0.63% in the long run with no short run effect. Overall, it is imperative to reassess the agronomic management strategies, developing and testing cultivars adaptable to the revealed climatic trend, with ability to withstand severe weather conditions in ensuring sustainable maize production.

Concerns over adverse environmental effects have been raised due to Vietnam's reliance on fossil fuels like coal. At the same time, efforts are being made to boost the usage of renewable energy while simultaneously lowering greenhouse gas emissions. This study examines whether there is an environmental Kuznets curve (EKC) relationship between gross domestic product (GDP) and coal consumption in Vietnam by controlling for renewable energy consumption and oil prices from 1984 to 2021. We adopt the autoregressive distributed lag (ARDL) framework to explore a long-run level relationship between the study variables. We find that the GDP elasticity of coal demand has been greater than one since the 1990s and about 3.5 in recent years, indicating that the coal intensity of GDP has increased with economic growth. Thus, the GDP-coal consumption relationship resembles an upward-sloping curve instead of an inverted U-shaped EKC. This relationship is robust when we use other estimation methods and account for two additional independent variables. While a 1% rise in renewable energy consumption results in a 0.4% reduction in coal consumption, the impact of oil prices on coal consumption is negative but insignificant. The findings allow us to provide policy implications for the sustainable development of Vietnam: (1) more stringent policies, for example, enacting a carbon pricing scheme, are needed to reduce coal consumption; (2) policies should be implemented to make renewable energy sources more affordable; and (3) as facing high oil prices, the country should diversify its energy mix by expanding the usage of renewable energy.