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This study empirically examines the impact of climate change on cereal production in selected lower-middle-income countries with a balanced panel dataset spanning 1971–2016. The study uses average annual temperature and rainfall to measure climate change. Besides this, CO 2 emissions, cultivated land under cereal production, and rural population are used as the control variables. Second-generation unit root tests, i.e., CIPS and CADF, are used to test the stationarity of the variables. Feasible generalized least square (FGLS) and fully modified ordinary least square (FMOLS) models are used to achieve the objective. Pedroni cointegration test confirms the presence of cointegration between cereal production and climate change variables. The findings show that a rise in the temperature reduces cereal production in lower-middle-income countries. In contrast, rainfall and CO 2 emissions have a positive effect on cereal production. For robustness purpose, the Driscoll-Kraay standard regression and dynamic ordinary least square (DOLS) models have also found similar results. Dumitrescu-Hurlin test has found the bidirectional causality of cereal production with temperature and CO 2 emissions. Also, unidirectional causality is running from rainfall and rural population to cereal production. The adverse effects of temperature on cereal production are likely to pose severe implications for food security. The paper recommends that governments of the sample countries should research and develop heat-resistant varieties of cereal crops to cope with the adverse effects of temperature on cereal production and ensure food security.

The present study aims to examine the impact of climate change on wheat and rice yield in Punjab, India, during 1981–2017. The study employs fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and pooled mean group (PMG) approaches. The Pedroni cointegration has established a long-run relationship of climate variables with rice and wheat crops. FMOLS and DOLS models show that minimum temperature has a positive effect on both wheat and rice. In contrast, the maximum temperature is found to be negatively contributing to both crops. Rainfall has a significant adverse impact on the production of wheat. In the study period, seasonal rainfall has been found detrimental for the production of wheat and rice crops, indicating that excess rainfall proved counterproductive. Moreover, the Dumitrescu-Hurlin causality test has revealed a unidirectional causality running from minimum temperature, rainfall, and maximum temperature for rice and wheat production. The findings of the study suggest that the government should invest in developing stress-tolerant varieties of wheat and rice, managing crop residuals to curb other environmental effects, and sustaining natural resources for ensuring food security.

The foremost aspect in welding is to improve the mechanical and microstructural properties in the weldment. The properties depend upon the weld bead characteristics. This is in turn depend upon the welding process. Shielded Metal Arc welding (SMAW) is the welding process which is simpler, usable and robust in nature. The bead characteristics such as bead width (BW), reinforcement (R), penetration (P), weld penetration shape factor (WPSF), weld reinforcement form factor (WRPF) has been selected as output weld characteristics which are influenced by parameters such as welding current (I), welding speed (S), arc length (A), electrode advance angle €, and joint gap (G). In this paper a response surface methodology is implemented which predicts the optimum results of 0.51% error from the observed results. To find the optimum results metaheuristic algorithms that is, Jaya (JA) algorithm and Firefly (FA) algorithm was implemented. The results obtained are within the maximum and minimum permissible limits and of 0.22% and 0.31% deviation from the observed values. This shows the accuracy and refinement of results using algorithms. The real world verification is done from the regression model. The sensitivity analysis and parameter effect to show variation in parameters influence the results has been presented.

To devise an appropriate climate policy dealing with environmental degradation, reliable measurement of CO 2 emissions is essential. In the recent past, most researchers have utilized production-based emissions in their studies, ignoring the important role of consumption-based emissions in environmental degradation. Therefore, the present research examines the drivers of consumption-based CO 2 emissions in SAARC nations over the period 1990 to 2018. By employing traditional and second-generation panel cointegration methodologies, the study, more specifically, explores the link between consumption-based CO 2 emissions and its five macroeconomic determinants, namely, GDP growth, energy consumption, FDI, trade openness (measured by composite trade share index), and urbanization. The study also applies the FMOLS and DOLS techniques for calculating the long-run elasticities of regressors with respect to the explained variable. The results establish a cointegration relationship between the variables and validate an “N-shaped EKC” for the SAARC region. It is also found that in the long run, energy consumption and urbanization amplify the consumption-based CO 2 emissions while FDI and trade openness improve the environmental quality by plummeting emissions. Most importantly, the study rejects the “pollution-haven hypothesis” for the SAARC region based on the outcomes of FDI and trade openness. Lastly, based on the results, some policies are recommended for the abatement of environmental degradation in SAARC countries. As the SAARC nations rely heavily on fossil-based energy, it is suggestive for these economies to enhance the level of energy efficiency and augment the share of renewable energy sources in the energy mix. Furthermore, the policy designers in this region should encourage trade openness and liberalize inward FDI for containing consumption-based emissions.

In recent years, environmental change has arisen as a ubiquitous problem and gained environmentalist’s attention across the globe due to its long-term harmful effects on agricultural production, food supply, water supply, and livelihoods of rural households. The present study aims to explore the asymmetrical dynamic relationship between climate change and rice production with other explanatory variables. Based on the time series data of India, covering the period 1991–2018, the current study applied the nonlinear autoregressive distributed lag (NARDL) model and Granger causality approach. The results of the NARDL reveal that mean temperature negatively affects rice production in the long run while positively affecting it in the short run. Furthermore, positive shocks in rainfall and carbon emission have negative and significant impacts on rice production in the long and short run. In comparison, negative rainfall shocks significantly affect rice production in the long and short run. Wald test confirms the asymmetrical relationship between climate change and rice production. The Granger causality test shows feedback effect among mean temperature, decreasing rainfall, increasing carbon emission, and rice production. While no causal relationship between increasing temperature and decreasing carbon emission. Based on the empirical investigations, some critical policy implications emerged. Toward sustainable rice production in India, there is a need to improve irrigation infrastructure through increasing public investment and to develop climate-resilient seeds varieties to cope with climate change. Along with, at the district level government should provide proper training to farmers regarding the usage of pesticides, the proper amount of fertilizers, and irrigation systems.

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.

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% and 92%, respectively, at a 1% significance level. Moreover, interaction effects suggest that these investments yield greater benefits in developing economies, reducing emissions by 56% and 118%, respectively, at the 1% significance level. This study underscores the importance of Green Finance policies tailored to the specific economic contexts of nations to maximize environmental sustainability.

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.Article highlightsRainfall in Punjab has become more irregular and concentrated in fewer months since 2000.Both daytime and nighttime temperatures in Punjab are rising, with warmer summers and springs.These climate shifts threaten wheat and rice crops, highlighting the need for resilient farming methods.Forecasts show minimum temperatures in Punjab will keep rising, affecting both weather and farming.

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 (CCO 2 ) 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% increase in economic growth is associated with a 0.73% rise in CCO₂ emissions for BRICS as a whole, while a 1% increase in renewable energy consumption leads to a 0.75% reduction in CCO₂. Green innovation and digitalization also show negative associations, with a 1% rise in green innovation reducing emissions by 0.041% and digitalization by 0.00036%. Conversely, financial globalization is positively associated with CCO₂, with a 1% increase linked to a 0.12% rise in emissions. BRICS countries possess substantial potential in renewable energy sources, and transitioning from fossil fuels to renewables can uphold environmental sustainability. Additionally, leveraging financial globalisation to augment investments in green technology, renewable energy and sustainable digital infrastructure with a focus on green growth prioritization can augment green employment and sustainable development. The study is the first of its kind that uses novel second-generation panel cointegration and econometric techniques that capture the cross-sectional dependency and slope heterogeneity.

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.