Climate trends and maize production nexus in Mississippi: empirical evidence from ARDL modelling.

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 (CO2). 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 CO2 (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 CO2 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.

Climate trends and soybean production since 1970 in Mississippi: Empirical evidence from ARDL model.

Studying historical response of crops to weather conditions at a finer scale is essential for devising agricultural strategies tailored to expected climate changes. However, determining the relationship between crop and climate in Mississippi (MS) remains elusive. Therefore, this research attempted to i) estimate climate trends between 1970 and 2020 in MS during the soybean growing season (SGS) using the Mann-Kendall and Sen slope method, ii) calculate the impact of climate change on soybean yield using an auto-regressive distributive lag (ARDL) econometric model, and iii) identify the most critical months from a crop-climate perspective by generating a correlation between the detrended yield and the monthly average for each climatic variable. Specific variables considered were maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), precipitation (PT), carbon dioxide emissions (CO2), and relative humidity (RH). All required diagnostic-tests i.e., pre-analysis, post-analysis, model-sensitivity, and assessing the models' goodness-of-fit were performed and statistical standards were met. A positive trend in Tmin (+0.25 °C/decade), and a negative trend in DTR (-0.18 °C/decade) was found. Although Tmax, PT, and RH showed non-significant trends, numerical changes were noted as +0.11 °C/decade, +3.03 mm/decade, and -0.06 %/decade, respectively. Furthermore, soybean yield was positively correlated with Tmin (in June and September), PT (in July and August), and RH (in July), but negatively correlated with Tmax (in July and August) and DTR (in June, July, and August). Soybean yield was observed to be significantly reduced by 18.11 % over the long-term and by 5.51 % over the short-term for every 1 °C increase in Tmax. With every unit increase in Tmin and CO2 emissions, the yield of soybeans increased significantly by 7.76 % and 3.04 %, respectively. Altogether, soybeans in MS exhibited variable sensitivity to short- and long-terms climatic changes. The results highlight the importance of testing climate-resilient agronomic practices and cultivars that encompass asymmetric sensitivities in response to climatic conditions of MS.

Income, coal consumption, and the environmental Kuznets curve in Vietnam.

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.

Assessing the long- and short-run asymmetrical effects of climate change on rice production: empirical evidence from India.

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.

A district-level analysis for measuring the effects of climate change on production of agricultural crops, i.e., wheat and paddy: evidence from India.

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.

Key drivers of consumption-based carbon emissions: empirical evidence from SAARC countries.

To devise an appropriate climate policy dealing with environmental degradation, reliable measurement of CO2 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 CO2 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 CO2 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 CO2 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.

Do energy consumption and environmental quality enhance subjective wellbeing in G20 countries?

G20 countries are responsible for more than 80% of global energy consumption and the largest CO2 emissions in the world. Literature related to the energy consumption-environmental quality-subjective wellbeing nexus is limited and lacks consensus. This paper analyses the impact of energy consumption and environmental quality on subjective wellbeing in G20 countries from 2006 to 2019 using a panel-corrected standard error (PCSE) model. Cantril life ladder data is used as a proxy of subjective wellbeing. For robustness, the Newey-West standard error model is used. The findings reveal that renewable energy consumption and environmental quality, i.e. lesser carbon emissions, enhance subjective wellbeing in G20 countries. In contrast, non-renewable energy consumption degrades subjective wellbeing. Moreover, the study also finds bidirectional causality between renewable energy consumption, non-renewable energy consumption, and economic growth. The policymakers of these countries should encourage renewable energy production and its consumption to reduce carbon emissions for conserving the environment and enhancing their people's subjective wellbeing.

Impact of climate change on cereal production: evidence from lower-middle-income countries.

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, CO2 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 CO2 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 CO2 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.