The purpose of the study is to examine the impact of tourism on economic growth in Sri Lanka through the Autoregressive Distributed Lag (ARDL) bounds testing approach. The analysis was carried out for the period from 1969 to 2009. By and large, our analysis reveals that tourism has a positive impact on economic growth in Sri Lanka both in the short-run and long-run.

This study empirically examines the impact of climate change on cereal production in selected lower-middle-income countries with a balanced panel dataset spanning the period 1971-2016. The study uses average annual temperature, average annual rainfall, and CO 2 emissions to measure climate change. Besides this, cultivated land under cereal production, and rural population are also 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) model is used to overcome the issues of cross-sectional dependence, serial correlation, and group-wise heteroscedasticity. The ndings show that a rise in the temperature reduces the cereal production in lower-middleincome countries. While other climate variables, i.e., rainfall and CO 2, affect cereal production positively. The sensitivity of long run elasticity has been checked with the help of Driscoll-Kraay standard regression. The adverse effects of temperature on cereal production are likely to pose severe implications for food security. In conclusion, the paper recommends that governments and cereal producers should carry out adaptation activities and programmes to cope with the negative effects of temperature on cereal production.

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For a couple of decades, environmental change has arisen as a ubiquitous problem and gained environmentalist's attention across the globe due to its long-term harmful effect on agricultural production, food supply, water supply and livelihoods of rural poor. The primary objective of this study is to explore the asymmetrical dynamic relationship between climate change and production of rice and controlled variables covering 1991-2018 by employing the nonlinear autoregressive distributed lag (NARDL) model and Granger causality approach.in India. The NARDL findings demonstrate a significant negative relationship between mean temperature and production of rice in the long run while positively influencing rice production in the short run. Moreover, positive shocks in rainfall and carbon emission have a negative and significant effect on India's rice production in the long and short run. In comparison, negative shock in rainfall has a significant positive impact on 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 our empirical investigations, some critical policy implications emerged. To sustain rice production, improve irrigation infrastructure through increasing public investment and 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, proper amount of fertiliser and irrigation systems.

G20 countries are responsible for more than 80% of global energy consumption and the largest CO 2 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.

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PurposeThe paper investigates the effects of climate change along with ecological and carbon footprint on rice crop production in India during 1982–2016.Design/methodology/approachThe autoregressive distributed lag (ARDL), canonical cointegration regression (CCR) and fully modified ordinary least square (FMOLS) models are used in the paper.FindingsA long-run relationship is found between climate change and rice production in India. Results report that ecological footprint and carbon footprint spur long-term rice production. While rainfall boosts rice crop productivity in the short term, it has a negative long-term impact. Further, the findings of ARDL models are validated by other cointegration models, i.e., the FMOLS and CCR models.Research limitations/implicationsThis study provides insights into the role of ecological footprint and carbon footprint along with climate variables in relation to rice production.Originality/valueIn the literature, the effects of ecological and carbon footprint on rice production are missing. Therefore, this is the first study to empirically examine the impact of climate change along with ecological footprint and carbon footprint on rice production in India.

This study empirically examines the impact of climate change on cereal production in selected lower-middle-income countries with a balanced panel dataset spanning the period 1971-2016. The study uses average annual temperature, average annual rainfall, and CO 2 emissions to measure climate change. Besides this, cultivated land under cereal production, and rural population are also 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) model is used to overcome the issues of cross-sectional dependence, serial correlation, and group-wise heteroscedasticity. The findings show that a rise in the temperature reduces the cereal production in lower-middle-income countries. While other climate variables, i.e., rainfall and CO 2, affect cereal production positively. The sensitivity of long run elasticity has been checked with the help of Driscoll-Kraay standard regression. The adverse effects of temperature on cereal production are likely to pose severe implications for food security. In conclusion, the paper recommends that governments and cereal producers should carry out adaptation activities and programmes to cope with the negative effects of temperature on cereal production.