Abstract
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.
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Abbreviations
- ADF:
-
Augmented Dickey-Fuller
- AIC:
-
Akaike information criterion
- ARCH:
-
Auto-regressive conditional heteroscedasticity
- ARDL:
-
Auto-regressive distributed lag
- CCKP:
-
Climate change knowledge portal
- CSM:
-
Crop simulation model
- CUSUM:
-
Cumulative sum test
- CUSUMQ:
-
Cumulative sum of squared test
- ECT:
-
Error correction term
- FGLS:
-
Feasible generalized least square
- LM:
-
Lagrange multiplier
- NARDL:
-
Non-linear autoregressive distributed lag
- PP:
-
Phillips-Perron
- RBI:
-
Reserve Bank of India
- RCP:
-
Representative Concentration pathways
- RESET:
-
Ramsay Regression Equation Specification Error Test
- WDI:
-
World Development Indicators
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Imran Ali Baig: conceptualization, data curation, formal analysis, writing—original draft. Abbas Ali Chandio: supervision. Ilhan Ozturk: editing and validation, supervision. Pushp Kumar: methodology, investigation, formal analysis. Zeeshan Anis Khan and Md. Abdus Salam: review, editing and made suggestions.
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Baig, I.A., Chandio, A.A., Ozturk, I. et al. Assessing the long- and short-run asymmetrical effects of climate change on rice production: empirical evidence from India. Environ Sci Pollut Res 29, 34209–34230 (2022). https://doi.org/10.1007/s11356-021-18014-z
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DOI: https://doi.org/10.1007/s11356-021-18014-z