Sugar Price Volatility in India's Food–Energy Nexus: Evidence from Ethanol Blending, Sugarcane Pricing and Climate Factors.

Main Article Content

Varsha Agrawal
Kumud Shukla
Harshit Bansal

Abstract

The Indian sugar industry is moving from a food commodity market to an integrated food–energy market with the growing implementation of the Ethanol Blended Petrol (EBP) programme. Price stability for sugar is crucial to the interests of farmers, consumers and industry, but market uncertainty has arisen due to ethanol diversion, climate variability and policy interventions. This analysis uses monthly retail sugar price data for the period June 2016 to May 2026 across three major Indian states, Karnataka, Maharashtra and Uttar Pradesh, to understand seasonal behaviour, volatility dynamics and the drivers of retail sugar price volatility. The methods include analysis of seasonal price trends, Augmented Dickey–Fuller (ADF) and ARCH–LM tests, as well as eGARCH (1,1) models, to estimate the persistence of volatility and asymmetric price responses. The findings show strong volatility persistence and asymmetric effects in Maharashtra and Uttar Pradesh, while price behaviour is comparatively stable in Karnataka. Simultaneously, the effects of ethanol blending percentage, sugarcane Fair and Remunerative Price (FRP), temperature and rainfall were also assessed using a multiple regression model. Ethanol blending has a significant effect on price volatility, while higher FRP has the opposite effect. The findings highlight the importance of balanced food–fuel policies, stable sugarcane pricing mechanisms, and evidence-based market management for improving sugar market stability during India's ongoing food–energy transition.

Article Details

How to Cite
Agrawal, V., Shukla, K., & Bansal, H. (2026). Sugar Price Volatility in India’s Food–Energy Nexus: Evidence from Ethanol Blending, Sugarcane Pricing and Climate Factors. CINEFORUM, 66(3), 68–79. https://doi.org/10.66669/cineforum.v66i3.1095
Section
Original Articles

References

Balcombe, K., & Rapsomanikis, G. (2008). Bayesian estimation and selection of nonlinear vector error correction models: The case of the sugar–ethanol–oil nexus in Brazil. American Journal of Agricultural Economics, 90(3), 658–668. https://doi.org/10.1111/j.1467-8276.2008.01136.x

Beck, S. (2001). Autoregressive conditional heteroskedasticity in commodity spot prices. Journal of Applied Econometrics, 16(2), 115–132.

Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1

Carpio, L. G. T. (2019). The effects of oil price volatility on ethanol, gasoline and sugar price forecasts. Energy, 181, 1012–1022. https://doi.org/10.1016/j.energy.2019.05.067

Department of Food and Public Distribution. (2024). Sugar production and ethanol diversion statistics. Government of India.

Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431. https://doi.org/10.1080/01621459.1979.10482531

Dutta, A. (2018). Cointegration and nonlinear causality among ethanol-related prices: Evidence from Brazil. GCB Bioenergy, 10(5), 335–342. https://doi.org/10.1111/gcbb.12495

Enders, W. (2015). Applied Econometric Time Series (4th ed.). Wiley

Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. https://doi.org/10.2307/1912773

Food and Agriculture Organization of the United Nations. (2023). FAOSTAT statistical database. Rome, Italy: FAO.

Food and Agriculture Organization of the United Nations. (2023). Food Outlook: Biannual report on global food markets. Rome: FAO.

Gardebroek, C., & Hernandez, M. A. (2013). Do energy prices stimulate food price volatility? Examining volatility transmission between US oil, ethanol and corn markets. Energy Economics, 40, 119–129. https://doi.org/10.1016/j.eneco.2013.06.013

Gilbert, C. L., & Morgan, C. W. (2010). Food price volatility. Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1554), 3023–3034. https://doi.org/10.1098/rstb.2010.0139

India Meteorological Department. (2024). Annual Climate Summary of India. Ministry of Earth Sciences.

Intergovernmental Panel on Climate Change. (2023). Climate Change 2023: Synthesis Report. https://doi.org/10.59327/IPCC/AR6-9789291691647.001

International Energy Agency. (2023). Renewables 2023: Analysis and forecast to 2028. Paris: IEA.

International Sugar Organization. (2024). Quarterly Market Outlook. London: ISO.

Kumar, V., et al. (2023). Heterogeneous climate effect on crop yield and associated risks to water security in India. International Journal of Water Resources Development, 40(2), 345–378. https://doi.org/10.1080/07900627.2023.2244086

Lesk, C., Rowhani, P., & Ramankutty, N. (2016). Influence of extreme weather disasters on global crop production. Nature, 529(7584), 84–87. https://doi.org/10.1038/nature16467

Letta, M., & Tol, R. S. J. (2019). Weather, climate and total factor productivity. Environmental and Resource Economics, 73(1), 283–305. https://doi.org/10.1007/s10640-018-0262-8

Ljung, G. M., & Box, G. E. P. (1978). On a measure of lack of fit in time series models. Biometrika, 65(2), 297–303. https://doi.org/10.1093/biomet/65.2.297

Lobell, D. B., & Field, C. B. (2007). Global scale climate–crop yield relationships and the impacts of recent warming. Environmental Research Letters, 2(1), 014002. https://doi.org/10.1088/1748-9326/2/1/014002

Ministry of Agriculture & Farmers Welfare. (2023). Agricultural Statistics at a Glance 2023. Government of India.

Ministry of Petroleum and Natural Gas. (2024). Ethanol Blended Petrol (EBP) Programme: Annual Progress Report. Government of India.

Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. https://doi.org/10.2307/2938260

OECD & FAO. (2022). OECD–FAO Agricultural Outlook 2022–2031. OECD Publishing. https://doi.org/10.1787/f1b0b29c-en

OECD & FAO. (2024). OECD–FAO Agricultural Outlook 2024–2033. OECD Publishing. https://doi.org/10.1787/4c5d2cfb-en

Ortiz-Bobea, A., Ault, T. R., Carrillo, C. M., Chambers, R. G., & Lobell, D. B. (2021). Anthropogenic climate change has slowed global agricultural productivity growth. Nature Climate Change, 11, 306–312. https://doi.org/10.1038/s41558-021-01000-1

Ray, D. K., Gerber, J. S., MacDonald, G. K., & West, P. C. (2015). Climate variation explains a third of global crop yield variability. Nature Communications, 6, 5989. https://doi.org/10.1038/ncomms6989

REN21. (2024). Renewables 2024 Global Status Report. Paris: REN21 Secretariat.

Serra, T., Zilberman, D., Gil, J. M., & Goodwin, B. K. (2011). Nonlinearities in the US corn–ethanol–oil–gasoline price system. Agricultural Economics, 42(1), 35–45. https://doi.org/10.1111/j.1574-0862.2010.00487.x

Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3–4), 591–611. https://doi.org/10.2307/2333709

Tadesse, G., Algieri, B., Kalkuhl, M., & von Braun, J. (2014). Drivers and triggers of international food price spikes and volatility. Food Policy, 47, 117–128. https://doi.org/10.1016/j.foodpol.2013.08.014

World Bank. (2024). Commodity Markets Outlook. Washington, DC: World Bank.

Zhao, C., Liu, B., Piao, S., et al. (2017). Temperature increase reduces global yields of major crops in four independent estimates. Proceedings of the National Academy of Sciences, 114(35), 9326–9331. https://doi.org/10.1073/pnas.1701762114.