Structural Vector Autoregressive Modeling for Carbon Dioxide Emissions—Evidence from India

carbon dioxide emissions
gross fixed capital formation
gross national product
population
forest cover
impulse response function
variance decomposition

How to Cite

Kumar, K. N. R. (2025). Structural Vector Autoregressive Modeling for Carbon Dioxide Emissions—Evidence from India. Agricultural & Rural Studies, 3(4), 20. https://doi.org/10.59978/ar03040020

Abstract

India, one of the world’s fastest-growing economies, faces the pressing challenge of reconciling rapid economic expansion with the imperative to reduce CO₂ emissions. This study examines the dynamic interrelationships among Gross Fixed Capital Formation (GFCF), Gross National Product (GNP), Population (POP), and Forest Cover (FC) in influencing CO₂ emissions using a Structural Vector Autoregression (SVAR) framework. The model captures both short- and long-run dynamics to uncover the persistence and transmission of shocks across variables. Short-run results reveal a positive and significant self-impact of CO₂ emissions, indicating emission inertia. While economic activity (GFCF and GNP) and population growth exert positive short-term effects on emissions, forest cover demonstrates a negative immediate impact, highlighting its short-run mitigating role. In the long run, CO₂ emissions remain sustained, reflecting structural dependence on carbon-intensive growth. Forest expansion contributes to gradual emission reduction, whereas economic and population growth persist as dominant emission drivers. Impulse Response Functions illustrate the nuanced short-term interplay between environmental and economic variables, while Structural Variance Decomposition indicates that economic and demographic shocks increasingly explain CO₂ variations over time. Diagnostic tests confirm model robustness, with residuals satisfying normality assumptions. Evidence of long-run bidirectional causality among key variables underscores the interdependence of growth and environmental sustainability. The findings emphasize the need for targeted policy interventions promoting low-carbon capital formation, technological innovation, and strengthened forest management to align India’s development goals with its environmental commitments.

https://doi.org/10.59978/ar03040020

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References

Adewole, A. I., Bodunwa, O. K., & Akinyanju, M. M. (2020). Structural vector autoregressive modeling of some factors that affect the economic growth in Nigeria. Science World Journal, 15(2), 11–15.

https://www.ajol.info/index.php/swj/article/view/202938

Ahmad, A. U., Abdullah, A., Abdullahi, A. T., & Muhammad, U. A. (2015). Stock market returns and macroeconomic variables in Nigeria: Testing for dynamic linkages with a structural break. Scholars Journal of Economics, Business and Management, 2(8A), 816–828.

https://www.researchgate.net/publication/288616691

Antonakakis, N., Chatziantoniou, I., & Filis, G. (2017). Energy consumption, CO₂ emissions, and economic growth: An ethical dilemma. Renewable and Sustainable Energy Reviews, 68, 808–824. https://doi.org/10.1016/j.rser.2016.09.105

Apergis, N., & Payne, J. E. (2010). Renewable energy consumption and economic growth: Evidence from a panel of OECD countries. Energy Policy, 38(1), 656–660. https://doi.org/10.1016/j.enpol.2009.09.002

Aqeel, A., & Butt, M. S. (2001). The relationship between energy consumption and economic growth in Pakistan. Asia-Pacific Development Journal, 8(2), 101–110.

https://www.unescap.org/sites/default/files/apdj-8-2-ResNote-1-AQEEL.pdf

Auclert, A., Malmberg, H., Martenet, F., & Rognlie, M. (2021). Demographics, wealth, and global imbalances in the twenty-first century. National Bureau of Economic Research. https://doi.org/10.3386/w29161

Ayobamiji, A. A., & Kalmaz, D. B. (2020). Reinvestigating the determinants of environmental degradation in Nigeria. International Journal of Economic Policy in Emerging Economies, 13(1). https://doi.org/10.1504/IJEPEE.2020.106680

Bernanke, B. S. (1986). Alternative explanations of the money-income correlation. Carnegie-Rochester Conference Series on Public Policy, 25, 49–99. https://doi.org/10.1016/0167-2231(86)90037-0

Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79(4), 655–673. https://www.jstor.org/stable/1827924

Blanchard, O. J., & Watson, M.W. (1986). Are business cycles all alike? In R. J. Gordon (Ed.), The American business cycle: Continuity and change (pp. 123–180). University of Chicago Press. http://www.nber.org/chapters/c10021

Bowden, N., & Payne, J. E. (2009). The causal relationship between U.S. energy consumption and real output: A disaggregated analysis. Journal of Policy Modeling, 31(2), 180–188. https://doi.org/10.1016/j.jpolmod.2008.09.001

Bozkurt, C., & Akan, Y. (2014). Economic growth, CO2 emissions and energy consumption: The Turkish case. International Journal of Energy Economics and Policy, 4(3), 484–494. https://www.econjournals.com/index.php/ijeep/article/view/878

Buckle, R. A., Kim, K., Kirkham, H., McLellan, N., & Sharma, J. (2002). A structural VAR model of the New Zealand business cycle. New Zealand Government, The Treasury. https://hdl.handle.net/10419/205501

Cheng, B. S., & Andrews, D. R. (1998). Energy and economic activity in the United States: Evidence from 1900 to 1945. Energy Sources, 20(1), 25–33. https://doi.org/10.1080/00908319808970040

Cravino, J., Lan, T., & Levchenko, A. A. (2020). Price stickiness along the income distribution and the effects of monetary policy. Journal of Monetary Economics, 110, 19–32. https://doi.org/10.1016/j.jmoneco.2018.12.001

Crippa, M., Guizzardi, D., Pagani, F., Banja, M., Muntean, M., Schaaf, E., Becker, W., Monforti-Ferrario, F., Quadrelli, R., Risquez Martin, A., Taghavi-Moharamli, P., Köykkä, J., Grassi, G., Rossi, S., Brandao De Melo, J., Oom, D., Branco, A., San-Miguel, J., & Vignati, E. (2023). GHG emissions of all world countries – 2023. Publications Office of the European Union. https://doi.org/10.2760/953322

Erbaycal, E. (2008). Disaggregated energy consumption and economic growth: Evidence from Turkey. International Research Journal of Finance and Economics, (20), 1–8. https://www.sid.ir/paper/608791/en

Ewing, B. T., Sari, R., & Soytas, U. (2007). Disaggregate energy consumption and industrial output in the United States. Energy Policy, 35(2), 1274–1281. https://doi.org/10.1016/j.enpol.2006.03.012

Ferreira, P., Soares, I., & Araújo, M. (2005). Liberalisation, consumption heterogeneity and the dynamics of energy prices. Energy Policy, 33(17), 2244–2255. https://doi.org/10.1016/j.enpol.2004.05.003

Garratt, A., Lee, K., Pesaran, M. H., & Shin, Y. (1998). A long-run structural macroeconometric model of the UK. The Economic Journal, 113(487), 412–455. https://doi.org/10.1111/1468-0297.00131

Halicioglu, F. (2009). An econometric study of CO₂ emissions, energy consumption, income, and foreign trade in Turkey. Energy Policy, 37(3), 1156–1164. https://doi.org/10.1016/j.enpol.2008.11.012

Hassan, A., Babafemi, O. D., & Jakada, A. H. (2016). Financial market development and economic growth in Nigeria: Evidence from VECM approach. International Journal of Applied Economics Studies, 4(3), 1–13.

https://www.researchgate.net/publication/321772982

Jakada, A. H., Mahmood, S., Ahmad, A. U., Muhammad, I. G., Danmaraya, I. A., & Yahaya, N. S. (2022). Driving forces of CO2 emissions based on impulse response function and variance decomposition: A case of the main African countries. Environmental Health Engineering and Management Journal, 9(3), 223–232. https://doi.org/10.34172/EHEM.2022.23

Jalil, A., & Mahmud, S. F. (2009). Environment Kuznets curve for CO₂ emissions: A cointegration analysis for China. Energy Policy, 37(12), 5167–5172. https://doi.org/10.1016/j.enpol.2009.07.044

Jena, P. K., Sharma, R., Dehury, T., & Nayak, S. (2023). Impact of monetary policy transmission on the demographic in India: A structural VAR approach. Asian Economics Letters, 4(3). https://doi.org/10.46557/001c.77815

Krishnan, R., Sanjay, J., Gnanaseelan, C., Mujumdar, M., Kulkarni, A., & Chakraborty, S. (Eds.). (2020). Assessment of climate change over the Indian region: A Report of the Ministry of Earth Sciences (MoES), Government of India. Springer. https://doi.org/10.1007/978-981-15-4327-2

Li, Y. (2023). An empirical study on the factors influencing carbon emissions in Heilongjiang Province based on VAR model. E3S Web of Conferences, 406, 03033. https://doi.org/10.1051/e3sconf/202340603033

Liew, V. K.-S. (2004). Which lag length selection criteria should we employ? Economics Bulletin, 3(33), 1–9. https://www.researchgate.net/publication/4827253

Love, I., & Zicchino, L. (2006). Financial development and dynamic investment behavior: Evidence from panel VAR. The Quarterly Review of Economics and Finance, 46(2), 190–210. https://doi.org/10.1016/j.qref.2005.11.007

Mohanty, A., & Wadhawan, S. (2021). Mapping India’s climate vulnerability – A district level assessment. Council on Energy, Environment and Water. https://images.hindustantimes.com/images/app-images/2021/11/ceew-study-on-climate-change-vulnerability-index-and-district-level-risk-assessment.pdf

Mollick, A. V. (2009). Employment responses of skilled and unskilled workers at Mexican maquiladoras: The effects of external factors. World Development, 37(7), 1285–1296. https://doi.org/10.1016/j.worlddev.2008.10.008

Narayan, P. K., Narayan, S., & Prasad, A. (2008). A structural VAR analysis of electricity consumption and real GDP: Evidence from the G7 countries. Energy Policy, 36(7), 2765–2769. https://doi.org/10.1016/j.enpol.2008.02.027

Narayan, P. K., & Prasad, A. (2007). Electricity consumption–real GDP causality nexus: Evidence from a bootstrapped causality test for 30 OECD countries. Energy Policy, 36(2), 910–918. https://doi.org/10.1016/j.enpol.2007.10.017

Nathaniel, S., Barua, S., Hussain, H., & Adeleye, N. (2021). The determinants and interrelationship of carbon emissions and economic growth in African economies: Fresh insights from static and dynamic models. Journal of Public Affairs, 21(1), e2141. https://doi.org/10.1002/pa.2141

Ng, S., & Perron, P. (2001). Lag length selection and the construction of unit root tests with good size and power. Econometrica, 69(6), 1519–1554. https://doi.org/10.1111/1468-0262.00256

Oryani, B., Koo, Y., & Rezania, S. (2020). Structural vector autoregressive approach to evaluate the impact of electricity generation mix on economic growth and CO₂ emissions in Iran. Energies, 13(16), 4268.

https://doi.org/10.3390/en13164268

Ozturk, I. (2010). A literature survey on energy–growth nexus. Energy Policy, 38(1), 340–349.

https://doi.org/10.1016/j.enpol.2009.09.024

Payne, J. E. (2010). A survey of the electricity consumption-growth literature. Applied Energy, 87(3), 723–731.

https://doi.org/10.1016/j.apenergy.2009.06.034

Ramos-Martín, J., & Ortega-Cerdà, M. (2003, February 12–15). Non-linear relationship between energy intensity and economic growth. ESEE Conference Frontiers 2, Tenerife, Spain.

https://www.researchgate.net/profile/Jesus-Ramos-Martin-2/publication/228424347

Robalo, P. B., & Salvado, J. C. (2008). Oil price shocks and the Portuguese economy since the 1970s. FEUNL Working Paper Series, No. 529. https://doi.org/10.2139/ssrn.1091172

Saidu, B. M., Ahmed, B. A., & Jakada, A. H. (2018). The determinants of long run economic growth in Nigeria. Asian Economic and Financial Review, 8(1), 1–8. https://doi.org/10.18488/journal.aefr.2018.81.1.7

Sbia, R., Shahbaz, M., & Hamdi, H. (2014). A contribution of foreign direct investment, clean energy, trade openness, carbon emissions and economic growth to energy demand in UAE. Economic Modelling, 36, 191–197.

https://doi.org/10.1016/j.econmod.2013.09.047

Silva, S., Soares, I., & Pinho, C. (2012). The impact of renewable energy sources on economic growth and CO2 emissions - a SVAR approach. European Research Studies Journal, 15(4), 133–144.

https://doi.org/10.35808/ersj/374

Sims, C. A. (1980). Comparison of interwar and postwar business cycles: Monetarism reconsidered. American Economic Review, 70(2), 250–257. https://www.nber.org/system/files/working_papers/w0430/w0430.pdf

Sims, C. A. (1986). Are forecasting models usable for policy analysis? Quarterly Review, 10(1), 2–16.

http://www.minneapolisfed.org/research/QR/QR1011.pdf

Stern, D. I. (1993). Energy and economic growth in the USA: A multivariate approach. Energy Economics, 15(2), 137–150. https://doi.org/10.1016/0140-9883(93)90033-N

Tiwari, A. K. (2010). Globalization and wage inequality: A revisit of empirical evidences with new approach. Journal of Asian Business Management, 2(1), 173–187.

Tiwari, A. K. (2011). A structural VAR analysis of renewable energy consumption, real GDP and CO₂ emissions: Evidence from India. Economics Bulletin, 31(2), 1793–1806. http://www.accessecon.com/Pubs/EB/2011/Volume31/EB-11-V31-I2-P164.pdf

Yıldırım, S., Ertuğrul, H. M., & Soytaş, U. (2015). Türkiye’de aylık istihdam serisinin durağanlığı: Geleneksel, yapısal kırılmalı ve mevsimsel birim kök test uygulamaları [Stationarity properties of monthly employment in Turkey: Conventional, structural break and seasonal unit root tests]. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 15(4), 91–102.