Environment, Development and Sustainability

, Volume 17, Issue 6, pp 1509–1525 | Cite as

Impact of economic growth and population on agrochemical use: evidence from post-liberalization India

Article
First Online:
Received:
Accepted:

Abstract

This paper analyzes the impact of population and per capita income on agrochemical use in India. Traditionally, few researchers have used I = PAT equation in its original form to study the impact of population and per capita income on agrochemical use. In this paper, a variant of I = PAT is used which relates per capita income and per hectare population with per hectare agrochemical use. The sample covers the period 1990–2008 for 25 Indian states. Our results suggest that per capita income has a nonlinear relationship with per hectare agrochemical use. Observed negative relationship between pesticide use per hectare and persons per hectare is indicative of public awareness regarding harms related with intensive use of pesticides; however, a positive relationship between fartilizer consumption per hectare and population pressure, found here, reiterates importance of fertilizers for food security. An examination into dematerialization of agriculture is also carried out at all India level which indicates that declining intensity of fertilizer and pesticide use in post-1990 period is mainly attributed to structural change in the economy. In summary, the paper concludes that India needs environment friendly agriculture policies and rural infrastructure to manage agriculture-related environmental problems.

Keywords

Agrochemicals I = PAT Driscoll–Kraay estimator Integrated pest management India 
This is a preview of subscription content, log in to check access.

References

  1. Antle, J. M., & Heidebrink, G. (1995). Environment and development: Theory and international evidence. Economic Development and Cultural Change, 43(3), 603–625.CrossRefGoogle Scholar
  2. Arahata, K. (2003). Income growth and pesticide consumption in the future: Applying the Environmental Kuznets Curve hypothesis. Paper prepared for presentation at the American Agricultural Economic Association.Google Scholar
  3. Ausubel, J. H., & Wagggoner, P. E. (2008). Dematerialization: Variety, caution and persistence. Proceedings of the National Academy of Sciences, 105(35), 12774–12779.CrossRefGoogle Scholar
  4. Baum, C. (2001). Residual diagnostics for cross section time series regression models. The Stata Journal, 1(1), 101–104.Google Scholar
  5. Beck, N., & Katz, J. (1995). What to do (and not to do) with time series cross section data. American Political Science Review, 89(3), 634–647.CrossRefGoogle Scholar
  6. Beck, N., & Katz, J. (1996). Nuisance versus substance: Specifying and estimating time series cross section models. Political Analysis, 6(1), 1–36.CrossRefGoogle Scholar
  7. Boserup, E. (1976). Environment, population and technology. Population and Development Review, 2(1), 21–36.CrossRefGoogle Scholar
  8. Brock, W. A., & Taylor, M. S. (2005). Economic growth and the environment: A review of theory and empirics. In P. Aghion, & S. Durlauf (Eds.), Handbook of economic growth, 1(28) (pp 1749–1821). New York: Springer.Google Scholar
  9. Chertow, M. R. (2001). The IPAT equation and its variants: Changing views of technology and environmental impact. Journal of Industrial Ecology, 4(4), 13–29.CrossRefGoogle Scholar
  10. Choi, I. (2001). Unit root tests for panel data. Journal of International Money and Finance, 20(2), 249–272.CrossRefGoogle Scholar
  11. Cole, J. R., & McCoskey, S. (2013). Does global meat consumption follows an environmental Kuznets curve? Sustainability: Science. Practice and Policy, 9(2), 26–36.Google Scholar
  12. Commoner, B. (1971). The closing circle: Nature, man and technology. New York: Knopf.Google Scholar
  13. Commoner, B. (1972). A bulletin dialogue on “The Closing Circle”: Response. Bulletin of the Atomic Scientists, 28(5), 42–56.Google Scholar
  14. Culas, R. J. (2007). Deforestation and environmental Kuznets curve: An institutional perspective. Ecological Economics, 61, 429–437.CrossRefGoogle Scholar
  15. Dasgupta, S., Laplante, B., Wang, H., Wheelar, D., et al. (2002). Confronting the environmental Kuznets curve. The Journal of Economic Perspectives, 16(1), 147–168.CrossRefGoogle Scholar
  16. Dietz, T., & Rosa, E. A. (1997). Effects of population and affluence on CO2 emissions. Proceedings of the National Academy of Sciences, 94, 175–179.CrossRefGoogle Scholar
  17. Dinar, A., Larson, D. F., Frisbie, J. A., et al. (2012). Clean Development Mechanism agricultural methodologies could help California to achieve AB 32 goals. California Agriculture, 66(4), 137–143.CrossRefGoogle Scholar
  18. Dinda, S. (2004). Environmental Kuznets curve hypothesis: A survey. Ecological Economics, 49, 431–455.CrossRefGoogle Scholar
  19. Driscoll, J. C., & Kraay, A. C. (1998). Consistent covariance matrix estimation with spatially dependent panel data. Review of Economics and Statistics, 80, 549–560.CrossRefGoogle Scholar
  20. Ecobichon, D. J. (2001). Pesticide use in developing countries. Toxicology, 160, 1–3.CrossRefGoogle Scholar
  21. Ehrlich, P. R., & Holdren, J. P. (1971). Impact of population growth. Science, 171, 1212–1217.CrossRefGoogle Scholar
  22. Ghimire, N., & Woodward, R. T. (2013). Under- and over-use of pesticides: An international analysis. Ecological Economics, 89, 73–81.CrossRefGoogle Scholar
  23. Ghosh, N. (2004). Reducing dependence on chemical fertilizers and its financial implications for farmers in India. Ecological Economics, 49, 149–162.CrossRefGoogle Scholar
  24. Greene, W. (1993). Econometrics analysis (2nd ed.). New York: Macmillion.Google Scholar
  25. Grossman, G. M., & Kruger, A. B. (1995). Economic growth and the environment. Quarterly Journal of Economics, 110(2), 353–377.CrossRefGoogle Scholar
  26. Hoechle, D. (2007). Robust standard errors for panel regressions with cross sectional dependence. The Stata Journal, 55(2), 1–31.Google Scholar
  27. Im, K. S., Pesaran, M. H., Shin, Y., et al. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53–74.CrossRefGoogle Scholar
  28. Jorgenson, A. K. (2007). Foreign direct investment and pesticide use intensity in less developed countries: a quantitative investigation. Society and Natural Resources, 20(1), 73–83.CrossRefGoogle Scholar
  29. Jorgenson, A. K., & Kuykendall, K. A. (2008). Globlizaion, foreign investment dependence and agriculture production: Pesticide and fertilizer use in less-developed countries. Social Forces, 87(1), 529–560.CrossRefGoogle Scholar
  30. Li, F., Dong, S., Li, F., Yang, L., et al. (2014). Is there an inverted U-shaped curve? Empirical analysis of the Environmental Kuznets Curve in agrochemicals. Frontiors of Environmental Science and Engineering, 8(2), 1–12.CrossRefGoogle Scholar
  31. Lichtenberg, E. (2002). Agriculture and the environment. In B. L. Gardner, & G. C. Rausser (Eds.), Handbook of Agricultural Economics, 1(2) (pp 1249–1313). New York: Springer.Google Scholar
  32. Liddle, B. (2014). Impact of population, age structure, and urbanization on carbon emission/energy consumption: evidence from macro level, cross country analyses. Population and Environment, 35, 286–304.CrossRefGoogle Scholar
  33. Longo, S., & York, R. (2008). Agricultural exports and the environment: A cross national study of fertilizer and pesticide consumption. Rural Sociology, 73(1), 82–104.CrossRefGoogle Scholar
  34. Maddala, G. S., & Wu, S. (1999). Comparative study of unit root tests with panel data and a new simple test. Oxford Bulletin of Economics and Statistics, 61(S1), 631–652.CrossRefGoogle Scholar
  35. Maston, P. A., Parton, W. J., Powar, A. G., & Swift, M. J. (1997). Agricultural intensification and ecosystem properties. Science, 277, 504–508.CrossRefGoogle Scholar
  36. Meadows, D. L., Meadows, D. H., Randers, J., & Behrens, W. W. (2004). Limits to growth: The 30 year update. Chelsea: Green Publishing Company.Google Scholar
  37. Panayotou, T. (1997). Demystifying the environmental Kuznets curve: Turning a black box into a policy tool. Environment and Development Economics, 2, 465–484.CrossRefGoogle Scholar
  38. Parks, R. (1967). Efficient estimation of a system of equations when disturbances are both serially and contemporaneously correlated. Journal of the American Statistical Association, 62, 500–509.CrossRefGoogle Scholar
  39. Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics, 61(S1), 653–670.CrossRefGoogle Scholar
  40. Pretty, J. (2008). Agricultural sustainability: concepts, principles and evidence. Philosophical Transactions of the Royal Society B, 363, 447–465.CrossRefGoogle Scholar
  41. Rao, E. V. S. P., & Puttanna, K. (2006). Stratgies for combating nitrate pollution. Current Science, 91(25), 1335–1339.Google Scholar
  42. Ruttan, W. V. (1971). Technology and environment. American Journal of Agricultural Economics, 53(5), 707–717.CrossRefGoogle Scholar
  43. Schreinemachers, P., & Tipraqsa, P. (2012). Agricultural pesticide and land use intensification in high, middle and low income countries. Food Policy, 37, 616–626.CrossRefGoogle Scholar
  44. Singh, A. P., & Narayanan, K. (2012). An study of environmental Kuznets curve for Indian agriculture. Unpublished M. Phil dissertation, IIT Bombay.Google Scholar
  45. Tilman, D., Cassman, K. G., Maston, P. A., Nayor, R., & Polasky, S. (2002). Agricultural sustainability and intensive production practices. Nature, 418, 671–677.CrossRefGoogle Scholar
  46. Waggoner, P. E., & Ausubel, J. H. (2002). A framework for sustainability science: A renovated IPAT identity. Proceedings of the National Academy of Sciences, 104, 10288–10293.Google Scholar
  47. Wilson, J. S., & Otsuki, T. (2004). To spray or not to spray: pesticides, banana exports and food safety. Food Policy, 29(2), 235–249.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Department of Humanities and Social SciencesIIT BombayMumbaiIndia

Personalised recommendations