Impacts of a carbon tax policy on Illinois grain farms: a dynamic simulation study

Abstract

Methods from dynamic modeling and econometrics are used in order to develop a computer model of Illinois grain farmers’ adjustment to a carbon tax policy. All relevant money and material inflows and outflows on Illinois farms and their reaction to a carbon tax policy are explicitly included into the framework. The approach taken here is interdisciplinary in that the agroecological subsystem, the production technology subsystem, and the interrelation of both systems are modeled in detail. This allows for capturing numerous feedback processes and nonlinearities that have been found to stabilize the system. Furthermore, the bottom-up representation of the system is essential for relying on stakeholder participation in the model building and validation phase. The findings from model simulations up to 2020 indicate that farm income will be negatively affected by a carbon tax policy, with smaller farms being subjected to a stronger effect than larger farms. Through the detailed representation of Illinois farms, we are able to track numerous effects of a carbon tax policy on the level of a single farm and to find measures to react to such a policy. The policy implications and the findings are discussed.

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References

  1. American Society of Agricultural Engineers (1998) ASAE standards. Agricultural machinery management data. ASAE D497.4 JAN98, St. Joseph, Michigan

    Google Scholar 

  2. American Society of Agricultural Engineers (1993) ASAE standards. Agricultural machinery management data. ASAE D497.2 1993, St. Joseph, Michigan

    Google Scholar 

  3. American Society of Agricultural Engineers (2001) Agricultural machinery management. ASAE EP496.2 JAN01, St. Joseph, Michigan

    Google Scholar 

  4. Andersen A, Carbone R, Fildes R, Hibon M, Lewandowski R, Newton J, Parzen E, Winkler R (1984) The forecasting accuracy of major time series methods. Wiley, Chichester

    Google Scholar 

  5. Baumol W, Oates W (1988) The theory of environmental policy. Cambridge University Press, USA

    Book  Google Scholar 

  6. Breusch T, Pagan A (1979) A simple test for heteroscedasticity and random coefficient variation. Econometrica 47:1287–1294

    Article  Google Scholar 

  7. Bullock DG, Bullock DS (1994) Quadratic and quadratic-plus-plateau models for predicting optimal nitrogen rate of corn: a comparison. Agronomy Journal 86:191–195

    Article  Google Scholar 

  8. Burniaux J-M, Martin JP, Nicoletti G, Martins JO (1992) GREEN: a global model for quantifying the costs of policies to curb CO2 emissions. OECD Economic Studies 19:49–92

    Google Scholar 

  9. Callan SJ (1991) The sensitivity of productivity growth measures to alternative structural and behavioral assumptions: an application to electric utilities, 1981–1984. Journal of Business and Economic Statistics 9:207–213

    Google Scholar 

  10. Cerrato ME, Blackmer AM (1990) Comparison of models describing corn yield response to nitrogen fertilizer. Agronomy Journal 82:138–143

    Article  Google Scholar 

  11. Deal B, Schunk D (2004) Spatial dynamic modeling and urban land use transformation: a simulation approach to assessing the costs of urban sprawl. Ecological Economics 51:1–2, 79–95

    Article  Google Scholar 

  12. Farm Business Farm Management Association (1980–2002) Annual summary of Illinois farm business records. University of Illinois at Urbana-Champaign, College of Agricultural and Consumer Sciences. Cooperative Extension Service. Circular 1329

  13. Farm Business Farm Management Association (2001a) Illinois historic crop yields. Farm Decision Outreach Central. College of Agricultural, Consumer and Environmental Sciences, Cooperative Extension Service. University of Illinois at Urbana-Champaign

  14. Farm Business Farm Management Association (2001b) Crop revenue and costs. FBM 0102. Farm Decision Outreach Central. College of Agricultural, Consumer and Environmental Sciences, Cooperative Extension Service. University of Illinois at Urbana-Champaign

  15. Fernandez-Cornejo J (1993) Demand and substitution of agricultural inputs in the central corn belt states. U.S. Department of Agriculture. Economic Research Service. Technical Bulletin No. 1816. Washington D.C.

  16. Fiddaman TS (1997) Feedback complexity in integrated climate-economy models. PhD dissertation, MIT Sloan School of Mangement

  17. Gallagher P (1986) U.S. corn yield capacity and probability: estimation and forecasting with nonsymmetric disturbances. North Central Journal of Agricultural Economics 8:109–122

    Article  Google Scholar 

  18. Gallagher P (1987) U.S. soybean yields: estimation and forecasting with nonsymmetric disturbances. American Journal of Agricultural Economics 69:796–803

    Article  Google Scholar 

  19. Godfrey LG (1978) Testing against general autoregressive and moving average error models when the regressors include lagged dependent variables. Econometrica 46:1293–1302

    Article  Google Scholar 

  20. Gopalakrishnan C, Khaleghi H, Shrestha R (1989) Energy-nonenergy substitution in US agriculture: some findings. Applied Economics 21:673–679

    Article  Google Scholar 

  21. Granger C (1969) Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37:424–438

    Article  Google Scholar 

  22. Han S, Simmons FW (2000) Soil management and tillage systems. In: Illinois agronomy handbook 2001–2002. College of Agricultural, Consumer and Environmental Sciences, Cooperative Extension Service. University of Illinois at Urbana-Champaign, pp 135–146

  23. Hannon B, Ruth M (2001) Dynamic modeling, 2nd edn. Springer. Berlin Heidelberg New York

    Book  Google Scholar 

  24. Helsel ZR (1992) Energy and alternatives for fertilizer and pesticide use. In: Fluck RC (ed) Energy and farm production. Elsevier, New York

    Google Scholar 

  25. Hoeft R, Peck TR (2000) Soil fertility. In: Illinois agronomy handbook 2001–2002. College of Agricultural, Consumer and Environmental Sciences, Cooperative Extension Service. University of Illinois at Urbana-Champaign, pp 84–124

  26. Hons FM, Saladino VA (1995) Yield contribution of nitrogen fertilizer, herbicide, and insecticide in a corn-soybean rotation. Communications in Soil Science and Plant Analysis 26:3083–3097

    Article  Google Scholar 

  27. Houghton JT, Ding Y, Griggs DJ, Noguer N, van der Linden PJ, Xiasou D (2001) Climate change 2001: The Scientific Basis. Cambridge University Press, Cambridge

    Google Scholar 

  28. Huntington HG (1994) Been top down so long it looks like bottom up to me. Energy Policy 22:833–839

    Article  Google Scholar 

  29. IASS—Illinois Agricultural Statistics Service (1995–2002) Illinois annual summary. Springfield, IL

  30. IPCC (1995) Climate change: the IPCC scientific assessment. Intergovernmental panel on climate change. Cambridge University Press, Cambridge

    Google Scholar 

  31. Jaffe AB, Newell RG, Stavins RN (1999) Energy-efficient technologies and climate change policies: issues and evidence. Climate Issue Brief No. 19. Resources for the Future, Washington, D.C.

    Google Scholar 

  32. Karagiannis G, Mergos GJ (2000) Total factor productivity growth and technical change in a profit function framework. Journal of Productivity Analysis 14:31–51

    Article  Google Scholar 

  33. Kenyon DE (2001) Producer ability to forecast harvest corn and soybean prices. Review of Agricultural Economics 23:151–162

    Article  Google Scholar 

  34. Knutson RD (1999) Economic impacts of reduced pesticide use in the United States: measurement of costs and benefits. AFPC Policy Issues Paper 99-2

  35. Knutson RD, Taylor CR, Penson JB, Smith EG (1990) Economic impacts of reduced pesticide use. Choices 4:25–31

    Google Scholar 

  36. Laitner JA, Bernow S, DeCicco J (1998) Employment and other macroeconomic benefits of an innovation-led climate strategy for the United States. Energy Policy 26:425–432

    Article  Google Scholar 

  37. Lakshminarayan PG, Babcock B (1996) Temporal and spatial evaluation of soil conservation policies. Center for Agriculture an Rural Development, Iowa State University, Ames, IA

    Google Scholar 

  38. Liang C-L (1996) Environmental policies and U.S. agriculture—an application of a general equilibrium model. PhD Thesis, Department of Agricultural Economics, Purdue University

  39. Ljung G, Box G (1979) On a measure of lack of fit in time series models. Biometrika 66:265–270

    Article  Google Scholar 

  40. Lovins A, Lovins HL (1991) Least cost climatic stabilization. Annual Review of Energy and the Environment 16

  41. McCarl GQ, Gowen M, Yeats T (1997) An impact assessment of climate change mitigation policies and carbon permit prices on the U.S. agricultural sector. Report for Climate Change Policies and Programs Division. U.S. Environmental Protection Agency, Washington D.C.

    Google Scholar 

  42. McIntosh CS, Williams AA (1992) Multiproduct production choices and pesticide regulation in Georgia. Southern Journal of Agricultural Economics 24:135–144

    Google Scholar 

  43. Nafziger ED, Mulvaney RL, Mulvaney KL, Paul LE (1984) Effect of previous crops on the response of corn to fertilizer nitrogen. Journal of Fertilizer Issues 1:136–138

    Google Scholar 

  44. Newell RG, Adam BJ, Stavins RN (1999) The induced innovation hypothesis and energy-saving technological change. Quarterly Journal of Economics 144:941–975

    Article  Google Scholar 

  45. Richardson GP (1995) Loop polarity, loop dominance, and the concept of dominant polarity. System Dynamics Review 11:67–88

    Article  Google Scholar 

  46. Ruth M, Hannon B (1997) Modeling dynamic economic systems. Springer, Berlin Heidelberg New York

    Book  Google Scholar 

  47. Ruth M, Amato A, Davidsdottir B (2000) Impacts of market-based climate change policies on the U.S. iron and steel industry. Energy Sources 22:269–280

    Article  Google Scholar 

  48. Said SE, Dickey DA (1984) Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika 71:599–607

    Article  Google Scholar 

  49. Siemens J, Hamburg K, Tyrell T (1990) A farm machinery selection and management program. Journal of Production Agriculture 3:212–219

    Article  Google Scholar 

  50. Smith EG, Knutson RD, Taylor CR, Penson JB Jr (1990) Impacts of chemical use reduction on crop yield and costs. Tennessee Valley Authority and Texas A&M University System Cooperation

  51. Sterman J (2000) Business dynamics. Systems thinking and modeling for a complex world. Irwin McGraw-Hill, New York

    Google Scholar 

  52. Taylor CR, Smith HA (1999) Aggregate economic evaluation of the elimination of organophosphates and carbamates. AFPC Policy Research Report No. 99-15. College Station TX: Texas A&M University

    Google Scholar 

  53. Tietenberg TH (1990) Economic instruments for environmental regulations. Oxford Review of Economic Policy 6:17–33

    Article  Google Scholar 

  54. Uri ND (1998) Impact of the price of energy on the use of conservation tillage in agriculture in the USA. Applied Energy 60:225–240

    Article  Google Scholar 

  55. U.S. Department of Agriculture (1970–2001) Agricultural prices annually. National Agricultural Statistics Service and Economic Research Service

  56. U.S. Department of Agriculture (1994) Fertilizer use and price statistics. Economic Research Service, Washington D.C.

    Google Scholar 

  57. U.S. Department of Agriculture (1996) Cropping practices survey 1990–1995. Economic Research Service, Washington D.C.

    Google Scholar 

  58. U.S. Department of Agriculture (1997) Agricultural resources and environmental indicators. Economic Research Service, Washington D.C.

    Google Scholar 

  59. U.S. Department of Agriculture (1999) Economic analysis of U.S. agriculture and the Kyoto Protocol. Office of the Chief Economist, Economic Research Service, Washington D.C.

    Google Scholar 

  60. U.S. Department of Energy, Energy Information Agency (1997a) Service report: analysis of carbon stabilization cases. Department of Energy, Office of Policy Analysis and Forecasting, Washington D.C.

    Google Scholar 

  61. U.S. Department of Energy, Energy Information Agency (1997b) Annual energy outlook 1998 with projections to 2020. (DOE/EIA-0383(98), December 1997). Washington D.C.

  62. U.S. Department of Energy, Energy Information Administration (2001) Annual energy review 2000. Washington D.C.

  63. von Weizsaecker E, Lovins A, Lovins H (1994) Factor four: doubling wealth, halving resource use. Earthscan, London

    Google Scholar 

  64. Villezca Becerra PA, Shumway CR (1993) State-level output supply and input demand elasticities for agricultural commodities. Journal of Agricultural Economics Research 44:22–34

    Google Scholar 

  65. Voss RD, Shrader WD (1982) Crop rotations—effects on yields and response to nitrogen. Iowa State University Cooperative Extension Service, Ames, IA

    Google Scholar 

  66. Westra J, Olson K (1997) Farmers’ decision processes and adoption of conservation tillage. Staff Paper Series P97-9. Department of Applied Economics, University of Minnesota

  67. Wiese AM (1998) Impacts of market-based greenhouse gas emissions reduction policies on U.S. manufacturing competitiveness. Research Study #090. American Petroleum Institute, Washington D.C.

    Google Scholar 

  68. Yohe G, Wallace R (1996) Near term mitigation policy for global change under uncertainty: minimizing the expected cost of meeting unknown concentration thresholds. Environmental Modeling and Assessment 1:47–57

    Article  Google Scholar 

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Correspondence to Daniel Schunk.

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Schunk, D., Hannon, B. Impacts of a carbon tax policy on Illinois grain farms: a dynamic simulation study. Environ Econ Policy Stud 6, 221–247 (2004). https://doi.org/10.1007/BF03353938

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Key words

  • Carbon tax
  • Climate change policy
  • Dynamic modeling
  • Impact analysis