Trade and environmental damage in US agriculture

World Review of Science, Technology and Sustainable Development

Volumn 2, Issue 2, 2005, Pages 168-190

  Managi, Shunsuke ^a   Karemera, David ^b  

^a YOKOHAMA NATIONAL UNIVERSITY   (Japan)

^b SOUTH CAROLINA STATE UNIVERSITY   (United States)

Author keywords

agricultural export; environmental damage; factor endowments; international trade; technological change

Indexed keywords

EID: 33744512766     PISSN: 17412242     EISSN: 17412234     Source Type: Journal    
DOI: 10.1504/WRSTSD.2005.007302     Document Type: Article

References (59)

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47. These data of environmental damage after 1996 are not available and therefore limit the study periods. In addition, although it is beneficial if we could compare the results to the other countries, environmental damage data defined in this paper is only available in the USA. The available environmental-related data in agriculture for the other countries is nationally aggregated agricultural input data such as fertiliser and pesticide use over 1980 and 2000 (OECD, 2002).
48. Factor endowments were found to account for much of the variation in output in Harrigan (1997). A number of studies, however, have called into question its empirical validity (e.g., Bowen et al., 1987).
49. See Jaffe et al. (1995) and Copeland and Taylor (2004) for a survey of the literature.
50. Alternative method of endogenous system modelling might be the use of systems dynamics approach (see Forrester, 1973; Weisbuch, 1990). The basic concepts of systems dynamics are: the relationships between elements in a system are as important as the elements themselves actions often lead to reactions that affect the original actor. Such loops of action/reaction are called feedback of the various inputs that contribute to some part of a system, there will always be one that most limits the dynamics of this part. This limiting factor provides the highest leverage point for change. Using these basic concepts, it is possible to model the ebb and flow of cause and effect in surprisingly complex systems. The systems approach is strong when the ‘big picture’ or enough of the picture can be modelled as a viable whole. System dynamics is commonly used to model the dynamics of population, ecological and economic systems that usually interact strongly with each other. Saeed (1998), for example, illustrates how experimentation with a system dynamics model may be used to determine the terms of trade for meeting long-term contentions for sustaining global economic relations and environment. Importance of system dynamics models of agricultural–environment -economy interactions is the focus on disequilibrium dynamics, with behavioural decision rules and explicit stocks and flows of capital, labour, resources and money. See also Saeed (1986) and Sterman (1985, 1986, 1989) for the literature of economic modelling.
51. See Cole and Elliot (2003) for detailed analysis of capital abundance and pollution.
52. Theoretical model in Antweiler et al. (2001) include only capital/labour as factor abundance since there is little reason to believe that other endowments factors have an independent effect on either the demand for a clean environment or the derived demand for pollution emissions. In empirical model, Antweiler et al. (2001) include several factors abundance for sensitivity analysis.
53. Aggregated national level of import and summation of import and export trend is similar for 1975–2002. The simple correlation between export and import is 0.97 (Council of Economic Advisers, 2003).
54. The fixed factor supply, competitive market and constant return to scale may be an over simplification of the reality in US agriculture. In the literature of US agriculture studies, however, these assumptions are commonly employed. See Robinson, Kilkenny and Hanson (1990) and Hanson and Rose (1997) for fixed factor supply assumption, Paul et al. (2002) for competitive market setting assumption and Ball, Butault and Nehring (2001) for constant return to scale assumption. Relaxing these assumptions needs to be considered in the future study.
55. Alaska and Hawaii are excluded since no data is available. Detailed data description is available on request.
56. The regional boundaries and dummies are defined as follows: Northeast: CT, DE, ME, MD, MA, NH, NJ, NY, PA, RI, VT Southeast: AL, FL, GA, KY, NC, SC, TN, VA, WV Central: IN, IL, IA, MI, MO, MN, OH, WI Northern Plains: KS, NE, ND, SD Southern Plains: AR, LA, MS, OK, TX Mountain: AZ, CO, ID, MT, NV, NM, UT, WY Pacific: CA, OR, WA.
57. 2, and BOD.
58. The elasticity of per capital income is between –0.385 and –1.392 whereas Antweiler et al. (2001) show it to be between –0.9 and –1.5.
59. See Copeland and Taylor (2004) for detailed review and discussion.