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Biophysical Context of the Economy: Implications for Economics

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Economics of a Crowded Planet

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Abstract

This chapter outlines a biophysical context for the economy, describing how natural science understands the structure and dynamics of the natural world, as the context for the economy. The exponential increase in the material scale of the economy has set in motion a coevolution with Earth’s natural systems. The perspective upon the economy is from the outside looking in, so as to help the social-science reader understand how and why natural scientists perceive the relationship between human activity and natural processes, and to provide a rationale for an economics of a crowded planet. That rationale begins with the material scale of the economy as a bounding condition for individual preference. It is predicated critically upon certain propositions about individual motivations and norms under planetary limitations.

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Notes

  1. 1.

    Maturana and Varela (1998).

  2. 2.

    Maturana and Varela (1998, p. 75).

  3. 3.

    Maturana and Varela (1998, p. 96).

  4. 4.

    Rodman (1995, p. 251).

  5. 5.

    Georgescu-Roegen (1971, pp. 197–200).

  6. 6.

    Maturana and Varela (1998, p. 74).

  7. 7.

    See Maturana and Varela (1998, p. 74).

  8. 8.

    Maturana and Varela (1998, p. 40).

  9. 9.

    Georgescu-Roegen (1971, p. 343) makes a similar point: “…even equations and symbolic operations are man-made. By the very nature of its actor, every intellectual endeavor of man is and will never cease to be human.” This is not to take a sophistic position that everything is a figment of the imagination but rather to acknowledge that we as human beings construct a reality—just as any other animal does—to make sense of our world and to operate within it.

  10. 10.

    A neighboring unity may not necessarily be adjacent. Nerve cells connected by elongated ganglia may be spatially distant but still capable of direct interaction. They are thus structurally coupled, in the sense defined by Maturana and Varela.

  11. 11.

    Georgescu-Roegen (1971, p. 270).

  12. 12.

    Georgescu-Roegen (1971, p. 203).

  13. 13.

    Maturana and Varela (1998, p. 52).

  14. 14.

    Holling and Sanderson (1996, p. 59).

  15. 15.

    Lorenz (1963).

  16. 16.

    May (1973a, b, 1974).

  17. 17.

    R.M. May, pers. comm.

  18. 18.

    Georgescu-Roegen (1971, p. 42).

  19. 19.

    Simon (1974).

  20. 20.

    Eldredge and Salthe (1984). See also Salthe (2012) for a recent discussion of this kind of ‘compositional’ hierarchy.

  21. 21.

    Holland (1998, pp. 225–231).

  22. 22.

    For example, Crutchfield et al. (2003) show how emergent properties arise from applying genetic algorithms to confer ‘fitness’ upon populations of cellular automata within a model selective environment. The GAs provide the algorithmic variation upon which selection acts. When certain target conditions are reached, all automata in the model ‘relax’ into the same state.

  23. 23.

    Holland (1998, p. 232).

  24. 24.

    Berman (1981, p. 283).

  25. 25.

    Holland (1998, p. 242).

  26. 26.

    Maturana and Varela (1998, p. 135).

  27. 27.

    Georgescu-Roegen (1971, p. 111).

  28. 28.

    For example, Darwin (1859).

  29. 29.

    Georgescu-Roegen (1971, pp. 129–130).

  30. 30.

    Georgescu-Roegen (1971, p. 169).

  31. 31.

    Costanza and Folke (1996, p. 17).

  32. 32.

    Costanza et al. (1993) and Perrings et al. (1995).

  33. 33.

    Georgescu-Roegen (1971, p. 277).

  34. 34.

    Georgescu-Roegen (1971, p. 278).

  35. 35.

    Georgescu-Roegen (1971, p. 209) cited in Simpson (1949).

  36. 36.

    H. Poincaré (1934–1956) Oeuvres, 11 vols., vol. X cited in Georgescu-Roegen (1971, p. 169).

  37. 37.

    See, for example, Nelson and Winter (1982), Holland et al. (1986), Holland (1998), Crutchfield and Schuster (2003), and Beinhocker (2006).

  38. 38.

    Sterman and Sweeney (2002, p. 207).

  39. 39.

    E.g., Malhotra and Thorpe (1991).

  40. 40.

    Holling (1978).

  41. 41.

    Galbraith (1973, p. 251).

  42. 42.

    Galbraith (1973, p. 291).

  43. 43.

    Galbraith (1973, p. 290).

  44. 44.

    Galbraith (1973, p. 292).

  45. 45.

    Berman (1981).

  46. 46.

    http://www.materialflows.net/decoupling-material-use-and-economic-performance.

  47. 47.

    Estimates vary somewhat. A 2012 study by Kallmeyer et al. inferred the mass of Earth’s biota around 683 Gt. This may have been an overestimate, as a more recent study by Bar-On et al. (2018) places it around 550 Gt. For the present analysis, and for the models to follow in Chapters 3 and 4, 600 Gt is assumed.

  48. 48.

    Geider et al. (2001).

  49. 49.

    US National Aeronautics and Space Administration, http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html.

  50. 50.

    Emissions Database for Global Atmospheric Research (EDGAR), http://edgar.jrc.ec.europa.eu.

  51. 51.

    Bar-On et al. (2018).

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  1. San Rafael, CA, USA

    Fraser Murison Smith

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Murison Smith, F. (2019). Biophysical Context of the Economy: Implications for Economics. In: Economics of a Crowded Planet. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-31798-0_2

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  • DOI: https://doi.org/10.1007/978-3-030-31798-0_2

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