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Modelling Interactions Between Economic Activity, Greenhouse Gas Emissions, Biodiversity and Agricultural Production

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Abstract

In this article, we develop a modelling approach which examines selected drivers of ecosystem functioning and agricultural productivity. In particular, we develop linkages between land use and biodiversity and between biodiversity and agricultural productivity. We review the literature for quantitative estimates of key relationships and their parameters for modelling human consumption, land use, energy use, and greenhouse gas emissions on biodiversity and agricultural productivity. We assemble these specifications into an iterative causal model and carry out a number of scenario projections of country-level consumption, production, land use, energy use, greenhouse gas emissions, species diversity, and agricultural production up to 2050. Finally, we dissect the projections into key drivers using structural decomposition and sensitivity analyses.

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Notes

  1. Purchasing Power Parities (http://www.worldbank.org/depweb/english/modules/glossary.htm#ppp).

  2. Strictly speaking, the species portion we relate to disappearing climates does not adhere to the IUCN definition of a “threatened” species. Our combined measure S is perhaps better termed as “species at risk”. However, for the sake of simplicity, we retain the IUCN term.

  3. The number of threatened species excludes introduced species, species whose status is insufficiently known (categorized by IUCN as “data deficient”), those known to be extinct, and those for which status has not been assessed (categorized by IUCN as “not evaluated”). Species are classified as vulnerable or endangered if they face a risk of extinction in the wild in the immediate future (critically endangered), in the near term (endangered), or in the medium term (vulnerable). Threat categories are assigned based on total population size, distribution, and rates of decline [170]. Note that cetaceans are not assigned to particular countries except for inshore or coastal species, and are therefore missing from this analysis. Similarly, only vascular plants are considered, only land-nesting or breeding marine species are counted, and marine turtles and most marine fish are excluded (cf. Naidoo and Adamowicz [136]).

  4. The Total Number of Known Species refers to the total number of a particular type of species in a given country. Data on known mammals exclude marine mammals. Data on known birds include only birds that breed in that country and not those that migrate or winter there. The number of known plants includes higher plants only: ferns and fern allies, conifers and cycads, and flowering plants. The number of known species is collected by the United Nations (UN) Environment Programme-World Conservation Monitoring Centre (UNEP-WCMC) from a variety of sources, including, but not limited to, national reports from the Convention on Biodiversity, other national documents, independent studies, and other texts. Data are updated on a continual basis as they become available; however, updates vary widely by country. While some countries (WCMC estimates about 12) have data that were updated in the last 6 months, other species estimates have not changed since the data were first collected in 1992.

  5. Note that we measure pasture yields as livestock (live weight) yields, and not as forage yields, because the former are documented by more extensive data than the latter.

  6. For example, a ceteris-paribus term for y = x 1 x 2 of determinant x 1 is x (1)2 Δx 1, where x 2 stays constant during the change in x 1.

  7. There exist exact decomposition methods [4, 40, 167]. However, the sizeable differences between residuals and ceteris-paribus terms that can occur between different non-exact decomposition methods, suggests a kind of arbitrariness in trying to allocate these residuals to any one of the determinants in order to achieve an exact decomposition. One might even argue that there is little trade-off between this arbitrariness and the “unexplained-ness” of residuals, and therefore not much gained in preferring an exact over a non-exact structural decomposition method.

  8. Non-arable land was calculated as the remainder of total land area and land types.

  9. Spatial autocorrelation can be modelled either in a spatial lag model where the explained variable appears also as a (spatially weighted) dependent variable, together with other determinants, or in a spatial error model, where the error term assumes a particular spatially autoregressive shape.

  10. According to the [85], ‘the A1 storyline and scenario family describes a future world of very rapid economic growth, global population that peaks in mid-century and declines thereafter, and the rapid introduction of new and more efficient technologies. Major underlying themes are convergence among regions, capacity building and increased cultural and social interactions, with a substantial reduction in regional differences in per capita income. The A1 scenario family develops into three groups that describe alternative directions of technological change in the energy system. The three A1 groups are distinguished by their technological emphasis: fossil intensive (A1FI), non-fossil energy sources (A1T), or a balance across all sources (A1B) (where balanced is defined as not relying too heavily on one particular energy source, on the assumption that similar improvement rates apply to all energy supply and end use technologies). […] The A2 storyline and scenario family describes a very heterogeneous world. The underlying theme is self-reliance and preservation of local identities. Fertility patterns across regions converge very slowly, which results in continuously increasing population. Economic development is primarily regionally oriented and per capita economic growth and technological change are more fragmented and slower than in other storylines.’

  11. Note that we ignore structural effects in domestic production recipes and diets, world trade, as well as geographic and legal restrictions to land occupation.

  12. Tilman et al. ([179], p. 282) writes that “because of the exponential nature of past global population and economic growth, we had anticipated exponential temporal trends for these [land] variables. Surprisingly, each was a linear […] function of time […].” This work confirms the quasi linear trend of agricultural land expansion, and explains this with the sub-linear relationship of agricultural output with economic growth (see Section 3.2).

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Authors and Affiliations

  1. ISA, School of Physics, A28, The University of Sydney, Sydney, NSW, 2006, Australia

    Manfred Lenzen, Christopher Dey & Barney Foran

  2. School of Chemistry F11, The University of Sydney, Sydney, NSW, 2006, Australia

    Asaph Widmer-Cooper

  3. Geography Department, University of Otago, PO Box 56, Dunedin, New Zealand

    Ralf Ohlemüller

  4. School of Biological Sciences A08, The University of Sydney, Sydney, NSW, 2006, Australia

    Moira Williams

  5. School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW, 2052, Australia

    Thomas Wiedmann

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Appendices

Appendix 1

Table 3 Specifications of \( {{\varvec{\rho}}_P}(t) = {{\varvec{\rho}}_P}\left( {2005} \right) + {{\bf d}}{{\varvec{\rho}}_P}t \) and \( {{\varvec{\rho}}_x}(t) = {{\varvec{\rho}}_x}\left( {2005} \right) + {{\bf d}}{{\varvec{\rho}}_x}t \)
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Appendix 2

Table 4 Specifications of elasticities η x,j and η P,j for j = crop and j = pasture
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Appendix 3

Table 5 2005 land use pattern \( {{{\bf L}}_j}{\widehat{{{\bf L}}}^{{ - 1}}} \) , percentage of threatened species S, 2005 agricultural productivity \( \frac{{{{\bf p}}(t)}}{{{{{\bf p}}_{{2005}}}}} \), and annual decline rate ρ L thereof
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Appendix 4

Table 6 GDP elasticity of energy use η e , and 2005 greenhouse gas emissions G (Mt/year)
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Lenzen, M., Dey, C., Foran, B. et al. Modelling Interactions Between Economic Activity, Greenhouse Gas Emissions, Biodiversity and Agricultural Production. Environ Model Assess 18, 377–416 (2013). https://doi.org/10.1007/s10666-012-9341-3

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