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Land-use modelling in New Zealand: current practice and future needs

Author

Listed:
  • Jo Hendy

    () (Interim Climate Change Commission)

  • Levente Timar

    () (Motu Economic and Public Policy Research)

  • Dominic White

    () (Motu Economic and Public Policy Research)

Abstract

New Zealand faces the challenge of using our land in ways that are not only resilient to future pressures and sustain our rural communities but also enhance our natural environment. For the public and private sectors to make robust land-use decisions under uncertainty, high-quality modelling tools and data are essential. The drivers of land-use decisions are complex and models provide a structured methodology for investigating these. While New Zealand is fortunate to have a range of different modelling tools, these have historically been used in a sporadic and ad hoc way, and underlying datasets are deficient in some areas. As the foundation for more strategic development of New Zealand’s modelling capability, this paper profiles the main land-sector and farm- and production-related models and datasets currently applied in New Zealand. It also explores priority policy areas where modelling is needed, such as achieving emission reduction targets; managing freshwater, biodiversity and soil quality; and understanding the distributional impacts of policy options as well as climate change. New Zealand’s modelling capability could be strengthened by collecting and sharing land-use data more effectively; building understanding of underlying relationships informed by primary research; creating more collaborative and transparent processes for applying common datasets, scenarios and assumptions, and conducting peer review; and conducting more integrated modelling across environmental issues. These improvements will require strategic policies and processes for refining model development, providing increased, predictable and sustained funding for modelling activity and underlying data collection and primary research, and strengthening networks across modellers inside and outside of government.

Suggested Citation

  • Jo Hendy & Levente Timar & Dominic White, 2018. "Land-use modelling in New Zealand: current practice and future needs," Working Papers 18_16, Motu Economic and Public Policy Research.
  • Handle: RePEc:mtu:wpaper:18_16
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    File URL: http://motu-www.motu.org.nz/wpapers/18_16.pdf
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    References listed on IDEAS

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    1. Graeme J. Doole & Olga Vigiak & David J. Pannell & Anna M. Roberts, 2013. "Cost-effective strategies to mitigate multiple pollutants in an agricultural catchment in North Central Victoria, Australia," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 57(3), pages 441-460, July.
    2. Graeme J. Doole & David J. Pannell, 2012. "Empirical evaluation of nonpoint pollution policies under agent heterogeneity: regulating intensive dairy production in the Waikato region of New Zealand," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 56(1), pages 82-101, January.
    3. Schilling, Chris & Kaye-Blake, William & Post, Elizabeth & Rains, Scott, 2012. "The importance of farmer behaviour: an application of Desktop MAS, a multi-agent system model for rural New Zealand communities," 2012 Conference, August 31, 2012, Nelson, New Zealand 136070, New Zealand Agricultural and Resource Economics Society.
    4. Daigneault, Adam J. & Morgan, Fraser, 2012. "Estimating Impacts of Climate Change Policy on Land Use: An Agent Based Modeling Approach," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124973, Agricultural and Applied Economics Association.
    5. R White & G Engelen, 1997. "Cellular Automata as the Basis of Integrated Dynamic Regional Modelling," Environment and Planning B, , vol. 24(2), pages 235-246, April.
    6. Doole, Graeme J., 2012. "Cost-effective policies for improving water quality by reducing nitrate emissions from diverse dairy farms: An abatement–cost perspective," Agricultural Water Management, Elsevier, vol. 104(C), pages 10-20.
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    More about this item

    Keywords

    Land-use; modelling; data management; Policy analysis;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • D58 - Microeconomics - - General Equilibrium and Disequilibrium - - - Computable and Other Applied General Equilibrium Models
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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