新西兰土地使用建模:当前实践和未来需求

Joanna Hendy, A. Ausseil, Isaac Bain, É. Blanc, David A. Fleming, Joel Gibbs, A. Hall, A. Herzig, P. Kavanagh, Suzi Kerr, Catherine Leining, Laetitia Leroy, Edmund Y. Lou, J. F. Monge, A. Reisinger, Jim Risk, T. Soliman, A. Stroombergen, L. Timar, T. J. van der Weerden, Dominic White, C. Zammit
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引用次数: 20

摘要

新西兰面临的挑战是如何利用我们的土地,不仅要适应未来的压力,维持我们的农村社区,还要改善我们的自然环境。为了使公共和私营部门在不确定的情况下做出强有力的土地利用决策,高质量的建模工具和数据是必不可少的。土地利用决策的驱动因素是复杂的,模型为调查这些驱动因素提供了一种结构化的方法。虽然新西兰有幸拥有一系列不同的建模工具,但这些工具在历史上一直以零星和特别的方式使用,并且在某些领域缺乏基础数据集。作为新西兰建模能力更具战略性发展的基础,本文概述了新西兰目前应用的主要土地部门和农业及生产相关模型和数据集。它还探讨了需要建模的优先政策领域,例如实现减排目标;管理淡水、生物多样性和土壤质量;了解政策选择和气候变化的分配影响。可以通过更有效地收集和分享土地利用数据来加强新西兰的建模能力;通过初步研究建立对潜在关系的理解;为应用共同数据集、情景和假设以及开展同行评议创建更具协作性和透明度的流程;并在环境问题上进行更综合的建模。这些改进将需要战略政策和流程来完善模型开发,为建模活动、基础数据收集和初级研究提供更多的、可预测的和持续的资金,并加强政府内外建模者之间的网络。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Land-Use Modelling in New Zealand: Current Practice and Future Needs
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.
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