Energy- and Multi-Sector Modelling of Climate Change Mitigation in New Zealand: Current Practice and Future Needs

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As New Zealand charts its course toward a low-emissions economy, the quality of energy-sector and multi-sector modelling is becoming increasingly important. This paper outlines why models are useful for answering complex questions, provides a stocktake of energy-sector and multi-sector models used for climate change mitigation modelling in New Zealand, and makes suggestions for improving future modelling work. 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 a more strategic development of New Zealand’s modelling capability, this paper profiles some of the energy-sector and multi-sector models and datasets currently applied in New Zealand. New Zealand’s modelling capability could be strengthened by collecting and sharing data more effectively; building understanding of underlying relationships informed by primary research; creating more collaborative and transparent processes for applying common datasets; increasing international collaboration; 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 activities, underlying data collection and primary research; and strengthening networks across modellers inside and outside of government. Many of the suggested improvements could be realised by creating an integrated framework for climate change mitigation modelling in New Zealand. This framework would bring together a suite of models and a network of researchers to assess climate change mitigation policies regularly. Core elements of the framework would include a central repository of data, input assumptions and scenarios, and a “dashboard” that synthesises results from different models to allow decision-makers to understand and apply the insights from the models more easily.