Level of detail (LoD) is an important factor in urban building energy modelling (UBEM), affecting functionality and accuracy. This work assesses the impacts of the LoD of the roof, window, and zoning on a comprehensive range of outcomes (annual heating load, peak heating demand, overheating, and time-series heating error) in a representative New Zealand house. Lower-LoD roof scenarios produce mean absolute error results ranging from 1.5% for peak heating power to 99% for overheating. Windows and shading both affect solar gains, so lower-LoD windows and/or shading elements can considerably reduce model accuracy. The LoD of internal zoning has the greatest effect on time-series accuracy, producing mean absolute heating error of up to 66 W. These results indicate that low-LoD “shoebox” models, common in UBEM, can produce significant errors which aggregate at scale. Accurate internal zoning models and accurate window size and placement have the greatest potential for error reduction, but their implementation is limited at scale due to data availability and automation barriers. Conversely, modest error reductions can be obtained via simple model improvements, such as the inclusion of eaves and window border shading. Overall, modellers should select LoD elements according to specific accuracy requirements.

Agricultural systems have entered a period of significant disruption due to impacts from change drivers, increasingly stringent environmental regulations and the need to reduce unwanted discharges, and emerging technologies and biotechnologies. Governments and industries are developing strategies to respond to the risks and opportunities associated with these disruptors. Modelling is a useful tool for system conceptualisation, understanding, and scenario testing. Today, New Zealand and other nations need integrated modelling tools at the national scale to help industries and stakeholders plan for future disruptive changes. In this paper, following a scoping review process, we analyse modelling approaches and available agricultural systems’ model examples per thematic applications at the regional to national scale to define the best options for the national policy development. Each modelling approach has specificities, such as stakeholder engagement capacity, complex systems reproduction, predictive or prospective scenario testing, and users should consider coupling approaches for greater added value. The efficiency of spatial decision support tools working with a system dynamics approach can help holistically in stakeholders’ participation and understanding, and for improving land planning and policy. This model combination appears to be the most appropriate for the New Zealand national context.

There is a clear research gap in understanding how future pathways and disruptions to the New Zealand (NZ) agricultural system will have an impact on the environment and productivity. Agriculture is in a period of significant change due to market disruptions, climate change, increasingly stringent environmental regulations, and emerging technologies. In NZ, agriculture is a key sector of the economy, therefore government and industry need to develop policies and strategies to respond to the risks and opportunities associated with these disruptors. To address this gap, there is a need to develop an assessment tool to explore pathways and interventions for increasing agricultural profitability, resilience, and sustainability over the next 5–30 years. A decision support tool was developed through Stella Architect, bringing together production, market values, land use, water use, energy, fertiliser consumption, and emissions from agricultural sectors (dairy, beef, sheep, cereals, horticulture, and forests). The parameters are customisable by the user for scenario building. Two future trend scenarios (Business as usual, Optimisation and technology) and two breakaway scenarios (Carbon farming, Reduction in dairy demand) were simulated and all met carbon emissions goals, but profitability differed. Future environmental regulations can be met by adjusting levers associated with technology, carbon offsets, and land use. The model supports the development and assessment of pathways to achieve NZ’s national agriculture goals and has the potential to be scaled globally.

Improving the sustainability of New Zealand’s housing stock is a significant challenge. Economic, indoor climate and energy use data indicates the affordability, healthiness and environmental impacts of new and existing houses in New Zealand are relatively poor compared with other OECD countries. Poor performance is largely due to the building energy and environmental performance requirements prescribed by regulations, which are low in New Zealand compared with other jurisdictions. Building performance requirements will need to be raised in order to improve the sustainability of housing. A wellbeing approach to determining public benefits from raising building performance requirements in regulations is outlined in this paper. This approach draws on the Living Standards Framework (LSF) developed by The New Zealand Treasury. This framework links 12 domains of wellbeing to each other and to 4 capital stocks, including housing, that underpin future wellbeing. The LSF is used to assess the impacts of raising building performance requirements on community wellbeing. Results indicate better knowledge of the relationships between the domains of wellbeing and the underpinning capital stocks is needed to improve the identification and analysis of preferred regulatory settings.