8. Knowledge gaps

In previous environmental reports, the ‘Knowledge gaps’ section identified specific gaps in each domain. This report takes a broader approach, identifying gaps that prevent us from understanding environmental change as a whole, and its impacts on people and their quality of life.  

This recognises the interrelated nature of the drivers and pressures of environmental change, and their cumulative impacts on individuals, communities and ecosystems.  

This section explores five themes to describe critical knowledge gaps that are holding us back – not only in understanding the drivers and consequences of environmental change, but also in effective actions and responses. These gaps echo challenges faced globally, highlighted by international initiatives such as the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) Knowledge Taskforce, which emphasises the need for robust, interdisciplinary insights to address shared environmental issues (see IPBES Knowledge gaps).  

Learning from both local experiences and international research can better address these gaps and equip Aotearoa New Zealand to tackle the complexities of environmental change. 

Environmental change is driven by many complex factors, such as international influences, human population growth, individual consumer choices, and technological and climate change (see section 1: Drivers).  

These drivers create environmental pressures that are interconnected, shaped by feedback loops and evolving over time. Understanding the dynamics is further complicated by spatial variations in drivers and pressures. A key knowledge gap about drivers, therefore, is disentangling these intricate relationships to determine which factors drive specific pressures across different times and places. 

Addressing this gap requires advances in modelling techniques. The models must analyse data to identify patterns and feedback processes, isolate pressures and drivers, and predict how various factors (eg, economic policies, climate conditions, technological advancements and population growth) interact over time.  

However, improving predictions for issues such as global warming and biodiversity loss highlights another critical gap: understanding how societies, economies and institutions are likely to respond. This requires further research in social sciences, including psychology, political science, economics, complexity science and socio-technical transitions. 

Another gap lies in the development of robust scenario-based models to explore possible futures. These models enable the testing of ‘what-if’ scenarios, each with distinct assumptions or conditions. This illustrates a range of possibilities rather than a single forecast. For example, they are used to examine how future urbanisation in a specific area might interact with changing climate patterns, or to predict localised effects of policy changes such as urban green design. Advancing research in predictive scenario modelling will be essential to decode environmental change and support effective decisions. 

Data gaps across all the domains in this report prevent deeper understanding of environmental patterns, processes and feedback loops. These data are critical for our understanding, and for informing modelling and decision making.  

Since it is not possible to collect on-the ground observations for every location in New Zealand, modelling techniques can be used to fill gaps in unmeasured areas, using patterns across time and space. Investing in new technologies such as satellite imagery or eDNA can improve the scale and coverage of environmental data. However, it is essential to establish clear monitoring standards and methods for consistency, and to ground truth these new technologies. This is especially important as new technologies and service providers are advancing at pace. 

Research on emerging pollutants and their impacts is another gap. The number of studies of microplastics, waste byproducts and liquid chemical pollutants in air, soils, and freshwater and marine environments is growing. However, we still need to quantify how widespread these are, understand their ecological impacts, and assess their role in climate change.  

Monitoring and evaluating the effectiveness of policies and management interventions is another area where data are either poor or lacking. Reliable evaluation requires environmental baselines, targeted monitoring design, and robust data management. Standardising data collected at local or regional scales to measure the impact of interventions (eg, good land management practices) helps make clear links between action and response. Advances in artificial intelligence could support this by mining historical data and speeding up data cleaning and processing. 

Data monitoring networks often rely on regional or local information, which may not represent national trends. Monitoring must allow for national representation, to understand the scale and extent of a problem. Global-scale models can also be used through downscaling to fit to our context. Clear data management (eg, standardisation, consistent terminology) and detailed metadata are essential to avoid misinterpretation and to facilitate greater reuse of data (eg, land use or ecosystem classification systems; Law et al, 2024; Sprague & Wiser, 2024). Many foundational datasets are curated by public organisations such as Crown research institutes or universities. Balancing open access to shared data with privacy and sovereignty protection is key to ensuring informed decisions that benefit everyone. 

The environmental domains we report on (land, freshwater, marine, air, and atmosphere and climate) are interconnected. Ecological and social processes frequently interact, often leading to cumulative effects on ecosystems and people. For example, pollution, climate change, invasive species and land-use change combine with and amplify each other’s impacts.  

Understanding these dynamics requires an integrated ecosystem-level approach. This would bring together diverse data sources to reveal cause-and-effect relationships, as well as tipping points (where a small change or event causes a significant and permanent change in environmental state), and to identify opportunities for action.  

There are three knowledge gaps here to overcome: 

• the limited availability of data 
• difficulties in integrating data as well as linking techniques, for example linking land use with water quality to identify cause and effect, and overcome complicating factors such as lag times and legacy effects 
• research and models that can link this environmental change to impacts on people. For example, the development of adaptation strategies would benefit from better understanding of the interdependencies among climate, urban infrastructure (including services), financial services (including banking and insurance) and governance systems. 

A similar set of challenges must be overcome to visualise interactions between climate, water, food, biodiversity and people (IPBES, 2024b). Changes to land use, for example, often affect freshwater and marine ecosystems and their services (eg, food production, and water for irrigation).  

Advanced models, decision tools and even ‘digital twins’ are critical to bridging these gaps. They can help simulate the interactions across systems, understand chain reactions and evaluate the impacts and co-benefits of interventions. An integrated understanding of the processes by which one part of a social-ecological system has flow-on effects to another is vital to address both current risks and future uncertainties.  

Mātauranga Māori (Māori knowledge) provides a rich and unique record of changes in the environment, and the impact on people and their quality of life. Mātauranga Māori does not separate the environment into domains, nor people from it. It therefore helps us understand the cumulative effects of environmental change on individuals, communities and ecosystems more holistically.  

Despite the significance of mātauranga Māori, gaps remain in bringing it into environmental reporting and how it affects the Māori worldview (te ao Māori). The following are some of the areas that remain to be addressed.  

• Systematically collect, manage and make available environmental indicators (ngā tohu o te taiao) and place-based knowledge – while respecting Māori data sovereignty.  
• Interweave mātauranga Māori with conventional scientific methods. Frameworks that respect and integrate both knowledge systems can provide a more comprehensive understanding of environmental state and trends. Environmental reporting often lacks the storytelling and cultural narratives central to mātauranga Māori. Incorporating these can provide richer, more meaningful insights into environmental change and impact on the environment (te taiao), people and their quality of life.  
• Actively value mātauranga Māori as a knowledge system. This creates a foundation for collaboration and mutual respect. This partnership is more likely to promote environmental stewardship. 
• Acknowledge the growing economic benefits of Māori involvement in primary industries such as agriculture, forestry, aquaculture and fisheries. This provides another avenue to address gaps in the way we utilise mātauranga Māori, as well as generate new knowledge. By leveraging cultural wisdom and sustainable practices, we can enhance economic outcomes while ensuring environmental sustainability.  

Addressing these gaps will not only enhance environmental reporting, but also uphold the principles of te Tiriti o Waitangi (the Treaty of Waitangi). This will foster a more inclusive and effective approach to environmental stewardship.  

There is limited understanding of how environmental change affects people’s quality of life. This makes it challenging to determine whether the changes have positive or negative effects. For example, ecosystems provide essential services – such as clean air, water and food – which support economic stability, health and community resilience. However, quantifying these contributions is complex, particularly with intangible benefits such as mental health support from green spaces, or cultural ties to landscapes.  

Our relationships with nature vary between people and communities, and also shift over time due to social changes. This makes it difficult to assign consistent values to these benefits, and highlights a significant knowledge gap – developing robust, reliable and adaptive valuation methods for ecosystem services, especially for non-market benefits (Ausseil et al, 2021; Maechler & Boisvert, 2024). To more inclusively capture the full spectrum of effects on people’s relationships with nature, these methods must account for social and cultural differences, and variations across different locations and over time. 

Equally important is understanding risks and resilience in the face of environmental challenges. Predicting health impacts, such as from air pollution, requires advanced modelling to anticipate exposure levels and outcomes. Similarly, identifying the communities most at risk, whether from rising sea levels, extreme weather or resource scarcity, is vital for targeted interventions and ensuring certainty for development in lower-risk areas. To create lasting solutions, we need new methods to measure adaptation progress and resilience. With improved assessment, we can better understand trade-offs and develop strategies that not only protect ecosystems and vulnerable populations, but also enhance quality of life in a rapidly changing world.