If we want to repair our relationship with nature, we first need to understand the system that has been steadily eroding it – my latest research paper.
Responses to declining nature connection often centre on individuals or on providing more green space. But these approaches can overlook the deep cultural, historical, and regional forces that shape our relationship with nature. Without a system‑level view, policies risk being piecemeal, short‑lived or ineffective. To understand why nature connectedness has been slipping, we need a wider perspective, from individual moments of noticing nature, out to regional histories of urbanisation, and further still to national cultural shifts driven by science and industry.
My latest research paper, (a first Richardson & Richardson with my son who’s completing a Masters in Data Science), goes into the engine room of that system, showing how cultural change, local environmental conditions, and psychological processes interact to power (or stall) our relationship with nature. This is the first model to link cultural change, regional environments and psychological pathways into a single system capable of explaining two centuries of decline and today’s regional differences.
Built from real-world data and research evidence the model simulates individuals and families over two centuries in eight cities and counties. It shows how national trends and local environments interact to shape nature connectedness, placing everyday behaviour and attention to nature within the wider system policymakers seek to influence.
It’s the most complex work I’ve attempted and the culmination of a year using new approaches such as machine learning and agent-based modelling to study long-term trends and global differences in recent papers in Earth, Ambio, Sustainability Science and Biological Conservation. As such, the paper also offers a theoretical synthesis of the emerging macro-nature connectedness field, shifting the agenda from individuals and green space to strategic interventions that address the forces driving long-term disconnection and the realities of local places. Put simply, nature connection isn’t viewed as an individual trait, but a system shaped over centuries—this latest research starts to reveal how.
What is the Nature Connection Engine?
This study introduces a hybrid agent‑based model – conceptualised here as a Nature Connection Engine – that unifies macro‑level cultural dynamics, regional socio‑environmental context, intergenerational mechanisms, attention to nature and behavioural feedbacks within a single systems framework.
So, putting it a little more simply, the model includes three key drivers of nature connection:
(1) macro‑level cultural context over time – outer oval
(2) contemporary regional environmental conditions – middle oval
(3) psychosocial processes, including attention and intergenerational transmission – inner oval
Even more simply, it’s a computer simulation of people and places over time.
Using UK cities and counties for training and testing, there were three research questions:
- How well does the agent-based model reproduce current regional nature connection while reflecting historical national trends in the human-nature relationship?
- How do macro-level and regional-level factors influence regional differences in the human-nature relationship?
- Can the model be used to generate future human-nature relationship trends for scenario exploration?
Bringing the Idea to Life
The Nature Connection Engine runs as a yearly simulation from 1800 to 2020, modelling how people, families and places evolve together over time. Each year, the model updates five core processes: how towns grow, how nature changes, how individuals age and form families, how their connection to nature shifts, and how this connection is passed to the next generation.
| AI and Machine Learning
The Nature Connection Engine is built using an agent‑based model coded in Python. It’s an idea that’s been in my head for a few years, but AI put the coding skills at my fingertips to make it happen. The new tools allowed me to express ideas in plain English and iteratively translate them into working code. It still required a “human in the loop” throughout — testing algorithms in Excel, checking assumptions, and refining the logic in my head during a summer of long bike rides — but AI made the whole process possible. I now see AI as an intellectual power tool: not a replacement for expertise, but a way to turn ideas into reality. |
An individual’s nature connectedness changes through a balance of influences: the nature around them, their attention to it, and wider cultural and regional pressures. These interacting forces allow realistic long‑term trends to emerge from small, everyday experiences.
The model was trained on four contrasting regions—Northumberland, Dorset, Birmingham and Leicester—and then tested on four others, including Liverpool and North Yorkshire, to check how well it generalises to regions it had never seen, giving confidence that its insights hold beyond the calibration areas. The model was also stress‑tested using sensitivity analyses. Much more sits behind the model, but this is the basic engine that reveals how two centuries of human–nature relationships can explain contemporary regional differences.
| Urban–Nature Balance: The Need for a Composite Index
To understand how urban environments shape the human–nature relationship, the model needs more than simple measures of “green space” or “urban areas”. Standard datasets often cluster at extremes, offering little insight into how people actually experience their surroundings. Through the process of building and testing the model, it became clear that these measures could not reproduce regional differences in the way that was needed. To address this, the model combines population density, the proportion of neighbourhoods classed as urban, and people’s perceived nature quality. Perception matters: our recent research shows that how natural a place feels can be as important as how much nature it objectively contains. This composite measure provides a more sensitive and realistic representation of the urban–nature interface, helping the model reveal how everyday environments shape our connection with nature — and why the same intervention can have different effects from one place to another. |
What can the model do?
Built from real‑world data, the model reliably mirrors both the long‑term national decline in nature connectedness and today’s regional variation, recreating two centuries of decline and reproducing contemporary differences across places. It not only fits the past, it fits the present and can help diagnoses the forces behind the trends over decades and differences we see today.
The model shows that regional conditions explain around two‑thirds of variation, with macro‑cultural trends (science, economy, secularisation) shaping the overall national trajectory. Urban areas are influenced more strongly by local socio‑environmental factors, while rural regions are more exposed to long‑term cultural pressures.
The Nature Connection Engine can also test interventions before they’re implemented, helping reveal why the same policy succeeds in one place but falters in another. The model found that children’s programmes raise nature connectedness across all regions and are the single most effective intervention, while neighbourhood greening strengthens the effect but rarely reverses decline on its own. However, combined action—children’s programmes plus nature‑based neighbourhoods—creates the conditions for sustained recovery, especially in urban settings where lower baseline connectedness allows for larger long‑term gains.
The model can be run into the future, and these scenarios show that under current trends, nature connectedness continues to fall, particularly in rural areas – something to think about. Encouragingly, model runs extended to 2100 show the potential for self‑sustaining improvement once supportive conditions are in place.
What next?
The model is complex, but reality is far more complicated and there can be further work to improve it, to make, for example, simulated Leicester more like real Leicester. So, I’m now seeking funding to:
- Incorporate more real urban and greenspace data to simulate neighbourhood‑level inequalities and opportunities.
- Extend the model with new mechanisms linking attention, experience, behaviour and feedback loops.
- Calibrate the future scenarios using results from the many individual projects running across the UK.
- Co‑design the outputs of the new version of the model to ensure relevance, usability and policy alignment.
The Vision
Policymakers lack tools that can reveal why interventions succeed in some places but not others, how cultural or economic trends influence outcomes, or how combinations of programmes could generate long‑term, self‑sustaining improvements in connectedness, wellbeing and pro‑nature behaviours.
The strength and stability of the initial model provide the foundations for a new policy tool— a Nature Connection Engine — a practical diagnostic and scenario‑testing approach that helps policymakers explore what works, where, why, and for whom. Under this view, nature connection becomes an engine for nature recovery and wider social benefits, rather than a narrowly individual or access‑based issue.
Such a model would provide a decision‑support dashboard that allows practitioners to test “what‑if” scenarios – e.g. rather than one successful community garden in Derby, what if there were 25? Together with the children’s programme that worked really well in Bradford? This will directly support urban planning, nature recovery, public health, wellbeing strategies and children’s nature engagement programmes.
That future is possible with a little more work, for now, the model shows how today’s regional differences emerged and why similar environments can lead to different levels of nature connectedness: individual relationships with nature are shaped by the interplay of culture, place and family. A key finding is that rural regions—despite greater access to nature—are vulnerable to long‑term cultural forces driving disconnection. The work also highlights that improving access to green space, while valuable, is a relatively shallow leverage point unless paired with interventions that shape attention and early‑life experience. In short, meaningful recovery requires changing not just the environments people live in, but the psychological and cultural conditions that sustain connection across generations.
Richardson, M., & Richardson, L. A. (2026). Reframing human–nature connectedness: A multi-scale agent-based model across time and region. Urban Forestry & Urban Greening, 120, 129399. https://doi.org/10.1016/j.ufug.2026.129399









