Making boundaries real for regional policy

The idea that we should combine estimates of nine “planetary boundaries” to define a safe operating space for the Earth and its systems exemplifies the problem. The definition of planetary boundaries, by Johan Rockstrom and others, is an influential reformulation of earlier thinking about limits to growth and sustainability. But it is an open question whether the safe operating space their rough numbers define is a workable notion for actual policy-making. If not, its main use would remain metaphorical, as a tool for persuading people to take seriously the idea that there are some limits to human use of ecosystem services.

John Dearing of the University of Southampton and co-authors have recently proposed a way of turning safe operating spaces into a usable framework with immediate implications for action. Their approach, applied in a paper appearing in Global Environmental Change in June this year, is both less and more ambitious than that embodied in the original sketch of overarching planetary boundaries.

It is less ambitious because it focusses on regional scales – using two rural localities in China as a detailed case study. On the other hand, following work published by Oxfam in 2012, it expands the original idea beyond biophysical or ecological parts of the Earth system. This framework adds measures of social justice to broaden the picture of costs and consequences.

To evaluate the concept of safe and just operating spaces at the regional scale, the researchers needed to assemble a complex set of data and formulate hypotheses about how different indicators are linked. Ecologically, they write, their aim is to develop a guide “for identifying regional boundaries from observations of system dynamical behaviour in real world time series.” Such observations may come from monitoring, surveying, remote sensing or sediment analysis. The reference to system dynamical behaviour reflects a concern to track non-linear responses, and to identify the “tipping points” that may compromise ecosystems. As they say,

“the original planetary boundaries framework recognized that the effects of crossing multiple thresholds at regional scales can aggregate to become a global concern. But the cascading effects of environmental degradation can have critical consequences for the sustainability of regional systems themselves, well before the effects are obvious at the global scale.”

Dearing and colleagues applied their framework to two regions in China – Shucheng County and Erhai Lake – each covering around 2000 square kilometres and supporting a million people. As elsewhere in rural China, rapid agricultural development in the last half century has raised living standards in both regions, but also had environmental costs.

In Shucheng County, for example, the data assembled by the researchers show that water, air and sediment quality have all breached what they call the “environmental ceiling”. At the same time, the population’s access to clean water, sanitation and education has yet to reach minimal standards. These indicators, along with half a dozen others, define the “social foundation” that complements the environmental indicators. The safe and just operating space then lies between these two complex boundaries.

That space will vary regionally. Erhai lake-catchment had a different pattern of environmental impacts, with no strong danger signs in air or sediment data but cause for concern about upland soil stability.

Looking at time series for all the indicators – social as well as ecological – then feeds into future scenarios. This could help people weigh up the livelihood benefits against the downsides of ecological degradation – the trade-offs that inevitably exist where people draw directly on local environmental resources to support themselves. It looks as though careful management of water quality, and investment in water treatment plants and sewage processing, would be good investment priorities in both regions.

The case studies are instructive, but where might this approach lead? Dearing suggests that it could be applied “wherever sufficiently long time series of key variables are available”. That calls for a flexible approach to using many different kinds of information. As he sees it,

“The data can be very good for social norms and some environmental variables where nations maintain decent official statistical records but usually difficult for regulating ecosystem services that include soil stability and water quality, where monitoring is very hit and miss – and often short term where it does exist. We utilise all records including reconstructions from sediments – as these are often the best records we have. In general, records for air quality and flooding are the longest”.

Once the different types of data available have been brought together, the analysis of the interactions they show presents further challenges. Dearing proposes that complex systems theory, with its ability to take account of non-linear change and tipping points, is a vital aid to such analysis. The new paper moves some way to demonstrating that, he believes:

“We think this is a novel study that confronts how we might incorporate tipping points, changes in variability, slow versus fast variables and so on in the management of social-ecological systems, without recourse to modelling. Perhaps it doesn’t cover all aspects of complexity theory but it does address the gap that exists between the growing sense that nonlinearities exist in social-ecological systems and our ability to study them in real world situations”.

In the future, this might feed into more formal modelling to develop this approach to regional management further, but he remains cautious about that at the moment.

“Modelling would be a next step – but only models that can capture the nonlinear, complex behaviour that we show in the time-series. Many management models do not capture complex, nonlinear interactions – which I think should worry us all. System dynamic and agent-based models are probably the way to go but I would want them to be able to simulate the most obvious nonlinearities that we see recorded in the time-series.”

Meanwhile, better analysis of existing data may be enough to guide action. As Dearing points out,

“Modelling is not the only step. We don’t expect our doctors to start modelling our bodies when we get ill. We expect them to use the best available information (tests, lifestyle, previous illness, family history) to make a diagnosis of the symptoms and determine a plausible course of treatment or action (lay off the doughnuts). I think we should do the same when we are concerned about the health of social-ecological systems”.

In that light, he argues, the concept of a safe and just operating space is a useful diagnostic aid. Now we’ll see if others can apply it in regions under environmental pressure.

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