Key concepts

The academic literature is full of terminology related to tipping points. Here we try to explain what the key terms mean. A separate glossary of the terms in bold is available.

What is a tipping point?

In everyday usage, a tipping point is where a small change makes a big difference to a system (Gladwell, 2000) (Figure 1) or “the point at which a series of small changes or incidents becomes significant enough to cause a larger, more important change” (Oxford English Dictionary). Here a system is any group of interacting or interrelated things that act according to a shared set of rules to form a recognisable, unified whole – for example, an ice sheet, or an economy. A tipping point is a type of threshold. The small change that causes a system to pass a tipping point can be described as a trigger. The resulting large change can be described as a qualitative change in what a system looks like or how it functions – for example from a Greenland ice sheet to a largely ice-free ‘green’ Greenland, or from an economy powered by fossil fuels to one powered by renewable energy. The change associated with passing a tipping point also commonly includes qualities of: abruptness (change is rapid relative to the drivers forcing it); self-perpetuation (change will continue even if the forcing is removed, until a new state is reached); and irreversibility (change is difficult or impossible to reverse) (Milkoreit et al., 2018). 

Here we define a tipping point as occurring when change in part of a system becomes self-perpetuating beyond a threshold, leading to substantial, widespread,  frequently abrupt and often irreversible impact (inspired by Armstrong McKay et al., 2022 and Milkoreit et al., 2018). This definition includes the possibilities of non-abrupt and reversible tipping points. 

A tipping system is any system that can pass a tipping point. The term tipping element was originally introduced to describe large parts (subsystems) of the climate system (greater than ~1,000km-length scale) that could pass a tipping point (Lenton et al., 2008). Some other disciplines have started to use ‘tipping element’ more broadly to describe those parts or subsystems of a larger system that can undergo tipping point dynamics (e.g. Otto et al., 2020). When used in other contexts a qualifier such as ‘social’ tipping element (Otto et al., 2020) is important to avoid confusion.

Two other terms are widely used in the academic literature often interchangeably with tipping points, and with each other (Dakos, 2019): Regime shift describes an abrupt and/or persistent shift in the current state of an ecosystem from one stable state to another (Biggs et al., 2009; Maciejewski et al., 2019) and critical transition describes an abrupt shift in a system that occurs at a specific (critical) threshold in external conditions (Scheffer, 2009). Thus both describe the change that may be associated with a tipping point, but not the tipping point itself. In this report, we use tipping event to describe the crossing of a tipping point and tipping dynamics to describe the resulting changes that unfold. (Where regime shift or critical transition are used, we define them on a case-by-case basis.) 

Sources of tipping point behaviour

The qualities of tipping points described above can come about because of several generic characteristics of the systems in which they occur, and the forces they are subject to.

A feedback mechanism (or feedback loop) is a closed loop of causality whereby a change in a system feeds back to amplify or dampen that change. Feedback mechanisms can be mathematically positive or negative, depending on whether they amplify or dampen the effects of a change. An example of amplifying/reinforcing positive feedback is when warming in the Arctic causes sea-ice to melt, exposing a much darker ocean surface that absorbs more sunlight, amplifying the warming. An example of damping/balancing negative feedback is when demand for specific goods in the economy exceeds supply, prices rise and this suppresses demand.

Tipping can occur when amplifying/reinforcing (positive) feedback mechanisms overwhelm damping/balancing (negative) ones and get strong enough to support self-perpetuating change. For example, when one person infected with COVID-19 can infect four others, who can infect 16, and so on, the spread of infection is self-perpetuating. Only a (small) subset of all amplifying (positive) feedback loops can get strong enough to support self-perpetuating change. Also, self-perpetuating change is transient – it cannot continue indefinitely because at some point it will reach a limit. In the spread of an epidemic or pandemic that limit can be when the majority of the population has become infected.

Systems typically exhibit at least one stable state or attractor that the system will return to from a set of initial conditions. The quality of ‘attraction’ or dynamical stability exists because of a predominance of damping (negative) feedback that resists change. For example, if you push back just a little bit on a chair, the resulting change in the balance of forces acts to bring you back upright. This is an example of perturbing the system away from a stable state. It will tend to return to that state – at least for some range of sizes of perturbation. But if you push back too far on a chair you may find yourself rapidly transitioning into an alternative stable state – sprawled on your back on the floor. This is an example of bi-stability – you and the chair are a system with two alternative stable states. In between there is a balance point, which is an unstable state, because a small nudge either way will send you back upright or on to the floor. There also exist systems with multi-stability (more than two alternative stable states). For a system with alternative stable states, there are three main ways that a tipping point can occur (Figure 2).

Sometimes when a system is forced by changing external ‘boundary’ conditions – such as global warming of an ice sheet – the state that it is in can lose stability. It may reach a bifurcation point where the current stable state disappears and the system moves to another (stable) state or attractor, with a corresponding qualitative change in behaviour. Such shifts can be smooth – such as when a previously stable system begins to oscillate. Or the system may undergo a catastrophic bifurcation where it moves discontinuously to a different state/attractor. This is the most widely discussed type of tipping point in the literature and is referred to as bifurcation tipping (Figure 2, left). An example is the loss of the Greenland ice sheet – as the surface melts it declines in altitude, putting it in warmer air and causing further melt. A bifurcation tipping point can be reached where this reinforcing feedback becomes self-propelling – meaning smaller sizes of the ice sheet are not stable, and the ice sheet is committed to irreversibly shrinking to a much smaller size, or disappearing altogether. 

When a system has alternative stable states (attractors) it can exhibit hysteresis, meaning the state the system is in depends on its history (Figure 3). When forced in one direction, the system may pass a tipping point from one stable state (attractor) to another, but when the forcing is reversed to the same level it may remain in the other state (attractor), and further reduction in forcing is needed until a different tipping point is reached. Such hysteresis is a key source of irreversibility when crossing a tipping point. For example, while the Greenland Ice Sheet requires some global warming to be tipped into irreversible loss, if the ice sheet is lost it will not regrow at the same temperature level, nor at the preindustrial temperature level – instead it would require global cooling. Hysteresis is an example of path dependence, where past events constrain future events. The existence of the Greenland Ice Sheet today is a legacy of the last ice age. In such cases, to predict future changes it is important to know a system’s history. 

In a system with alternative stable states (attractors), where the current state has lost some of its stability (but a bifurcation point has not been reached), it can be vulnerable to small perturbations termed noise (i.e. stochastic variability). A nudge in the wrong direction can be enough to tip the system out of its present state, past the unstable state into an alternative state. This phenomenon is called noise-induced tipping (Figure 2, middle). In reality where a system is subject to both noise and steady forcing towards a bifurcation point, the tipping out of the initial state usually happens due to noise before the bifurcation point. In the climate system, the weather can be thought of as noise (short-term internal variability). In the Greenland Ice Sheet example, a summer heatwave may melt enough of the ice sheet to take it past the tipping point, whereas without that heatwave the tipping point would not have been crossed.

Sometimes a small change in the rate at which a system is forced can produce a large change in outcome. Forcing a system rapidly may bring it towards an unstable state because the system’s damping feedbacks are not acting fast enough to counter the forcing. Then just a small further increase in the rate of forcing may be enough to cause the system to tip. Whereas slower forcing to the same level would not cause it to tip. This is referred to as rate-induced tipping (Figure 0.2, right). An example in a human system are some power grid blackouts (Ritchie et al., 2023): Power grid controllers act as a damping feedback in the system trying to increase electricity supply (by switching on power stations) to match increases in demand. However, if demand for electricity rises faster than they expect, this can lead to a blackout. 

A further important source of tipping can be a cascade effect (or domino effect or chain reaction). This is a causal chain whereby a small change in a subsystem causes a further change to another subsystem, and a further one, and so on, resulting in a large overall change to a wider system. For example, the extermination of wolves from Yellowstone National Park triggered a cascade that changed the whole ecosystem, and reintroducing wolves tipped the system back through another cascade. Within one species, cascading change can spread through networked populations of (human or non-human) agents through the process of contagion, whereby information or behaviour is passed from one agent to another. Simple contagion only requires contact with one other agent for adoption of new information or behaviour to occur. Complex contagion depends on contact with multiple agents before adoption occurs. Equally, when adding nodes or links to a network, a point can be reached where percolation occurs and a previously disconnected network becomes globally connected, allowing change to spread abruptly throughout.

Focal types of system and tipping point

Many types of systems can exhibit tipping points. This report focuses on a subset of types of systems, relevant to global change, in which tipping points can occur. 

The systems we consider are all complex systems consisting of a large number of interconnected components that interact with each other, often giving rise to feedback loops, nonlinearity, and emergent properties (which cannot be reduced to the properties of the component parts). Some of the systems we consider are complex adaptive systems characterised by the ability to change in response to changing (internal or external) conditions in a way that maintains or enhances their function. They are typically composed of interacting heterogeneous agents, which may be humans or other organisms, with their own behaviours, preferences and decision-making processes.

The Earth system is the complex system at the surface of the planet Earth, comprising the atmosphere, hydrosphere (including oceans and freshwaters), cryosphere (including ice sheets), biosphere (living organisms) and lithosphere (land, soils, sediments and parts of the Earth’s crust) (Lenton, 2016). The climate system is the parts of the Earth system that govern the climate at the surface of the Earth. Referring to the climate system rather than the Earth system tends to involve a shift in emphasis towards shorter timescales and those subsystems most affecting climate (e.g. the atmosphere and oceans). 

A climate tipping point occurs when change in part of the climate system becomes self-perpetuating beyond a threshold, leading to substantial and widespread Earth system impacts. For example, the irreversible loss of the Greenland Ice Sheet would ultimately lead to around seven metres of global sea-level rise. The climate tipping points we are particularly interested in here are ones that occur beyond a particular threshold level of global warming. Earth system tipping points include climate tipping points and other cases of large-scale self-perpetuating change beyond a threshold involving non-climate variables – for example, tipping points into or out of oceanic anoxic events in Earth’s past. 

Ecosystems are complex, sometimes adaptive systems composed of living organisms (ecological agents) coupled to their physical and chemical environment in a particular spatial (geographic) area. Ecosystems are smaller in spatial scale than the whole biosphere, which is sometimes referred to as the ‘global ecosystem’. 

An ecological tipping point occurs when change in a biological population, community, or ecosystem becomes self-perpetuating beyond a threshold. For example, when increased fires or grazing trigger a tropical woodland to tip into a savanna. Changes resulting from tipping points in ecosystems are also often referred to as regime shifts, or sometimes as critical transitions. They can be triggered by both natural and human-induced disturbances, such as habitat loss, species invasions, pollution and climate change. 

Social systems are complex, often adaptive, collective human systems, which have rich dynamics (Parsons, 2010) and operate within an ecological and Earth system context (Otto et al., 2020; Eker and Wilson, 2022; Winkelmann et al., 2022). Social systems are composed of massively entangled formal and informal organisations and networks. They may be an interconnected web of hierarchical, bureaucratic organisations or networks of small formal and informal groups, communities or family systems, all of which have their own institutions and/or norms. In common language, ‘system change’ refers to changing social systems. 

Social systems, like physical and ecological systems, can have stable states (attractors) that resist change; they can exhibit path dependency and hysteresis; they can undergo non-linear change with positive feedback; and they can cross social tipping points into new stable states, over various timescales. For example, in the diffusion of innovation whereby new ideas, products or services spread through social systems over time, there can be critical mass tipping points where, for example, one more person adopting a behaviour or technology causes everybody else to adopt. Similar dynamics can underlie tipping points into escalating political protests, riots, or revolutions. Communities may also tip into a state of anomie characterised by a breakdown of social norms, social ties and social reality.

Humans have greater agency and ability to learn than other species, and a growing collective awareness of their impacts on the larger systems of which they are a part. This gives us humans greater potential to alter the fate of those larger systems than is the case for other species.

Different types of social systems can be identified. A socio-behavioural system encompasses social norms, behaviours and lifestyles, communities and their cultures, and institutions. A social-ecological system includes interacting social and ecological components which together shape the behaviour and functioning of the system. For example, fisheries include both the aquatic ecosystems, and the people who live in, depend on, and shape these systems. A socio-technical system (or social-technological system) comprises interacting social and technological components often with a common goal (or goals). Examples include transportation networks, energy systems, and healthcare systems. They are often designed to meet societal needs, but they also shape and are shaped by social norms, values and practices. A social-ecological-technological system comprises interacting social, ecological and technological components – for example, food systems.

Corresponding types of tipping point can be identified. A social-ecological tipping point is one that arises because of the coupling of the social and ecological components (and is not present in either of them independently). A socio-technical tipping point is one that arises because of the coupling of social and technological components (and is not present in either of them independently). A social-ecological-technological tipping point is one that arises because of the coupling of social, ecological and technological components. For example, the ‘Green Revolution’ in agriculture in the 1960s and 1970s that led to a reduction in poverty through greater crop yields from genetic selection and the use of fertilisers.

A tipping cascade occurs when passing one tipping point triggers at least one other tipping point. It can occur within climate, ecological or social realms, or across them. For example, a climate tipping point can trigger ecological tipping points with cascading impacts that trigger social tipping points. 

In this report we often add a normative interpretation of the impacts and consequences of reaching particular tipping points in different systems. We use the emotional meanings of ‘positive’ and ‘negative’ as simple normative labels, aware that these should not be confused with their mathematical meanings (particularly in the context of feedback loops). Thus, in the most general sense, a positive tipping point is one that is predominantly beneficial for humans and the natural systems we depend upon, and a negative tipping point is one that is predominantly detrimental for humans and the natural systems we depend upon. 

More specifically, we define positive tipping points as those that accelerate change which reduces the likelihood of negative Earth system tipping points, and/or increases the likelihood of achieving just social foundations. Both are needed to ensure a sustainable future within safe and just Earth system boundaries (Gupta et al., 2023; Rockström et al., 2023; Raworth, 2017).

We acknowledge that ‘positive’ and ‘negative’ are value judgements; one person’s positive outcome may be another’s negative outcome, and distinguishing between the two is often subject to debate. However, the normative force in our usage of these terms is based on the science of biophysical capacities and the ethics of human wellbeing. Almost all people, regardless of their differences, believe that human flourishing is better than human suffering, and share a common interest in achieving sustainability. We define the latter  as an aggregate measure of the biophysical capacities (planetary boundaries) and social foundations that can ensure a minimum level of wellbeing for a given population, indefinitely. Achieving a sustainable future will require a high level of collective responsibility and action, especially in relation to the global challenge of climate change. It is, however, a highly contested concept: different actors and groups tend to disagree about the speed and depth of transformation required. 

Related concepts 

Several key concepts related to tipping points are widely used in this report. 

Before reaching a tipping point, a system typically loses resilience, which is defined here in a narrow sense to refer to its capacity to resist (or absorb) change and continue to function in its present state. In quantitative analyses of tipping points, resilience is often defined as the capacity of a system to return to a stable state (attractor) after a perturbation, measured as its recovery rate from disturbance. In development practice, the resilience of social and social-ecological systems is often used in a normative way (i.e. resilience is good/desirable). It is also sometimes used more broadly than we do here, to refer to the capacities to persist, adapt, or transform in response to change (Moser et al., 2019, Folke, 2016).

The loss of resilience is a generic early indicator of approach to a bifurcation tipping point (Figure 4). It is a manifestation of the weakening of damping negative feedback in a system before strong amplifying positive feedback takes over at a tipping point. This causes a phenomenon called critical slowing down, whereby a system approaching a tipping point tends to undergo larger changes in response to perturbations and takes longer to recover from them. The associated loss of resilience can be detected in changing statistical indicators of system behaviour (Scheffer et al., 2009). In the context of undesirable, negative tipping points in systems, these are often referred to as early warning signals. In the context of desirable, positive tipping points in systems, we refer to them as early opportunity indicators.  

The change in a system that accompanies a tipping point is sometimes described as a transformation of that system. We use transformation more specifically to refer to rapid and fundamental changes in human systems required to achieve sustainability (Patterson et al., 2017). Dramatic socio-cultural, political, economic and technological changes are required to move societies toward more desirable futures in the Anthropocene (Pereira et al., 2018, Bennett et al., 2016), yet their empirical assessment remains challenging (Salomaa and Juhola, 2020). In contrast, transition has a narrower usage to describe managed, often sector-specific, processes of social-technological change. 

Where there is the desire and agency to try and cause a positive tipping point in a system, it is important to understand the strategic interventions that can bring it about and how effective they may be. Meadows (1999) originally identified a series of general leverage points or ‘places to intervene in a system’, and identified their relative effectiveness (from most to least):

1. The mindset or paradigm out of which the system arises; 
2. The goals of the system; 
3. The distribution of power over the rules of the system; 
4. The rules of the system; 
5. Information flows; 
6. Material flows and nodes of material intersection; 
7. Driving positive feedback loops; 
8. Regulating negative feedback loops; 
9. Constants, parameters, numbers. 

More recently, examples of leverage points that can trigger positive tipping points in social-ecological-technological systems have been termed sensitive intervention points (Barbrook-Johnson et al., 2023; Mealy et al., 2023; Farmer et al., 2019; Hepburn et al., 2020) or social tipping interventions (Otto et al., 2020). Super-leverage points have been proposed, which are capable of catalysing tipping cascades across multiple sectors (Meldrum et al., 2023). 

Enabling conditions are the system conditions that can allow a tipping point to be triggered (Lenton et al., 2022). For example, with respect to positive tipping points, enabling conditions include the diffusion of social norms promoting sustainable behaviours, price reductions and availability of sustainable alternatives. Feedback processes between policy, technological and behavioural change (e.g. in terms of social norms, availability, prices and political support) can create favourable conditions that can enable positive tipping points (Smith, 2023; Fesenfeld et al., 2022). In this context, demand-side solutions are ones that reduce greenhouse gas emissions and other harmful stressors by changing consumption habits, norms and lifestyles; whereas supply-side solutions are ones that do so through technological innovations and their diffusion.

What is not a tipping point?

The term ‘tipping point’ has become increasingly popular in the media and public discourse in recent years, with many journalists and commentators using it to describe a wide range of phenomena. Sometimes the term is misused, creating misunderstanding and its own risks (Milkoreit, 2023). Wrongly asserting a negative tipping point could lead to a false sense of inevitability, leading to disempowerment, nihilism or despair. Wrongly asserting a positive tipping point could lead to false optimism, potentially interrupting difficult but necessary actions to affect change.

Tipping points are general features of a system. Events, people or historical junctures are not tipping points. They might have something to do with the crossing of a tipping point, but they are not its defining feature. For example if a fishery collapses, it is not the last fish caught or the person that caught it that represents the tipping point, because in a counterfactual situation the system would have tipped if a different fish was caught or a different person (or creature) caught it. Thus an election or a treaty are not tipping points (although they may have something to do with them). 

Situations where a big change makes a big difference to a system are not tipping points. They are cases of linear, proportional change. Equally, many cases where a change gets amplified by positive feedback are not strong enough to produce a tipping point of self-perpetuating change. Hence it is critical to assess how strong amplifying feedback loops are, and to consider what damping feedback loops are present, before asserting a potential tipping point. Equally, in cases of cascading consequences it is important to assess how strong they are before asserting a tipping point. 

When talking about tipping points in this report, we describe them in terms of general system features and distinguish that from the actions and forces that can bring a system towards a tipping point – the strategic interventions that can create enabling conditions and can trigger tipping.