Climate change induced socio-economic tipping points: review and stakeholder consultation for policy relevant research

The literature review was structured along two criteria emerging from existing review articles (table SI 1.1 available online at stacks.iop.org/ERL/15/023001/mmedia):

• (1)  
  The type of systems on which the tipping point concept is applied: e.g. physical, biological and social systems, which is a major source of discussion on tipping point definitions (Russill and Nyssa 2009, Russill 2015).
• (2)  
  The characteristics of tipping point definitions: e.g. stable states, abruptness, feedbacks and limited reversibility (Milkoreit et al 2018).

We searched 'Tipping Point*' AND 'Climat* Chang*' in the Web-of-Science core collection database (n = 508) (table SI 1.2), enriched by forward and backward citation tracking. To address criterion 1, this literature was positioned in a diagrammatic representation of systems exhibiting tipping points (figures 1, 3), after Barker (2003). To address criterion 2, after Milkoreit et al (2018), three qualitative questions were answered for each branch of literature: (1) How are system states distinguished? (2) What is the role of mechanisms underlying state stabilisation and shifts? (3) How is abruptness or nonlinearity defined? In addition, the purpose and policy relevance of each branch were addressed. The review process methodologically followed Berrang-Ford et al (2015), see the supplementary information, SI 1. This supplement also contains an overview of model approaches used in studies describing climate-induced tipping points in socio-economic and coupled socio-ecological systems (table SI 1.3).

To derive an overview of policy relevant tipping points in Europe, we organized a stakeholder workshop and carried out 28 additional semi-structured interviews resulting in a list of 22 SETP candidate examples (table SI 2.1). The whole process followed a co-design and co-delivery protocol and used a set of research co-production success factors identified from the literature (Watkiss et al 2018). 24 stakeholders attended the workshop, representing a variety of sectors in Europe, including health, finance, public governance, tourism, agriculture, transport, insurance and nature conservation. Workshop attendants were divided in five thematic groups (table SI 2.2). Each group was asked to draw an inventory of SETPs in their sector, followed by a discussion on causal mechanisms. Similarly, the 28 semi-structured interviews (table SI 2.3) were used to identify SETP examples, to get insight in the mechanisms leading to their occurrence and to learn about past experiences and policy recommendations (table SI 2.4).

Climate tipping points describe critical thresholds at which large-scale components of the Earth's climate system, at least sub-continental in scale, switch to a qualitatively different state due to a small perturbation (Lenton et al 2008). The classic example is the potential collapse of the North Atlantic deep water formation and the associated Thermohaline Circulation (THC): an ocean current mechanism which is a key determinant of the global climate (Steflen et al 2004). Other examples are: the self-amplifying melting of the West-Antarctic and Greenland ice-sheet, sea-ice melt in the Arctic, and state changes in the El-Nino Southern Oscillation (Levermann et al 2012, Lenton 2013).

Concerning stable states (characteristic 1 in table 1), early models of the THC described two clearly distinguished stable flow regimes and critical transitions (or bifurcations) between them (Stommel 1961, as cited by Lenton 2013), although there is still a debate on whether unambiguous stable states can be distinguished (Caesar et al 2018, Thornalley et al 2018, Nature Editorial 2018). The mechanisms (characteristic 2 in table 1) causing different stable states, as well as abrupt transitions between states, are reinforcing and dampening feedback processes in the climate system leading to nonlinear system behaviour. Abruptness (characteristic 3 in table 1) can be understood in two ways: change is rapid compared to a geological timescale or rapid compared to a policy-relevant time horizon, such as 100 years (Lenton et al 2008, Kopp et al 2016).

The policy relevance of climate tipping points is mainly to inform about safe levels of climate change with respect to temperature stabilisation. Climate tipping points seem to have played a crucial role in the history of the Earth, and therefore, they also might have large impacts on current societies (Lenton 2013). This is for example reflected in the IPCC Fifth Assessment Report (IPCC 2014b)—with 'large singular events' as one of the 5 'reasons for concern' and in the IPCC 1.5 °C-warming report (IPCC 2018). They also have been used as major justification for ambitious mitigation policies (Knutti et al 2016, Lemoine and Traeger 2016), such as the European Union Long-Term Strategy (EC 2018). Framing of climate tipping points is predominantly negative. They are often seen as being beyond the limits of adaptation (Watkiss et al 2015), so they can only be addressed by insurance-like mitigation (Weitzman 2014).

The second branch of literature is ecological tipping points, better known as 'critical transitions' causing 'regime shifts' in biological or ecological systems (Scheffer and Carpenter 2003, Biggs et al 2018). This branch of literature is much wider than climate change discourse alone, but here, we focus on climate related examples. For example, increasing temperature and nutrient concentrations can tip lakes into eutrophic states (Scheffer et al 2015). Climate change could potentially cause drought-induced vegetation shifts, such as desertification in the Sahara (Martínez-Vilalta and Lloret 2016). Increasing CO₂ concentrations may shift coral reefs to entirely different ecosystems (Hoegh-Guldberg et al 2007, Marzloff et al 2016, IPCC 2018). These systems often exhibit hysteresis: turning back to the original state is usually not possible by simply reversing the conditions (de Zeeuw and Li 2016).

Concerning stable states, the literature emphasises that the term 'dynamic regimes' would be more appropriate, because ecological systems are hardly ever completely stable in the sense that no fluctuations occur (Scheffer et al 2001). Concerning mechanisms, Van Nes et al (2016) distinguish two ways in which an ecosystem can be tipped to an alternative state. The first mechanism is erosion of system resilience. Normally, a small perturbation would have been within the coping capacity due to system resilience, but with the resilience eroded, it can cause a state shift. This highlights that many causal factors may have contributed before a perturbation in one variable causes the actual tipping to occur and these could have additive or even synergistic interactions (Scheffer and Carpenter 2003, Ratajczak et al 2018). The second mechanism is an unusual large perturbation in one driver which on its own pushes the system over a critical point, from which it cannot return (Van Nes et al 2016). This literature emphasises that internal system-feedbacks play a determining role in both the stabilisation and the rapid change between different dynamic regimes (Scheffer et al 2001). The clearest examples of tipping points are found in small-scale, heterogeneous and strongly interconnected systems (Scheffer et al 2012). Abruptness is defined as rapid change compared to typical rates of change in the ecosystem (Ratajczak et al 2018).

The policy relevance of this literature is that it gives insights as to how a desired state of an ecosystem can be maintained or achieved by describing a 'safe operating space' (Scheffer et al 2015). For instance, preventing tipping points in iconic ecosystems such as coral reefs received major attention in the recent 1.5 °C-warming report (IPCC 2018). Monitoring and early warning systems can be useful to indicate when ecosystems approach a tipping point, noting these are also relevant for climate tipping points (Scheffer et al 2009, Wang et al 2012, Camarero et al 2015, Clements and Ozgul 2018).

Whereas the above literature mainly draws on complex dynamic systems theory, the literature about transformation tipping points is more qualitative and descriptive, i.e. closer to Gladwell's (2000) popular understanding of tipping points. The roots of this literature are in innovation and change theory (Rogers 1962) which studies why and how ideas and trends spread. It seeks to build on these mechanisms to deliver transitions in society that meet the adaptation and mitigation challenges posed by climate change (Loorbach and Rotmans 2006, Moser and Dilling 2007, van der Brugge 2009).

Distinction of clear stable states is emphasised less in this body of literature. The tipping point is an approximate indicator of the point separating a state A, in which a new strategy, behaviour, idea or technology is only adopted by a minority of early adopters, from a state B, in which it is adopted by a large majority (Loorbach and Rotmans 2006, Moser and Dilling 2007, Patenaude 2011, Sperling 2018). Others define states as institutional settings with different actors and altered actor networks (Westley et al 2011, Fuchs and Thaler 2017). In contrast to the biophysical literature, this literature mainly talks about so-called 'positive tipping points' (David Tàbara et al 2018): the envisioned state B is more desirable than the current state A. One difficulty here is that only in retrospect, it is possible to properly describe shifts as being radical (Fuchs and Thaler 2017). Another complication is that these descriptions are subjective; whereas transformative change may pose opportunities for some stakeholders, it may be destructive for others, in particular those that depend on the established system (Young 2012).

The mechanisms that could cause a shift towards a 'fundamentally improved state' (Burch et al 2017) are diverse (Farmer et al 2019). In the mitigation domain, these could include policy instruments or market forces (EC 2018, Mercure et al 2018) as well as financial thresholds for competing technologies (IRENA 2018). However, it can also arise via other means. Westley et al (2011) highlight how institutional entrepreneurs can lower the threshold between the current and the envisioned state, causing tipping to occur. Geels and Schot (2007) articulate that slowly changing drivers may put pressure on an existing regime, so that at a certain point a window of opportunity may cause a breakthrough of a niche innovation. The policy relevance of this literature is that it gives insights into how governments could formulate policies and incentives in order to achieve successful change towards societies that embrace adaptation and mitigation strategies. It provides checklists for the design of 'transformative-oriented' climate policy (David Tàbara et al 2019, Fazey et al 2018).

The fourth branch is the climate adaptation literature. Here, adaptation tipping points indicate thresholds where the magnitude of climate change causes an adaptation action to fail in meeting key performance indicators, policy or management objectives (Kwadijk et al 2010). The origins of this branch are in the adaptation decision support literature and in particular the focus on decision making under deep uncertainty (Walker et al 2013, Ray and Brown 2015, Watkiss et al 2015). This field studies exceedance of threshold conditions under different futures using exploratory modelling approaches such as robust decision making (Hall et al 2012) and scenario discovery (Bryant and Lempert 2010). One of these adaptation decision support approaches is 'dynamic adaptive policy pathways', developed as a scenario-neutral approach to policy making (Haasnoot et al 2013). In order to illustrate these pathways using adaptation route-maps, the approach uses tipping point terminology to indicate the 'best-before' dates of policy actions for meeting a plan's objectives. It is best known from coastal and water management applications such as the Dutch Delta Program (Bloemen et al 2018) and the London Thames Estuary project (Ranger et al 2013), but has also been applied to other sectors including agriculture and forestry (Petr et al 2015, Prober et al 2017).

State descriptions of performance of actions across the pathway are binary: they either meet objectives (state A) or fail (state B). Descriptions of these thresholds can be formal, such as standards or laws, or informal, based on acceptability thresholds as assessed by stakeholders. In climate adaptation studies, the mechanism causing an 'adaptation tipping point', i.e. the exceedance of the threshold, is a change in climate conditions (e.g. sea-level rise (SLR), river discharge), causing a certain action or portfolio of actions to fail. Although this is a sudden and therefore an obvious nonlinear and abrupt switch, it does not necessarily correspond to a nonlinearity in the physical or socio-economic domain, nor does it have to be the result of internal feedback mechanisms for state stabilisation and change (Werners et al 2013, Kopp et al 2016). However, some shifts in adaptation actions are so transformative in nature that they could qualify as tipping points consistent with the other branches of literature. This is, for instance, explicitly recognised in the IPCC AR5 with the explicit definition of 'transformational' versus 'incremental' adaptation (Kates et al 2012, IPCC 2014a).

Based on the above analysis of the tipping point literature, we propose the following typology of tipping points for climate change (figure 2). The state shifts have different positions in the cause-effect chain from climate change to adaptation and/or mitigation, as shown in figure 3. Climate tipping points highlight that increasing greenhouse-gas concentrations may trigger strong nonlinear responses of large elements of the climate system. Ecological tipping points may have climate change as one of their drivers, and describe cases that lead to state shifts in ecological systems. For both, the state shifts occur in the biophysical system. Transformation tipping points reflect radical changes in human response to the impacts or challenges of climate change, and can lead to different mitigation or adaptation strategies, while also some of the adaptation tipping points reflect climate-induced exceedance of criteria or policy thresholds requiring transformative adaptation. Note that the literature explicitly mentions adaptation tipping points, but not mitigation tipping points. This notwithstanding the ample research addressing the huge technological and behavioural changes implied by decarbonisation pathways leading to a 2 or 1.5 °C warming world. Transformational response, encompassing adaptation and mitigation, describes shifts in terms of climate action (by policy makers, private companies or individuals). We thus summarize adaptation and mitigation tipping points under the umbrella of transformational response to climate change, resting in the socioeconomic domain (figure 2).

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Note that we contrast these tipping points in terms of transformational response (figure 3, top-right) with tipping points in terms of socio-economic impact (figure 3, bottom-right). Although both occur in the socio-economic system, the state shifts result from different mechanisms. Impact refers to cases where a state shift occurs in the socio-economic structure due to autonomous climate change and inaction or insufficient action from the human side. Response refers to cases where the socio-economic structure is deliberately and fundamentally altered, or planned to be altered, by human action, in anticipation or response to (potential) impacts (see Kates et al 2012). Also, an abrupt shift in response (such as a global carbon tax) may take a long time before it materializes in socio-economic benefits, whereas the consequences of an impact tipping point may directly hit the economy. In summary, SETPs encompass socio-economic impact tipping points, adaptation tipping points and mitigation tipping points.

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From the above exploration, we are now ready to define a climate change induced SETP. It is 'a climate change induced, abrupt change of a socio-economic system, into a new, fundamentally different state (beyond a certain threshold that stakeholders perceive as critical).' These have the following characteristics: (1) Stable states: substantially different, more-or-less stable states or dynamic regimes at either side of some critical threshold. This distinction can be made qualitatively: showing by narrative how state A is fundamentally different from state B, or quantitatively: showing an S-shaped nonlinearity between climate drivers and socio-economic indicators. (2) Mechanism explaining nonlinear behaviour. One should be able to provide a rationale—e.g. a causal pathway—on why these states are more or less stable and why there is a sudden transition between the two. (3) Rapid, abrupt change. The state change should be rapid compared to the change usually observed in the system. Additionally, for the scope of this paper we look for policy relevant examples where climate change is a significant driver of the tipping point. Also, we focus on impact SETPs (rather than transformational response SETPs), because these seem to have least coverage in the existing literature.

Reduced snow conditions in Alpine regions are among the most visible early impacts of climate change in Europe. Numerous studies have identified a major risk to the Alpine winter sports industry (e.g. Köberl et al 2015, Marty et al 2017, Steiger et al 2017). In Austria, recurring unfavourable snow conditions have led to the collapse of several low-lying ski resorts (Falk 2013). This has caused societal concerns because of the economic significance of the sector and its contribution to cultural identity (Interview #22, see table SI 2.3).

The state (Characteristic 1, table 2) of an individual resort can be described in terms of financial profitability. The mechanism (Characteristic 2, table 2) causing a state shift can be understood as follows. When a resort does not succeed in making a profit for a number of successive years, it will reduce its reserves and eventually make a decision to withdraw from the sector, or in certain conditions, it may fall into bankruptcy. As a rule of thumb, a resort needs 100 days of good snow conditions a year to be financially viable. Based on this rule, the number of viable existing resorts in the Alpine region could reduce from the baseline 91% (in 2007) to 61% and 30% under warming of 0°, 2° and 4 °C, respectively (Abegg et al 2007, as cited in Damm et al 2014). Bankruptcy could occur abruptly (Characteristic 3, table 2), because there is large intra-annual variability in snow conditions on top of the slowly changing trend (Marke et al 2015). In the immediate term, bankruptcy is not necessarily irreversible, and some resorts have been successfully reopened (Falk 2013), but in the face of climate change, the viability of low-lying resorts for snow tourism in the medium to long-term is fundamentally irreversible, irrespective of supporting policies—e.g. the government privatizing unprofitable ski lifts and train lines (Interview #23).

From this case we take the following insights. First, the question whether collapse of a ski resort can be seen as a clear tipping point strongly depends on the scale of analysis. On a village scale, the disappearance of a resort may have large impacts, because a major share of local income is derived from it. On a larger spatial scale, the impacts are usually smaller because of economic diversification and substitution effects when tourism and employment shift to other areas. This is strongly region dependent (Abegg et al 2007), as can be seen by comparing two Austrian regions: Styria and Tyrol. Styria has many low-lying resorts but a diverse economy, so that individual resorts are likely to tip, but their overall economic impact may be limited. In contrast, Tyrol is very much dependent on income from winter tourism, but the resorts are situated at much higher altitudes, making them less likely to tip (Interview #22). Second, adaptation by artificial snow-making may significantly reduce the likelihood of tipping by prolonging the skiing season in the short or even medium term. However, this is very costly and has large environmental impact. Thirdly, there is a potential adaptation strategy to diversify. Several regions are considering a more fundamental shift towards gaining a large share of income from summer tourism. For example, they seek to revive the 'Sommerfrische', a historic fashion where rich families retreated from cities to spend the hot summer at higher altitudes (Interview #23). Whereas higher temperatures threaten winter tourism, it may be opportune in summer when people wish to escape the hot cities in Southern Europe.

In Southern Europe, climate change is projected to have disproportionally large impacts from the combination of higher temperatures and most likely lower precipitation (Vautard et al 2014, IPCC 2014b), with most studies showing large detrimental impacts on agriculture (Ciscar et al 2014). In the past, large parts of north-west and central Spain have seen widespread abandonment of farmland and migration from rural areas (Collantes et al 2014, Iglesias and Garrote 2017) due to a complex interplay of socio-economic, geographical and environmental factors (Terres et al 2015). Interviewees expressed concerns that climate change may trigger even more farmland abandonment with considerable social, and to a lesser extent, economic consequences (Interview #25, 26). Indeed, several studies have found that climatic determinants may explain land abandonment patterns (Gellrich and Zimmermann 2007, Arnaez et al 2011, García-Ruiz et al 2011, Hatna and Bakker 2011).

The state (Characteristic 1, table 2) of a piece of land can be described in financial terms. Whether the land provides a viable financial rate of return is determined by land, input and production costs; productivity and quality of outputs; market prices; and ancillary income from subsidies or other farm activities. Furthermore, the most profitable farming activity at any location is dependent on the local climate and biophysical context. Climate change may alter the relative productivity of crops in certain regions, making the crop more favourable (unfavourable) if, on average, climate moves closer to (further away from) the optimum conditions for cultivating that crop (Prishchepov et al 2013). If land eventually is abandoned, this also impacts wider rural communities. At some point, what once was a lively agricultural area may have become what one of the interviewees described as the 'Spanish Laponia': abandoned farmlands, villages with little economic activity inhabited by mainly elderly people and the schools, shops and churches closed (Interview #25). The mechanism causing farmland abandonment (Characteristic 2, table 2) is the choice-process of individual farmers, influenced by several feedback mechanisms. With migrants leaving the area, the rural area gets less attractive to live in—especially for younger people, and the infrastructure remains underdeveloped (Interview #25). This process is very hard to reverse, which is reflected by many failed governmental attempts to repopulate rural areas (Interview #6). Concerning abruptness (Characteristic 3, table 2), abandonment may happen either sudden or gradually, depending on the interaction between environmental and socio-economic conditions (Estel et al 2015, Levers et al 2018).

From this case we take the following insights. First, the clearest cases of tipping points are found on the community scale, where reinforcing mechanisms can rapidly change the socio-economic structure to a state of abandonment. Second, in many places, adaptation by irrigation has been effective in countering further farmland abandonment and rural exodus (Iglesias et al 2018, Interview #25). Southeast Spain and the Ebro Basin has taken-up a large amount of irrigation, and with that, a change of crops towards water-intensive cultivation such as maize (García-Ruiz and Lana-Renault 2011). This, however led to severe overexploitation of water resources, aggravated by inappropriate crop choices due to subsidy schemes (González-Gómez et al 2012). Considering that the main social contribution of irrigation is rural employment (Gómez-Limón and Picazo-Tadeo 2012), decreasing water availability may also tip some of the currently irrigated areas to abandonment (Interview #26).

SLR constitutes a major threat for the densely populated coastal zones in Europe (Hinkel et al 2010, IPCC 2014b, Vousdoukas et al 2018) and worldwide (Hinkel et al 2014, IPCC 2019). Although flood defences can be upgraded to reduce damage, this is economically efficient during the 21st century for only 13% of the global coastline (comprising 90% of today's global floodplain population and 96% of today's global floodplain assets) (Lincke and Hinkel 2018). For the protected areas, however, rising sea-levels increase the potential of larger and larger flood disasters in the case of defence failure, because the floodplains, and flood depth in case of a disaster, continue to grow with SLR irrespective of protection. Furthermore, coastal protection often attracts further development in the floodplain, which further increases potential flood damages (Brown et al 2014, Hinkel et al 2018). After the occurrence of a major flood disaster, affected regions can either decide to rebuild in the same site or to retreat from the coast, which is the tipping point considered here.

The state (Characteristic 1, table 2) of a coastal settlement can be described in terms of its population exposure: high and increasing exposure before and low and decreasing exposure after the tipping point. Three social mechanisms (Characteristic 2, table 2) can cause a state shift from high to low population exposure. The first process is migration, which refers to permanent or semi-permanent voluntary human mobility (Adger et al 2015). Migration is generally a slow process, driven by complex interrelations between many push and pull factors, and there is generally limited evidence that SLR (to date) has caused coastal migration (Adger et al 2015, McLeman 2018). The second process, displacement, refers to the involuntary and unforeseen movement of people due to armed conflicts or disasters (McLeman 2018, Mortreux et al 2018). Displacement is a fast process taking place during, or in the direct aftermath, of a disaster. The third process is relocation or managed retreat, initiated, supervised and implemented by governments (Hino et al 2017, Mortreux et al 2018). It is a slower process than displacement, but it is often initiated after people have been displaced by disaster. With rising sea levels, it is expected that governments will be confronted with this decision more frequently.

The change in population exposure induced by both forced displacement and planned relocation following a disaster is abrupt (Characteristic 3, table 2) as compared to normal changes in population exposure due to two reasons. First, displacement always is abrupt as this is caused by a fast flood disaster. Second, relocation decisions by governments generally provide incentives such as buy-outs for households to move out of the hazard zone. For example, in the aftermath of the coastal flooding brought by Xynthia, the French Government offered to fully compensate all home-owners, based on the value of the real estate prior to the storm and most homeowners accepted within a year (Lumbroso and Vinet 2011). These kinds of incentives are in addition to existing individual incentives for migration and thus lead to faster population change.

From this case we take the following insights. First, the SETP of retreat from a coastal zone may be more likely to be triggered by an extreme event (Interview #10). Second, however, it could also be that adaptation to the increasing flood risk in itself causes a reconfiguration of the system (Interview #27, 28) which can be so transformative that it is a 'response to climate change' tipping point. This is for example seen in The Netherlands, where new studies on extreme SLR (DeConto and Pollard 2016, le Bars et al 2017) have triggered the debate on a long-term water management policy (Haasnoot et al 2018). Some radically different strategies are mentioned: the construction of a large dike in front of the entire coast combined with a permanent closure of all estuaries; elevating new buildings above sea level (Aerts et al 2008); or strategic retreat from areas with economic stagnation and population decline (Olsthoorn et al 2008, van der Meulen 2018). Third, in this context it is not only the magnitude, but also the rate of SLR that could cause a policy shift. It typically takes decades to prepare for large infrastructural adaptation measures such as flood defences (Hallegatte et al 2012). The rate of change may exceed the capacity of society to adapt in the traditional way and trigger a shift towards more transformative policies.

Table 3 lists the characteristics that SETPs may share, drawing from the stakeholder interviews on other SETP candidates (table SI 2.3, 2.4).

Concerning SETP descriptions, there are two observations. First, it seems that the clearest examples of SETPs are found on small system scales (table 3). This not only holds for ski resorts, but also for agriculture, where interviewees (#2, 5, 6, 24, 25, 26, numbers refer to table SI 2.3, 2.4) could provide several examples of local, abrupt transformations, but hesitated to claim that this would lead to significant country-scale economic effects. Vice versa, even the interviewees that opposed the whole idea of the existence of tipping points in socio-economic systems, agreed that some of the dynamics could be seen on a small system scale, which they however did not consider of enough concern to be called 'real' tipping points (#27, 28). Second, it appears that descriptions of SETPs are prone to a large degree of subjectivity. This can relate to the fundamental question whether there are critical thresholds at all, at which strong nonlinear behaviour occurs. For example, interviewee #20 says: 'In the health sector, we never speak of thresholds, it is not that when a temperature surpasses a certain degree that suddenly something happens.', whereas interviewee #18 gave several examples of outbreaks of infectious diseases following environmental conditions gradually moving beyond threshold conditions. Similarly, Interviewee #3 expects that in case of a catastrophic dike-breach in the Netherlands, 'they will think (...): there is no way of recovery', and the delta will be irreversibly changed. In contrast, interviewee #28 argues that 'even if there would be a large flood event, we probably will rebuild. We will not move to higher grounds. That is so contrary to Dutch nature.' On the same casus, Interviewee #27 highlights another dimension of subjectivity. Coastal retreat could be a tipping point from a social, but not an economic point of view—from which it simply is a continuation of the rational cost-benefit analysis which also underpins the current policy. 'People don't like change, but whether it is a dramatic problem from an economic point of view, remains a question.'

Concerning mechanisms causing SETPs, interviewees distinguished several mechanisms that can push the socio-economic system over a tipping point. Most frequently mentioned, was a series of unfavourable conditions, that eventually tips the system into a new state. Above, we illustrated this for ski resorts and interviewees highlighted similar dynamics for farmland abandonment, collapse of insurance markets and willingness to rebuild after devastating hurricanes (table 3). In many cases, these extremes are perturbations on top of a gradual trend moving towards threshold conditions. Many of these thresholds are an interplay between climatic and financial factors: for profitable operation, agriculture needs a minimum amount of water and ski resorts a minimum amount of days with sufficient snow cover. In some cases, this implies that favourable conditions (for example for a certain crop) may shift from one region to another, which on the local level causes relatively abrupt shifts (for example in agricultural practices). Sometimes, governments need to radically reform their policies to maintain certain levels of performance. As we illustrated for the case of SLR, it can be the rate rather than the magnitude of change which exceeds society's capacity to respond and causes a system shift. As interviewee 10 puts it: 'One of the biggest threats to society is that the changes in the climate and the weather happen so rapidly, that we do not, or inadequately, have abilities to adapt to these changes.'

In almost every case, interviewees highlighted a combination of climatic and co-determining socio-economic conditions, which together explain a state shift: the resilience of a system against biophysical change already has been depleted by economic, demographic or social mechanisms (table SI 2.4). Nonlinear positive (self-enforcing) feedbacks in the socio-economic system may accelerate a transition. For example, increasing risk raises insurance premiums, which reduces their affordability, which in turn decreases the insurance uptake, which raises the premiums even further (Interview #3, 15). Similarly, concerns about the profitability of ski resorts may hinder further investments in the winter tourism sector, and rumours about accelerating SLR may worsen the investment climate of a delta which reduces the funds available for flood protection.