In the age of the Anthropocene, human societies have emerged as a planetary-scale geological force shaping the future trajectory of the whole Earth system . Cumulative greenhouse gas emissions and extensive modifications of the biosphere have accelerated since the Neolithic and industrial revolutions, especially through the rapid globalisation of social–economic systems during the 20th century, threatening the stability of the interglacial state  that has enabled the development and well-being of human societies . Political and societal developments during the 21st century and their feedback interactions with the planetary climate and biophysical environment will be decisive for the future trajectory of the Earth system . Business as usual is taking the planet into a “hothouse Earth” state unprecedented for millions of years in geological history , while calls for rapid decarbonisation of the global economic system to meet the Paris climate agreement  will also have complex consequences involving an intensified entanglement of social, economic, and biophysical processes and their resulting feedback dynamics, up to the planetary scale . Despite extensive debate about the Anthropocene , and growing recognition of the limitations of current Earth system models for analysis and policy advice in the context of these shifting dynamics  , little has been done to address the fundamental challenge of systematically reviewing the conceptual foundations of Earth system modelling to include dynamic social processes, rather than externalising them .

To understand planetary-scale social–ecological dynamics, models of World–Earth systems are urgently needed . Epistemologically, we conceptualise World–Earth systems as planetary-scale systems consisting of the interacting biophysical subsystems of the Earth, and the social, cultural, economic and technological subsystems of the World of human societies. It should be noted here that in the context of global change analysis and modelling, the term “Earth system” was intended to include human societies and their activities and artefacts . However, in currently influential science and policy contexts, notably the Intergovernmental Panel on Climate Change (IPCC) , “Earth system models” deal only with the physical dynamics of the atmosphere, ocean, land surface and cryosphere, and a limited set of interactions with the biosphere. While some might see tautology in the term “World–Earth systems”, we use it to highlight that human societies, their cultures, knowledge and artefacts (the “World”) should now be included on equal terms in a new family of models to conduct systematic global analyses of the Anthropocene. A fully co-evolutionary approach is needed, in the sense of representing social–ecological feedback dynamics across scales.

Future World–Earth modelling efforts will largely be pieced together from existing conceptualisations and modelling tools and traditions of social and biophysical subsystems, which encode the state of the art in our understanding of the Anthropocene. Current efforts in World–Earth systems modelling are highly stylised (e.g. ) or tend to be proof-of-concept prototypes . None currently operate in a process-detailed, well-validated and data-driven mode. To serve these nascent efforts in enabling World–Earth systems analysis of the Anthropocene, this article addresses the core question of which are the relevant categories within which World–Earth models, as essential “scientific macroscopes” , should operate. The problem for both scientific integration and real-world application is that the characteristic basis of the interactions of social and biophysical subsystems is often not explicit in current models. Often, the interactions between these subsystems are not recognised at all. By framing a taxonomy around the current dominant distinctions – and disciplinary divides – we can begin to explore links and feedback mechanisms between taxa in more structured, systematic and transdisciplinary ways. With this taxonomy, we develop initial tools and terminologies that enable model builders and model users to be clear about their social, cultural, epistemological and perhaps also axiological standpoints.

We want to emphasise that this taxonomic approach does not presuppose that there is “one world” (an ontological position) when models of different worlds are combined, nor do we intend it to serve as a universal blueprint for models of essentially everything. Instead, we argue that a taxonomy can help to more efficiently focus modellers’ attention on the ontological and epistemic commitments within their models. This approach opens Earth system analysis to deeper dialogues with proponents of non-human actors as shapers of the world  or even the possibility of no world at all .

While the present article proposes a conceptual basis for World–Earth modelling, the proposed taxonomy is employed in the companion paper by to develop the operational World–Earth modelling framework copan:CORE. Here, this framework is cast into software and applied to construct and study an example of a novel World–Earth model that seeks to overcome the long-standing challenge of endogenising the choice of discount factors (describing how much societies value the present relative to the future) in climate mitigation studies.

Diverse scientific modelling communities aim to capture different aspects of social–ecological dynamics embedded in the Earth system up to planetary scales. Some processes operating in the Earth system are commonly described as being governed by the “natural laws” and generalisable principles of physics, chemistry and (to some extent at least) ecology (e.g. atmosphere and ocean circulation as governed by the physical laws of fluid and thermodynamics), whereas others are thought to be dominated by human behaviour, decision-making and collective social dynamics (e.g. the regularities underlying individual and social learning). This tendency for separate treatment of these different kinds of processes in the natural and social sciences gives rise to problems when dealing with the many real-world subsystems that operate in both domains simultaneously. What is more, different scientific communities use different methods and adhere to different viewpoints as to the nature and character of such subsystems and their interactions. There is now a number of conceptualisations of social–ecological or coupled human–environment systems in environmental, sustainability and Earth system science (e.g. ), but we see a pressing need to structure modelling efforts across communities, providing a joint framework while maintaining the conceptual flexibility required for successful cross-disciplinary collaboration.

Here, we propose a taxonomic framework for structuring the multitude of subsystems that are represented in current mathematical and computer simulation models. The motivation for proposing such an ordering scheme is

to provide the means for collecting and structuring information on what components of social–ecological systems relevant to global change challenges are already present in models in different disciplines;

In this article, we use the term subsystem to refer to any dynamic component in models of World–Earth systems. In this broad category, we can include both the kinds of subsystems that are governed mainly by “natural laws” of physics, chemistry or ecology (e.g. seasonal precipitation, ocean nutrient upwelling) and those that are governed mainly by human behaviour, decision-making and collective social dynamics (e.g. international food trade, carbon taxes). Many scientific communities similarly make this distinction between biophysical (“natural”, ecological, environmental) subsystems and socio-cultural (social, human, “anthroposphere”) subsystems. We also highlight socio-metabolic subsystems at the overlap of societal and natural “spheres” of the Earth system (Fig. ). We suggest that explicit attention to these subsystems and their interactions is needed in order to deepen the understanding of transformative change in the planetary social–ecological system, making a valuable contribution to the design and operational development of future, more comprehensive World–Earth models for charting sustainability transitions into a safe and just operating space for humanity .

A further note on the term biophysical: here, we use this word as a shorthand term to refer to Earth's interacting living and non-living components, encompassing geophysical (e.g. climatic, tectonic), biogeophysical, biogeochemical and ecological processes. These categories are significant in Earth system science because feedbacks involving these processes tend to have different dynamic characteristics. Accordingly, they have been dealt with very differently in Earth system analysis and modelling .

The co-evolution of Earth's geosphere and biosphere is a central concept in Earth system science , but the global models that currently dominate the field represent just a snapshot of the system, focused on the biophysical dynamics that play out over decades to centuries. We use the term co-evolution to describe the complex dynamics that arise from the reciprocal interactions of subsystems, each of which changes the conditions for the future time evolution of the other (not excluding but also not limited to processes of Darwinian co-evolution involving natural selection). Earth system models (ESMs) include key physical feedbacks and increasingly permit the investigation of biophysical feedbacks, but as we have indicated, they lack socio-metabolic and socio-cultural subsystems, relying on narrative-based inputs for dealing with anthropogenic changes. Integrated assessment models (IAMs) used in the global change context  include some interactions of social and biophysical subsystems in order, say, to assess potential economic consequences of climate change and alternative climate policy responses. However, they lack the kinds of interactions and feedback mechanisms (e.g. by impacts of climatic changes on socio-metabolic subsystems, or by the effects of socio-cultural formation of public opinion and coalitions in political negotiations on environmental policies) that societies throughout history have shown to be important; this is revealed, for example, by studies of social–ecological collapse and its connection to past climate changes . To explore and illustrate the consequences of these typically neglected interactions and feedbacks, we have studied a conceptual model that gives rise to complex co-evolutionary dynamics and bifurcations between qualitatively different system dynamics: a model of socially transmitted discount rates in a greenhouse gas emissions game, discussed in Sect. .

For completeness, we also provide brief definitions of our working terminology: a “link” or “interaction” is a causal influence of one subsystem on another that is operationally non-decomposable into smaller links; a “mechanism” is a micro-description of how exactly this causal influence is exerted; a “process” is a set of links that “belong together” from some suitable theoretical point of view; a “loop” is a closed path in the network of links; and an “impact” of a link is the change in the target system attributable to this link.

We should note here that this taxonomy is dealing with causal narratives from different scientific disciplines that are encoded in models; as such, it does not require any a priori theories and hypotheses about causality. Causal narratives are our starting point because they are necessary for and are explicitly encoded in simulation modelling – and our classification lets us interrogate them more systematically and exposes them explicitly.

The biophysical taxon (ENV) contains the processes and subsystems that are typically included in current comprehensive Earth system models, but it views them from the perspective of the Anthropocene shift to human “co-control”. These subsystem models are governed by deterministic and stochastic mathematical equations, often developed from first principles about the physical relationships involved. There is a case for subdividing the biophysical taxon into an ecological sub-taxon (subsystems associated with life) and a geophysical sub-taxon (subsystems not associated with life), as they have distinct, albeit co-evolving, dynamics , and this subdivision would correspond to widely accepted geosphere–biosphere conceptualisations of the Earth system . However, we apply our principle of compactness, because geosphere–biosphere links and processes have been comprehensively documented over the past few decades, as they underpin current Earth system and global integrated assessment modelling. Rather than retracing these links (after all, the existing models are not going to be completely reconfigured in light of the issues we explore in this paper), we have opted to take today's state of the art in biophysical global modelling as our main point of departure, following the principle of compatibility introduced above.

Earth system models have developed from coupled atmosphere–ocean general circulation models, progressively coupling in components describing biogeochemical and biogeophysical dynamics. On the decadal to millennial timescales relevant for the analysis of anthropogenic climate change and its medium-term consequences, examples of these modelled subsystems where human-controlled dynamics are prominent concerns include atmospheric chemistry, ocean productivity, sea ice, land vegetation, and major elemental cycles such as those of nitrogen, phosphorus and sulfur . Furthermore, as it becomes clearer that palaeoclimate models can play a vital role in “deep future” studies of human-controlled processes in the Anthropocene, Earth system dynamics operating on longer timescales are relevant . Thus, for these purposes, the biophysical taxon would include subsystems involving the lithosphere (e.g. rock weathering, isostatic depression and rebound associated with the advance and retreat of ice sheets on land) and even external drivers such as large-body impacts , if these provide “natural experiments” or analogues for future change.

Research fields dealing with models of subsystems belonging to the biophysical taxon include, among others, geophysics, meteorology, oceanography, biology, ecology, biogeochemistry and geology. Few of these sciences have yet grasped the methodological and theoretical tools for dealing with the human dimensions of anthropogenic change. From our planetary-scale perspective, the ENV taxon exhibits a substantial overlap with categories such as models of “the environment”, “nature” or “ecology”, with their specific disciplinary connotations, although many of these models have tended to be small-scale, context-specific and idiographic. An exception to this is global dynamic vegetation models such as LPJ , which focus on representing the physical dynamics of ecological processes and structures in an Earth system context and not on ecological dynamics as such (i.e. interactions between living organisms). We note a current drive for further refinements of ecological dynamic network processes in large-scale modelling  within the ENV taxon that may improve global-scale conceptualisations of ecosystems in ways compatible with both Earth system modelling and socio-ecological systems research and resilience thinking.

The socio-metabolic taxon contains processes and subsystems that form the material basis and products of societies, making direct interconnections between human societies and the biophysical environment that sustains them. This taxon comprises models of demographics and social structure (e.g. population size, age and sex distribution and health parameters; and social categories with material or resource-use consequences, such as class, clan, caste and ethnicity). It also includes “the technosphere”: society's artefacts, factors of production and technologies (e.g. labour, land, capital, natural resources, raw material and energy; tools, machines and infrastructure; and cultivated landscapes, domesticated animals and plants respectively), and economic systems (manufacturing, distribution and consumption of goods and services) .

The broad field of economics currently dominates descriptions of parts of the socio-metabolic taxon in quantitative models, but many other disciplines such as geography, industrial metabolism, social ecology, and science and technology studies also play a role. In modelling terms, this taxon typically involves representations of both the biophysical planet Earth and the socio-cultural World of human societies. This implies hybrid models of the type that are currently included in integrated assessment models of global change, and it entails strong simplifying assumptions. We suggest that our approach can bring much-needed clarity and transparency about the role of such models in understanding World–Earth systems (see similar arguments in ). One should note that IAMs and economic models are typically expressed in terms of financial value and not material flows that directly interact with subsystems in ENV (with mostly empirical input–output theories of economics being an exception, ).

The socio-cultural taxon contains processes and subsystems that are described in models of the behaviour of human minds and their immaterial legacies, abstracted from their biophysical foundations and often described as lying in the realm of human agency . Of the three taxa proposed, processes and subsystems in the socio-cultural taxon are the least formalised in mathematical and computer simulation models to date, despite substantial efforts in this direction in many fields of the social sciences (e.g. ) and a likelihood that they may be only partly formalisable. Research fields dealing with models of processes and subsystems in the socio-cultural taxon include sociology, anthropology, behavioural economics, political science and social ecology. Our taxonomic approach can enable the diverse modelling activities now underway to engage more directly with the incipient World–Earth modelling effort.

Examples of modelled subsystems in this taxon include individual and collective opinions, behaviours, preferences and expectations, and their social network dynamics; information and communication networks; institutions and organisations; financial markets and trade; political processes; and social norms and value systems . Notably, the CUL taxon can also include processes of digital transformation and artificial intelligence that increasingly restructure and shape the socio-cultural sphere of human societies. It also provides a locus for debating the challenge of reflexiveness in science, especially in fields where modelling plays a vital role in shaping knowledge and action . For instance, future World–Earth modelling will have to grapple with ways to recognise Earth system science as an endogenous generator of scientific conceptions of “Earth”. Relevant for modelling efforts, socio-cultural subsystems can vary on substantially different timescales. Near-instantaneous information exchanges are possible on online social networks and within and between increasingly advanced algorithms (e.g. algorithmic trading systems on financial markets), whereas elections and governance processes act on the order of years. Formal institutions (e.g. laws) change on the order of decades and informal institutions (e.g. religions) develop over time frames on the order of centuries to millennia .

Our model-centred taxonomy is inspired by previous systemic conceptualisations of human societies embedded in the Earth system, building upon them in a way that may help to bridge across diverse disciplines and theoretic traditions.

In one of the earliest Earth system conceptualisations, distinguishes the inanimate matter of the geosphere, the living biosphere and the noosphere of networked consciousness, the latter reverberating in recent conceptualisations of the technosphere and planetary human–Earth system interactions . Along these lines, Fig. 34 introduced the ecosphere (directly corresponding to our ENV taxon, entailing geophysical and ecological interactions), the anthroposphere (broadly related to MET but with some socio-cultural features), and the global subject (closely related to CUL).

Conceptualisations in resilience theory, ecological economics and sustainability science emphasise the interactions and interdependence of biosphere and society , with many sustainability practitioners adding the economy to make “three pillars” or a “pie of sustainability” consisting of economy embedded in society embedded in biosphere . These fields have typically focused on local to regional geographic scales or specific sectors and have not placed much emphasis on global modelling; however, in general terms, their view of society contains aspects of our MET taxon, although “the economy” is more restricted than MET. argues that the field of ecological economics would benefit from more attention to the creative processes of “art”, which we would frame as CUL aspects that are largely absent from current conceptualisations in that field and also more broadly (as also argued by , and ).

explicitly develop the concept of social metabolism, in terms of the set of flows between nature and culture, in order to describe deliberate global sustainability transitions. Governance-centred classification schemes in social–ecological systems research , in the tradition of Ostrom , can also be brought into our taxonomy. Categories of the governance (sub)system link CUL and MET, and the (sub)system to be governed (ENV and MET) links the biophysical resources to be used with the social agents who will use them.

The taxonomy approach means that things that were previously included in models as opaque and unquestioned systems can be unpacked and critically examined. This would be of particular benefit to model users who were not the model builders. For example, education may be explicitly linked to demography (as in various integrated assessment models), so it would typically be treated as a quantifiable and accumulable process in the MET taxon – i.e. investment in women's education results in a lower birth rate and, therefore, less future land use. In CUL, education would perhaps be treated in a more relational way – dealing with factors such as the spread of ideas, the development of communities and changes in power structures.

This taxon encompasses interactions between biophysical subsystems of the type studied in current process-detailed Earth system models such as those in Phase 5 of the Coupled Model Intercomparison Project (CMIP5) model ensemble used in the IPCC reports . For example, this includes modelled geophysical fluxes of energy and momentum between the atmosphere and ocean, interactions between land vegetation, atmospheric dynamics and the hydrological cycle, or, more generally, exchanges of organic compounds between different compartments of biogeochemical cycles (excluding human activities here).

To date, a detailed representation of these biophysical interactions is largely missing in current first attempts at modelling social–ecological dynamics at the planetary scale (e.g. ). However, emerging socio-hydrological  and agent-based land-use dynamics models at regional scales  include some processes involving interactions between biophysical subsystems such as the atmosphere, hydrological cycles and land vegetation.

This taxon describes modelled interactions through which biophysical subsystems exert an influence on socio-metabolic subsystems. Relevant examples in the context of global change in the Anthropocene include the impacts of climate change on human societies , such as damage to settlements, production sites, and infrastructure and supply chains ; impacts on agriculture or human health; and impacts on provisioning and regulating ecosystem services such as resource flows .

Some of these interactions such as climate change impacts are now being included in IAMs (a prominent example being the DICE model; ) and stylised models (e.g. Sect.  in this paper; ), but challenges remain, for example, in estimating damage functions and the social cost of carbon . Influence from weather and climate on agriculture are studied on a global scale using model chains involving terrestrial vegetation models such as LPJ  and agricultural economics models such as MAgPIE . As another example, models of the distribution of vector-born diseases such as malaria are employed to assess the impacts of climate change on human health .

This taxon contains modelled interactions through which the state of the biophysical environment directly influences socio-cultural subsystems. These links can be mediated through the observation, monitoring and assessment of environmental change from local to global scales (e.g. chemical pollution, deforestation or rising greenhouse gas concentrations in the atmosphere) by social actors that, in turn, are processed by public opinion formation and policymaking in socio-cultural subsystems . The links described by the ENV → CUL taxon also relate to cultural identity connected to the environment, sense of place  and, more generally, what has been described as cultural ecosystem services . For example, modelled the effect of changes in extreme events resulting from climate change on the risk perception of individuals. Changes in risk perception may result in changes in emission behaviour given the perceived behaviour of others (social norms) and structural conditions in society, thereby feeding back on future climate change.

The ENV → CUL taxon also play a role in regional-scale models of poverty traps, where decline in natural capital reduces traditional ecological knowledge as a form of cultural capital , or in models of human perceptions of local scenic beauty in policy contexts . At the moment, most models deal with these interactions only at a sub-global level; however, there is increasing recognition of the need for the more dynamic understanding that formal modelling can provide of such complex psychologically and culturally mediated aspects of human behaviour in the Anthropocene .

This taxon describes modelled interactions between MET subsystems that connect the material manifestations and artefacts of human societies. Examples include the energy system driving factories, supply chains connecting resource extractors to complex networked production sites, or machines constructing infrastructure such as power grids, airports and roads.

Certain processes involving such interactions (e.g. links between the energy system and other sectors, such as industrial production) are represented in IAMs in an abstracted, macroeconomic fashion. Agent-based models resolving the dynamics of supply chains also exist, and they allow for the impacts of climate shocks on the global economy to be described in much greater detail (e.g. ). Another class of examples are population models that may include factors such as the influence of income on fertility . However, to our best knowledge, process-detailed models of the socio-industrial metabolism or the technosphere comparable in complexity to biophysical Earth system models have not been published so far.

This taxon encompasses modelled influences exerted by socio-metabolic subsystems on the biophysical environment including various forms of the “colonisation of nature” . Prominent examples in the context of global change and sustainability transformation include human impacts on the environment addressed by the planetary boundaries framework  such as anthropogenic emissions of greenhouse gases , nitrogen and phosphorous, other forms of chemical pollution and novel entities (e.g. nanoparticles, genetically engineered organisms), land-use change and induced biodiversity loss, and exploitation and use of natural resources . This taxon also includes various forms of the conversion of energy and entropy fluxes in the biophysical Earth system by human technologies such as harvesting of renewable energy by wind turbines and photovoltaic cells  or different approaches to geoengineering .

The interactions described by the MET → ENV taxon are central in IAM and ESM studies of the global environmental impacts of human activities in the Anthropocene such as anthropogenic climate change as driven by greenhouse gas emissions and land-use change . The latter two key processes are also frequently included in emerging studies of planetary social–ecological dynamics using stylised models .

This taxon describes modelled influences and constraints imposed upon socio-cultural dynamics by the material basis of human societies (socio-metabolic subsystems). These include, for example, the effects, needs and constraints induced by the biophysical “hardware” that runs socio-cultural processes: infrastructure, machines, computers, human bodies and brains, and the associated availability of energy and other resources. It also includes the effects of technological evolution, revenues generated from economic activity, supply of valued goods (e.g. on opinion formation and behavioural change in the socio-cultural domain), or the consequences of change in demographic distribution of pressure groups on political systems and institutions.

As a recent example, the model mentioned above (Sect. ) has one parameter to reflect structural constraints in society that affects the degree to which emission behaviour can be changed. MET → CUL links also appear in models of resource use in social–ecological systems, where social learning of harvesting effort depends on the harvest rate  and fish catches influence perceptions about the state of the fishery , or in models of economic impacts on individual voting behaviour .

This taxon contains modelled self-interactions between subsystems in the socio-cultural domain that have been described as parts of the noosphere , the global subject  or the mental component of the Earth system . Examples include the interaction of processes of opinion dynamics and preference formation on social networks, governance systems and underlying value systems  as well as interactions between different institutional layers such as governance systems, formal and informal institutions .

Some of these processes related to human behaviour and decision-making  have already been studied in models of social–ecological systems on local and regional scales and have been modelled in various fields ranging from social simulation to the physics of social dynamics . However, thus far, they are largely not included in IAMs of global change or stylised models of planetary social–ecological systems .

This taxon encompasses modelled influences that socio-cultural subsystems exert on the biophysical environment. An example of such a class of interactions is environmental governance realised through formal institutions , for instance, where the designation of a piece of land as a nature protection area excludes certain forms of land use which have a direct impact on environmental processes there. Similarly, nature protection areas for biodiversity conservation have been represented in marine reserve models . Another related example of CUL → ENV links are nature-related values and informal institutions such as respecting sacred places in the landscape and following social taboos regarding resource use . Different forms of environmental governance have been modelled via so-called decision or sustainability paradigms .

Direct CUL → ENV links arguably cannot be found in the real world, in that socio-cultural influences on environmental processes must be mediated by their physical manifestations in the socio-metabolic domain (e.g. in the case of nature protection areas through the constrained actions of resource users, government enforcement efforts and infrastructure such as fences). However, such direct CUL → ENV links may be implemented in models, even on the global scale, such as in trade-off assessments of multiple land uses (e.g. ).

Finally, this taxon contains modelled links pointing from socio-cultural to socio-metabolic subsystems. Examples include socio-economic policies and governance choices such as taxes, regulations or caps that influence the economy (e.g. carbon caps or taxes in the climate change mitigation context) or demographics (e.g. family planning and immigration policies) as well as the physical manifestations of financial market dynamics such as real estate bubbles. CUL → MET interactions also encompass the influence of cultural values, norms and lifestyles on economic demand and consumption as well as consequent changes in industrial production, building, transportation and other sectors.

Policy measures such as taxes, regulations or caps are much studied by IAMs of anthropogenic climate change , whereas the influences of value and norm change on economic activities, such as general resource use  and fishing , have been studied in the social–ecological modelling literature – but at a mostly local to regional level.

Beyond the taxonomy of interactions introduced above, higher-order taxonomies could also be derived. For example, a taxonomy of feedback loops can be derived from the 3×3 taxonomy of links, leading to six taxa for feedback loops of length two in models of World–Earth systems: given a pair of interaction taxa A→B and B→A, the resulting taxon for loops between A and B may be denoted as A  B. Many such feedback loops relevant for sustainability are not or only rigidly treated in current ESMs and IAMs. For example, the ENV MET feedback loop is typically not sufficiently represented in IPCC-style analyses, because the impacts of climate change on human societies are not explicitly modelled or ill-constrained in IAMs (Sect. ). Furthermore, feedback loops of the type CUL  X, where X may be subsystems from ENV, MET or CUL are mostly missing altogether, in large part because CUL is not represented (or is only fragmentarily included) in current ESMs and IAMs.

Longer and more complex paths and subgraphs of causal interactions between subsystems could be classified by further higher-order taxonomies (e.g. inspired by the study of motifs, small subgraphs, in complex network theory; ). This approach quickly leads to a combinatorial explosion – for example, for three-loops of the type A→B→C→A involving three modelled subsystems A,B,C and their interactions, enumeration and counting of all possible combinations shows that there are already 11 distinct taxa for feedback loops of this kind. However, there are systematic methods available for classifying and clustering causal loop diagrams that could be leveraged to bring order into more complex models of World–Earth systems . Overall, such higher-order taxonomies could help in the design of models or model suites that can deal with different aspects of (non-linear) interactions between World–Earth subsystems and serve as tools for understanding the emergent co-evolutionary macrodynamics.