Adaptation to severe climate change

Globally, governments are still in the early stages of exploring a transition to new non-fossil fuel economies. Currently the global economy including agriculture, and horticulture are heavily dependent on fossil fuels, and there is a very high risk that political compromise will delay the end of outmoded practices. The integral role that fossil fuels play in the current global economy cannot be overstated, and any significant reduction in their role will result in a total remodelling of economic indicators. What is more, the close correlation of economic growth, and the implicit assumptions relating to positive GDP data, are seriously problematic when seeking to mitigate anthropogenic climate change and environmental degradation.

Given the urgency to reduce greenhouse gas emissions to limit self-reinforcing climate feedback loops, even a 2 or 3 year delay before embarking on a path of drastic emissions reductions may lead the climate into a territory where any attempts to limit the average temperature rise to 2°C, or less, become futile.

In order to mitigate the risk that humanity will not move fast enough to avoid social and environmental crises, we must prepare ourselves by concentrating on our ability to adapt, which in turn is dependent on our ability to explore a broad range of potential climate change scenarios and a broad range of possible mitigation pathways.

From a scientific perspective, based on the data available and the observable trends, actions to de-carbonise the economy which are framed with a 2050 deadline are inadequate. Firstly, the time frame is not aggressive enough to prevent potentially catastrophic levels of warming, and secondly, given the complexity of the climate there is also an urgent need for actions based on the precautionary principle.

Scientific uncertainty is not a justification for government to take a light regulatory approach when voters and their entire ecosystem face such existential moral hazard. In the same way that no one would allow their children to board an airplane if the risk of a deadly crash would be 10% (or even 0.1%), we should not have blind confidence in our collective ability to de-carbonise the global economy in time to prevent severe climate changes.

Adaptation actions must consider scenarios of at least 4-6°C of warming by 2100 and the likely consequences of such levels of warming in terms of threats such as:

  • sea level rise, ocean acidification, and the salination of freshwater aquifers,
  • impact on agriculture and food production,
  • the spread of diseases, and the human and animal health risk of temperature increases,
  • increases in extreme / catastrophic weather events,
  • limits to the ability of local communities to cope with these consequences.

Preparing for adaptation to severe climate change must seriously consider the risk of social collapse at local, regional, national, and even transnational levels. In a global economy, when a major climate-induced social collapse occurs, we can not assume smooth continued operation of local or global economic and financial systems. Furthermore any de-carbonisation initiatives that depend on concepts such as financialised emission trading schemes may turn out to be ineffective.

These risks underscore the need for a climate change modelling tool chain that assists domain experts from various disciplines, as well as policy designers, to systematically and with relative ease explore, and communicate a broad range of options to reduce the risk and to contain the impact.

At the moment, the way we respond and adapt to climate change impacts is not well coordinated or communicated. Many of the risks, impacts and actions to adapt are dealt with across a number of different legislative and regulatory regimes.

There are gaps in our information. We have some knowledge about the physical impact of sea-level rise on our coastlines and communities but we currently don’t know much about the impact that rising seas and temperatures will have on economic and ecological systems. We also do not know what the impact of ongoing extreme weather events would be on production in the primary sector. Together these impacts could seriously disrupt current geographical, geological and meteorological advantages enjoyed by certain economic sectors.

We do not fully understand the ecological interdependencies related to the acidification of the oceans and the potential collapse of the complex food chain dependent on phytoplankton, and the oxygenation of the seas and atmosphere. We do not know if we are approaching a tipping point in ocean temperatures brought about CO2 absorption and rising acidification. We do not fully understand the water cycle and how the eutrophication of oceans and the salination of waterways interact with freshwater aquifers, impacting water resources and land-based food capacity.

There is more work to do to understand the possible impacts on our health, biodiversity and culture beyond the traditional timescales projected by economists, statisticians and politicians. A new transdisciplinary approach to climate change mitigation is needed to take precautions against the worst impacts that could affect all aspects of human societies, both locally and globally.

Exploring climate change mitigation and adaptation

Economic assumptions are never neutral and this is especially true of GDP and growth expectations that can hide equity costs within pricing markets on 10, 50, and 100 year timescales. The assumptions made in any economic models must also be explicit about equity issues and the level of commitment to achieve specific equity targets.

An evidence-based approach must be based on known patterns of physical resource flows and resource demands, and on explicit assumptions about changes to these patterns due to the need for climate change mitigation, and will, therefore, promote well informed political debate.

Climate change mitigation and adaptation is a complex transdisciplinary challenge. Any potentially useful modelling tool chain must be able to take into account the following constraints and limitations:

  1. The reality of human cognitive limits, including the limits of quantitative methods, the limits of qualitative methods, and the limits of language.
  2. The influence of ideologies and cultural norms on human behaviour, in combination with cultural inertia.
  3. Changes of government and potentially significant changes in climate change related policies every few years.
  4. The growing likelihood of extreme weather events of new levels of severity and the effects on agricultural production, economic infrastructure, and human lives.
  5. The potential failure to limit local and global temperature increase to 1.5ºC or 2ºC even within the next 20 years.
  6. The potential breakdown of established economic ideology due to local and global climate disasters within the next 20 years.
  7. The need to rapidly reduce the ecological and energy footprint of human civilisation, and the level of incompatibility of reduced resource consumption with the established economic ideology.
  8. The human potential, creativity, and resilience that can be unlocked by trusted collaboration at human scale.
  9. The potential need to replace established financial economic paradigm with a viable resource and waste based alternative on short notice, and the ability to iterate on economic paradigm in order to adapt to rapid climate change and to deal with acute ecological disasters.

A modelling and simulation tool chain that does not consider the above constraints and possibilities will be of very limited use for the exploration of climate change mitigation and adaptation pathways, and will not be able to assist policy designers and implementers.

The S23M team envisages a modelling framework and a tool chain design that assists modellers, policy designers, policy implementers, the public, and industry representatives from all economic sectors to incrementally learn from each other about the unfolding reality of climate change, the changing social and economic support needs of local communities, and the need to invest in new types of economic infrastructure that are of strategic importance for our collective ability to adapt to climate change.

Uncertainties around climate and social norms

Just as the world wars wreaked havoc on society and the environment, the climate crisis creates a similar disjuncture with the past. The future of human societies is going to be dominated by two broad trends that are already visible now.

  1. Increasing numbers of climate related increasingly severe weather events (severe rainfalls and flooding, cyclones and coastal erosion, heat waves, droughts, etc.), and downstream effects on agricultural production and ecosystem functions. The inherent level of uncertainty around the rate at which global temperatures will continue to rise and the rates at which national economies will be able to rapidly reduce green house gas emissions leads to a corresponding level of uncertainty around the frequency and magnitude of future severe weather events.
  2. Increasingly levels of climate change related anxiety in the population, which may rise to the surface following severe weather events or disasters, leading to rapid shifts in social norms (financial economic growth is no longer the main or only target of economic policy, etc.). Further changes in social norms are inevitable, but the timing is impossible to predict – leading to significant uncertainty about future climate change mitigation related goals and legislation.

This means that classical financial economic modelling techniques and metrics such as GDP are no longer useful for assessing the impact of climate change and for assessing the cost of climate change mitigation actions. Whilst we can’t predict the future, we do know that the future will not be a continuation of historic economic trends, and it may also not be influenceable in any adequate way via classical economic tools such as interest rates, tax rates (carbon tax, etc.) and market mechanisms (emissions trading schemes, etc.).

Instead, going forward, national and local governments are well advised to rely on the development of agent based models using the resources, events, and agents (REA) paradigm to combine:

  1. available physical climate models,
  2. available scientific data about local bioregions and microclimates,
  3. available data on historic and current regional economic activities categorised by sectors and industries, measured in physical quantities of resources (kg) and goods (quantities of specific categories of goods),
  4. with the tacit knowledge of subject matter experts and local practitioners in relevant disciplines.

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REA models of resource flows and economic activity can be developed and validated incrementally by groups of subject matter experts, both at the macro level as well as at the regional (meso) level, and they can serve as a formal foundation for agent based simulations of economic activities in combination with a range of very different climate change and climate change mitigation scenarios.

This approach allows modellers to run simulations that take a precautionary approach in relation to the uncertainties around severe weather events and around shifts in social norms outlined above. The REA data resulting from the simulations can be translated from physical metrics into local monetary metrics based on the different assumptions about social norms and economic rules that underpin the various simulation runs.

The resulting portfolio of possible climate change mitigation scenarios, including formal representations of all the assumptions about future social norms and economic rules, provide policy designers with a tool box for educating politicians and the public about the available options and associated investments in specific climate change mitigation and adaptation activities.

A growing number of researchers and practitioners are working on resource based accounting methods, and some are already working on the development of regenerative economic ecosystems at a bioregional level that are specifically designed for resilience against climate change.

An economic ideology independent reasoning framework

Preparation for potentially severe climate change and economic disruption is only possible with the help of economic and ecological modelling and simulation tools that don’t make implicit (hard-coded) assumptions about the way economic systems work. In this context financial economic modelling techniques are at best inadequate if not useless.

Over a period of 30 years and longer significant shifts in economic ideology are inevitable in order to adapt effectively to changes in climate, to the effects of increasingly extreme weather events, and to related social challenges.

Governments need suitable multi-dimensional economic and ecological modelling tools for reasoning about human collaboration and resource flows at various levels of scale that can be configured on demand, to reflect emergent economic and ecological practices that may differ radically from current “best practice”.

The MODA + MODE human lens provides thirteen categories that are invariant across cultures, space, and time – it provides an economic ideology independent reasoning framework for transdisciplinary collaboration. The human lens allows us to make sense of the world and the natural environment from a human perspective, to evolve our value systems, and to structure and adapt human endeavours accordingly.

humanlens

The human lens is a meta language that can be used to design multi-paradigm and multi-dimensional modelling and simulation tools for resource flows between economic agents as well as resource flows between ecological systems and economic systems.
The human lens is comprised of:

  1. The system lens, to support the formalisation and visual representation of knowledge and resource flows in complex socio-technological systems based the three categories of resources, events, and agents (the REA paradigm, an accounting model developed by E.W. McCarthy in 1982 for representing activities in economic ecosystems). The system lens can be applied at all levels of organisational scale, resulting in fractal representations that reflect the available level of tacit knowledge about the modelled systems.
  2. The semantic lens, to support the formalisation and visual representation of values and economic motivations of the agents identified in the systems lens. The semantic lens provides a configuration framework for articulating ethical, cultural, and economic value systems as well as a reasoning framework for evaluating socio-technological system design scenarios and research objectives with the help of the five categories of social, designed, symbolic, organic, and critical.
  3. The logistic lens, to support the formalisation and visual representation of value creating activities and heuristics within socio-technological systems. The logistic lens provides five categories for describing value creating activities: grow (referring to the production of food and energy), make (referring to the design, engineering, and construction of systems), sustain (referring to the maintenance of production and system quality attributes), move (referring to the transportation of resources and flows of information and knowledge), and play (referring to creative experimentation and other social activities). The logistic lens can be used to model and understand feedback loops across levels of scale (from individuals, to teams, organisations, and economic ecosystems) and between agents (companies, regulatory bodies, local communities, research institutions, educational institutions, citizens, and governance institutions). The categories of the logistic lens assist in the identification of suitable quantitative metrics for evaluating performance against the value system articulated via a configuration of the semantic lens.

All 13 human lens concepts reflect foundational aspects of human cognition and the human capacity for symbolic thought within an ecological context, and are found in all cultures under various labels.

The human lens concepts are recognisable in all historic human cultures, and they will continue to be relevant in another 1,000 years – this is what is meant by “independent of economic ideology”. This is important because language is always a contentious topic in a transdisciplinary context, since each discipline uses a different language. The human lens can be used to model all aspects of the relationships between economic agents and all aspects of collaboration within economic agents.

resource-flows-logistic-lens

Furthermore the fractal characteristic of the human lens allows the representation of groups of collaborating economic agents and the representation of abstract relationships between such groups.

The grow category in the logistic lens can be used to instantiate specific subcategories in the land use sector for forestry, horticulture, dairy farming etc. Additionally the base categories of the logistic lens provide a framework for the transport (move, referring to the transportation of resources and flows of information and knowledge), electricity (grow, referring to the production of food and energy), and industry sectors (make, referring to the design, engineering, and construction of systems). The base categories in the logistic lens are designed to encourage zero waste system designs, the sustain category (referring to the maintenance of production and system quality attributes) can be used to instantiate models that focus on maintenance, repair, and decomposition for reuse of economic resources and that explicitly indicate, and as needed, quantify, residual waste streams. Finally the play category (referring to creative experimentation and other social activities) can be used to instantiate models that focus on important cultural practices, on the education sector and on research and innovation).

A distinguishing feature of the MODA + MODE meta paradigm is that it allows for consistent formalisation of discipline specific paradigms and local domain specific languages, such that domain experts are able to continue to use their preferred paradigms and terminologies.

Agent based economic and ecological models can be created and populated with available data and assumptions (scenarios) about economic sectors and ecological practices at various levels of scale in time and space, and these models can then be used to:

  1. Visualise qualitative and quantitative economic dependencies and resource flows, including but not limited to links from sectoral models to thoroughly understand the interactions between the energy and land use sectors.
  2. Run agent based simulations of activities in the economic and ecological spheres to explore different scenarios and their implications.
  3. Generate corresponding multi-dimensional economic and ecological accounting tools that can be used to coordinate human economic activities.

logisticflow

All the categories and semantic links between categories and instances in the example model above are easily made available for processing by software tools. The example shows concrete resources (orange), events and activities (blue), as well as agents (green) and their motivations (red).

The semantic lens allows us to create explicit models of different worldviews and paradigms, so that all relevant value systems and cultural differences are not only acknowledged, but become an integral part of the language used to describe economic activities and their purpose.

semantics.png

Visual semantic models can be used to trace motivations back over several levels to specific cultural or individual values. In this way assumptions and worldviews are made explicit, and cultural context can be integrated into economic and logistical models to any desired level of detail. In particular local knowledge and values can be reflected in the configuration of economic models, alongside scientific knowledge about the natural world and the climate, facilitating the co-design of mitigation and adaptation activities in collaboration with local populations.

The human lens in combination with an inclusive consultative and transdisciplinary approach provides results that are traceable to underlying datasets and economic assumptions and that can assist policy designers in answering important questions under a range of different climate change scenarios:

  1. What emissions reductions are technically and economically feasible when factoring in the interactions between sectors and economy-wide constraints?
  2. What are the economic consequences of different levels of emissions reductions, different types of policy interventions, and different scenarios of technological and economic change?
  3. What distributional impact could emissions budgets or emissions policies have on different sectors, regions, generations and socio-economic groups?
  4. What impact will domestic emissions policies have on the ability to meet global emission reduction targets?
    What impact will overseas markets and policies have on local emissions, production and trade?

In an increasingly unpredictable world that can easily be disrupted by severe climate related events, a modelling and simulation tool as described above may be essential for preventing or limiting social collapse, allowing local populations to rapidly explore the viability of new sequences of adaptive actions, before jointly agreeing on and committing to specific (and potentially radical) changes in economic and ecological practices.

Modelling and simulation tool chain design

A suitable modelling tool that supports the human lens and representations of both quantitative and qualitative / semantic models can be implemented with the help of category theory (which is the abstract “systems integration language of mathematics”) and with denotational semantics (to map formal models to concrete computational platforms and data storage technologies) .

A basic implementation of the human lens for qualitative modelling is achievable with a Unified Modeling Language (UML) modelling tool and via the configuration of a UML Profile that includes suitable UML stereotype definitions for the thirteen human lens concepts.

An even more basic implementation is afforded by markers and a whiteboard or by pencil and paper, but then of course the models can’t be used to drive automation and agent based simulation tools.

The full potential of the human lens can be harnessed with a tool like S23M’s Open Source Cell Platform that provides an unlimited multi-level instantiation capability and that enforces strict semantics for agent based modelling.

The Cell Platform provides a clean formalisation based on the axioms of category theory that is recursively bootstrapped from the structure of an ordered pair, without any spurious complexity induced by the underlying implementation technology (the Java Virtual Machine – JVM). Additionally the Cell Platform:

  • Uses denotational semantics (a unique machine readable semantic identity for each concept) to completely separate the concern of naming from the concern of semantic modelling, allowing each agent to introduce preferred labels and symbols.
  • Enables communication and collaboration between agents based on artefacts (information resources), and events which equates to native support for the REA paradigm.
  • Provides an API in the language of category theory that exposes the recursive construction of models, and that hence allows extensions, restrictions, and other variations of all concepts.
  • Allows agents to make selected models discoverable, to make selected models visible to other agents, and to declare semantic equivalences between concepts in different models that are then recognisable by the reasoning engine within the Cell Platform.
  • Provides support for 4-state information quality logic (true, false, unknown, not applicable) to allow agents to easily process incomplete data and any structures they may find in the models from other agents – without resulting in ambiguous semantics.
    Supports logic and reasoning entirely within the abstract language of category theory, since semantic equivalences are defined between semantic identities rather than between human assigned labels.

The S23M team envisages a transdisciplinary modelling and simulation framework for climate change mitigation and adaptation scenarios that allows domain experts from various disciplines to contribute models of sectors of the economy and aspects of ecosystems within their preferred paradigm and in their preferred terminology into a modelling tool chain that includes explicit support for:

  1. Managing and enforcing the limits of applicability of specific models.
  2. Recording all assumptions that are associated with a model or specific scenario in a form that is accessible to software tools.
  3. Reviewing and flagging inconsistencies between the assumptions associated with different models or aspects of the domain of interest.
  4. Formal version and variant management for all models, data sets, and sets of assumptions that are associated with specific climate change mitigation pathways.
  5. Formal traceability between sets of assumptions, and related models and results of agent based simulations.
  6. The human lens meta language, to enable
    • Specification of suitable qualitative and quantitative goals in suitable metrics, including metrics for the quantification of resource flows and green house gas emissions in physical units, and chemical types/properties.
    • Modelling cultural norms and expected or potential shifts in cultural norms.
    • Modelling the economic ideology and expected or potential shifts in economic ideology.
    • Modelling cross-sector dependencies and resource flows at different levels of scale, including desirable shifts in such dependencies.
    • Modelling of concrete economic agents that are of strategic economic importance, including the dependencies between these agents – to provide a foundation for performing dynamic agent based simulations under a range of different assumptions.
    • Semantic integration between different aspect and sector models, including the specification of any required transformations of input and output data structures.
  7. As needed, translating the results of agent based simulations back into traditional financial economic metrics, to assist experimental and iterative development of suitable policies and regulatory frameworks for achieving desired national and regional level outcomes.

The purpose of such a modelling and simulation tool chain is to allow rapid exploration and iterative refinement of different mitigation and adaptation scenarios. Policy makers and the wider population need to see “first hand” (as far as that is possible) that busyness as usual and related measures to “decarbonise” the economy via traditional economic tools within the framework of financial economics are inadequate for dealing with the challenge presented by the climate crisis.