Climate scenarios are descriptions of plausible future climate conditions and are used to understand a potential range of future climate change and its impacts, as well as to identify opportunities for climate mitigation and adaptation.
Scenarios have been used for planning purposes for decades. As computational power has grown, the ability to build complex scenarios has grown with it, alongside the power to test assumptions, conduct sensitivity analysis and solve complex optimization problems. The oil price shocks of the 1970s accelerated the use of planning tools to maintain short-term financial stability and manage long-term energy system transitions – this was also one of the driving forces for the creation of the International Energy Agency, whose scenarios are widely used today to understand how the world might decarbonize.
Scenarios can be exploratory or normative. Exploratory scenarios build a wide range of possible future emissions scenarios to explore the consequences of these different pathways. They are not predictions, but rather their wide range helps develop an understanding of plausible outcomes for decision-making. Normative scenarios, on the other hand, describe a pre-specified future that can be achieved with only a specific set of actions. These scenarios can be used to explore the potential consequences of meeting a specific emissions reduction target – particularly the technology and policy options needed to get us there.
The process of developing these scenarios starts with making assumptions about population growth, economic growth, land use changes, and energy consumption in the future. These assumptions feed into models of society and economy called integrated assessment models (IAMs) that output emissions scenarios or trajectories of future GHG emissions – these are exploratory scenarios when the emissions profile is an output, and normative scenarios when the emissions profile is a target. Subsequently, the emissions scenarios determine the future radiative forcing (i.e., net energy gain by the Earth’s atmosphere), which is a key input for projecting the future climate.
The scale of assumptions, the lack of quality baseline data for most of the world, and the uncertainty of the linkages between socio-economic factors and the earth system impacts every modelling effort, producing results which may not be completely representative of the future it is trying to analyze. For instance, estimating temperature changes as per carbon budgets is probabilistic in nature. The Intergovernmental Panel on Climate Change (IPCC) estimates that there is 67% chance of staying below 1.5°C if 320 Gt of carbon dioxide is emitted from 2022. This probability reduces to 50% if 420 Gt are emitted instead. Communicating the assumptions and level of uncertainty associated with scenarios is increasingly important to ensure disclosures by financial institutions and businesses are not misinformed and risk management approaches by these entities and government are calibrated to the uncertainty.
Transition risk refers to the risks and opportunities from a transition to a low-carbon economy and brings additional subtleties to climate scenario modeling. Under a transition compliant with the Paris Agreement, most sectors of the economy, such as energy generation or transportation, will see dramatic changes to the demand for energy fuels alongside a switch from carbon-intensive to decarbonized technologies. Markets may or may not be currently fully pricing in this shift, which will have a large long-term impact on a public or private entity, particularly in certain sectors like oil and gas exploration. These impacts can be quantified by modeling the entity’s future cash flows, taking into account the changing revenues and costs due to the low-carbon transition. WTW’s Climate Transition Analytics team quantifies the difference in discounted future cash flows between a business-as-usual and a climate transition scenario as the Climate Transition Value-at-Risk (CTVaR).
Climate scenarios form the foundation of how we quantify CTVaR – the mismatch between what is priced into the market today, and where risks and opportunities lie in a low-carbon world. Our business-as-usual scenario represents a world where policy ambition is limited to existing commitments - which are inadequate to meet 1.5°C, or even 2°C temperature targets. Meanwhile, our climate transition scenarios imagine a world where we meet temperature targets through a combination of effective climate policies, technology improvements and behavioral change. In this world, the demand for fossil fuels drops significantly, while there is a massive increase in renewables penetration, even more than what is currently priced into the business-as-usual scenario.
We build these scenarios through a combination of original modelling, academic research, industry publications and other sources. Since all of the analysis is done in-house, we can adapt our analysis to meet client needs, or to keep abreast of technology, market and regulatory trends.
Moreover, as the world transitions to a decarbonized energy system, our business-as-usual scenario must keep up – for example, we consider that the growth in electric vehicles (EVs) needed in a 2°C world is already aligned with the EV production plans for major carmakers, which means that we would consider the ‘CTVaR’ for electric vehicles to be zero. For most sectors of the economy, however, there is still a mismatch between where the markets currently are and where they need to be for the world to stay within safe climate thresholds. Figure 1 illustrates our view on a 1.5°C compliant transition in passenger transport – the numbers are based on our own sectoral decarbonisation models.
Our transition scenarios are normative, i.e., they start from a defined end goal, in this case, a temperature, and hence, an emissions target for 2100, and work backward to the present, from which regional and sectoral emissions allowances can be inferred. Our climate transition controversies (CTCs) represent commodity and technology pathways within each sector (for example, electric vehicles or plastics production), that together create a world that is aligned with the emissions target set by the ‘normative’ transition scenario. Our asset-level models, particularly in the natural resources sector, can be used to quantify the potential cash flows from individual wells or mines based on their product portfolio and production costs, including their risk of stranding in various scenarios.
For example, our corporate consulting team helped a mining equipment and services company quantify the transition risks of selling to new oil sands projects, which may be stranded assets in a transition away from fossil fuels, and the upside if they focused on energy transition metals such as copper or nickel, whose demand is expected to grow as electrification continues to accelerate.
While climate change impacts are already starting to be felt around the world, the most wide-ranging and uncertain consequences still lie in the future. Building trajectories of greenhouse gas emissions (GHGs) is central to understanding the future of climate change.
Physical climate risks refer to the negative impacts of climate change on the physical world – severe heatwaves, storms, and sea level rise, for example, can affect agriculture, forestry, real estate, and public infrastructure and cause direct human and economic loss. Despite global efforts towards decarbonization, some of these impacts are locked in [1] and decision-makers need to understand how these could affect businesses, financial systems, or governments.
‘Physical’ climate scenarios are central to how we understand and communicate physical risk for specific geographies, sectors, sub-sectors, investment portfolios, and businesses. They come in many varieties, and each has their appropriate use – scenarios can be qualitative narratives that explore potential outcomes of regional climate change or of a particular risk management decision, simple statistical models used to explore different assumptions, or they can be quantitative projections of climate change generated by complex models of the Earth system (Figure 2).
Whatever their type, there is uncertainty embedded within scenarios, for instance, in the response of the climate system to a change in emissions (climate sensitivity). This uncertainty increases at the local levels at which planning and decision-making take place.
Physical climate scenario analysis is exploratory in nature – we analyse a wide range of future scenarios with at-risk assets or economic indicators to build an understanding of a range of losses and challenges. We derive historical hazard data from archives, for instance of the US National Oceanic and Atmospheric Administration (NOAA), US National Aeronautics and Space Administration (NASA), and the European Centre for Medium-Range Weather Forecast (ECMWF). Future projections are obtained from the World Climate Research Programme framework, CMIP, and from the Coordinated Regional Climate Downscaling Experiment (CORDEX). We use our in-house modeling expertise to then either downscale and/or bias-correct these datasets to reduce model errors.
Using these scenarios to analyse and stress test physical risk for assets, communities or investment portfolios can be beneficial if their limitations are understood and accounted for in adaptation decision-making. For instance, our team supported a national development bank in understanding the potential impacts of different climate scenarios on its assets and portfolio for different time frames and different geographical scales, to give an early snapshot indication of where risks may lie.
Scenarios represent simplified worlds – whether exploratory, business-as-usual or 2°C temperature rise compliant. They are not predictions and should not be used as such – the world is extremely complicated, and models are simplified versions of reality at best (wars and pandemics, for example, represent key unknowns). Instead, we create or use scenarios through a combination of original modeling, commercial data from leading companies, academic research, and reports from international groups to model what the world could or should look like, and what the risks for public and private entities, and the financial system as a whole, are if unaligned with a normative transition or if exposed to the physical impacts of climate change.
Scenario modelling is a subjective exercise involving value judgments and numerous assumptions on the range of futures and how these affect assets, communities, long-term discount rates, equitable distribution of the remaining carbon budget, sectoral emissions constraints, and what is already ‘priced in’ by markets, amongst others. But it is an exercise that is increasingly essential and valuable to organizations seeking to build a comprehensive picture of their climate risks, understand how they affect their business, meet evolving disclosure requirements, and make the strategic decisions necessary to minimize and manage these risks.
