Hotter

Geoff Royston

Climate change is a hot topic. And getting hotter with the recent report from the associated Intergovernmental Panel, the build-up to the United Nations summit meeting in Glasgow this November – and Bill Gates’ new book, How to Avoid a Climate Disaster. Reading this prompts me to offer a few comments from an analytical perspective, centering on three areas – understanding the issue, developing options for solutions, and coping with deep uncertainty.

Bill Gates’ book ranges widely – from cars to cement, from cities to cows (a surprisingly large contributor to global warming gas emissions) – but it starts with the observation that the underlying issue is the global need to continue to make more energy available to more people, not least for improving the lives of the poorest, without releasing any more carbon into the atmosphere. Which brings me to my first analytical point, about understanding the issue.

THE ATMOSPHERIC BATHTUB Until as recently as 2019 the UK was committed only to reduction in its carbon emissions, not elimination. But only elimination will do. An early simple, but compelling, illustration of why that is so came from the system dynamics modelling community (which also deserves acknowledgement for an early warning on global environmental problems, including CO2 pollution, with the report The Limits to Growth, published way back in 1972). It centres on a simple ‘bathtub’ model: if you turn the taps to slow, but not stop, the flow of water into a bath, the water level will still keep rising (unless you pull out the plug). Similarly, with just reducing net emissions of carbon gases – their atmospheric stock level will still keep rising, and the world will continue to get hotter.

MITIGATION AND ADAPTATION As yet the world, despite two major commitments to action (the Kyoto and Paris agreements), is nowhere near on track for attaining the target of 100% reduction in carbon emissions by mid-century (see Figure 1).

In a world of constantly rising demand for energy, most of which is currently produced by fossil fuels (for understandable reasons, petrol is cheaper per litre than cola, and generates ten times as much energy per kilo as TNT), getting to net zero carbon is going to be hard. Especially to do quickly; past transitions in energy sources have all taken many decades. Which means, as Bill Gates notes, that there needs to be strenuous efforts not only at eliminating net carbon emissions – mitigation – but also on coping better with climate change – adaptation.

How to Avoid a Climate Disaster discusses many approaches to mitigation and adaptation. Innovative devices, redesigned processes, new policies ……… (read the book!). Which brings me to my second analytical point, about the contribution of analysis and modelling to solutions.

INFORMATION AS ENERGY? Bill Gates mentions he is funding computer modelling of all the US power grids, and that this has shown that building a national grid and associated intelligent control systems would reduce resources needed for energy production by 30 percent. (The UK was first in the world to create such a grid, back in 1935!). More generally, closer matching of supply and demand for electricity, e.g. through dynamic pricing, can improve

FIGURE 1 CURRENT v REQUIRED CARBON EMISSIONS FOR LIMITING GLOBAL WARMING TO 1.5° (BASED ON FIGURES IN THE UN ENVIRONMENTAL PROGRAMME EMISSIONS GAP REPORT 2020)

efficiency. Information and analysis can reduce the requirement for energy and resources – in effect substituting for them. (Though improved efficiency in energy production may not reduce carbon emissions if it just lowers energy prices: it needs to be accompanied by regulation, especially carbon pricing.)

Gates also observes that steps that would give sizeable reductions in carbon emission by 2030, e.g. building gasfired electricity generating stations to replace coal-fired ones, can be very different from the things we need to do to get to zero by 2050. Exploration of the interplay and best sequencing of steps towards net zero is clearly a task where analysis and modelling can contribute.

And when it comes to adaptation, good information and sound analysis also play a vital role. For example, Gates mentions early warning systems for storms and floods; phone apps that can help farmers in low-income countries to identify crop pests and diseases; and information systems and logistics models that support emergency workers in disaster situations.

UNCERTAIN ELEPHANTS? Climate change deniers make great play of the fact that the projections of global heating and associated events are based on models entailing sizeable uncertainties. This is, well, undeniable, but such uncertainty (which is mostly about the speed and distance of travel, not about its general direction) is not an unacknowledged elephant in the room of climate science. The current pandemic has provided a salutary reminder that complex dynamic systems can be hard to model with any precision (especially when people’s behaviour is involved) – and climate modelling is no exception. Nevertheless, in both cases, there is no sensible choice but to try, and for policy makers to pay attention to the results.

Uncertainties surrounding climate change mean that we have to be prepared for a range of possible future climates for the Earth, some compatible with human society as we know it, some not. Which takes me to my last analytical point, on scenario planning, an approach that can be particularly useful when probabilities are difficult or impossible to assign and classical cost-benefit analysis is not practicable.

PRECAUTIONARY TALES The first task in thinking and planning for uncertain futures in any domain is to envisage some credible scenarios. Then to consider strategies that are robust to as many of these as possible; including coping with the worst-case scenario. Climate change certainly has some worst-case scenarios, particularly those involving reaching climate tipping points via positive feedback effects (a stalwart of system dynamics modelling) such as melting ice caps reducing the reflectivity of the Earth to sunlight, thus further increasing warming, leading to more melting ………

In such cases there can be calls for invoking the precautionary principle – that faced with a potentially serious hazard but where little is known – or even realistically knowable – about likelihood or impact, steps should nevertheless be taken to mitigate the risk. However, the precautionary principle is not without difficulties – what is an appropriate level of mitigation for an uncertain level of risk, especially when the related precautions may be costly or themselves be of uncertain effect? That certainly looks like the sort of situation presented by some climate change scenarios: e.g. what role, if any, should geoengineering play?

Cass Sunstein (of Nudge fame and also the person responsible, under the Obama administration, for work to develop a value for the economic cost of carbon emissions) addresses this sort of issue in his new book, Avoiding Catastrophe: Decision Theory for COVID-19, Climate Change and Potential Disasters of All Kinds.

While warning of the dangers of dismissing quantified approaches too readily, and hence arguing that every attempt should be made to estimate at least ball-park probabilities and impacts, Sunstein also argues there are nevertheless a few situations where the precautionary approach looks appropriate. Situations, for example, where the worst case is grave, and the costs and risks of mitigation are not too high. This could well be the position for global heating, where the worst case looks irreversibly bad and there seem to be affordable mitigation options that could if necessary be reversed. As the title of another recent climate change book, by Mike Berners-Lee, reminds us: There is No Planet B.

© Penguin Random House Dr Geoff Royston is a former president of the OR Society and a former chair of the UK Government Operational Research Service. He was head of strategic analysis and operational research in the Department of Health for England, where for almost two decades he was the professional lead for a large group of health analysts.