Black Swans Traversing a Black Sky

Jul 31, 2020 | Estimating emission reductions

Black is the new blue.

Blue sky thinking applies innovative solutions to difficult problems. Black sky thinking, a phrase borrowed from the space industry, goes even further in terms of imagination, speculation and conjecture.

And where better to start than with climate change? If we take our palpable and very evident failure to make a dent in global greenhouse gas emissions, in absolute (tCO2e) or relative (tCO2e/$ GDP, tCO2e/capita) terms, it quickly becomes apparent that climate change is a wickedly difficult problem in urgent need of some black sky thinking.

Some truly radical, if inadvertent, mitigation actions have been pursued by governments around the world in the form of coronavirus lock-downs and curfews. Global greenhouse gas emissions are forecast to fall by about 6% in 2020 compared with 2019. Of course, such measures are not socially desirable or economically sustainable, and are already producing significant emissions rebounds as they are relaxed. But they do at least serve as a reminder that, when they really want to, governments can act rapidly and forcefully.

Mega-projects such as damming the Strait of Hormuz to generate hydro-electricity or foresting the Sahara Desert to sequester atmospheric carbon certainly qualify as black sky thinking, as do some of the more ambitious solar radiation management proposals.

High-albedo black sky thinking. Credit: Inference

But black sky thinking need not be big and shiny and entail large expenditures.

Arguably, the single biggest step in getting a better handle on climate change is to develop improved estimates of greenhouse gas emissions and their corollary, emission reductions.

Countries proudly boast about how many megatonnes of emissions they will avoid by 2030. Companies actively tout becoming carbon-neutral. Projects as diverse as clean cookstoves in rural Africa and mangrove plantations in south-east Asia provide incredibly confident annual abatement schedules, to several decimal places if requested.

And yet the unspoken truth is that such figures are necessarily estimates. Sometimes they are informed judgements and sometimes wild guesses (and usually somewhere in between), but they are speculative estimates nonetheless. They come with confidence intervals and biases that are sometimes better understood, and potentially more interesting, than the estimates themselves.

It’s not sexy. But the application of some black sky thinking to how to improve emissions quantification methods would certainly go a long way. After all, to (mis-)quote Peter Drucker, if you can’t measure it, you can’t improve it.

Unfortunately, assessing the mitigation impact of an investment or a project or a policy – or a combination thereof – is intrinsically difficult. Greenhouse gas emissions are, in the main at least, invisible and odourless. Emission reductions – the absence of emissions that would otherwise have occurred – are even more intangible, reliant upon a counterfactual baseline that, by definition, cannot be observed.

Incredibly granular – and hence, inevitably, complex – methodologies have been developed for the Clean Development Mechanism (CDM) to address precisely this problem, to ensure the proverbial ‘tonne is a tonne’. But the CDM’s methodologies are far from perfect and, anyway, cover only a small range of technologies and sectors. They also exclude policies and measures (as opposed to investments), which rather limits their applicability.

CDM tool for calculating emissions from solid waste disposal sites: certainly precise, but how accurate? Credit: Clean Development Mechanism

A looser framework of GHG accounting principles, designed to accommodate a broader range of interventions, including policies and capacity building, is operated by the Global Environment Facility (GEF). The GEF’s Financial Mechanism sibling, the Green Climate Fund (GCF), currently has no GHG methodologies of its own and has, instead, tended to draw from – or at least seek inspiration from – CDM, GEF and multilateral development bank approaches, accompanied by scrutiny from the GCF Secretariat and the Independent Technical Advisory Panel during the project review process.

Some of the challenges associated with the quantification of emission reductions stem from practical, often prosaic, measurement difficulties – the need to use statistical sampling where comprehensive data collection is infeasible, instrument limitations, the use of default Tier 1 IPCC emission factors in the absence of locally-calibrated parameters, data confidentiality or accessibility issues, and budget constraints, to name just a few.

Technological developments, such as satellite remote sensing and inexpensive sensors connected through the Internet of Things, and knowledge-sharing initiatives, such as the IPCC’s emission factor database, can go some way to addressing at least some of these difficulties.

Other challenges are somewhat more fundamental and stem from the ‘dimensionality’ of projects and policies. Pinning down the spatial, temporal and financial scope of a mitigation intervention can be surprisingly difficult, particularly where leakage, rebound or second-round systemic effects serve to blur the boundaries of the intervention.

Perhaps the most overlooked source of uncertainty is uncertainty itself. We live in a fast-moving, intrinsically uncertain world. The global economy doubles in size every 20 years – except when it doesn’t. Technological advance is breath-taking – except when it isn’t.

Black swans – surprising and impactful events that seem, in retrospect, tantalisingly predictable – abound. Obvious examples that have had a huge impact on greenhouse gas emissions include the 1970s energy crisis and the collapse of Russian and Ukrainian industry in the 1990s, leading to the ‘hot air’ that bedevilled the Kyoto Protocol.

Just recently, a blowout – sudden, brief and completely unexpected – turned a natural gas well in eastern Ohio into a ‘super-emitter’, leaking more methane in 20 days than all but three European nations emitted over the entire year.

And, of course, we are currently living through a black swan pandemic whose course and duration remain shrouded in uncertainty.

Less obvious, but no less important, black swans include the Chinese one-child policy – arguably, the single most effective climate mitigation policy ever implemented – and the Fukushima nuclear accident which, among other things, led Germany to start closing its nuclear power plants and thereby inadvertently increase annual CO2 emissions by a startling 13%.

Acknowledgement that we live in a high-entropy world of discontinuous change and seemingly random black swan events requires a profound shift in mind-set: black sky thinking writ large.

It means moving away from the smooth curves, elegant formulae and rational actors beloved of emissions modellers.

More disconcertingly, it serves to gnaw away at the underlying logic of mitigation.

If the emissions reduction performance of a project or policy is so buffeted by uncertainty – and, crucially, if the counterfactual baseline that the project or policy is supposed to be improving is so unknowable – how can we be confident that our efforts are worthwhile? How can different mitigation approaches be compared? How can we gauge their cost-effectiveness? How can emission reductions be packaged up and traded?

Clearly, some greater humility about our emission reduction claims is in order.

Recognition of the estimation challenge also demands consideration of that concept that is loved and loathed in equal measure by the climate community: the theory of change.

To be continued…