Светлое будущее будет темным, но холодным

In this context, the introduction of personal carbon allowances (PCAs), a mitigation policy proposal developed in the 1990s5, is ripe for revisitation. This policy aims to link personal action with global carbon reduction goals. A PCA scheme would entail all adults receiving an equal, tradable carbon allowance that reduces over time in line with national targets. In its original design, the allowance could cover around 40% of energy-related carbon emissions in high-income countries, encompassing individuals’ carbon emissions relating to travel, space heating, water heating and electricity. Allowances were envisioned to be deducted from the personal budget with every payment for transport fuel, home-heating fuels and electricity bills. People in shortage would be able to purchase additional units in the personal carbon market from those with excess to sell. New, more ambitious PCA proposals include economy-wide emissions, encompassing food, services and consumption-related carbon emissions, for example.

In the 2000s, when the UK government explored the adoption of PCA scheme to reduce carbon emissions from households, the idea was rejected due to claimed low social acceptability, technological barriers and high implementation costs. PCAs were defined in the early 2010s as “a big idea that never took off”, and ‘“a policy ahead of its time”. No large-scale national programmes have so far investigated PCAs as a policy option. By 2021, arguably, the policy window of opportunity provided by the COVID-19 crisis, in combination with the need to address worsening climate and biodiversity crises26, and by the advancements in information and communication technologies, particularly artificial intelligence (AI), could improve the feasibility and attractiveness of PCAs to policymakers and the public.

Similar to a carbon tax, a policy with which it is often compared, the economic mechanism of PCAs is envisaged to influence decision-making by assigning a visible carbon price to the purchase and use of fossil-fuel-based energy in the first instance, and possibly also to consumption-related emissions in more advanced PCA designs.

For instance, machine-learning algorithms could be trained to automatically gather all the available information on someone’s emissions, and to fill data gaps and accurately estimate an individual’s carbon emissions on the basis of limited data inputs such as stops at petrol stations, check-ins at venues and travel histories.