Babies and Carbon: framing matters
A closer look at the climate mitigation gap study.
Recent media coverage has widely spread the idea that avoiding baby-making is the number one far-and-away best way to reduce carbon emissions. The idea was based on this study. A widely shared graph was extracted from the study to vividly demonstrate that having a child is so significantly more impactful than any other carbon-lowering lifestyle choice as to make all your dedicated bicycle riding, industrial meat renouncing, and recycle-bin-using endeavours basically worthless.

Here is the dramatic graph, as published in Science Alert.
It’s crushing for the environmentally aware, liberating for the inconvenienced and a really good example of the scientific method misapplied…
So how did the study misapply scientific methods, what would better methodology look like, and why is getting it right so important?
The Abstract starts out by stating the aims of the research with a summary of its methodology:
“ Here we consider a broad range of individual lifestyle choices and calculate their potential to reduce greenhouse gas emissions in developed countries, based on 148 scenarios from 39 sources.”
The methodology admirably involves collating a large number of data sources to reduce the impact of outliers, but with the imposed rule that the scenarios all come from developed countries.
The next sentence makes the ambitious claim that the findings will lead to recommendations that will be both widely applicable and high-impact. The sentence suggests that both systemic change and individual change will happen together:
“We recommend four widely applicable high-impact (i.e. low emissions) actions with the potential to contribute to systemic change and substantially reduce annual personal emissions”
At this point, a couple of questions come to mind before continuing to read the paper:
· How does isolating developed countries as a group contribute to the conclusions?
· Is systemic change merely the sum of individual choices?
Reading on, the Introduction ironically hints at some of the limitations of the research that were previously glossed over: individual lifestyle changes won’t be enough in the long-term.
“This has prompted calls (Anderson 2015) for near-term, profound emissions cuts that may require changes in lifestyle choices from the high-carbon individuals estimated to produce nearly 50% of emissions (Gore 2015). National policies and major energy transformations often take decades to change locked-in infrastructure and institutions, but behavioural shifts have the potential to be more rapid and widespread”
The Methods section of the paper provides a rationalisation for the choice to limit data to developed countries:
“The choice to focus on developed regions was motivated by the higher emission and consumption levels in those regions, which demand steeper emissions cuts in order to attain the same, low per capita emissions target that will avoid dangerous planetary warming (Girod et al 2013).”
The choice in the Methods to draw only on data from developed countries faces a significant problem when you consider the differences in carbon emissions per capita that exists in the world, as illustrated in the graph below.

The graph illustrates the pattern of developed countries having higher CO₂ emissions per capita and developing countries having lower CO₂ emissions per capita.
It makes no sense to calculate the potential for individual lifestyle changes to reduce individual CO₂ emissions based purely on projections from existing data from the highest carbon producing systems that the world has.
In the case of the calculations of carbon emissions from a child, not only are the calculations projected from “historical rates” of developed world children, but the carbon emissions of the children AND the children’s descendants are all assigned to the parents. This an incidence of multiple counting. There is even more multiple counting embedded in the emissions data of a child, because why does a child create carbon emissions? A child anywhere in the world creates carbon emissions merely by living, but a child in the developed world would usually create more emissions based on a typical lifestyle of being driven in a car, using fossil fuel-based energy and creating a lot of non-biodegradable waste. The graph, therefore does not compare like with like, but compares one action (having a child) that includes multi-generational impacts plus the impacts of all the other actions in the graph, with the other actions that are considered in isolation and only for a period of a year.
This leads to the next question of: is systemic change merely the sum of individual choices?
Some criticisms of the distorted carbon foodprint graph have suggested that the very widespread failure to identify the flawed thinking of the research that produced it is a symptom of our times: we have developed a collective cognitive bias towards assuming that people should use their individual consumer power to fix socio-environmental problems without the support of other people or institutions.
“ -isms” can be pretty divisive, but they do help to think about the bigger picture. As the mitigation gap study is about recommendations for what individuals could do in the specific case of moving to low carbon living, I’ll look at how this plays out in the car driving example by considering bicycle riding as an alternative.
For a bicycle rider, riding on the typical road is not a choice to be taken lightly. The image on the left shows a typical road in which the bicycle path is part of the general road, with no protection from cars alongside. It’s a hazardous and unpleasant set-up for bicycle riders, and a huge disincentive to adopt cycling. The image on the right shows the same road with a separated bicycle path, thus supporting bicycle riders to safely choose cycling as a commuting option.
Beyond safe separations, there are a number of other design considerations that would open up cycling as an option to specific groups of people (beyond the die-hard cyclists). Consistent lighting could be important to nighttime female cyclists, even surfacing could be important to elderly cyclists and cycle path connections to train or tram stops could be important for longer distances.
Each of these design elements requires an investment from the relevant level of government, that would only become worthwhile if there were enough bicycle riders to make the investment for. In terms of progressive staging of infrastructure, road surfacing upgrades might come first, then improved lighting, then more connection points. So if there were only a few bicycle riders, then the cycle path improvements would never be made and larger numbers as well as specific groups of people would not even take up cycling. On the other hand, if increased numbers of people take up cycling as an alternative to car driving, and they do this in communication with their city about improvements that they need, they open the door for better cycling conditions for more people. And so, people’s choices to adopt an alternative to car driving, if communicated and connected, creates more change than the sum of each individual choice.
There’s a lot more that can be said about the specific actions advocated by this research paper and about how ignoring broader human needs tends to result in the failure of purist policy recommendations. For example, if people cut out meat entirely but replace it with processed food alternatives, they push up their carbon emissions again.
Research like this shows the importance of realistically considering human needs in conjunction with environmental needs, and becoming more specific about what types of ecological production systems and human choice landscape systems address both. Individuals need the support of economic and social systems to be able to adopt these ways of living and using resources, and the research that is done should include an understanding of the environments that people live in.
In short, population size is of course a contributing factor to carbon emissions, but this is because population size is a magnifier of systemically high carbon lifestyles, not because population is itself the cause. High carbon systems are embedded in the way that we live, move, eat and consume. This may seem overwhelming but it also means that there are multiple points of opportunity to adopt low carbon lifestyles that already exist and sometimes have always existed. Social and institutional support would help.
Research has the important role of accurate problem identification and it is therefore of questionable value when it misidentifies root causes. We need good research into system-level causes of climate change, and also what role individuals can play in helping shift these systems towards sustainable versions. It’s also so important for this research to be publicly accessible (as this study was) so people can understand where statistics come from and be part of conversations about change.
Do you have any links to a peer-reviewed study on the carbon impacts of a particular location’s transition from a high carbon system, e.g. after a successful public transport policy, or a switch to food without synthetic pesticides? Please share below.


