Dissecting ‘Overpopulation’ Numbers

Part Two: The Perils of Per Capita

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Continuing our examination of the misuse (deliberate or not) of numbers, statistics and formulas by advocates of the “too many people” explanation of environmental destruction.

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Part One showed that the global population numbers frequently quoted by populationist authors conceal immense differences between countries. The principal conclusion was that “CO2 emissions are a problem of rich countries, not poor ones.” This means that programs that seek to mitigate global warming by reducing birth rates in poor countries are at best misguided.

Now we look at the misuse of “per capita” statistics. [1]

See also
Part One: Population Where?
Appendix to Part Two: Rates versus Ratios

* * * * * * *

By Ian Angus

In an official briefing, the largest and most influential populationist group in the world explains why it favors a “population-based climate strategy”:

“The most effective national and global climate change strategy is limiting the size of the population. … A non-existent person has no environmental footprint: the emissions ‘saving’ is instant and total.

“Given an 80-year lifespan and annual per capita emissions (2006) of 9.3 tonnes of CO2 … each Briton ‘foregone’ – each addition to the population that does not take place – saves 744 tonnes of CO2.”

The UK-based Optimum Population Trust (OPT) goes on to quantify the lifetime saving from preventing one birth at £30,000 – a “nine million per cent” return on a 35-pence investment in condoms.[2]

We might view that as a feeble attempt at humor, had OPT not published a clearly serious study in August 2009, purporting to prove that birth control is the most cost-effective way to reduce carbon emissions. The methodology involved forecasting the number of unwanted births that might be eliminated between now and 2050 if modern birth control were universally available – and then multiplying the number of non-people by the current per capita emission rates in the countries they wouldn’t be born in. Result – 34 fewer gigatonnes of CO2, at a cost of only $7/tonne.[3]

Prominent US populationist Lester Brown made a similar calculation to confidently predict that if the world’s population by 2050 matches the UN’s “low” projection instead of the “medium” projection, we will reduce our energy needs by the equivalent of 2,792 million tons of oil. He arrived at that improbably precise figure by multiplying the difference between the two population projections by per capita energy use.[4]

The source

OPT, Brown, and the many others who make similar use of per capita emissions figures, are using an argument that seems to have originated with Paul and Anne Ehrlich, for forty years the world’s most persistent and influential promoters of populationism. Their 1968 book The Population Bomb is the best-selling book on environmental issues of all time, and to this day it’s rare to find a magazine article on population that doesn’t cite or interview them.

In 1970, the Ehrlichs published a college-level text book whose central premise was set out in the Introduction: “The explosive growth of the human population is the most significant terrestrial event of the past million millennia. … Spaceship Earth is now filled to capacity or beyond.” [5]

They tried to express their views mathematically:

“It is axiomatic that the total impact of a society on the ecosystem can be expressed by the relation

I = P*F

“Where I is the total impact, P is the population size, and F is the impact per capita.” [6]

For F, you can substitute “per capita emissions” or “ecological footprint” or any other term that purports to measure individuals’ effect on the environment. Then, according to the formula, one person’s impact will be 1 times F, two people’s impact will be 2 times F, and so on.

Although longer versions of the formula have been proposed, this one is still widely used, both in narratives like the OPT statement quoted above, and in formal demographic studies. [7]

The median isn’t the message

At first glance, I = P*F appears obvious and undeniable – that’s why it remains central to populationist arguments. But its apparent simplicity conceals a common error: it mistakes an abstraction (an average or median) for concrete reality.

To illustrate this, we can turn to another field entirely. In 1982, the noted evolutionary biologist Stephen Jay Gould discussed his reaction to learning that he had a type of cancer for which the median survival time was just 8 months. His article was titled, profoundly, “The Median Isn’t the Message.”

“We still carry the historical baggage of a Platonic heritage … with its emphasis in clear distinctions and separated immutable entities, leads us to view statistical measures of central tendency wrongly, indeed opposite to the appropriate interpretation in our actual world of variation, shadings, and continua.

“In short, we view means and medians as the hard ‘realities,’ and the variation that permits their calculation as a set of transient and imperfect measurements of this hidden essence. If the median is the reality and variation around the median just a device for its calculation, the ‘I will probably be dead in eight months’ may pass as a reasonable interpretation.

“But all evolutionary biologists know that variation itself is nature’s only irreducible essence. Variation is the hard reality, not a set of imperfect measures for a central tendency. Means and medians are the abstractions.”

Instead of despairing, Gould set out to learn how survival times varied concretely, and to find how he could maximize his chances of living longer than the “median.” In fact, he lived 20 more years. [8]

The populationist equivalent of “The median survival time for people with my kind of cancer is 8 months, so in 8 months I’ll be dead,” is: “Canada’s per capita emission rate is 17.2 tonnes, so reducing the population by one will cut emissions by 17.2 tonnes.”

Measures such as “emissions per capita” or “ecological footprint” are created by dividing a country’s total population into the total emissions generated in that country, or the total resources used there, or something similar. For comparing countries, a per capita figure can be more meaningful than a total – for example, the usual media assertion that China is the largest emitter of greenhouse gases conceals the fact that China has over four times the population of the number two country, the USA.

But per capita statistics can be very misleading if we forget that they are abstractions and treat them as concrete measures of the impact of individuals on the environment.

Just as Gould’s median survival time concealed a great range of possible outcomes, so per capita emission rates (or any other form of pollution per capita) conceal great variations in the pollution that can actually be attributed to specific persons or groups.

What’s included?

Perhaps the most obvious problem is that a large proportion of green house gas emissions are not caused by individual behavior at all. Simon Butler and I made this point in a recent article about claims that reducing immigration will reduce emissions.

“most emissions are caused by industrial and other processes over which individuals have no control.

“In Canada, for example, no change in the number of immigrants will have any effect on the oil extraction industry at the Alberta Tar Sands, described by George Monbiot as ‘the world’s biggest single industrial source of carbon emissions.’

“Reducing immigration to the United States will have no effect whatsoever on the massive military spending – up 50% in the past decade – which ensures that the Pentagon is the world’s biggest consumer of oil. …

“Closing Australia’s borders would have had no effect on the climate denial policies of the previous Liberal Party government, or on the current Labor government’s determination to continue Australia’s role as ‘the world’s largest ‘coal mule.’”[9]

Reducing immigration won’t change any of those things, nor will reducing the population by other means. In fact, reducing population is more likely to have the paradoxical effect of increasing per capita emissions – proving once again that this statistic is an abstraction.

So at the very least, before claiming to predict the emission saving that will result from preventing one or a hundred or a million births, OPT should calculate a per capita figure that only includes emissions that are caused directly by individuals.

Wealth Matters

But even that wouldn’t make per capita emissions a useful guide to the effect of increasing or decreasing population. It may improve the numerator (emissions), but it still uses a denominator (population) that, as Marx wrote, is itself an abstraction.

According to an old joke, if you stand with one foot in the oven and the other in the freezer, on average you’re reasonably comfortable. That’s exactly the problem with “per capita” – it’s an average that conceals inequalities within the population.

A study published by the Canadian Centre for Policy Alternatives illustrates this. Drawing on path-breaking research by a former Director at Statistics Canada, Size Matters “finds that the ecological footprint of high-income Canadian households is substantially greater than that of everyone else.”

“The richest 10% of Canadian households are leaving behind an ecological footprint of 12.4 hectares per capita. To put that finding in context, their per capita ecological footprint is 66% higher than the national average. …

“The ecological footprint of the richest 10% of Canadians is nearly two-and-a-half times that of the poorest 10%.” [10]

The average ecological footprint increases gradually from 5.03 hectares in the poorest group to 8.87 hectares for those just below the top – and then it jumps by 40% to 12.42 hectares for those in the top 10%. As a CCPA news release announcing the report says, “When it comes to environmental impact, it really is a case of the rich and the rest of us.” [11]

Canada is not unique. A survey of household GHG emissions in India found even more extreme disparities – “the highest income group in India, constituting merely 1% of the population, emits four and a half times as much CO2 as the lowest income group consisting of 38% of the population.” A presentation accompanying that study concluded:

“There is no average India but a major divide between the emissions between the CO2 emissions of the rich and the poor.” [12]

Global Inequality vs Per Capita Pollution

In The Political Economy of the Environment, James K. Boyce considers the impact of global inequality on the environment, comparing the wealth not of countries but of people. He cites 1988 UN figures showing that the richest 20% of individuals in the world had average incomes of $22,800 each, while the poorest 20% averaged $163 each – a 140 to 1 ratio. He concludes:

“The amount of environmental degradation associated with a dollar’s worth of production and consumption is likely to vary across individuals and countries.

“Whether degradation per dollar is higher for poor people or for rich people is a question on which little systematic evidence has been collected. If the amount of degradation per dollar were roughly the same for both groups, the richest 20 percent of the world’s people would account for 140 times as much environmental degradation as the poorest 20 percent.

“Put differently, the total amount of degradation for which the poorest fifth is responsible could equal that for which the richest fifth is responsible only if the degradation per dollar for the poor were 140 times greater – a rather implausible suggestion.

“This simple comparison suggests that environmental degradation driven by the economic activities of the rich is likely to surpass, by a substantial margin, that driven by the economic activities of the poor.” [13]

The gap between rich and poor has expanded since 1988. In 2005 Thomas Pogge of Columbia University reported that the income ratio between the richest 10% of the world’s population and the poorest 10% was 320 to 1.[14] This makes Boyce’s conclusion even more credible today.

This gap is, inevitably, reflected in access to birth control: According to leaders of International Planned Parenthood, “In every country for which we have data, women in the highest quintile [richest 20%] have much better access to contraceptives when compared with the lowest quintile.” [15]

Despite this, groups such as Optimum Population Trust and the Sierra Club’s Population Justice Project claim that future Third World emissions can be significantly reduced by meeting “unmet needs” for birth control – that is, by providing reproductive health services to the world’s poorest people, who have the least access to birth control, who cause the least environmental damage today, and whose children are the least likely to contribute to increased emissions in coming decades.

As noted demographer Wolfgang Lutz has pointed out:

“within each country the rich have fewer children and emit significantly more than the poor. India, for example, has a per capita carbon emission of only 0.21 tons. Although this is one of the lowest in the world there is every reason to assume that the richest 10 percent in India emit at least 10 times more than the bulk of the population and that the expected future population growth of India comes almost entirely from the poor segments of the population. If this is true, the actual impact of population growth on carbon emission will be much less than national averages would imply.” [16]

We can restate his final sentence:

Reducing the number of babies born to poor women in India will have much less actual impact on carbon emission than national averages (per capita emission numbers) would imply.


A 2009 study of the impact of global inequality on green house gas (GHG) emission levels, by Dr. David Satterthwaite of the UK’s International Institute for Environment and Development, found that “a significant proportion of the world’s urban (and rural) populations have consumption levels that are so low that they contribute little or nothing to such emissions.”

Among his conclusions:

“It is very misleading to discuss responsibility for GHG emissions per person using national averages because of the very large differences in per capita emissions within each nation between the highest-income and lowest income groups – perhaps a 100-fold or more difference between GHG emissions per person if we could compare the wealthiest 1 per cent and the poorest 1 per cent in many nations …

“If GHG emissions were allocated to people (not nations) on the basis of the contribution of their consumption to GHG emissions, it is likely that the wealthiest one-fifth of the world’s population would account for more than 80 per cent of all GHG emissions (they have more than 80 per cent of the world’s income) and an even higher proportion of historical contributions to GHG emissions. The consumption of the one fifth of the world’s population with the lowest income levels may account for only around 1 per cent of all GHG emissions.” [17]

Part One of this article dissected the global population numbers that populationists so often cite, and concluded that CO2 emissions are a problem of rich countries, most of which have low birth rates, not poor ones with high birth rates. This means that the often-claimed correlation between global population growth and global warming is an illusion.

In Part Two, we’ve seen that per capita numbers, which are also used to support populationist conclusions, conceal substantial inequalities within populations and between people. When we dissect the numbers, we find that most emissions and pollution cannot be attributed to the actions of individuals, and that insofar as some can be, they are mainly caused by rich people, not poor ones. This means that using  ‘per capita’ numbers or national averages to calculate the environmental impact of population changes will produce misleading results. (In short: I=P*F is wrong.)

If you are studying ants, the environmental impact per insect and per nest may be a useful basis for predicting what will happen if the ant population increases. That’s because you are studying largely undifferentiated units that behave in very predictable ways.

But human beings aren’t insects. Over the centuries, human populations have interacted with the environment in many different ways, and our societies are deeply divided by wealth, class, and power. Because they  ignore those differences and reduce social complexity to numbers, populationists inevitably diagnose social ills incorrectly, and just as inevitably they propose cures that won’t work.


Reference notes

[1] Judging by some of the responses I received to the first article in this series, I should point out that disagreeing with the analysis and solutions proposed by populationists is not the same as believing that the world’s resources are infinite, or that economic growth can continue forever, or that population size is irrelevant. As someone said, “Saying that I’m not at the South Pole does not mean that I am at the North Pole.”

[2] “A Population-Based Climate Strategy – An Optimum Population Trust Briefing.” Accessed May 26, 2010 at http://www.optimumpopulation.org/opt.sub.briefing.climate.population.May07.pdf

[3] Thomas Wire. Fewer Emitters, Lower Emissions, Less Cost: Reducing Future Carbon Emissions By Investing in Family Planning. Accessed May 26, 2010 at http://www.optimumpopulation.org/reducingemissions.pdf

[4] Lester Brown et al. Beyond Malthus: Nineteen Dimensions of the Population Challenge. WW Norton, New York 1999. p. 47

[5] Paul R. Ehrlich and Anne H. Ehrlich. Population Resources Environment: Issues in Human Ecology. WH Freeman & Company, San Francisco. Second Edition. 1972. pp 1, 3

[6] Ibid, p. 252. This is an early version of the IPAT formula: Impact = Population times Affluence times Technology. Although it contains more terms, IPAT is effectively identical to – and has the same weaknesses as — its predecessor.

[7] See, for example, Brian O’Neill et al. Population and Climate Change. Cambridge University Press, 2001. p. 117.

[8] Stephen Jay Gould. “The Median Isn’t the Message.” Bully for Brontosaurus: Reflections in Natural History. WW Norton, New York, 1992. pp 473-478

[9] Ian Angus and Simon Butler. “Should Climate Activists Support Limits on Immigration?” Climate and Capitalism, January 24, 2010. Accessed May 27, 2010 at https://climateandcapitalism.com/?p=1562

[10] Hugh Mackenzie, Hans Messinger, and Rick Smith. Size Matters: Canada’s Ecological Footprint, By Income. Canadian Centre for Policy Alternatives, June 2008. p. 5. Accessed May 27, 2010 at http://www.policyalternatives.ca/publications/reports/size-matters.

[11] Ibid.

[12] Hiding Behind the Poor. Greenpeace India Society, October 2007. Report, news releases and presentation accessed May 28, 2010 at http://www.greenpeace.org/india/footer/search?q=Hiding+Behind+the+Poor

[13] James K. Boyce. The Political Economy of the Environment. Edward Elgar, Northampton, MA, 2002. p.6

[14] Summary of a discussion of the book Worlds Apart, sponsored by The Carnegie Endowment for International Peace, September 28, 2005. Accessed May 28, 2010 at http://www.carnegieendowment.org/files/Worlds_Apart_Discussion.pdf

[15] Carmen Barroso and Steven W. Sinding, “Cairo: The Unfinished Revolution.” Laurie Mazur, ed., A Pivotal Moment: Population, Justice, and the Environmental Challenge. Island Press, Washington, D.C. 2010. p. 250

[16] Wolfgang Lutz. “World Population Trends: Global and Regional Interactions Between Population and Environment.” In Lourdes Arizpe et al, eds. Population and Environment: Rethinking the Debate. Boulder: Westview Press. 1994. p. 59

[17] David Satterthwaite, “The implications of population growth and urbanization for climate change.” Environment & Urbanization. 2007 Vol. 21(2) p. 545


  • Ian,

    I’ve got a few points on the statistics of all this, which while a bit complicated I think will be helpful for the layperson (and those more knowledgeable about climate change and/or statistics might like to point out any errors).

    Firstly: distributions, such as of income, are indeed important to understand, rather than just a central tendency, but the latter is often important as well. With regard to this tendency, an important point is that a median is not the same as the average, or mean. The mean of a set of values comes from adding all the values up and dividing by how many values there are; the median is lining all the values up and picking the one in the middle. They are in fact the same in the familiar bell curve distributions which many measurements fall into, particularly natural ones, but not skewed ones, like income, and even more wealth, under capitalism, or the cancer survival rates in Gould’s essay http://www.phoenix5.org/articles/GouldMessage.html

    This is important because means as a summery distort a skewed distribution a lot more than a median. If Gould had been given the mean survival rate he would have freaked out more as it would have been even less than the median 8 months he was told, because the bunching towards the bottom pulls the average below what the values for most individuals actually is, whereas the median indicates that half the people so far measured have lasted less than 8 months, and half more, stretching out to the 20 years Gould got and more. Because under capitalism there’s a relatively small range between even welfare recipients and well-paid workers compared to a few bourgeois squillionaires bunching the distribution well towards the top end, an average income figure will be well above what most people earn, while the median tends to be around a typical full time wage. So it can be a useful summary, though often a more detailed summary, like citing a range around the mean or median (as appropriate) or chopping up the distribution into bits such as deciles, is often more useful.

    Secondly: In preparing for a talk on population, consumerism and the environment for the Socialist Alliance in Melbourne in a couple of weeks, l’ve been thinking about valid ways of statistically showing actual relationships between environmental impact, population, “affluence” and “technology”. That’s because the (very partial) value of the I=PAT formula is that these factors are in some ways related, even if they don’t in and of themselves tell us the whole story, particularly about causation. What I’ve been mucking around with might prove useful I think in helping us understand the attraction of such formulae, because they relate to real if partial phenomena, and also help us put the case for the real explanations and solutions.

    What I’ve done is get together, for 12 (so far) countries across the size and wealth spectrums, and recorded annual CO2 emissions (a measure of I), population, GNP (a measure of A). I’m not really sure what the populationists means by T – how much tech? How advanced it is? When the iphone G4 is released does T go up a bit? I’ve got something to say about a valid measure of T below, but as I can’t think of anything easy to look up now I’ll ignore it. Which is lesson number one: when constructing a model the researcher, not God or the universe, decides what variables to include.

    Anyway I got my I, P and A numbers, and put them into the stats application. I got that to graph I vs P – and there’s definitely a moderate to strong linear positive relationship (for the stats heads, r=0.66). Countries with higher populations tend to produce more C02. If we do that for I vs A, we get a stronger positive relationship (r=0.80). Countries with higher GNPs tend to produce more C02. So you can see why many concerned punters will think, well it’s the population and the affluence causing this shit we’re in.

    If we want to see if we can make a more detailed mathematical model of how these variables relate together, with I as the dependent variable and the others as the independent variable (i.e. I = something to do with P and A) it actually makes no mathematical sense when we have the independent linear relationships mentioned above just to multiply P and A together. What we (or the computer) do is a technique called a regression, which works out a “line of best fit” of the form:

    I = a +bxP + cxA

    Where a,b and c are constants that my trusty Mac Mini works out. These don’t tell you directly about the different contributions of the two variables though, as their scales are completely different, but the program also works out “standardized coefficients” which convert variables to the same scale and so tell you about the proportional contributions. Here we get 0.51 for P, and 0.70 for A. That is, GNP makes about 150% of the contribution to CO2 production that population does in this model (stats heads will be interested that for the model r squared = 0.90, that is these variables account for 90% of the variation in I). So this exercise seems to be useful in providing evidence that, other things being equal, amount of stuff produced is of more concern than population in CO2 emissions (actually if there’s interactions between independent variables you should put in another terms that does multiply them, dxPxA to account for the interaction, but this seems to have little effect here so I’ve ignored it).

    I think we could add technology to our model in some way. While as mentioned a single measure of technology makes no sense to me, maybe there’s relatively straightforward measures of 2 variables: a “good T”, e.g. an index of how much energy is produced by turbines and solar panels, and a “bad T” e.g. an index of much energy is produced by oil and coal. I reckon these would make a contribution to the equation, with “good T” having a negative coefficient (more good stuff associated with less CO2), and a “bad T” having a positive one (more good stuff associated with more CO2). They’d have to be interactions between our A and these Ts (the effect of extra A would depend on the extent to which it was produced with good or bad T). This could also aid our arguments, by suggesting the importance of changing the tech c.f. population and affluence.

    But, the populationists might respond, well you’ve shown that population as such still has this big effect, so maybe we still should slash immigration or sterilize undesirables, or whatever their particular paneceas are. This though misunderstands two basic limitations of this sort of exercise. Firstly, the First Commandment of observational science, in which we observe a number of variables extracted from complex real world systems at one point in time, is: CORRELATION AIN’T CAUSATION. We’ve only shown association, and the pattern of causation might lie in underlying factors not so amenable to direct observation, and/or various interactions we haven’t uncovered (as opposed to experimental science, in which we have a lot more control over a limited number of variables and can plausibly show mathematical models of causation).

    Secondly, a related limitation is that these associations are only valid from time the data was observed. Regression can be used for “prediction”, but only in the sense we can “predict”, from our equation, the CO2 emissions of a country we didn’t use for the analysis, at the same point in time that we obtained the measurements we did use. A pointless exercise, as we could just look the actual number up. The point of the regression in this and many cases is to get an idea of the relative contribution of the different factors.

    I.e. taking these two limitations together, it’s not valid to say, hey let’s slash population by a certain amount by stopping all immigration next year and presto CO2 will fall by b times that amount. This policy have all sorts of effects, such as a probably catastrophic economic collapse and a rise in xenophobia, a situation which would make developing greener tech and enacting progressive social change harder.

    In short, we can’t actually prove anything through observation of complex real world systems, only build a case through varied analyses and sources of data. I think we can integrate the above into our arguments along these lines:

    * Yes we acknowledge the evidence shows that, all else being equal, more people and more stuff is associated with CO2.
    * But how do we address the issue of population, and of how stuff is produced?
    * Immigration restrictions and population control are politically perilous, and the former does nothing on the global level. The historical evidence shows that the effective way to reduce population growth is to increase living standards and women’s rights globally. But capitalism is a block to this.

    * Greener tech will ameliorate or reverse the effects of more stuff, even current evidence shows (I reasonably assumed above) and developing the tech will actually require more of many kinds of stuff (education, infrastructure). Again though capitalism is in the way of developing and generalizing this tech rapidly enough.

    * Therefore we need socialism (and the struggle for reforms along the way there).

  • If I understand correctly the text, it proposes to take into account sub-groups of a given population when trying to calculate ecological impact, such as: the richest 20% or the poorest 20%.

    It’s not clear to me that apportioning GHG emissions to individuals or groups based on their wealth is that straight-forward.

    I looked for some data on GHG emissions by sector. See for example the graph at:

    In some cases, emissions can reasonably be linked to individual wealth: Air transportation at 1.6%; clearly rich people fly more than poor people.

    Livestock and Manure at 5.1% is a little more difficult to apportion. One person can only eat so much meat! In western countries, I would guess that meat consumption is fairly well distributed across socio-economic groups. In poorer countries, that is likely not true.

    Than there are things like Cement production at 3.8%. Sure an individual (likely a rich one!) can resurface their driveway in concrete, but most cement probably gets used in “common” projects: a concrete highway, an office tower, a stadium, etc. Who gets apportioned the GHG emissions for these structures: the developers/owners or the users?

    In short, looking at that graph it seems that the main cause of GHG emissions is basically economic activity. This activity is driven by the needs of the population of a nation, of a city, of a suburb. How do we change the behaviour of these groups of people so their economic activity produces less GHG emissions?

  • So then, all we need to do is kill all people who become rich beyond a very modest threshold, and allow human numbers to grow uncontrolled until there are horizon to horizon crowds of extremely poor people on every continent. By the logic promoted in these articles their contribution to global heating will STILL be negligible.

    I find it barely credible that anyone can still continue to argue that a human population spike, unlike population spikes in any other species, is nothing much to worry about, even when it gets into that last shoot-up phase of an exponential growth curve. (Growth-and-crash curve really; in the real world runaway population-blooms always do crash)

    Of course the Pampered Twenty Percent are a disproportionate problem. You have to be very obdurately blind not to see that. But to pretend that shooting-up human numbers, at any level of prosperity or poverty, isn’t a dire problem for the planet is crazy. And one thing I’d give you very good odds on is that if we don’t do something at very high priority to stop the shoot-up, and to start our numbers climbing carefully way down again, then Gaia will do it her way, with indescribable horrors.

    I don’t see much prospect of the humane, human-mediated approach actually happening. As Dmitry Orlov puts it, it wasn’t in our power to control our numbers when they were on the way up, and it won’t be in our power to control them on the way down either. But to pretend that current levels of overpopulation isn’t a problem is just wilful blindness. You aren’t a devout Catholic by any chance, are you Ian?

    • Sadly, Rhisiart’s comment is typical of what passes for logic in some populationist circles.

      Since I don’t agree that “the explosive growth of the human population is the most significant terrestrial event of the past million millennia” (Ehrlich), I must therefore be an opponent of birth control, an advocate of unrestricted exponential growth, and a crypto-Catholic.

      I answered that absurdity in the article above , but Rhisiart (sadly typical again) doesn’t seem to have bothered reading to the end, so I’ll repeat:

      Judging by some of the responses I received to the first article in this series, I should point out that disagreeing with the analysis and solutions proposed by populationists is not the same as believing that the world’s resources are infinite, or that economic growth can continue forever, or that population size is irrelevant. As someone said, “Saying that I’m not at the South Pole does not mean that I am at the North Pole.”

  • I think you concept is outdated but essentially correct. As a teacher of environmental science I can tell you the equation I = P x F has not been used for at least a decade. The equation I have taught is I = P x A x T. P is still population. A is for Affluence and replaces F and T has been added for use of technology which implies a faster thruput of energy and other resources.

    • Thanks Thomas

      I agree that I=P*A*T (also created by Paul Ehrlich) is more commonly used by scientists today — but really it is just an expansion of I=P*F. In the expanded version, F (impact per person) is divided into two components. Brian O’Neill describes this in the book I cited in footnote 7.

      The OPT statement I cited was, of course, pure I=P*F!!

      Limiting the discussion to I=P*F allowed me to explain simply what’s wrong with per capita calculations, but exactly the same criticism applies to I=P*A*T. Both A and T must be per capita or national average figures, and so will be just as misleading as F in the simpler formula.

      For more thoughts on IPAT. see Ben Courtice’s recent Climate & Capitalism article, “‘I=PAT’ means nothing, proves nothing.”

  • Given an 80-year lifespan and annual per capita emissions (2006) of 9.3 tonnes of CO2 … each Briton ‘foregone’ – each addition to the population that does not take place – saves 744 tonnes of CO2.”

    You morons, 744 tonnes of CO2 amounts to about 250 tonnes of coal. I could fit that amount of coal in my garage. It’s dick all.

    So let me get this straight. We are going to try to limit the population because each person generates about 10 tonnes of carbon a year. 10 tonnes of CO2 costs about $100 on the carbon exchange, and they consider this too expensive. These people are idiots, where did they buy their degrees? Surely even an 80 year old generates more than $100 annually in tax revenue, this makes no economic sense to try to limit population when all it costs is about $100 per year in carbon. Most people spend far more than this on beer or coffee every year, most spend far more than this just to watch their tv. This sounds more like ideology talking than common sense.