By crushing emissions, the recession is saving our lives

If it weren't for the global slowdown, our planet would be in a far worse state than it already is.

In the penultimate blog of this series we consider the third dimension of this era of "Great Uncertainty", the profound environmental challenge we face. The story of our environmental crisis is the story of a series of symbolic breaches. On 10th May this year the Earth Systems Research Laboratory (an environmental observatory and part of the US National Oceanic and Atmospheric Administration) perched 11,000 feet up atop the Mauna Loa volcano in Hawaii recorded the first ever average daily carbon dioxide level in excess of 400 parts per million (ppm). CO2 levels last reached such levels some 5 million years ago.

400 ppm, just like every other such symbolic ceiling, was long considered an unattainable figure, a level we could simply not allow ourselves to hit – a kind of doomsday portend and the point at which we would need to become (if we were not already) very, very scared that the damage we had inflicted on the planet was likely to prove irreparable and irreversible.  But it came and went, just like all the others – and most of us, I suspect, no longer give it very much thought. Indeed, it may well be that we are becoming increasingly immune to such symbolic breaches as the process of environmental and ecological grieving becomes ever more familiar.

But most of us know we can’t carry on like this. We know, in particular, that we can’t afford to forget for a moment this third dimension of the Great Uncertainty, even as we grapple with its first two features. Nor can we seek to solve those aspects of the situation at the expense of worsening our prospects in relation to this third issue. At heart, we face not just a crisis of growth, but, much more significantly, a crisis for growth.

This is of course immensely difficult terrain on which think and act. But there are some things we can say and do.

First, we can remind ourselves of why the task is so urgent – and we need to do so. There are some things, climate change denial notwithstanding, that we can be pretty certain about. Interestingly, though perhaps unremarkably when you think about it, they are not about symbolic breaches like passing through the 400 ppm CO2 threshold. They are about the planet’s "carrying capacity"; and the point is that for CO2, alas, it’s a lot less than 400 ppm.

This concept allows us to identify a series of planetary boundaries – what Johan Rockstrom called "the safe operating space for humanity with respect to the Earth system… associated with the planet’s biophysical subsystems or processes".  Here, with the benefits of the latest science, we can start to counter-pose current figures on environmental degradation with expert best approximations of the planet’s carrying capacity (the point beyond which we simply cannot go without threatening human life, certainly as we know it, on earth).

The results are startling and alarming in equal measure. Adapted and updated from Rockstrom, they are summarised in the table below for just a small sub-set of the planetary carrying capacities we might consider:

Earth system processes Parameter Boundary Current level
Climate change Atmospheric CO(ppm) 350 >400
Biodiversity loss Extinction rate (no. of species per million per year) 10 >100
Nitrogen cycle Amount of nitrogen removed from the atmosphere for human use (million tonnes per year) 35 >120
Freshwater use Human consumption of freshwater (km3 per year) 4000 c. 3000
Ocean acidification Global mean saturation state of aragonite in surface sea water 2.75 2.9
Landmass usage Per cent of global landmass used for crops 15 c. 12

Data like this show that we are already in the "red zone" (where we exceed planetary carrying capacity) with respect to a number of earth-system processes and moving rapidly into it in a number of the others.

Second, we need to recognise that the global financial crisis has done more to reduce the pace (or at least slow the acceleration) of the process of global environmental degradation than anything directly intended to have such an effect. That is because it has served to reduced aggregate global growth rates. Of course, we need to be extremely careful here. For one’s enemies’ enemies do not always make good friends – and we can have environmentally unsustainable non-growth just as much as we can have environmentally unsustainable growth. Indeed, what is clear is that we have had both: the post-2008 story is only of the move from the latter to the former.

Nevertheless, what such reflections reveal is just how crucial the question of growth is to our capacity to respond to the global environmental crisis. Almost certainly, we will need to wean ourselves off growth if we are to do anything that takes us out of the "red zone" (and time-lag effects, it scarcely need be pointed out, are very considerable indeed).

So how might we do this? That’s not easy to specify in detail yet, but the starting point is, on the face of it, deceptively simple (though one should not underestimate the political difficulties of what we here propose). It is that we work collectively and globally to change the global currency of economic success – replacing the convention of growth (for that is what it is) with something else.

In effect, we need urgently to devise a more balanced and sustainable array of genuinely global (indeed, planetary) collective public goods whose promotion might eventually replace the blind and narrow pursuit of economic output as the global currency of economic success.

What’s more, it’s not too difficult to imagine what might be entailed here. Alongside GDP we would need to build a new index of economic success – a compound index, inevitably. It might include things like changes in the Gini coefficient (in the direction of greater societal equality), changes in per capita energy use (rewarding increased energy efficiency and sustainability), changes in per capita carbon emissions and other planetary boundary statistics (rewarding the greening of residual growth) and perhaps a range of more routine development indices (changes in literacy rates and so forth).

This alternative Social, Environmental and Developmental index – let’s call it SED – would be recorded and published alongside GDP and would immediately allow the production of a new hybrid GDP-SED index. Over a globally agreed timescale, the proportion of SED relative to GDP in the hybrid index would rise – from zero (now) to 100 per cent (at some agreed point in the future).

In the interim, we would, of course, gauge whether our economies were "growing", "flat-lining" or "in recession" according to the new hybrid index, moving in effect from GDP to SED in how we measured economic performance.

The changes to our modes of living over that period of time would be immense – and would need to be immense. But it’s surely what is required if we are to rectify our planetary imbalance and, even so, it’s only a necessary, not a sufficient, condition of exiting that dangerous planetary "red-zone".

This is the fourth in a five-post series on the "Great Uncertainty".

Photograph: Getty Images

Professors Colin Hay and Tony Payne are Directors of the Sheffield Political Economy Research Institute at the University of Sheffield.

OLIVER BURSTON
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How science and statistics are taking over sport

An ongoing challenge for analysts is to disentangle genuine skill from chance events. Some measurements are more useful than others.

In the mid-1990s, statistics undergraduates at Lancaster University were asked to analyse goal-scoring in a hypothetical football match. When Mark Dixon, a researcher in the department, heard about the task, he grew curious. The analysis employed was a bit simplistic, but with a few tweaks it could become a powerful tool. Along with his fellow statistician Stuart Coles, he expanded the methods, and in doing so transformed how researchers – and gamblers – think about football.

The UK has always lagged behind the US when it comes to the mathematical analysis of sport. This is partly because of a lack of publicly available match data, and partly because of the structure of popular sports. A game such as baseball, with its one-on-one contests between pitcher and batter, can be separated into distinct events. Football is far messier, with a jumble of clashes affecting the outcome. It is also relatively low-scoring, in contrast to baseball or basketball – further reducing the number of notable events. Before Dixon and Coles came along, analysts such as Charles Reep had even concluded that “chance dominates the game”, making predictions all but impossible.

Successful prediction is about locating the right degree of abstraction. Strip away too much detail and the analysis becomes unrealistic. Include too many processes and it becomes hard to pin them down without vast amounts of data. The trick is to distil reality into key components: “As simple as possible, but no simpler,” as Einstein put it.

Dixon and Coles did this by focusing on three factors – attacking and defensive ability for each team, plus the fabled “home advantage”. With ever more datasets now available, betting syndicates and sports analytics firms are developing these ideas further, even including individual players in the analysis. This requires access to a great deal of computing power. Betting teams are hiring increasing numbers of science graduates, with statisticians putting together predictive models and computer scientists developing high-speed software.

But it’s not just betters who are turning to statistics. Many of the techniques are also making their way into sports management. Baseball led the way, with quantitative Moneyball tactics taking the Oakland Athletics to the play-offs in 2002 and 2003, but other sports are adopting scientific methods, too. Premier League football teams have gradually built up analytics departments in recent years, and all now employ statisticians. After winning the 2016 Masters, the golfer Danny Willett thanked the new analytics firm 15th Club, an offshoot of the football consultancy 21st Club.

Bringing statistics into sport has many advantages. First, we can test out common folklore. How big, say, is the “home advantage”? According to Ray Stefani, a sports researcher, it depends: rugby union teams, on average, are 25 per cent more likely to win than to lose at home. In NHL ice hockey, this advantage is only 10 per cent. Then there is the notion of “momentum”, often cited by pundits. Can a few good performances give a weaker team the boost it needs to keep winning? From baseball to football, numerous studies suggest it’s unlikely.

Statistical models can also help measure player quality. Teams typically examine past results before buying players, though it is future performances that count. What if a prospective signing had just enjoyed a few lucky games, or been propped up by talented team-mates? An ongoing challenge for analysts is to disentangle genuine skill from chance events. Some measurements are more useful than others. In many sports, scoring goals is subject to a greater degree of randomness than creating shots. When the ice hockey analyst Brian King used this information to identify the players in his local NHL squad who had profited most from sheer luck, he found that these were also the players being awarded new contracts.

Sometimes it’s not clear how a specific skill should be measured. Successful defenders – whether in British or American football – don’t always make a lot of tackles. Instead, they divert attacks by being in the right position. It is difficult to quantify this. When evaluating individual performances, it can be useful to estimate how well a team would have done without a particular player, which can produce surprising results.

The season before Gareth Bale moved from Tottenham Hotspur to Real Madrid for a record £85m in 2013, the sports consultancy Onside Analysis looked at which players were more important to the team: whose absence would cause most disruption? Although Bale was the clear star, it was actually the midfielder Moussa Dembélé who had the greatest impact on results.

As more data is made available, our ability to measure players and their overall performance will improve. Statistical models cannot capture everything. Not only would complete understanding of sport be dull – it would be impossible. Analytics groups know this and often employ experts to keep their models grounded in reality.

There will never be a magic formula that covers all aspects of human behaviour and psychology. However, for the analysts helping teams punch above their weight and the scientific betting syndicates taking on the bookmakers, this is not the aim. Rather, analytics is one more way to get an edge. In sport, as in betting, the best teams don’t get it right every time. But they know how to win more often than their opponents. 

Adam Kucharski is author of The Perfect Bet: How Science and Maths are Taking the Luck Out of Gambling (Profile Books)

This article first appeared in the 28 April 2016 issue of the New Statesman, The new fascism