…. and CO2, Energy Use, Energy Efficiency & Food production, All Coupled to GDP.
The figures have been changed to reflect continued research, and the text is in the process of being updated.
To draw the whole history of global climate change I needed to go back to 1780 to find the time when human caused CO2 emissions and resulting increases in the concentration of atmospheric CO2, measured in PPM (parts per million) suddenly broke away from the pre-industrial background (Figure 1). Notice how the variation in the pre-industrial (yellow) and industrial (blue) CO2 concentration curves differ. Finding that point at which human caused climate change began became the anchor point for interpreting the more recently collected data.
In Figure 2. The first thing you see is the highly irregular line for the recorded average surface temperature of the earth. The Atmospheric CO2 curve in Figure 1, and the modern CO2 emissions curve in Figure 2 are both relatively smooth, so the highly irregular temperature measurements are most likely due to the difficulty of measuring the temperature of the complex ever moving and changing weather systems. So we assume that the “real” temperature curve would reflect the steady increase in CO2, just hidden by the noisy data. To see it we project an exponential curve (the dashed green line) starting from 1780, scaled to best fit the 1850 to 2016 temperature record. That becomes the most likely “real” rate of climate change. That is also the rate of exponential temperature increase that is quite likely to also continue, …until something earth-shattering takes place to change the economy so long accustomed to ever more rapid expansion.
The shape of the curve since 1780 also shows that industrial CO2 emissions have been growing at an approximately exponential rate from the start, at about 1.62% per year, and so doubling CO2 pollution every 43 years. Global temperature would have started rising back then too, even if initially immeasurable, of course, as it is the rising concentration of CO2 that measures the degree to which CO2 acts as a blanket to hold in heat from the sun. So when looking at more modern data one can mathematically put the starting point for other curves at 1780 too (Figure 2).
There are a great many interesting issues raised by these relationships between the trends and the local variations seen in the data. Some of the shapes in the data do seem to reflect real phenomena, like how the variations in the temperature data are large and lasting at first and then smaller and briefer later. I think that’s likely to be a real indicator of further accelerating increase in the future. Of course that’s just a guess based on poorly understood fluctuations, so only an interesting hypothesis.
A great opportunity to check this method against the IPCC’s elaborate calculations is to compare my results with the IPCC’s Oct 7 just released warning that by 2040 we could have 2.7 deg F warming, and have crossed very dangerous thresholds. My approach is based on projecting growth curves from the ~1780 emergence of fast growing rates of CO2 concentration in the atmosphere, and projecting into the future from that point with a curve that as closely as possible fits the detailed temperature record, as summarized in Figure 3. Some will say it’s luck though my method is highly constrained if you study it, but my method predicts the IPCC estimate quite closely.
Since “efficiency” shows no sign of slowing this process down, only it would seem accelerating it, we need to ask what it has really done for us. Industrial efficiency has been such a hallmark of economic development throughout modern history, as we consumed ever more ever faster with accompanied increasing pollution and other impacts. How then did the world consensus develop that efficiency is reliable way to make all these curves turn down rather than up. It’s truly a deep mystery, of how every-day intuition simply misled us, despite loads of clear evidence to the contrary, and of course also well publicised observations by scientists all along that the opposite was happening, like from Stanley Jevons in the late 1800’s.
We also seem to have just forgotten that what GDP actually measures is the economy’s total real valued consumption. With ever faster increasing real valued consumption, priced as the market basket of goods and services of a typical family. Somehow that original meaning disappeared when “sustainability” became so socially important, and governments, economists and activists all invented concepts for having multiplying consumption without the effects, also resting on the idea that efficiency would ultimately “dematerialize” GDP, forgetting that GDP is itself a measure of material value.
A more hidden pattern in the data is that CO2 is *accumulating* in the atmosphere, and so has its warming effect for around 200 years. This seems to be why there is such serious concern that we find a way to sequester a large part of the CO2 we’ve already put in the atmosphere. Even if we stop (somehow) adding more to the atmosphere, the climate could continue to warm for some time.
When you put the most modern indicators of economic growth all on the same curve (Figure 4), you see the clear steady relation between them called “coupling.” Each curve shows a different growth rate, but each one is also in constant relation to all the others! That’s what “coupling” means. This way of presenting the data also demonstrates a powerful method of comparing the relation of indicators of growth that have different units of measure. Here the units of each curve have been scaled so that the curve’s value in 1971, in proportion to the value of GDP, is the same as the ratio of their growth rates. So with the growth rate of energy being about 2/3 that of GDP, the starting value of energy use in 1971 is set at about 2/3 the value of GDP. At the bottom of the graph the units labels ending in ” _i ” are the ones “indexed” to GDP in this way.
It’s a powerful technique for understanding complex systems, such as the world economy, that behave smoothly as a whole. The most important observation is just that. The system as a whole and these whole system indicators are not separate variables, and the smoothness of the curves shows the system as a whole behaving smoothly as a whole over time. At the right are the individual growth rates for each indicator, and the period of years in which each doubles in size. So in total consumption (GDP) has been doubling in real terms every 22 years, if not in your neighborhood, at least in total for the system as a whole. That’s the real meaning of exponential growth, as systems for multiplying systems, doubling and doubling till something naturally upsets the process. Nature offers two basic options for how they either stabilize or destabilize to reach their limits. That’s what we now need to understand to chart a course away from fooling ourselves about growth without consequences. In nature growth, just like pregnancy, ALWAYS has consequences.
From our local views of the world that often does not seem to be at issue, though it really is the main force behind all the changes everyone is struggling to adapt to. Individual businesses, cities and countries generally have a quite irregular experience, as their roles in the whole continually change. What the smoothness of the curves and the change in the system as a whole really means is that the world economy is working the just the way it is (financially) supposed to. It is being globally competitive the way money managers manage it, and continually reallocating resources and business to where they will be best utilized, resulting in most every part having somewhat irregular experience to make the whole behave smoothly. The uniformity of these global indicators also says is that their origins all point back to ~1780, when modern economic growth began. We have reasonable measures US economic growth from ~1790, …and so went the world!
Smooth exponential curves and the systems generating them are, of course, among the things of nature with inherent “shelf lives”, relying on systems of developing organization of multiplying scale and complexity, certain to cross thresholds of transformative change. In nature, growth systems generally develop to one of two kinds of transformation, stabilization or destabilization, the crashing of a wave that doesn’t last for example or the thriving business that can last for generations. What characterizes the difference for the emerging systems that last is that, while becoming strong with compound growth (like the systems that don’t last also do), they become responsive and refine their systems to stay strong. In economic terms that’s remaining profit seeking they “internalize their externalities” to mature toward a peak of vitality rather then failure. It’s a choice made in mid-stream.
Understanding what will make that difference in outcome for our global growth system will partly come from people getting a better understanding of how we got here, as shown in the Figures 1, 2 & 3. The growth of technological civilization relies on ambition, creativity and resources, and methods that we could potentially change. How economic growth is largely managed by the application of business profits to multiplying business developments, what makes GDP to grow. If our decisions were to internalize our externalities that is also one of the things that might change, without really changing human ambitions, creativity or resources.
I will get around to describing the data sources for the figures, maybe making the data available, and perhaps adding other notes. I didn’t need to do anything very tricky, just a very little math and Excel. The dotted lines are the exponential trendlines that are so easy to make.
For partial reference to the data sources and more discussion of the method see the prior post on this data: Evidence of Decoupling Still Zero