9 Billion. The Population Explosion Is At And End.
From: Alfred Einstead (whopkins_at_csd.uwm.edu)
Date: 09/10/04
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Date: 9 Sep 2004 18:15:36 -0700
The Population Explosion Is Over (was: The Population Explosion Myth)
>From March 6, 2003
>From Akira Knaff (Akira@nospam.com):
>http://www.junkscience.com/news/eberstad.html
>
>The Population Implosion
>By Nicholas Eberstadt
>Copyright 1997 Dow Jones & Co., Inc.
>--------------------------------------------------------------------------------
> Most of the news from this week's 23rd General Population Conference in Beijing
> has focused on the threat of overpopulation. But this danger may be a myth.
> Over the past several years, some of the world's best demographers have begun
> a dramatic reassessment of the world's demographic future. They are now
> seriously considering the possibility that the world's population will
> peak in our lifetimes, and then commence an indefinite decline.
Things have become much more clear since then. The following mathematical
regularity has emerged and has remained valid for the past 30 years:
P(1989 + t) + P(1989 - t) = 10.39 billion +/- 5 million.
Actual figures, millions (from mid-year estimates by US Census Bureau):
1974+2004: 4014 + 6378 = 10392
1975+2003: 4087 + 6305 = 10392
1976+2002: 4159 + 6233 = 10392
1977+2001: 4231 + 6160 = 10391
1978+2000: 4304 + 6085 = 10389
1979+1999: 4379 + 6010 = 10389
1980+1998: 4454 + 5933 = 10387
1981+1997: 4530 + 5855 = 10385
1982+1996: 4610 + 5776 = 10386
1983+1995: 4691 + 5696 = 10387
1984+1994: 4771 + 5615 = 10386
1985+1993: 4853 + 5535 = 10388
1986+1992: 4937 + 5453 = 10390
1987+1991: 5023 + 5369 = 10392
1988+1990: 5110 + 5285 = 10395
1989+1989: 5197 + 5197 = 10394
The curve is an exact S curve, with 1989 as the turning point.
The best-fitting logistic curve has limits at 1 billion at the dawn of
the Industrial Era and 9 billion around 2100-2150 -- thus showing that
this portion of the historical curve is an entirely separate event
from the rest of human population curve preceding, consequent to the
Industrial Revolution, itself.
The following developments have emerged since the mid 1990's:
* No nation left on Earth as of 1999 has a growth rate over 5%
* As of 2002, hardly any is over 3% (just 13), or much beyond 2%,
whereas the world, itself, was at 2% just a few years ago.
* The population curve closely fits an S curve through the time
of the Industrial Revolution from the 1800's on, that tops
off under 9 billion.
* It is almost exactly symmetric about 1989, the inflection point,
for 1974-2004: within 5 million.
* The curve is concave down, the worldwide growth is under 1%
and is coming to a screeching halt -- everywhere.
http://www.csd.uwm.edu/~whopkins/FourthWave/images/Inflection.gif
The new development is linked, in part, to the growing dominance of women,
worldwide, amongst the ranks of college-level students; as is explained in
greater depth in the reply to Kapitza's 1996 paper below.
These issues will ultimately form part of the book _Progeny_, which
is the 3rd volume of _The_Federation_Series_. A brief preview of the
trilogy, which is being presented as the successor to the Toffler Future
Shock trilogy, was provided at the Transvision 2004 conference in Toronto.
An early draft of the followup report may be found at
http://www.csd.uwm.edu/~whopkins/tv04.htm
We'll address this in 4 parts
(1) The inflection point is past and the curve tops off at 9 billion
or less
(2) The close fit of the S curve to historical data
(3) The actual growth rates in the Middle East and worldwide, as of
1999 and 2002
(4) An update to and critique of the concluding comments in the 1996
"The phenomological theory of world population growth"
(Physics-Uspekhi 39(1) 57-71).
===================
(1) The inflection point is past and the curve tops off at 9 billion
or less
We're already past the inflection point.
http://www.csd.uwm.edu/~whopkins/FourthWave/images/Inflection.gif
The curve is almost EXACTLY symmetric around 1989.
P(1989 + t) + P(1989 - t) = 10.39 billion
where P(t) = world population:
(the best-fitting S curve with 1989 as an inflection point,
see below, tops off 9.229 billion; the best fitting S curve overall
at 8.742 billion).
Actual figures, millions (from mid-year estimates by US Census Bureau):
1974+2004: 4014 + 6378 = 10392
1975+2003: 4087 + 6305 = 10392
1976+2002: 4159 + 6233 = 10392
1977+2001: 4231 + 6160 = 10391
1978+2000: 4304 + 6085 = 10389
1979+1999: 4379 + 6010 = 10389
1980+1998: 4454 + 5933 = 10387
1981+1997: 4530 + 5855 = 10385
1982+1996: 4610 + 5776 = 10386
1983+1995: 4691 + 5696 = 10387
1984+1994: 4771 + 5615 = 10386
1985+1993: 4853 + 5535 = 10388
1986+1992: 4937 + 5453 = 10390
1987+1991: 5023 + 5369 = 10392
1988+1990: 5110 + 5285 = 10395
1989+1989: 5197 + 5197 = 10394
The best-fitting S curve, with 1989 as the inflection, fitting to
1950-2004, is given by:
* linear fit against: population (1 - a exp(kt)) = b + c exp(kt)
* k = 0.040785, 99.968% goodness of fit, 55 data points
* Transition range of population in millions: 1160 -> 5194 -> 9229
* Inflection time 1989
* Maximum rate: 82.273 million/year
showing that the S curve is NOT a transition spanning the entire
range of the population curve all the way into the past, but just
that period of time starting with the Industrial Revolution in
the early to mid 1800's.
Overall, ignoring the constraint, the best-fitting S curve is as follows:
* linear fit against: population (1 - a exp(kt)) = b + c exp(kt)
* k = 0.045136, 99.976% goodness of fit, 55 data points
* Transition range of population in millions: 1401 -> 5071 -> 8742
* Inflection time: 1987.45
* Maximum rate: 82.835 million/year
======================
(2) The close fit of the S curve to historical data
The projections by the US Census Bureau fail to note this regularity
P(1989 + t) + P(1989 - t) = 10.39 billion
and so, naturally, go astray once you get past 2004, as seen below.
1973,2005 3939 + 6450 = 10389
1972,2006 3862 + 6523 = 10385
1971,2007 3786 + 6595 = 10381
1970,2008 3708 + 6668 = 10376
1969,2009 3633 + 6740 = 10373
1968,2010 3558 + 6812 = 10370
1967,2011 3486 + 6884 = 10370
1966,2012 3416 + 6957 = 10373
1965,2013 3346 + 7029 = 10375
1964,2014 3277 + 7100 = 10377
1963,2015 3206 + 7171 = 10377
1962,2016 3137 + 7240 = 10377
1961,2017 3080 + 7309 = 10389
1960,2018 3040 + 7377 = 10407
1959,2019 2998 + 7445 = 10443
1958,2020 2945 + 7511 = 10456
1957,2021 2889 + 7576 = 10465
1956,2022 2833 + 7640 = 10473
1955,2023 2780 + 7702 = 10482
1954,2024 2729 + 7764 = 10493
1953,2025 2681 + 7824 = 10504
1952,2026 2635 + 7884 = 10519
1951,2027 2593 + 7942 = 10535
1950,2028 2555 + 8000 = 10555
========
For the above-mentioned model, Sigma, the following comparisons are
made against standard historical references, and against the USA and
UN references.
It starts to pick up around the beginning of the Industrial
Revolution in the 19th century and stays close afterwards.
Year Sigma Bir Dur Hau FoF Tho UN73 RaMc
1800 1402 954 900 900 813-1125
1850 1415 1241 1265 1200 1200 1128-1402 1175
1900 1540 1633 1650-1710 1656 1625 1600 1550-1762 1600
1950 2544 2527 2516 2500 2400 2486
Year Sigma UN99
1800 1402 980
1850 1415 1260
1900 1540 1650
1910 1617 1750
1920 1734 1860
1930 1912 2070
1940 2172 2300
1950 2544 2520
1960 3050 3020
1970 3696 3700
1980 4460 4440
1990 5282 5270
1999 6007 5980
2000 6084 6060
Year Sigma USA
1950 2544 2555
1951 2588 2593
1952 2634 2635
1953 2681 2681
1954 2729 2729
1955 2779 2780
1956 2830 2833
1957 2883 2889
1958 2937 2945
1959 2993 2998
1960 3050 3040
1961 3108 3080
1962 3168 3137
1963 3229 3206
1964 3292 3277
1965 3356 3346
1966 3421 3416
1967 3488 3486
1968 3556 3558
1969 3626 3633
1970 3696 3708
1971 3768 3786
1972 3841 3862
1973 3915 3939
1974 3990 4014
1975 4066 4087
1976 4143 4159
1977 4221 4231
1978 4300 4304
1979 4380 4379
1980 4460 4454
1981 4541 4530
1982 4622 4610
1983 4704 4691
1984 4786 4771
1985 4869 4853
1986 4951 4937
1987 5034 5023
1988 5117 5110
1989 5200 5197
1990 5282 5285
1991 5365 5369
1992 5447 5453
1993 5529 5535
1994 5610 5615
1995 5691 5696
1996 5771 5776
1997 5850 5855
1998 5929 5933
1999 6007 6010
2000 6084 6085
2001 6160 6160
2002 6235 6233
2003 6309 6305
2004 6382 6378
Projections:
Sigma UN99
2010 6793 6790
2020 7369 7500
2030 7804 8110
2040 8115 8580
2050 8330 8910
2100 8696 9460
2150 8737 9750
2200 8741 10000
Sigma USA
2005 6454 6450
2006 6524 6523
2007 6593 6595
2008 6661 6668
2009 6728 6740
2010 6793 6812
2011 6857 6884
2012 6920 6957
2013 6981 7029
2014 7040 7100
2015 7099 7171
2016 7156 7240
2017 7211 7309
2018 7265 7377
2019 7317 7445
2020 7369 7511
2021 7418 7576
2022 7466 7640
2023 7513 7702
2024 7559 7764
2025 7603 7824
2026 7646 7884
2027 7687 7942
2028 7727 8000
2029 7766 8056
2030 7804 8112
2031 7840 8167
2032 7875 8221
2033 7909 8274
2034 7942 8326
2035 7973 8378
2036 8004 8428
2037 8033 8478
2038 8062 8527
2039 8089 8576
2040 8115 8623
2041 8141 8670
2042 8165 8716
2043 8189 8761
2044 8211 8805
2045 8233 8848
2046 8254 8891
2047 8274 8932
2048 8293 8972
2049 8312 9012
2050 8330 9050
REFERENCES:
U.S. Census Bureau, Population Division,
International Programs Center
Last Revised: 30 Apr 2004 14:08:41 EDT
http://www.census.gov/ipc/www/worldpop.html
SOURCES:
Bir: Biraben, Jean-Noel, 1980, An Essay Concerning Mankind's
Evolution, Population, Selected Papers, December, table 2.
Dur: Durand, John D., 1974, "Historical Estimates of World Population:
An Evaluation," University of Pennsylvania, Population Center,
Analytical and Technical Reports, Number 10, table 2.
Hau: Haub, Carl, 1995, "How Many People Have Ever Lived on Earth?"
Population Today, February, p. 5.
FoF: McEvedy, Colin and Richard Jones, 1978,
"Atlas of World Population History,"
Facts on File, New York, pp. 342-351.
1993: Kremer
Tho: Thomlinson, Ralph, 1975, "Demographic Problems, Controversy
Over Population Control," Second Edition, Table 1.
UN73: United Nations (UN), 1973, The Determinants and Consequences
of Population Trends, Population Studies, No. 50., p.10.
UN99: United Nations, 1999, The World at Six Billion, Table 1,
"World Population From" Year 0 to Stabilization, p. 5,
USA: U.S. Bureau of the Census (USBC), 2004,
"Total Midyear Population for the World: 1950-2050",
RaMc: Rand McNally & Co.
======================
(3) The actual growth rates in the Middle East and worldwide, as of
1999 and 2002
Population growth rate (in percents):
>From the 1999 CIA World Factbook and 2002 World Almanac. The
2002 figures are cited as "Natural Increase".
1999 for Serbia & Montenegro averaged at 0.03:
0.02 Serbia, 0.07 Montenegro
2002 1999
3.88 3.86 Marshall Islands
3.52 3.14 Sao Tome and Principe
3.39 3.45 Oman
3.38 3.34 Yemen
3.29 2.65 Chad
3.14 3.39 Saudi Arabia
3.09 2.96 Congo, Democratic Republic of the
3.05 3.88 Eritrea
3.02 4.92 Liberia
3.02 3.11 Comoros
3.02 2.80 Madagascar
3.01 3.37 Maldives
3.01 3.01 Mali
2.98 3.18 Solomon Islands
2.97 3.30 Benin
2.96 2.83 Uganda
2.93 2.99 Mauritania
2.91 3.32 Senegal
2.89 4.13 Somalia
2.88 3.35 Gambia, The
2.84 3.19 Iraq
2.80 2.95 Niger
2.79 2.71 Sudan
2.78 2.68 Guatemala
2.77 2.70 Burkina Faso
2.70 2.42 Belize
2.68 2.16 Ethiopia
2.67 2.14 Tanzania
2.64 2.24 Honduras
2.61 2.65 Paraguay
2.60 1.51 Djibouti
2.59 4.34 Sierra Leone
2.58 3.51 Togo
2.58 2.92 Nigeria
2.54 3.15 Syria
2.48 2.74 Laos
2.47 1.43 Tajikistan
2.46 2.55 Equatorial Guinea
2.43 2.26 Papua New Guinea
2.42 2.40 Libya
2.41 2.79 Cameroon
2.40 2.31 Guinea-Bissau
2.38 3.54 Burundi
2.37 3.95 Afghanistan
2.37 2.35 Cote d'Ivoire
2.32 2.51 Nepal
2.31 1.78 Kiribati
2.28 3.05 Jordan
2.28 2.84 Nicaragua
2.25 2.49 Cambodia
2.25 1.53 El Salvador
2.23 0.82 Guinea
2.20 2.18 Pakistan
2.20 2.16 Congo, Republic of the
2.19 2.84 Angola
2.17 2.25 Bhutan
2.15 1.44 Cape Verde
2.13 2.04 Philippines
2.11 3.30 Micronesia
2.06 1.78 Ecuador
2.01 1.62 Dominican Republic
2.00 0.00 Nauru
1.96 2.08 Malaysia
1.96 1.58 Turkmenistan
1.95 3.88 Kuwait
1.95 2.12 Zambia
1.91 1.96 Bolivia
1.87 2.05 Ghana
1.85 2.04 Central African Republic
1.83 1.91 Swaziland
1.82 1.84 Morocco
1.81 1.93 Peru
1.81 1.32 Uzbekistan
1.78 1.73 Mexico
1.76 1.28 Fiji
1.75 2.10 Algeria
1.73 0.80 Tonga
1.72 1.82 Egypt
1.71 0.68 Kyrgyzstan
1.70 2.38 Brunei
1.70 2.02 Vanuatu
1.67 1.85 Colombia
1.67 1.59 Bangladesh
1.67 1.53 Haiti
1.64 1.09 Saint Lucia
1.62 2.00 Bahrain
1.60 1.89 Costa Rica
1.60 1.46 Indonesia
1.57 1.71 Venezuela
1.55 1.80 Lesotho
1.55 1.68 India
1.53 0.87 Grenada
1.50 1.57 Malawi
1.50 1.37 Vietnam
1.49 0.71 Suriname
1.47 1.45 Mongolia
1.43 1.78 United Arab Emirates
1.42 1.59 Kenya
1.41 1.53 Panama
1.40 1.34 Tuvalu
1.38 1.61 Lebanon
1.38 1.57 Namibia
1.36 0.36 Antigua and Barbuda
1.30 2.54 Mozambique
1.29 1.81 Israel
1.28 2.43 Rwanda
1.26 0.64 Jamaica
1.25 1.05 Albania
1.24 1.94 Palau
1.24 1.57 Turkey
1.22 1.45 Korea, North
1.21 1.39 Tunisia
1.20 1.36 Bahamas, The
1.18 0.57 Saint Vincent and the Grenadines
1.17 3.62 Qatar
1.17 1.07 Iran
1.17 0.03 Serbia & Montenegro
1.13 1.23 Chile
1.08 1.29 Argentina
1.06 -1.41 Dominica
1.02 1.48 Gabon
1.02 1.10 Sri Lanka
0.97 1.18 Mauritius
0.96 1.34 Saint Kitts and Nevis
0.93 2.30 Samoa
0.92 0.77 China
0.91 -0.32 Guyana
0.91 1.16 Brazil
0.91 0.93 Thailand
0.89 1.00 Korea, South
0.89 0.63 Azerbaijan
0.86 1.15 Singapore
0.83 0.93 Taiwan
0.83 0.73 Uruguay
0.78 1.61 Myanmar
0.77 0.57 Iceland
0.67 -0.09 Kazakhstan
0.67 0.99 New Zealand
0.65 0.38 Ireland
0.58 0.64 Macedonia
0.57 0.90 Australia
0.55 0.85 United States
0.54 0.67 Cyprus
0.50 0.49 Malta
0.50 0.40 Cuba
0.49 -1.35 Trinidad and Tobago
0.49 3.20 Bosnia and Herzegovina
0.49 2.24 Andorra
0.49 0.04 Barbados
0.48 1.08 Liechtenstein
0.47 1.05 Botswana
0.44 1.32 South Africa
0.37 1.06 Canada
0.34 0.88 Luxembourg
0.32 0.47 Netherlands
0.31 0.64 San Marino
0.30 0.27 France
0.28 0.40 Norway
0.17 -0.38 Armenia
0.17 0.20 Japan
0.15 1.02 Zimbabwe
0.14 0.20 Switzerland
0.14 0.10 Croatia
0.13 -0.13 Portugal
0.12 0.24 United Kingdom
0.11 0.65 Seychelles
0.11 0.38 Denmark
0.09 0.15 Finland
0.08 0.10 Moldova
0.08 0.04 Slovakia
0.06 0.06 Belgium
0.02 0.05 Poland
0.01 0.41 Greece
0.01 0.10 Spain
-0.01 0.09 Austria
-0.01 -0.08 Italy
-0.07 -0.04 Slovenia
-0.07 0.29 Sweden
-0.13 0.01 Germany
-0.15 -0.23 Romania
-0.17 -0.01 Czech Republic
-0.29 -0.40 Lithuania
-0.33 0.31 Monaco
-0.34 -0.74 Georgia
-0.39 -0.20 Hungary
-0.44 -0.09 Belarus
-0.45 -0.33 Russia
-0.48 -0.82 Estonia
-0.65 -0.52 Bulgaria
-0.68 -1.25 Latvia
-0.71 -0.62 Ukraine
===================
(4) An update to and critique of the concluding comments in the 1996
"The phenomological theory of world population growth"
(Physics-Uspekhi 39(1) 57-71).
The Kapitza curve is of the form:
tan(a population) = b tan(c (time - time-0))
which has 3 natural phases: (a) an initial nearly-linear phase, (b) a
hyperbolic singular phase of the form population = k/(time-1 - time),
(c) a levelling-off S-shaped phase, given by an arc-cotangent curve.
The parameters a, b, c, time-0 are estimated based on fitting phase (c)
to recent data and the resulting fit generally matches the entire history
of the world population curve. The curve tops off at 14 billion.
Using revised parameters, based in particular on the regularity noted above,
gives you 10 billion.
Kapitza's comments in the concluding section:
"In the foreseeable future we can hardly expect to significantly change and
influence the overall growth pattern. The sheer size of the world population
and given the pace of events it is difficult to imagine how the world
community can have a major effect on global population growth. The fundamental
understanding of growth is still rather limited and definitive advice for
action is hard to provide, apart from very general recommendations. Probably
the most important issue is by all means to ensure the stability and security
for the world to be, as the prerequisite for resolving global problems."
These statements have to be modified in light of the latest developments
throughout the world. In fact, what originally may have looked like a
growth up to 14 billion, now shows itself, given the new data provided, to
be a growth only up to 10 billion. The growth rates in recent years have
come down substantially, far in excess of what the UN initially estimated.
This development may seem like a mystery upon first sight, but the mystery is
resolved when it's juxtaposed alongside another, equally significant,
development that -- itself -- has emerged as somewhat of a mystery.
In recent years, throughout the world, the proportion of college-level
students that are female has risen substantially, not just up to 50%, but in
many places both in the developed world and elsewhere, *well beyond 50%*
into the 60's and even 70's. Part of the reason for this occurrence is
tied to a progressive loss of direction and focus of the male half of the
human race, tied to the rise of *Cabin Fever*, manifesting in the
form of the *Rapa Nui Symdrome*, particularly since the closing of
the age of exploration. As a symptom, it points to the growing restlessness
of the human race, and its surging desire for a new era of exploration.
This aspect of the story is discussed in the Fourth Wave.
But another, equally substantial, part of the story is found by looking at
the actual numbers of students attending colleges over time throughout the
world. It's not so much that males are dropping out. They are *still*
attending colleges at roughly the same rate as before (and are, therefore,
also stagnating in a world that has become increasingly knowledge-dependent).
It's that the number of females attending colleges has skyrocketed.
What does this have to do with anything, especially the population issue?
In retrospect the connection is obvious and should have been foreseen long
ago. Women who are going to college are deferring childbirth. Nations
with an increased number of females attending college also have a birth
rate that is dropping through the floor. Even places like Egypt, India,
Pakistan, once major centers of population growth, the growth rate is
down under 1%/year. As true as it is that men have lost their classical
birthright in recent years -- the directive to go forth and explore new
horizons -- these developments show clearly that women too have lost their
classical birthright -- bringing new life into an increasingly overcrowded
world -- and that both developments are tied directly to the population
curve. In the case of women, with the prospect of childrearing becoming
less accessible they are compensating by doing the only other thing readily
available to them: going to college and entering the ranks of the professonal
elite. Whereas all the male energies that drive forth the directive to
explore have been thwarted and are now manifesting in forms that are the
classical red-flag pattern of the Rapa Nui Syndrome, the female energies,
having been released by loss of the child rearing prerogative, are being
diverted into education and the professions, with the surge of all these
energies will overwhelming these sectors in a tidal wave of estrogen.
This development has engineered a downward jolt in the entire evolution of
the population curve. Birthrates are dropping everywhere, and the result
is that the population explosion is coming to a screeching halt. It will
not get much above 10 billion.
The original author stated that if the model is to be supported by further
research (as has just been done here), and the insight it provides is valid,
then it may help to lead to greater understanding of the present state of
affairs. Indeed, the growing contrast between the model and what's actually
occurring puts the other developments just described in a whole new light.
The Kapitza curve, itself, must still stand against its greatest challenge:
the rival assumption of a logistic curve.
It turns out, when fitting the recent population data, 1950-2004 to a
logistic curve, that NO LOGISTIC CURVE STARTING AT 0 will fit well. Instead,
the best fitting curve requires a POSITIVE starting value of around 1 billion
set in RECENT time, around the start of the Industrial Revolution.
Correspondingly, the asymptotic value will be reduced to 9 billion. The
resulting curve fits the recent history EXTREMELY WELL and shows that the
population explosion of the past 150 years should really be considered as
an entirely separate development, corollary to the Industrial Revolution,
rather than a continuation of the population increases that took place
before. Trying to fit something to the curve on both sides of the
Industrial Revolution will achieve limited results, at best.
The difference between the best-fitting logistic curve and the one posed by
the Kapitza will become readily apparent as time progresses, and one
will be able to decide which model is more appropriate probably by 2010 or
earlier still.
Kapitza continues on to state the following: the model posed can offer a
common frame of reference for anthropology and history, demography and
sociology, for studies in human evolution and genetics. For doctors and
politicians it may elicit an understanding of the sources of stress and
tensions in this transient period, so unique in human development, both for
an individual and at a broader societal level. In this case interdisciplinary
studies and experiences are worth the effort in developing the model and the
promise it can provide in facing the predicament of humankind.
Indeed, a proper understanding of the population curve, and its relation to
the threshold phenomena corresponding to what the futurist Toffler termed
"waves", provides an underpinning to the sequel to the Tofflerian trilogy
currently being developed: the Federation Series. It is the focal point
that ties together the central developments that each volume covers. The
cabin fever underlying the decline of males is the central theme of the
second volume, _The_Fourth_Wave_. This has arisen as a consequence
of the closing up of the world, which in turn may be seen as a direct
consequence of the demographic transition the world has been undergoing
during this time. The rise of women within the ranks of the educated and
professional elite, particularly when seen in conjunction with the decline of
males, is the central theme of the first volume, _The_Fall_Of_Mankind_.
Finally, the outcomes of these developments, the emergence of a world
federation and of a spacefaring civilization spanning the inner solar
system, is the central focus of the third volume, _Progeny_.
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