{"title":"人口和二氧化碳排放双曲线增长的终结","authors":"Victor M. Yakovenko","doi":"10.1016/j.physa.2025.130412","DOIUrl":null,"url":null,"abstract":"<div><div>Using current empirical data from 10,000 BCE to 2023 CE, we re-examine a hyperbolic pattern of human population growth, which was identified by von Foerster <em>et al.</em> in 1960 with a predicted singularity in 2026. We find that human population initially grew exponentially in time as <span><math><mrow><mi>N</mi><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow><mo>∝</mo><msup><mrow><mi>e</mi></mrow><mrow><mi>t</mi><mo>/</mo><mi>T</mi></mrow></msup></mrow></math></span> with <span><math><mrow><mi>T</mi><mo>=</mo><mn>2080</mn></mrow></math></span> years. This growth then gradually evolved to be super-exponential with a form similar to the Bose function in statistical physics. Around 1700, population growth further accelerated, entering the hyperbolic regime as <span><math><mrow><mi>N</mi><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow><mo>∝</mo><msup><mrow><mrow><mo>(</mo><msub><mrow><mi>t</mi></mrow><mrow><mi>s</mi></mrow></msub><mo>−</mo><mi>t</mi><mo>)</mo></mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></math></span> with the extrapolated singularity year <span><math><mrow><msub><mrow><mi>t</mi></mrow><mrow><mi>s</mi></mrow></msub><mo>=</mo><mn>2030</mn></mrow></math></span>, which is close to the prediction by von Foerster <em>et al.</em> We attribute the switch from the super-exponential to the hyperbolic regime to the onset of the Industrial Revolution and the transition to massive use of fossil fuels. This claim is supported by a linear relation that we find between the increase in the atmospheric CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> level and population from 1700 to 2000. In the 21st century, we observe that the inverse population curve <span><math><mrow><mn>1</mn><mo>/</mo><mi>N</mi><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span> deviates from a straight line and follows a pattern of “avoided crossing” described by the square root of the Lorentzian function. Thus, instead of a singularity, we predict a peak in human population at <span><math><mrow><msub><mrow><mi>t</mi></mrow><mrow><mi>s</mi></mrow></msub><mo>=</mo><mn>2030</mn></mrow></math></span> of the time width <span><math><mrow><mi>τ</mi><mo>=</mo><mn>32</mn></mrow></math></span> years. We also find that the increase in CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> level since 1700 is well fitted by <span><math><mrow><mi>arccot</mi><mrow><mo>[</mo><mrow><mo>(</mo><msub><mrow><mi>t</mi></mrow><mrow><mi>s</mi></mrow></msub><mo>−</mo><mi>t</mi><mo>)</mo></mrow><mo>/</mo><msub><mrow><mi>τ</mi></mrow><mrow><mi>F</mi></mrow></msub><mo>]</mo></mrow></mrow></math></span> with <span><math><msub><mrow><mi>τ</mi></mrow><mrow><mi>F</mi></mrow></msub></math></span> = 40 years, which implies a peak in the annual CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions at the same year <span><math><mrow><msub><mrow><mi>t</mi></mrow><mrow><mi>s</mi></mrow></msub><mo>=</mo><mn>2030</mn></mrow></math></span>.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"661 ","pages":"Article 130412"},"PeriodicalIF":3.3000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The end of hyperbolic growth in human population and CO2 emissions\",\"authors\":\"Victor M. Yakovenko\",\"doi\":\"10.1016/j.physa.2025.130412\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Using current empirical data from 10,000 BCE to 2023 CE, we re-examine a hyperbolic pattern of human population growth, which was identified by von Foerster <em>et al.</em> in 1960 with a predicted singularity in 2026. We find that human population initially grew exponentially in time as <span><math><mrow><mi>N</mi><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow><mo>∝</mo><msup><mrow><mi>e</mi></mrow><mrow><mi>t</mi><mo>/</mo><mi>T</mi></mrow></msup></mrow></math></span> with <span><math><mrow><mi>T</mi><mo>=</mo><mn>2080</mn></mrow></math></span> years. This growth then gradually evolved to be super-exponential with a form similar to the Bose function in statistical physics. Around 1700, population growth further accelerated, entering the hyperbolic regime as <span><math><mrow><mi>N</mi><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow><mo>∝</mo><msup><mrow><mrow><mo>(</mo><msub><mrow><mi>t</mi></mrow><mrow><mi>s</mi></mrow></msub><mo>−</mo><mi>t</mi><mo>)</mo></mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></math></span> with the extrapolated singularity year <span><math><mrow><msub><mrow><mi>t</mi></mrow><mrow><mi>s</mi></mrow></msub><mo>=</mo><mn>2030</mn></mrow></math></span>, which is close to the prediction by von Foerster <em>et al.</em> We attribute the switch from the super-exponential to the hyperbolic regime to the onset of the Industrial Revolution and the transition to massive use of fossil fuels. This claim is supported by a linear relation that we find between the increase in the atmospheric CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> level and population from 1700 to 2000. In the 21st century, we observe that the inverse population curve <span><math><mrow><mn>1</mn><mo>/</mo><mi>N</mi><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span> deviates from a straight line and follows a pattern of “avoided crossing” described by the square root of the Lorentzian function. Thus, instead of a singularity, we predict a peak in human population at <span><math><mrow><msub><mrow><mi>t</mi></mrow><mrow><mi>s</mi></mrow></msub><mo>=</mo><mn>2030</mn></mrow></math></span> of the time width <span><math><mrow><mi>τ</mi><mo>=</mo><mn>32</mn></mrow></math></span> years. We also find that the increase in CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> level since 1700 is well fitted by <span><math><mrow><mi>arccot</mi><mrow><mo>[</mo><mrow><mo>(</mo><msub><mrow><mi>t</mi></mrow><mrow><mi>s</mi></mrow></msub><mo>−</mo><mi>t</mi><mo>)</mo></mrow><mo>/</mo><msub><mrow><mi>τ</mi></mrow><mrow><mi>F</mi></mrow></msub><mo>]</mo></mrow></mrow></math></span> with <span><math><msub><mrow><mi>τ</mi></mrow><mrow><mi>F</mi></mrow></msub></math></span> = 40 years, which implies a peak in the annual CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions at the same year <span><math><mrow><msub><mrow><mi>t</mi></mrow><mrow><mi>s</mi></mrow></msub><mo>=</mo><mn>2030</mn></mrow></math></span>.</div></div>\",\"PeriodicalId\":20152,\"journal\":{\"name\":\"Physica A: Statistical Mechanics and its Applications\",\"volume\":\"661 \",\"pages\":\"Article 130412\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2025-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Physica A: Statistical Mechanics and its Applications\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0378437125000640\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/31 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"PHYSICS, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physica A: Statistical Mechanics and its Applications","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378437125000640","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/31 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
摘要
利用公元前10000年至公元2023年的现有经验数据,我们重新研究了von Foerster等人在1960年确定的人口增长双曲线模式,并预测2026年将出现奇点。我们发现人口最初呈指数增长的时间为N(t)∝et/ t, t =2080年。这种增长随后逐渐演变为超指数增长,其形式类似于统计物理学中的玻色函数。1700年左右,人口增长进一步加速,进入N(t)∝(ts−t)−1的双曲状态,外推奇点年ts=2030,这与von Foerster等人的预测接近。我们把从超级指数型到双曲线型的转变归因于工业革命的开始和向大量使用化石燃料的过渡。我们发现,从1700年到2000年,大气中二氧化碳浓度的增加与人口之间的线性关系支持了这一说法。在21世纪,我们观察到人口逆曲线1/N(t)偏离直线,遵循洛伦兹函数平方根所描述的“避免交叉”模式。因此,我们预测在时间宽度τ=32年的ts=2030年将出现人口峰值,而不是奇点。我们还发现,自1700年以来CO2水平的增长与τF = 40年的arccot[(ts−t)/τF]很好地拟合,这意味着CO2年排放量在同年ts=2030年达到峰值。
The end of hyperbolic growth in human population and CO2 emissions
Using current empirical data from 10,000 BCE to 2023 CE, we re-examine a hyperbolic pattern of human population growth, which was identified by von Foerster et al. in 1960 with a predicted singularity in 2026. We find that human population initially grew exponentially in time as with years. This growth then gradually evolved to be super-exponential with a form similar to the Bose function in statistical physics. Around 1700, population growth further accelerated, entering the hyperbolic regime as with the extrapolated singularity year , which is close to the prediction by von Foerster et al. We attribute the switch from the super-exponential to the hyperbolic regime to the onset of the Industrial Revolution and the transition to massive use of fossil fuels. This claim is supported by a linear relation that we find between the increase in the atmospheric CO level and population from 1700 to 2000. In the 21st century, we observe that the inverse population curve deviates from a straight line and follows a pattern of “avoided crossing” described by the square root of the Lorentzian function. Thus, instead of a singularity, we predict a peak in human population at of the time width years. We also find that the increase in CO level since 1700 is well fitted by with = 40 years, which implies a peak in the annual CO emissions at the same year .
期刊介绍:
Physica A: Statistical Mechanics and its Applications
Recognized by the European Physical Society
Physica A publishes research in the field of statistical mechanics and its applications.
Statistical mechanics sets out to explain the behaviour of macroscopic systems by studying the statistical properties of their microscopic constituents.
Applications of the techniques of statistical mechanics are widespread, and include: applications to physical systems such as solids, liquids and gases; applications to chemical and biological systems (colloids, interfaces, complex fluids, polymers and biopolymers, cell physics); and other interdisciplinary applications to for instance biological, economical and sociological systems.