In the 1972 bestseller Limits to Growth (LtG), the authors concluded that, if global society kept pursuing economic growth, it would experience a decline in food production, industrial output, and ultimately population, within this century. The LtG authors used a system dynamics model to study interactions between global variables, varying model assumptions to generate different scenarios. Previous empirical-data comparisons since then by Turner showed closest alignment with a scenario that ended in collapse. This research constitutes a data update to LtG, by examining to what extent empirical data aligned with four LtG scenarios spanning a range of technological, resource, and societal assumptions. The research benefited from improved data availability since the previous updates and included a scenario and two variables that had not been part of previous comparisons. The two scenarios aligning most closely with observed data indicate a halt in welfare, food, and industrial production over the next decade or so, which puts into question the suitability of continuous economic growth as humanity's goal in the twenty-first century. Both scenarios also indicate subsequent declines in these variables, but only one—where declines are caused by pollution—depicts a collapse. The scenario that aligned most closely in earlier comparisons was not amongst the two closest aligning scenarios in this research. The scenario with the smallest declines aligned least with empirical data; however, absolute differences were often not yet large. The four scenarios diverge significantly more after 2020, suggesting that the window to align with this last scenario is closing.

中文翻译：

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更新限制增长：将 World3 模型与经验数据进行比较

1972 年畅销书《增长的极限》(LtG)，作者得出结论，如果全球社会继续追求经济增长，本世纪内粮食生产、工业产出和最终人口都会下降。LtG 作者使用系统动力学模型来研究全局变量之间的相互作用，改变模型假设以生成不同的场景。从那时起，特纳之前的经验数据比较表明，与以崩溃告终的情景最接近。这项研究构成了对 LtG 的数据更新，通过检查经验数据在多大程度上与跨越一系列技术、资源和社会假设的四种 LtG 情景一致。该研究受益于自上次更新以来数据可用性的提高，其中包括一个场景和两个变量，这些变量之前没有进行过比较。与观察到的数据最接近的两种情景表明，在未来十年左右的时间里，福利、食品和工业生产将停滞不前，这对持续经济增长作为 21 世纪人类目标的适宜性提出了质疑。这两种情况也表明这些变量随后会下降，但只有一种情况——下降是由污染引起的——描述了崩溃。在早期比较中最接近的场景不在本研究中两个最接近的对齐场景中。跌幅最小的情景与经验数据最不相符；然而，绝对差异往往还不大。2020 年后，这四种情景的分歧明显更大，这表明与最后一种情景保持一致的窗口正在关闭。