1. Introduction

In the vast expanse of the universe, our planet has been a remarkable cradle of life for billions of years. It has navigated through countless challenges since its inception, particularly in the last three hundred years of the industrial revolution, when humanity finally broke free from the long-lasting poverty trap. In the meantime, we have also incurred escalating costs, including income inequality, climate change, biodiversity loss, and the depletion of limited nonrenewable resources. Conventional scientific methodologies and approaches are increasingly coming to their limits as we try to better understand the high complexities of the human-planet relationship, and to steer through the planetary health challenges.

在浩瀚的宇宙中，我们的星球数十亿年来一直是生命的摇篮。自诞生以来，它经历了无数挑战，特别是在工业革命的最后三百年，人类终于摆脱了长期的贫困陷阱。与此同时，我们也付出了越来越大的代价，包括收入不平等、气候变化、生物多样性丧失以及有限的不可再生资源的枯竭。随着我们试图更好地理解人与星球关系的高度复杂性并应对星球健康挑战，传统的科学方法和途径越来越受到限制。

2. PHAS Framework

As a response, in October 2023, Peking University launched a groundbreaking project to construct a Planetary Health Axis System, or PHAS, an AI-driven large dynamic global digital system, aiming to track human civilization footprints in relation to the well identified Planetary Boundaries. In order to accomplish such a huge challenging task, the PHAS builds on a four major axes system, including human health, species health, environment health and societal health, which are quantified by over 48000 key variables across the planet. In the following, this video presents a brief introduction of the PHAS project.

为此，北京大学于2023年10月启动了一项开创性的项目，构建“星球健康坐标系统”（PHAS）。这是一个由人工智能驱动的大型动态全球数字系统，旨在追踪人类文明相对于已确定的星球边界的足迹。为了完成这项艰巨的任务，PHAS构建了四大轴心系统，包括人类健康、物种健康、环境健康和社会健康，并通过全球超过48000个关键变量进行量化。以下视频将简要介绍PHAS项目。

3. PHAS Roadmap

PHAS builds a roadmap using AI to model the comprehensive health of the planet. The PHAS roadmap consists of four core modules  as shown.

PHAS构建了利用人工智能模拟地球的综合健康状况的路线图。PHAS 路线图包含四个核心模块，如图所示。

Module 1 presents a stochastic control optimization process for the ultimate objective function of human civilization with trade-offs among the multi-targets of development. It brings a paradigm shift from focusing on GDP growth to human sustainable development for infinite time horizon. Instead of discounting future welfares, PHAS treats each generation equally by adding them into an infinite time average summation, following ergodic theory to ensure its convergence.

模块1提出了一个随机控制优化过程，用于实现人类文明的最终目标函数，并在发展的各个目标之间进行权衡。它带来了一种范式转变，即从关注GDP增长转向关注无限时间范围内的人类可持续发展。PHAS算法并非对未来福利进行折现，而是将每一代人平等地添加到一个无限时间的平均总和中，并遵循遍历理论来确保其收敛性。

Module 2 builds a large data training platform to integrate all available data and current knowledge using iterative AI imputation models equipped with special trained LLM. In order to avoid the “hallucination problem” in LLM, PHAS incorporates a vertically fine-tuned pre-trained large model to extract prior information from literature, and to synergistically use data and human expertise through iterative training cycles.

模块 2 构建了一个大数据训练平台，利用配备经过特殊训练的 LLM 的迭代式 AI 插补模型，整合所有可用数据和现有知识。为了避免 LLM 中的“幻觉问题”，PHAS 采用了垂直微调的预训练大型模型，从文献中提取先验信息，并通过迭代训练周期协同利用数据和人类专业知识。

Module 3 develops the  PHAS core engine using deep causal neural network on graph, empowering the capacity to model the complexity of planetary health. It is a combination with non-invertible dimensionality expansion (nIDE) and scalable deep causal graph learning(SCGL). The nIDE helps to transform nonlinear complexity into high-dimensional linear complexity asymmetrically to ensure causality. The SCGL detects the connection network among these complexities.

模块 3 使用基于图的深度因果神经网络开发 PHAS 核心引擎，增强其对地球健康复杂性进行建模的能力。它结合了不可逆维度扩展 (nIDE) 和可扩展深度因果图学习 (SCGL)。nIDE 有助于将非线性复杂性非对称地转换为高维线性复杂性，以确保因果关系。SCGL 则用于检测这些复杂性之间的连接网络。

Module 4 constructs an expert-data-AI convergence system to cease effective overfitting regularization of complex algorithms with real experts. PHAS generates unit reaction pathway results from statistics on current trained model and using decomposition tools to facilitate systematic quantitative and qualitative expert verification. Critically, the knowledge generated during these processes is absorbed in the network, leading to an ongoing learning system.

模块4构建了一个专家-数据-人工智能融合系统，以消除真实专家对复杂算法进行有效过拟合正则化的问题。PHAS基于当前训练模型的统计数据，并利用分解工具来促进系统性的定量和定性专家验证，从而生成单元反应路径结果。至关重要的是，这些过程中产生的知识会被网络吸收，从而形成一个持续学习的系统。

4. PHAS Outperform

Concerning the performance of PHAS model, it indeed offers a great deal of advantages over popular models in predicting capabilities. For instance, an evaluation of point prediction and graph recognition was conducted across both synthetic and authentic data-sets, demonstrating greater performance over conventional approaches such as statistical models, generic general large language models, general complex networks, localized natural science models, and general equilibrium models. In the context of loss function, the performance is measured by the accuracy on the predicted value with respect to the real or set value.  

就 PHAS 模型的性能而言，它在预测能力方面确实比主流模型展现出诸多优势。例如，在合成数据集和真实数据集上进行的点预测和图识别评估表明，其性能优于传统方法，例如统计模型、通用大型语言模型、通用复杂网络、局部自然科学模型和一般均衡模型。在损失函数方面，性能是通过预测值相对于真实值或设定值的准确率来衡量的。

As indicated in the left panel, following the Y axis measured by a normalized accuracy scale for experimental value, experimental connections and real world value respectively, the results well demonstrate the superiority of the PHAS Full Model by the higher bars over most of the compared models.  In terms of average scales  on the X axis in the right panel, the overall performance of PHAS model outperforms the other methods.

如左图所示，以分别测量实验值、实验连接和真实世界的标准化准确度尺度的 Y 轴为基准，结果以更高的柱状图形式充分展现了 PHAS 完整模型的优势，超过了大多数比较模型。以右图 X 轴的平均尺度为基准，PHAS 模型的整体性能优于其他方法。

In special, we test the model performance of predicting the distribution of climate change, especially on temperature. Different moments and quantiles are tested to measure the performance. The result shows that given similar but a bit better on the mean prediction, PHAS performs much better on high order moments and tail quantiles.

我们特别测试了模型预测气候变化分布（尤其是温度分布）的性能。我们测试了不同的矩和分位数来衡量其性能。结果表明，在均值预测方面，PHAS 模型在高阶矩和尾部分位数上的表现与模型相似，但略优于其他模型，但性能更佳。

Moreover, performs on time decay loss are tested in a special case of food water use. Comparations are taken among the accuracy of predictions on different time length, say 1 year to 20 years. Results show that with the highest average accuracy, PHAS Full model provides lowest relatively time decay loss. This means the PHAS model performance would not decay much as looking deep into the future.

此外，我们还在食品用水这一特殊情况下测试了时间衰减损失的性能。比较了不同时间长度（例如1年至20年）的预测精度。结果表明，在平均精度最高的情况下，PHAS Full 模型的时间衰减损失相对较低。这意味着 PHAS 模型的性能在展望未来时不会出现大幅下降。

5. Weighting System & PHAS Matrix

Based on its multidisciplinary science foundation, PHAS provides a comprehensive planetary health metrics using a well-grounded weighting system.

PHAS 基于其多学科科学基础，使用完善的加权系统提供全面的星球健康指标。

Unlike traditional data-driven health index measures, PHAS derives its Planetary Health Metrics, for both the globe at the top layer and regionals at lower layers, from its system foundation of optimality. Based on the previously introduced modeling foundation, the weighting system of PHAS is derived from the optimal solution to a dynamic control problem aimed at maximizing humanity's ultimate objective function. Such a weighting system is further enhanced by iterations on well-defined database and human feedbacks from multidisciplinary panel of leading experts. The PHAS-derived Planetary Health Metrics presents human’s optimal expected ultimate objective function achievable from the current state at the current time point.

与传统的数据驱动健康指数测量不同，PHAS 的星球健康指标（包括顶层的全球和底层的区域）均基于其系统优化基础。基于先前介绍的建模基础，PHAS 的权重系统源自一个动态控制问题的最优解，旨在最大化人类的最终目标函数。该权重系统通过在定义明确的数据库上进行迭代，以及来自多学科顶尖专家小组的人工反馈，得到了进一步的增强。基于 PHAS 得出的星球健康指标呈现了人类在当前时间点从当前状态可实现的最优预期最终目标函数。

In general terms, the top-level measure provided by PHAS can be interpreted as a scalar value ranging from 0 to 100, where 100 represents the optimal level of the ultimate objective function expected to be achieved by humanity on a completely unpolluted, undamaged, and unconsumed planet, while 0 corresponds to the expected termination of human civilization. This measure offers a relative indication of the current overall health status of the planet and humanity.

概括而言，PHAS 提供的顶层指标可以理解为一个从 0 到 100 的标量值，其中 100 代表人类在一个完全未受污染、未受破坏和未消耗的星球上预期实现的最终目标函数的最优水平，而 0 则代表人类文明的预期终结。该指标可以相对地反映地球和人类当前的整体健康状况。

The PHAS simulation functions permit scenario analyses and other policy evaluations. Using the Planetary Health Metrics as an example, where Y axis is normalized from 0 (the worst) to 100 (the best). X axis represents the yearly timeline, PHAS model indicates how the global level of planetary health would continue to deteriorate without actions today, shown in Panel 1.

PHAS 模拟功能支持情景分析和其他政策评估。以星球健康指标为例，Y 轴从 0（最差）到 100（最好）进行标准化。X 轴代表年度时间轴，PHAS 模型显示，如果不立即采取行动，全球行星健康状况将如何持续恶化，如面板 1 所示。

6. Policy Lab

Alternatively, a 20% reduction of fossil fuel use would slowdown the current trend, but not a halt of it indicated in Panel 2. Similarly in Panel 3, a carbon emission tax increase from a world average of 0.3% to 5% would also help reduce the downward sloping of planetary health, but still not to alter the trajectory. However, if the global innovation investment can increase over 20%, it would lead to a reversal of the trend back to the pre-2000 levels as demonstrated in Panel 4.

或者说，减少20%的化石燃料使用量会减缓当前的趋势，但不会像图2所示那样完全停止。同样地，如图3所示，将碳排放税从世界平均水平的0.3%提高到5%，也有助于减缓地球健康状况的下降趋势，但仍无法改变其发展轨迹。然而，如果全球创新投资能够增加20%以上，则将导致趋势逆转，回到2000年前的水平，如图4所示。

7. Replicate & Go Beyond

In terms of other applications, PHAS is fully capable of replicating known scientific knowledge while yielding more systematic additions with stronger significance. Conventional approaches establish point-to-point relationships between elements through limited data analysis under specific conditions or assumptions. PHAS can generate consistent findings in a single integrated process.

在其他应用方面，PHAS 完全能够复制已知的科学知识，同时产生更系统、更有意义的补充。传统方法在特定条件或假设下，通过有限的数据分析建立元素之间的点对点关系。而 PHAS 可以在单一集成流程中生成一致的发现。

Taking a case of climate change and human health, PHAS employs interpretability-oriented approximation to manifest conditional causal graphs for generating comprehensible pathway network. Virtually all known scientific discoveries can find corresponding representations in this network, such as temperature effects on disease prevalence.

以气候变化与人类健康为例，PHAS 采用面向可解释性的近似方法，展现条件因果图，从而生成易于理解的路径网络。几乎所有已知的科学发现都能在该网络中找到对应的表征，例如温度对疾病流行率的影响。

Moreover, beyond conventional science, the PHAS model offer additional capabilities. For example, PHAS can identify longer-range and more complex correlation pathways, and its discoveries exhibit bidirectional exclusivity. This implies the PHAS-generated network constitutes a comprehensive mapping from origins to endpoints with inherent significance. Such advantages by PHAS enable comparative assessment and priority settings across the system pathways and key elements, providing relative pathway flux to help optimize policy making.

此外，除了传统科学之外，PHAS 模型还提供了额外的功能。例如，PHAS 可以识别更长距离、更复杂的关联路径，并且其发现具有双向排他性。这意味着 PHAS 生成的网络构成了从起点到终点的全面映射，并具有内在意义。PHAS 的这些优势使得跨系统路径和关键要素进行比较评估和优先级设置成为可能，从而提供相对路径通量，以帮助优化政策制定。

8. Call for Collaboration

PHAS is still on its second year, being a learning and growing AI system. Active utilization and systematically captured user-feedback will be an essential part of the interactive learning system and we do look forward to engaging with you to make PHAS an important and useful tool to help steer mankind through the overarching challenge of our time. Thank you.

PHAS 是一个不断学习和成长的人工智能系统，目前正处于研发的第二年。积极利用和系统性地收集用户反馈将成为这个交互式学习系统的重要组成部分。我们期待与您携手，使 PHAS 成为一个重要且实用的工具，帮助人类应对时代面临的重大挑战。谢谢。