Studying the topology and drivers of networks has given us crucial insights about complex systems such as health16,17, ecosystems18, financial systems19 and our societies20. Network theory provides analytical tools to determine how such mathematical representations of systems can evolve through time and how they might respond to perturbations21. We apply a network approach to the SDGs to estimate what we call the sustainome—the system of SDG interactions. The sustainome can be represented as a network, where the vertices are the SDGs (goals or targets) and the edges are relationships between them. We estimate the sustainome network of interactions at two scales: among the 169 SDG targets and among the 17 SDGs them-selves. The concept of the sustainome is inspired from the conceptual definition of sustainomics22, defined as the study of how to achieve sustainability by maintaining six capitals (infrastructure, finance, communities, people, ecosystems and biodiversity) while generating the flows we require from those capitals to achieve the SDGs.

Relationships among goals can be defined in a number of ways, from shared concepts in their definitions4 to dependencies in indicator trajectories19. Within a sustainome framework, interactions between the SDGsare represented as the associations between progress towards each SDG (see Methods). For example, if initiatives are implemented to increase gross domestic product, will they be associated with a degradation of biodiversity? Several organizations have monitored macroscale indicators associated with the SDGs in most countries over the past decades, which allows us to determine global interactions among the SDGs.

The World Bank (see Methods) developed a set of 331 indicators to inform the SDGs (Supplementary Table 1) using data collected regularlyover the past 27 years for 263 countries23. The World Bank associated each of these indicators to SDG targets so that the indicators can be used as a measure of progress towards SDGs and their targets. Afterwards, the United Nations developed a set of indicators under the auspice of the Interagency and Expert Group on SDG Indicators. This set of indicators largely overlaps with that of the World Bank, which has more country representation and for more years. The World Bank data also allowed us to use indicators that were not given as a disaggregated percentage by age class and gen-der (which could not be re-aggregated without knowing the age and gender composition of each country and each year), thus minimizing collinearity issues in subsequent analyses. Therefore, we used the World Bank indicators to inform interactions between their respective identified 71 targets and 17 SDGs (98 SDG targets still lack indicators).

研究网络的拓扑结构和驱动因素让我们对复杂系统，诸如健康、生态系统、金融系统和我们的社会，有了至关重要的了解。网络理论提供了分析工具来确定这些系统的数学表示是如何随着时间发展的，以及他们是如何响应扰动的。我们将网络方法应用到可持续发展目标，以评估我们称作的sustainome——可持续发展目标相互关系系统。可以将这种系统看作一个网络，顶点是可持续发展目标，边缘是它们之间的相互关系。我们在两种尺度上评估可持续发展相互关系系统网络：169个可持续发展目标的指标和17个可持续发展目标。可持续发展目标相互关系系统的概念灵感来自于可持续经济的概念定义，这个定义是研究如何通过维持六种资本（基础设施、金融、社区、人们、生态系统和生物多样性）来实现可持续发展，同时从这些资本中产生我们实现可持续发展目标所需要的流动。

目标之间的相互关系可用多种方式来定义，从他它们定义中的共享概念到指标轨迹间的相互依赖关系。在可持续发展目标相互关系系统框架中，可持续发展目标之间的相互关系表现为各可持续发展目标进展之间的关联。例如，如果实施了增加国内生产总值的提议，这将是否与生物多样性的退化有关系？一些组织已经检测了大多数国家过去几十年与可持续发展目标有关联的宏观尺度的指标，这有助于我们确定全球可持续发展目标之间的相互关系。

世界银行利用263个国家过去27年定期收集的数据，制定了一套331指标，为可持续发展目标提供信息。世界银行将这些指标与可持续发展目标联系起来，以便这些指标能够衡量可持续发展目标及其指标的进展情况。后来，联合国在可持续发展目标跨机构专家组的支持下制定了一套指标。这套指标大部分与世界银行的那套指标重复，后者有大部分国家的代表权而且持续时间更长。世界银行的数据也使我们能够使用未按年龄阶段和性别百分比分类的指标，在不知道每个国家每年的年龄和性别组成的情况下是不能再重分类的），这样在后来的计算中就会尽量减少共线性的问题。因此，我们使用世界银行的指标来表示各自确定的71个指标和17个目标之间的相互关系（98个指标仍然缺失）。