Yuqian Zhang

Human economic activities drive the production and consumption of goods and services, contribute to the achievement of the United Nations Sustainable Development Goals (SDGs). However, the extent of economic growth’s influence on the SDGs remains unclear. To fill this knowledge gap, here, we quantified the environmental effects of economic activities and explored correlations between environmental effect and achieving SDGs. We developed six Environmental Footprint Indices, with a higher score indicating better efficiency or lower burden. Here we show that the various Environmental Footprint Indices had synergistic and trade-off effects on most SDG targets indices, but the synergistic effects prevailed. As income increased, the correlation between Environmental Footprint Indices and SDG target indices gradually strengthened. improved production efficiency and consumption changes notably advance SDGs, especially in low-income group countries. Our work provides scientific insights into the impact and prospects of environmental regulation required for achieving the SDGs by 2030.

Seesus is an open-source Python package that evaluates whether a textual expression aligns with the concept of sustainability as defined by the United Nations Sustainable Development Goals (SDGs). It labels a statement with the 17 SDGs as well as 169 specific targets and categorizes the statement into social, environmental, or economic sustainability. Compared to existing SDG classification software in Python, seesus is the only one that is tuned to a finer scale-the SDG target level-and offers transparency and controllability, allowing users to examine and customize the syntax of text classification according to their needs.

The global interest in moving towards a sustainable future has grown exponentially at all levels. The United Nations' Sustainable Development Goals (SDGs), adopted by world leaders in 2015, provide an integrated framework to track progress toward sustainability (UN, 2019). Textual data, such as public statements posted on websites, organization reports, and scientific publications, is a rich source for evaluating the planned and ongoing efforts, as well as achievements towards sustainability. However, no computational tool exists to date that can accurately and efficiently identify SDG-related statements from these large amounts of text data. To fill this gap, we developed the SDGdetector package in R (R Core Team, 2021) to map textual data to specific goals and targets under the UN SDG framework for quantitative analysis. This is the first open-source, high-resolution, and high-accuracy analytical package that can identify which and how many SDG goals and targets are declared in any type of text-based data frame or corpus. This package thus enables a unique way to monitor individuals' and organizations' commitments and efforts towards advancing the 17 SDGs and 169 associated targets.