Gong Mimi, Yu Ke, Zhou Changchang, Liu Zhouyi, Xu Zhenci, Xu Ming, Qu Shen
Center for Systems Integration and Sustainability, Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824, USA.
Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100081, China.
Fundam Res. 2024 Feb 28;5(1):360-369. doi: 10.1016/j.fmre.2024.01.005. eCollection 2025 Jan.
To achieve the United Nations Sustainable Development Goals (SDGs) by 2030, it is essential to understand the interlinkages between the goals. Previous research has investigated these interactions by focusing on their correlations. However, few studies have systematically prioritized them from a structural perspective through the complementarity measurements and empirically validated their policy effectiveness, such as which goals and indicators impact other SDGs most, especially in China. This study introduces a new concept known as the 'SDG space' by employing the "Product Space" approach in network science and economics. It measures the complementarities between SDGs and indicators through their network structures in investigating effective policy design. Using the most recent available but unpublished data for 31 Chinese provinces, the SDG space was constructed at the 17 SDG and 118 indicator levels by analyzing the probability of comparative advantage between each SDG or indicator pair co-occurring in the same place. Historical data confirm that in the 'SDG Space' network, a goal connected to other well-developed goals would enjoy better future growth and vice versa. The structure reveals that SDG 4 (Quality Education), 15 (Life on Land), and 1 (No Poverty) are critical goals with transformative synergies to other SDGs. Furthermore, we identified strong complementarities between land-based ecosystems and clean water and climate actions using the finer-grained indicator-level space. These findings help pave the way for China toward a sustainable future by providing science-based policy recommendations for decision-makers. They can be generally applied to other countries and regions to assist in navigating toward sustainable development.
要在2030年前实现联合国可持续发展目标（SDGs），了解这些目标之间的相互联系至关重要。以往的研究通过关注目标之间的相关性来探究这些相互作用。然而，很少有研究从结构角度通过互补性测量对其进行系统排序，并对其政策有效性进行实证验证，比如哪些目标和指标对其他可持续发展目标影响最大，尤其是在中国。本研究通过运用网络科学与经济学中的“产品空间”方法，引入了一个名为“可持续发展目标空间”的新概念。在研究有效政策设计时，通过目标和指标的网络结构来衡量可持续发展目标与指标之间的互补性。利用中国31个省份最新可得但未发表的数据，通过分析每对可持续发展目标或指标在同一地区共同出现的比较优势概率，在17个可持续发展目标和118个指标层面构建了可持续发展目标空间。历史数据证实，在“可持续发展目标空间”网络中，与其他发展良好的目标相联系的目标未来将有更好的增长，反之亦然。该结构表明，可持续发展目标4（优质教育）、15（陆地生物）和1（消除贫困）是对其他可持续发展目标具有变革性协同作用的关键目标。此外，我们利用更细化的指标层面空间，确定了陆地生态系统与清洁水及气候行动之间的强互补性。这些发现为决策者提供基于科学的政策建议，有助于中国迈向可持续未来。它们通常可应用于其他国家和地区，以协助其朝着可持续发展方向前进。
