The 2030 agenda of the United Nations provides a framework of 17 Sustainable Development Goals (SDGs) and 232 indicators for its members to fulfill. The overall achievement critically depends on how nations understand the interactions between these SDGs and set priorities for development pathways. This study provides a comprehensive network analysis of global SDG complementarities, measured by the co-occurrences of comparative advantages in the same region. We construct the ‘SDG space’ at goal and indicator levels with the most recently available data and then validate its robustness by comparing it to the commonly used correlation network and confirm its predictive power using historical data. Network analysis reveals a strongly connected socioeconomic-related core and an environmental-related periphery, with ‘bridge’ indicators connecting different clusters. The goal-level space identifies the ‘bridge’ goals as SDG 17 (Partnerships for the Goals), SDG 8 (Decent Work and Economic Growth), SDG 15 (Life on Hand) in the environmental-related cluster, while identifying SDG 7 (Affordable and Clean Energy), SDG 6 (Clean water and Sanitation), and SDG 16 (Justice and Strong Institutions) in the socioeconomic cluster. The indicator-level space provides details to explain how they act as ‘bridges’ in the network. In particular, 16-9: Free Press Index is the ‘bridge’ indicator with the highest betweenness centrality value and acts as the bottleneck indicator in China for its overall sustainable development. Improving it can enhance connected indicators’ performance, leading to positive cascading effects on different aspects of sustainability.
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 Using the most recent available but unpublished data for 31 Chinese provinces, the SDG space was constructed at the 17 SDG and 118 indicator levels by analyzing the probability of comparative advantage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.
Based on the best available activity data at a city level from top down and bottom up methods, a 2013-based emission inventory of NH3 was established for the Henan Province using an emission factors method. The 3 km×3 km spatial gridded distribution was carried out by using GIS technology. The results showed that the total amount of atmospheric NH3 emission in Henan Province in 2013 was 1035.3 kt, and the average emission intensity reached levels of 6.4 t/km2. Livestock and nitrogen fertilizer applications were the top two emission sources, accounting for 52.71% and 31.53% of the total emissions, respectively. Beef, laying hen, and goats were the main contributors in the livestock category, accounting for 34.98%, 16.63%, and 14.02% of the total emissions, respectively. There were different characteristics between emission source contributions and emission intensities in each city. Nanyang, Zhoukou, Shangqiu, and Zhumadian were the prefecture-level cities with large emissions, accounting for 11.53%, 9.84%, 9.62%, and 9.57% of the total amount in Henan Province, respectively. The NH3 emission intensities of Puyang and Louhe were larger than those of other cities, reaching up to 10.7 t·km-2 and 10.2 t·km-2, respectively. The spatial distribution revealed that emissions in the middle eastern region were relatively higher; whereas, the western region emissions were relatively low. The areas with high emissions were concentrated in the plains and densely populated areas.
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