Finding pathways to synergistic development of Sustainable Development Goals in China

Effective monitoring and assessment of sustainable development are crucial for achieving the UN's Sustainable Development Goals (SDGs). While the SDG index offers cross-country and regional comparisons, it lacks guidance on improving SDG performance. Hundreds of sustainable development indicator frameworks exist, but inconsistencies and conflicts among them highlight the ambiguity of sustainable development. The UN's SDG framework provides a common standard, but its global indicators may not be suitable for sub-national assessments. Therefore, monitoring and assessing SDG progress at the sub-national level is a priority, often hampered by data limitations and assessment methodology challenges. Three main approaches exist for measuring SDG progress: UN global reports, voluntary national reviews, and quantitative assessments like the SDG index. While the SDG index is useful for comparative analysis, its aggregated nature can mask offsetting effects where improvements in some indicators are offset by declines in others. This is especially relevant at the sub-national level due to regional variations in advantages and challenges. This study addresses the lack of detailed analysis on the offsetting effects of SDG implementation and spatial differences, as well as the knowledge gap on how to mitigate these effects by examining the case of China at the provincial level. China's SDG progress has significant global implications and involves incorporating SDGs into various sector development plans and creating demonstration zones. While prior research has analyzed spatial and temporal variations of China's SDGs, the offsetting effects haven't been thoroughly explored. Clarifying these effects and identifying solutions offers valuable insights for regional, national, and global sustainable development policies. This study examines the changing characteristics of SDGs and their offsetting effects in China since 2015, spatial similarities and differences among 31 provinces, and identifies priority SDGs for future development to minimize offsetting effects. The focus is on revealing the offsetting effects and suggesting strategies for improvement, not solely quantifying temporal variations, making the results timely and relevant for future policy formulation.

The literature review extensively cites existing studies and reports on sustainable development indicators, SDG frameworks, and assessment methodologies. It highlights the limitations of existing aggregated indices like the SDG index in capturing the nuances of regional progress and offsetting effects. The authors emphasize the need for more localized, granular analyses to effectively guide policy interventions. Several studies on China's progress towards the SDGs are referenced, noting existing analyses of spatial and temporal variations but identifying a gap in understanding offsetting effects. The authors highlight the importance of quantitative assessments to supplement narrative reports and provide precise guidance on prioritizing future tasks and resource allocation.

The study uses a multi-faceted methodology to analyze SDG progress in China at the provincial level. First, an indicator framework was created for China based on the UN's global indicator framework, existing literature, and five principles: policy relevance, universal applicability, reasonable indicativeness, timeliness, and statistical reliability. This resulted in a framework with 88 indicators across 16 SDGs (excluding SDG14 due to data limitations). Data was gathered primarily from official Chinese statistics, including yearbooks on various sectors, prioritizing reliable, widely applicable, and current data. Historical data from 1990 (or when available) to 2018 was used to establish baselines and target values for indicators. The SDG index was calculated by normalizing raw data to scores between 0-100, aggregating into target scores, then SDG scores, and finally, the overall SDG index score. The offsetting effect was analyzed by counting the number of SDGs and targets with increased or decreased scores in different provinces. One-way ANOVA and LSD tests assessed statistical significance of SDG index scores in 2018 across provinces, while the Mann-Whitney U test analyzed differences in scores between 2015 and 2018. Spatial variations were explored using hierarchical cluster analysis with Euclidean distance and ward sum of square deviation methods. The number of clusters was determined using gap statistics. Finally, multiple factor analysis (MFA) was employed to quantify interactions between three SDG categories ('Essential Needs', 'Governance', and 'Objectives') and identify influential SDGs using the RV coefficient. Pearson correlation coefficients were also used to analyze the directionality of interactions (synergy or trade-off).

China's overall SDG index score showed only marginal improvement from 2015 to 2018 (P>0.05), with uneven progress across provinces. Beijing had the highest score (79.3), while Gansu had the lowest (57.7) in 2018. A significant finding was the prevalence of offsetting effects: a national-level decline in an SDG score typically involves declines in at least 15 provinces. Conversely, a national-level increase may mask decreases in many provinces. This was also observed at the target level within SDGs. For example, improvements in SDG3 (Good Health and Well-Being) masked declines in specific targets like child mortality. Similarly, increased SDG9 scores (Industry, Innovation, and Infrastructure) hid declines in indicators such as industrial value added. The degree of change in individual SDG scores significantly impacted changes in the SDG index. While eastern provinces had higher 2018 scores, western provinces showed a higher number of SDGs with declining scores. Cluster analysis revealed four groups of provinces with similar patterns of SDG score changes, highlighting that provinces with different geographical characteristics and economic levels can share similar development challenges. For instance, eastern and central provinces, despite higher development levels, exhibited declining scores in SDG2 (Zero Hunger), SDG8 (Decent Work), and SDG12 (Responsible Production). Tibet formed a separate cluster, indicating different challenges compared to other regions. Analysis of SDG interactions showed strong synergies between the three SDG categories in 2015, but these weakened in 2018. The analysis highlighted SDG7 and SDG12 as priority goals. SDG7 (Affordable and Clean Energy) showed inconsistent progress across provinces, while SDG12 (Responsible Production and Consumption) declined in most provinces.

The findings highlight the limitations of relying solely on aggregated SDG indices, emphasizing the need for more detailed analyses to identify offsetting effects and regional disparities. The widespread decline in SDG2 (Zero Hunger) and SDG12 (Responsible Production and Consumption) scores despite overall food production increases suggests a need to address food safety issues and irrational consumption patterns. The study reveals that geographically and economically diverse provinces may face similar development challenges. The clustering of provinces into groups with similar SDG change patterns suggests the need for more integrated and context-specific policy approaches. The identified priority goals, SDG7 and SDG12, reflect the need for more balanced clean energy development and a transformation of production and consumption patterns to address the offsetting effects and improve overall SDG progress.

This study provides valuable insights into the complexities of SDG implementation in China. The emphasis on offsetting effects, spatial variations, and SDG interactions offers a more nuanced understanding of sustainable development progress. Prioritizing SDG7 and SDG12 is crucial for more balanced development. Future research should focus on refining SDG indicator frameworks, addressing data limitations, exploring non-linear interactions among SDGs, and investigating effective policy interventions for achieving SDG synergies.

The study acknowledges limitations concerning indicator selection, data availability, and the linear nature of the SDG interaction analysis. The chosen indicator framework, while informed by existing standards, may not fully capture the complexity of SDG implementation. Data limitations, particularly for some SDG indicators at the sub-national level, could affect the results. The analysis primarily focuses on linear relationships between SDGs, neglecting potentially significant non-linear interactions and feedback loops. These limitations suggest avenues for future research that could further refine the understanding of SDG dynamics.