ABSTRACT Extensive efforts have been dedicated to deciphering the interactions associated with Sustainable Development Goals (SDGs). However, these developments are hampered by a lack of

efficient strategies to avoid beneficial synergies being offset by harmful trade-offs. To fill these gaps, we used causal diagnosis and network analysis methods to construct 1302 directed

networks of SDGs for 31 provinces in China from 2000 to 2020. We observed a dramatic offsetting effect of SDG synergies and trade-offs in China from 2000 to 2020, with approximately 27% of

trade-off indicator pairs turning into synergies and about 25% of the synergy indicator pairs turning into trade-offs. However, our findings suggested that prioritising the progress of

24 March 2022 COUNTRY-LEVEL EVENNESS MEASURE IN ASSESSING PROGRESS TOWARDS SUSTAINABLE DEVELOPMENT GOALS (SDGS) Article Open access 31 August 2024 ASSESSMENT OF THE COUPLING COORDINATION

RELATIONSHIP BETWEEN THE GREEN FINANCIAL SYSTEM AND THE SUSTAINABLE DEVELOPMENT SYSTEM ACROSS CHINA Article Open access 21 May 2024 INTRODUCTION The Sustainable Development Goals (SDGs) set

by the United Nations are integrated and indivisible. The SDG framework aims to balance the economic, social, and environmental aspects of sustainable development (United Nations, 2015;

Voulvoulis et al., 2022; Cheng et al., 2022). Action toward one goal (i.e., SDG, target, or indicator) may enhance or detract from the performance of others, respectively, defined as synergy

or trade-off among the SDGs (Nilsson et al., 2016; Scharlemann et al., 2020; Sebestyén et al., 2019; Weitz et al., 2018; Wu et al., 2022). According to the consensus from previous studies,

addressing trade-offs and fostering synergies remain critical to rescuing the SDGs from failing (Pradhan, 2023). However, effective strategies to achieve this objective are yet to be

quantitatively investigated (Nilsson et al., 2018; Ospina-Forero et al., 2022). Existing studies could primarily be categorised into qualitative evaluation by experts’ knowledge or official

documents (Breu et al., 2021; Nilsson et al., 2016; Pham-Truffert et al., 2020; Sebestyén et al., 2019; Weitz et al., 2018) and quantitative correlation analysis, including pearson’s

correlation coefficient (Bali Swain and Ranganathan, 2021), spearman’s correlation analysis (Anderson et al., 2022; Kroll et al., 2019; Pradhan et al., 2017; Warchold et al., 2021), multiple

factor analysis (Zhang et al., 2022a, 2022b) and autoregressive correlation (Lusseau and Mancini, 2019). These studies highlight that SDG interactions varied according to the country’s

income (Lusseau and Mancini, 2019; Warchold et al., 2021), level of sustainable development (Wu et al., 2022), population groups (Warchold et al., 2021), and regions (Bali Swain and

Ranganathan, 2021; Warchold et al., 2021). Accordingly, the most dominant SDGs (Pham-Truffert et al., 2020), the most frequent SDG interactions (Pradhan et al., 2017), and the influence of

levers and obstacles for achieving the SDGs (Anderson et al., 2022) have been discussed. Meanwhile, some studies have explicitly investigated the causation of SDG interaction networks based

on methods like expert opinion (Dawes et al., 2022; Van Soest et al., 2019), granger causal analysis (Dörgo et al., 2018), systems model (Anderson et al., 2022; Pedercini et al., 2019),

statistical structure learning (Ospina-Forero et al., 2022), and physics-inspired approaches (Ospina-Forero et al., 2022). To compare the impact of databases on the assessment outcomes,

researchers have created a unified SDG database to understand the SDG interactions (Warchold et al., 2022). Nonetheless, the synergies and trade-offs among the SDGs could change in a complex

manner over time. Counting only the number of SDG synergies and trade-offs over time may mask the offsetting effects of shifts between synergies and trade-offs. In other words, the benefits

of synergising one goal with the others may be offset by the negative impacts of emerging trade-offs associated with another one. While some studies have discussed the offsetting effects of

changes in performance across the SDGs (Gusmão Caiado et al., 2018; Salvia et al., 2019; Warchold et al., 2021), very few studies have reported offsetting effects in SDG interactions over

time. Additionally, an effective approach to overcoming the offsets is yet another critical gap that requires filling. Addressing these limitations facilitates a deeper comprehension of the

dynamic changes in SDG interactions. Some studies have apprised policymakers about the closed loops and active and passive relations arising from the SDG causal interactions (Breu et al.,

2021; Messerll, 2000; Pham-Truffert et al., 2020). Theoretically, closed loops are cycles of interactions and can serve as a self-reinforcing sub-network (Breu et al., 2021; Pham-Truffert et

al., 2020), which may be virtuous or vicious. In such a sub-network, promoting an SDG indicator can automatically enhance or inhibit the other SDG indicators. Theoretically, a passive

relation in the SDG network signifies that the influence undergone by the SDG is greater than it exerted on the others. Passive SDGs are susceptible to positive or negative influences from

other SDGs. Conversely, an active relation signifies that the SDG’s influence on others is greater than that the influence it undergoes (Breu et al., 2021; Pham-Truffert et al., 2020).

Active SDGs’ progress can stimulate or inhibit other SDGs. The shifts between the active and passive SDGs reflect state changes for SDGs in synergy and trade-off networks. However, previous

studies mainly relied on expert experience to identify causality among the SDGs while disregarding their spatial and temporal variation (Breu et al., 2021; Pham-Truffert et al., 2020).

Hence, quantifying the dynamics of closed-loop and active-passive relationships in SDG causal interaction networks at fine scales is crucial for exploring ways to promote synergies and

mitigate trade-offs among the SDGs. To address the existing knowledge gaps, in this study, we analysed changes in SDG synergies and trade-offs at the indicator level in China. China was

selected due to its robust data availability and research experience on SDG interactions at a subnational scale. Furthermore, China has implemented multiple policies to accelerate the SDGs

(Wang et al., 2020). However, the current rate fails to guarantee to achieve all goals (Lu et al., 2019), which remains a common challenge faced by multiple countries worldwide. We aim to

explore China’s SDGs solutions as a valuable platform for global or other national analyses. Therefore, this study addresses the following questions: (i) What are the offsetting effects and

spatial variations of the shift between synergies and trade-offs among SDGs in China over the past 20 years? (ii) What are the spatio-temporal characteristics of the closed loops in China’s

SDG networks? (iii) What are the spatial and temporal dynamics of active-passive relations among SDGs in China? We determined the causal direction and intensity among SDG indicators to

answer these questions by the spatio-temporal geographically weighted regression method (GTWR). Based on the causal direction and strength among SDG indicators, we constructed 1302

interaction networks among SDG indicators (Fig. S1 and Table S1) at the provincial scale in China (including 31 provinces, excluding Hong Kong, Macau, and Taiwan) yearly from 2000 to 2020.

Within the networks, each node acted as a specific SDG indicator. A directional link connected every pair of nodes, the weight of which denoted their interaction intensity, represented as a

regression coefficient. The coefficients varied across time and space and were obtained yearly for each province. We constructed networks for synergy and trade-off because one SDG, as an

independent variable, can have a positive or negative impact on another SDG, as a dependent variable. For each province, we explored the offsetting effects of changes in SDG synergies and

trade-offs over time, identified closed loops in SDG networks, and analysed the dynamics of active-passive relations in the networks. METHODS DATA SOURCES To thoroughly evaluate the progress

of SDGs at the provincial scale in China, we collected provincial statistical data from 2000 to 2020 from the National Bureau of Statistics and Statistical Yearbook. To ensure the

representativeness of the SDG indicators, we constructed a provincial SDG indicator system for China by referring to the official UN SDG indicator framework, the Sustainable Development

Report 2021, and relevant literature (United Nations Statistics Division, 2017; Jeffrey et al., 2021; Zhang et al., 2022a). The SDG indicator data are available for all provinces in China

and sufficiently complete for the research period. We obtained 86 indicators that corresponding to 80 targets and 16 goals, i.e., at least two targets per goal except SDG 14 (Table S1).

Since SDG14 is primarily concerned with marine ecosystems, most provinces in China lack relevant indicator data (Zhang et al., 2022a). Therefore, we did not take into account the

interactions between SDG14 and other SDGs. Depending on the nature of the indicators, we classified them as positive, negative, and moderate indicators (Jeffrey et al., 2021; Warchold et

al., 2021; Zhang et al., 2022a). Among these, higher levels of sustainability required larger values for positive indicators (e.g., SDG 1.3.1 Unemployment insurance coverage rate), smaller

values for negative indicators (e.g., SDG 1.5.1 Number of people affected by natural disasters), and closer to the middle threshold for moderate indicators (e.g., SDG 5.1 Proportion of

female employees in employment). To ensure data comparability across the indicators, SDG indicators were rescaled from 0 to 100, where 0 represented the worst possible performance, and 100

described the optimum performance (Zhang et al., 2022a). The rescaling formulas as follows: $${{{\mathrm{Positive}}}}\!:x^{\prime} = \frac{{x -

x_{{{{\mathrm{min}}}}}}}{{x_{{{{\mathrm{max}}}}} - x_{{{{\mathrm{min}}}}}}} \times 100$$ (1) $${{{\mathrm{Negative}}}}\!:x^\prime = \frac{{x_{{{{\mathrm{max}}}}} -

x}}{{x_{{{{\mathrm{max}}}}} - x_{{{{\mathrm{min}}}}}}} \times 100$$ (2) $${{{\mathrm{Moderate}}}}:x^{\prime} = \left\{ {\begin{array}{*{20}{c}} {100 - \frac{{x_{{{{\mathrm{int}}}}} -

x}}{{\max \left( {x_{{{{\mathrm{int}}}}} - \min \left( x \right),\max \left( x \right) - x_{{{{\mathrm{int}}}}}} \right)}},x \,< \,x_{{{{\mathrm{int}}}}}} \\ {100 - \frac{{x -

x_{{{{\mathrm{int}}}}}}}{{\max \left( {x_{{{{\mathrm{int}}}}} - \min \left( x \right),\max \left( x \right) - x_{{{{\mathrm{int}}}}}} \right)}},x \,>\, x_{{{{\mathrm{int}}}}}} \\ {100,x =

x_{{{{\mathrm{int}}}}}} \end{array}} \right.$$ (3) where _x_′ is the normalised value after rescaling, _x_ is the raw data value, _x_max, _x_min donate the upper and lower bounds,

respectively, which are defined at the 2.5th percentile of the distribution. Each indicator distribution was censored so that all values exceeding the upper bound scored 100, and values

below the lower bound scored 0 (SDR2021). _x_int donates the optimal value, which is obtained by referring to relevant literature (Wu et al., 2022), while max(_x_) and min(_x_) are the

maximum and minimum values of the original data for the moderate indicator. After data normalisation, the variance inflation factor was used to exclude multicollinearity indicators.

Considering the initial datasets’ degree of missing and reliability, a total of 65 SDG indicators were used for the study. DIRECTIONAL INTERACTIONS AMONG SDGS Considering the spatio-temporal

non-stationarity of the SDG indicators, we used a spatio-temporal geographically weighted regression (GTWR) to analyse the influence of one SDG indicator on the other based on the

normalised data. Most SDG networks constructed in the current study are undirected weighted networks (Lusseau and Mancini, 2019) or directed unweighted networks (Dörgo et al., 2018). GTWR

method can obtain the direction and strength of interaction between SDG indicators between SDG indicators. The GTWR method can obtain the causal direction and intensity among SDG indicators,

where the causal intensity among SDG indicators is determined by the regression coefficient of GTWR. The regression coefficients of this model were descriptors of the spatio-temporal

relationship between SDG indicators and varied with the spatio-temporal location (Fotheringham et al., 2015; Huang et al., 2010). Positive regression coefficient values indicated a positive

influence of independent variable indicators on dependent variable indicators, overall implying a synergistic effect. In contrast, negative values signified that one indicator hindered the

other, implying a trade-off effect. The greater the coefficient’s absolute value, the stronger the synergy or trade-off between the indicators. The GTWR model can be expressed as: $$Y_i =

\beta _0\left( {u_i,v_i,t_i} \right) + \mathop {\sum}\limits_{k = 1}^d {\beta _k\left( {u_i,v_i,t_i} \right)X_{ik} + \varepsilon _i} \quad i = 1,2 \cdots n$$ (4) where _Y__i_ is the

dependent variable for the observation _i_; _X__ik_ is the _k_th explanatory variable for the observation _i_. \((u_i,\, v_i,\, t_i)\) represents the space-time coordinates of observation

_i_; _u__i_ and _v__i_ are the projected spatial coordinates, whereas _t__i_ is the projected temporal coordinate. \(\varepsilon _i\) is the error term; \(\beta _0{(u_i,\, v_i,\, t_i)}\) is

the intercept value; \(\beta _k{(u_i,\, v_i,\, t_i)}\) is the regression coefficient, which is a parameter measuring the influence of the explanatory variable _X__ik_ on dependent variable

_Y__i_. Through an augmented Dickey-Fuller test and a spatial autocorrelation for a significant spatio-temporal non-stationarity test, we conducted a spatio-temporal geographically weighted

regression of SDG indicators for each province in China from 2000 to 2020. Regression was run 65 times. Each regression analysis used a specific SDG indicator as the dependent variable. The

remaining indicators served as independent variables. A total of 651 observation items in 21 years of data from 31 provinces were used for each regression. We calculated the coefficient

matrices for the indicator-level interactions for each province year by year. We utilised the adjusted R2 to assess the fit and effect of the regression model. In general, the closer the

adjusted R2 is to 1, the better the fit. Adjusted R2 greater than 0.8 were considered credible. To avoid over-interpretation, a coefficient threshold was determined by several experiments to

best fit the resulting network structure with the desired property. Coefficient values greater than 0.1, less than −0.1, and between −0.1 and 0.1 indicated synergy, trade-off, and

non-significance of the interactions between the two indicators, respectively. NETWORK ANALYSES We constructed 1302 complex networks of SDG synergy and trade-offs for each province in China,

year by year (Fig. S1) based on the coefficient matrices of the indicator-level interactions using the Networkx package in Python. The established complex networks were directionally

weighted due to the directionality of the spatio-temporal geographically weighted regression coefficients. In the network, the nodes denoted SDG indicators, the directional link from node X

to node Y represented the impact of node X on node Y, and the weight of the link was determined by the regression coefficient. The absolute value of the coefficient size represented the

interaction intensity between nodes. The betweenness centrality of a link was calculated as the sum of the fraction of all pairs of shortest paths that pass through (Ulrik, 2008). The

greater the betweenness centrality of a link, the more important it becomes in the network (Qi et al., 2017). We selected the important links with the top 300 betweenness centrality as

sub-networks. We identified the closed loops in the sub-networks using the simple cycle algorithm.To perform an in-depth discussion of the inner mechanism, we considered simple loops with 3

to 5 nodes in the search for loops. Furthermore, due to the long search time of complex networks, we set a 3-hour time limit for each province’s annual network detection loop. We counted the

frequency of indicator-level nodes in the critical loops that appeared more than thrice. After constructing SDG synergistic and trade-off networks, we calculated the out-degree and

in-degree of each SDG indicator in the networks at the provincial level in China. We identified the active and passive relations among SDGs using the network node’s out-degree and in-degree

(Breu et al., 2021; Messerll, 2000; Pham-Truffert et al., 2020). The node’s out-degree and in-degree reflect its degree of influence on other nodes and the influence other nodes have on it,

respectively (Dawes, 2022). There is no a priori justification for favouring one measure with greater weight than another (Xu et al., 2020). Therefore,we calculated the out-degree and

in-degree among goals by arithmetic averaging the corresponding indicators. When the active ratio was greater than 1, the SDG was defined as having an active relation, pushing more effects

on other SDGs. If an SDG is more active in the network, it means it has a greater chance ofhaving a stronger positive or negative impact on other SDGs in the network. Conversely, when the

active ratio is less than 1, the SDG is defined as having a passive relation (Fig. 1) (Breu et al., 2021; Messerll, 2000; Pham-Truffert et al., 2020). Suppose an SDG is more passive in the

network. In that case, it is more unstable and dependent on other SDGs,as well as more susceptible to positive or negative influences from other SDGs. We calculated the overall change of the

SDG with active and passive relations according to Eq. (5). The SDG with the largest overall change was defined as the typical goal for active and passive changes for each province in

synergy networks (Table S2) and trade-off networks (Table S3). $${{C}}_{{{t}}_1 \to {{t}}_2} = \sqrt {\left( {{{x}}\left( {{{t}}_2} \right) - {{x}}\left( {{{t}}_1} \right)} \right)^2 +

\left( {{{y}}\left( {{{t}}_2} \right) - {{y}}\left( {{{t}}_1} \right)} \right)^2}$$ (5) where \(C_{t_1 \to t_2}\) is the overall change of the SDG from year _t_1 to year _t_2; _x_ represents

the active ratio of SDG in year _t_; _y_ represents the interaction degree of SDG in year _t_. If an SDG has become active and has changed the most overall over the last 20 years,

strengthening or controlling the development of it is more conducive to promoting the active development of other SDGs; if an SDG has become passive and has changed the most overall over the

last 20 years, it is crucial to promote the development of this SDG by identifying what goals are specifically influenced by it. RESULTS OFFSETTING EFFECTS OF CHANGES IN SYNERGIES AND

TRADE-OFFS Our results confirmed the existence of an offsetting effect of changes in SDG synergies and trade-offs in China over the last two decades. At the country scale, the number of

indicator pairs for synergies and trade-offs remained largely stable from 2000 to 2020. However, the synergies indicators’ and trade-offs were dynamic, with complex interchanges occurring

over time (Fig. 2a and Fig. S2). Over the past 20 years, approximately 27% of trade-off indicator pairs changed to synergies, and about 25% of synergistic pairs changed to trade-offs (Fig.

2a). Among them, the effect of SDG3 on SDG15 changed from trade-offs to synergies, accounting for a higher proportion. In contrast, its effect on SDG4 changed from synergies to trade-offs,

with a higher proportion (Fig. 2b). These changes might be responsible for the offsets in newly generated synergies by the newly emerged trade-offs. That is, the newly generated synergy of

SDG3 on SDG15 is offset by the newly generated trade-off of SDG3 on SDG4. Furthermore, we find that approximately 50% of the interactions between SDG indicator pairs have been

non-significant in China over the past 20 years (Fig. 2a), indicating that the synergy and trade-off relationship in China is not an either-or relationship. Additionally, our results

demonstrated that the changes between SDG synergies and trade-offs had significant variations across provinces (Fig. 2c, d). The offsetting effect of the synergies of SDG3 on SDG15 by its

trade-offs on SDG4 was more pronounced in the Northeastern and Central provinces of China. These observations signified that the beneficial effects of developing the terrestrial ecosystem

through health promotion might be offset by its trade-off effects on education in these regions. Similarly, in the Eastern coastal region of China, the effect of SDG6 on SDG2 shifted from

trade-offs to synergies while shifting from synergies to trade-offs on SDG4. This implied that the negative effect of clean water on education might have masked some of its beneficial

effects on food security. Meanwhile, we found that SDG3 and SDG11, while promoting SDG6, were inhibited by their internal indicators in Southern China. These observations emphasised the

importance of resolving conflicts among indicators within the SDGs as well as promoting synergies among the SDGs. CLOSED LOOPS IN SDG NETWORKS Our results demonstrated closed loops in

China’s synergy networks (excluding Xinjiang and Tibet) but not in China’s trade-off networks (Fig. 3). These loops provided an opportunity to mitigate the offsetting effects of trade-offs

on synergies among SDGs over time. In Southeast China, a virtuous cycle consisting of SDG1.1.1 (eradicate extreme poverty), SDG15.5.1 (red list index), and SDG15.1.1 (forest cover rate)

occurred several times (Fig. 3e). It illustrated that poverty alleviation policy had a positive synergistic effect on local terrestrial ecosystems. In return, the improvement of terrestrial

ecosystems further promoted the eradication of extreme poverty. Common virtuous cycles were more likely to occur in neighbouring provinces and in consecutive years (Fig. 3 and Table S4).

Therefore, integrated policies based on common self-reinforcing cycles can effectively contribute to developing a sustainable regional development. Progress in one indicator in a virtuous

cycle could automatically reinforce other indicators. Conversely, failure to progress in an indicator may trigger a vicious cycle where failures are multiplied across indicators. We

identified the high-frequency indicators for virtuous cycles in China (Table S5), including SDG12.2.2 (domestic material consumption), SDG6.5.1 (water resource management), SDG4.1.2

(education completion rate), 4.b.1 (educational investment), 4.a.1 (educational facilities), and SDG15.5.1 (red list index). Prioritising the progress of high-frequency indicators in

virtuous cycles, such as improving resource utilisation and water management, and increasing investment in education and ecological funding and its related facilities can multiply the

positive systemic effects of the SDG system overall. CHANGES IN ACTIVE AND PASSIVE RELATIONS AMONG SDGS Although closed loops were not observed in China’s trade-offs, the active-passive

relations changed dramatically in the SDG synergistic and trade-off networks. At the country scale, we found that in China’s synergy networks, approximately 19.1% of the indicators had

converted from passive to active relations between 2000 and 2020. About 19.3% of the indicators changed from active to passive relations (Table S6). For China’s trade-off networks,

approximately 20.1% of the indicators were changed from passive to active relations between 2000 and 2020. Approximately 16.5% of the indicators changed from active to passive (Table S6). At

the country scale, SDG1 and SDG7 changed from passive to active relations in both synergy and trade-off networks, implying that processes of these SDGs significantly influenced the other

SDGs. In contrast, SDG5 and SDG16 changed from active to passive relations in both networks, demonstrating the vulnerability of these SDGs (Fig. 4a). Our study also highlighted that the

changes in the active and passive relations of the various SDGs from 2000 to 2020 were spatially variable at the provincial scale (Fig. 4b). In the synergistic network, we found that from

passive to active relations, SDG13 (climate action) and SDG4 (quality education) were the typical goals in Southern China and Northeast China, respectively, implying that progress in these

goals could lead to improvement in more SDGs. Meanwhile, some synergy relations changed from active to passive, mainly SDG16 (peace, justice, and strong institutions), SDG5 (gender

equality), and SDG11 (sustainable cities and communities) in most provinces. It signifies that the progress toward these goals was more dependent on the progress toward the other SDGs.

Additionally, in the trade-off network, SDG1 (no poverty) was the typical goal that changed from passive to active in the Northern provinces but with decreasing trade-off intensity. In

contrast, in the South, SDG7 (affordable and clean energy) was the predominant goal that shifted from passive to active with increasing trade-off intensity. These results suggested that

addressing the trade-offs for SDG1 and SDG7 with other goals remains an important task for the future. However, for the change from active to passive trade-offs, we found that SDG11 and

SDG16 were the typical goals in most provinces and were similar to those in the synergistic network. This result reflected that while SDG11 and SDG16 were important nodes in the network that

were dependent on the progress of other SDGs, they could delay the transmission of trade-off relations in the networks. DISCUSSION By combining causal diagnosis and network analysis

methods, our study quantified the SDG interactions for 31 Chinese provinces at the indicator level from 2000 to 2020. Our results revealed the offsetting effects of SDG synergies and

trade-offs interconversion . We provided novel insights and innovations for addressing trade-offs and promoting synergies. First, we defined synergy and trade-off relationships based on the

causal relationships between SDGs. Most articles explore the synergies and trade-offs between SDGs by correlation analysis that could not identify the directionality of the interactions

(Bali Swain and Ranganathan, 2021; Anderson et al., 2022; Kroll et al., 2019; Pradhan et al., 2017; Warchold et al., 2021). We advanced the current status by identifying the causal direction

and strength between SDGs using the GTWR method. Based on the SDG causal network, the spatial and temporal changes in the causal interactions of SDGs are explored in the paper. Second, we

found that while the number of synergies and trade-offs among SDG indicators in China remained largely stable over the years 2000 to 2020, SDG interactions underwent complex changes with

spatial and temporal variations. Among them, the effect of SDG3 on SDG15 in Northeastern and Central China changed from trade-off to synergy from 2000 to 2020. This change might be

attributed to the growing popularity of green and low-carbon lifestyles, which contributed to better air quality and improved terrestrial ecosystems (Shi et al., 2022; Zhang et al., 2021a,

2021b). Similarly, in the Eastern coastal zone, the relationship between SDG6 and SDG2 shifted from trade-offs to synergies, possibly attributed to the role of control and management of

coastal water pollution on the improvement of water quality and food safety (Wang et al., 2022). Nonetheless, these benefits might be offset by the emerging trade-offs between SDG3 and SDG4.

This was possibly due to the potential conflict between investments in education and health care (Li and Huang, 2009; UN Department of Economic and Social Affairs, 2012), and the effect of

the new crown epidemic on the education system and international visits (Mok et al., 2021). These results illustrated the complexities of SDG interactions. They emphasised that the

offsetting effects of changes in SDG synergies and trade-offs might impede achieving SDGs in any country or region. Therefore, to address this offsetting effect, we leverage virtuous cycles,

promote active synergy and inhibit active trade-offs in the SDG network from mitigating beneficial synergies from being offset by harmful trade-offs among SDGs. Third, we identified closed

loops in the SDG synergy and trade-off network in China and its 31 provinces to explore effective ways to address trade-offs and promote synergies. An important and interesting finding of

our study was that China’s provinces had closed loops in the synergistic network, but not in the trade-off network. The discovery of virtuous cycles offers the possibility of finding

specific paths for advancing SDGs in China. High-frequency indicators in virtuous cycles are potential entry points for policy development that can efficiently facilitate the multiplication

of positive effects in virtuous cycles. Likewise, we found that several spatially adjacent provinces shared common virtuous cycles in consecutive years. This finding was consistent with

existing research that demonstrates that China’s urban spatial form tends to develop into urban agglomerations (Han et al., 2021). Our study further identified high-frequency indicators,

including SDG12.2.2, SDG6.5.1, SDG4.1.2, 4.a.1, 4.b.1 and SDG15.5.1 in China. Prioritising SDG12 (responsible consumption and production) could reduce the consumption of natural resources

like wood, promote water security, and have long-term benefits for food security (Zhang et al., 2022a). Enhancing water resources management (SDG6.5.1) has the potential to improve water

resource efficiency, protect the ecology, and promote economic development (Wang et al., 2018). Promoting quality education (SDG4) could improve the health of the population (Huang, 2022)

and increase the drive for technological innovation (Zhou and Luo, 2018). In addition, achieving SDG15 (life on land) through strengthening nature reserve management and policy guidance (Cui

et al., 2021), protecting native forests, and improving the structural complexity of terrestrial ecosystems (Hua et al., 2016; Zhang et al., 2021a, 2021b) could promote biodiversity

recovery for better environmental improvement and green economic development. Fourth, while there were no closed loops in the SDG trade-off networks, we highlighted the dynamics of the

active-passive relations among SDGs to deepen our understanding of SDG interactions. Previous studies have established a typology of SDGs that act as buffers and multipliers by defining the

activity ratio of SDGs to reflect the active and passive relations among SDGs (Breu et al., 2021; Pham-Truffert et al., 2020). However, these studies constructed the SDG networks based on

expert experience gleaned from a systematic literature review (Breu et al., 2021; Pham-Truffert et al., 2020). To address this limitation, we used reliable statistical data and causal

diagnostics to reduce the uncertainty of such analyses while also providing simultaneously offer the possibility of uncovering the spatio-temporal variability of the active-passive

relations. Our study revealed that SDG13 (climate action) in Southwest China and SDG4 (quality education) in Northeast China had typically changed from passive to active relations in the

synergy network. This finding confirmed that climate actions could reduce the economic losses of disasters and contribute to achieving other SDGs (Guo et al., 2019). In addition,

accelerating education development can help to stimulate employment, raise incomes, and advance the progress of other SDGs (Vladimirova and Le Blanc, 2016). These changes can be viewed

positive signs towards achieving China’s SDGs. However, SDG1 (no poverty) in Northern China and SDG7 (affordable and clean energy) in Southern China changed from passive to active relations

in China’s trade-off networks. This meant that solely pursuing these goals in isolation without considering their trade-off impacts on the other goals might jeopardise their ability to

progress smoothly in China. Therefore, maintaining stable implementation and optimisation of poverty alleviation policy is critical in promoting sustainable development in China (Liu et al.,

2021). Similarly, formulating plans to promote clean energy development must consider its negative impact to minimise trade-offs with other SDGs (D’Odorico et al., 2018; World Wide Fund for

Nature, 2018; Zahoor et al., 2022; Zhang et al., 2022c). In addition, SDG11 and SDG16 were susceptible to synergies and trade-offs with other SDGs in China. Existing studies show that SDG11

and SDG16 are prone to changes in the local economy level, infrastructure development, and industrialisation (Liu et al., 2021). The development of these SDGs requires rational planning of

the local economy by policymakers to achieve orderly construction, moderate development, and efficient city operation. In addition, the trade-off effects generated by other SDGs must be

integrated when formulating policies to efficiently promote SDG11 and SDG16 in China. CONCLUSIONS In conclusion, our study observed the offsetting effect of changes in SDG synergies and

trade-offs over time and their spatial differences. To address these effects, we identified the virtuous cycle and the high-frequency indicators as crucial policy development criteria to

accelerate SDG development. Exploring the dynamics of the active and passive types of SDG revealed that policies cannot be static, and the formulation of SDG development policies must be

adaptive in the face of changing conditions. Enhancing the development of active synergistic SDGs while curbing the negative impact of active trade-off SDGs can contribute to overall SDG

sustainable development. Promoting the steady development of quality education, strengthening education funding, developing education infrastructure and climate regulation are all measures

that can effectively lead to the development of other SDGs. Policies that promote no poverty and clean energy proactive development need to be designed to curb the trade-off effects of these

proactive trade-off SDG goals on other SDGs. For example, when developing policies to build clean energy facilities in China’s provinces, it is necessary to consider whether these clean

energy facilities are compatible with local natural conditions along with their impact on the local ecological environment. Leveraging virtuous cycles and inhibiting active trade-offs are

beneficial to control beneficial synergies from being offset by harmful trade-offs among the SDGs. It is recommended that provinces with the same virtuous cycle formulate a unified SDG

development policy that can efficiently promote the common regional development while reducing development costs. By promoting the transition from material-oriented consumption to

service-oriented consumption in China, strengthening the unified management of water resources, and solidly promoting the high-quality development of education, and other measures to promote

the steady development of high-frequency indicators, which in turn will accelerate the positive flow of the virtuous cycle network in China and facilitate the overall sustainable

development of China’s SDG.This study deepened our understanding of SDG synergies and trade-offs and facilitated evidence-based policymaking. In the future, we will further update the latest

SDG data to analyse the causal relationships among SDGs in China’s provinces and explore the impact of the COVID-19 pandemic on the process of achieving SDGs for each province in China.

Moreover, we will collect city-level SDG data and analyse the synergistic and trade-off relations at the city-level SDG in China, the virtuous cycle and the future simulation assessment of

SDGs. Our results could facilitate the achievement of the 2030 Agenda in China within a limited time frame, resucing it from failing. Meanwhile, our analytical framework could also provide

lessons for other countries or assessments at different scales. DATA AVAILABILITY The provincial data used in this study can be obtained from the National Bureau of Statistics via the

website (http://www.stats.gov.cn/). The datasets generated during and/or analysed during the current study are not publicly available due to confidentiality issues but are available from the

corresponding author on reasonable request. REFERENCES * Anderson CC, Denich M, Warchold A et al. (2022) A systems model of SDG target influence on the 2030 Agenda for Sustainable

Development. Sustain Sci 17:1459–1472. https://doi.org/10.1007/s11625-021-01040-8 Article  PubMed  Google Scholar  * Bali Swain R, Ranganathan S (2021) Modeling interlinkages between

sustainable development goals using network analysis. World Dev 138:105136. https://doi.org/10.1016/j.worlddev.2020.105136 Article  Google Scholar  * Breu T, Bergöö M, Ebneter L et al.

(2021) Where to begin? Defining national strategies for implementing the 2030 agenda: the case of Switzerland. Sustain Sci 16:183–201. https://doi.org/10.1007/s11625-020-00856-0 Article 

Google Scholar  * Cheng D, Xue Q, Hubacek K et al. (2022) Inclusive wealth index measuring sustainable development potentials for Chinese cities. Glob Environ Chang 72:102417.

https://doi.org/10.1016/j.gloenvcha.2021.102417 Article  Google Scholar  * Cui W, Liu J, Jia J et al. (2021) Terrestrial ecological restoration in China: identifying advances and gaps.

Environ. Sci Eur 33. https://doi.org/10.1186/s12302-021-00563-2 * D’Odorico P, Davis KF, Rosa L et al. (2018) The global food-energy-water nexus. Rev Geophys.

https://doi.org/10.1029/2017RG000591 * Dawes JHP (2022) SDG interlinkage networks: analysis, robustness, sensitivities, and hierarchies. World Dev 149:105693.

https://doi.org/10.1016/j.worlddev.2021.105693 Article  Google Scholar  * Dawes JHP, Zhou X, Moinuddin M (2022) System-level consequences of synergies and trade-offs between SDGs:

quantitative analysis of interlinkage networks at country level. Sustain Sci. https://doi.org/10.1007/s11625-022-01109-y * Dörgo G, Sebestyén V, Abonyi J (2018) Evaluating the

interconnectedness of the sustainable development goals based on the causality analysis of sustainability indicators. Sustain 10. https://doi.org/10.3390/su10103766 * Fotheringham AS, Crespo

R, Yao J (2015) Geographical and temporal weighted regression (GTWR). Geogr Anal 47:431–452. https://doi.org/10.1111/gean.12071 Article  Google Scholar  * Guo N, Ren Y, Tang X (2019) The

temporal and spatial evolution of natural disasters in China. GeoJournal 84:1515–1530. https://doi.org/10.1007/s10708-018-9934-8 Article  Google Scholar  * Gusmão Caiado RG, Leal Filho W,

Quelhas OLG et al. (2018) A literature-based review on potentials and constraints in the implementation of the sustainable development goals. J. Clean. Prod 198:1276–1288.

https://doi.org/10.1016/j.jclepro.2018.07.102 Article  Google Scholar  * Han Z, Jiao S, Zhang X et al. (2021) Seeking sustainable development policies at the municipal level based on the

triad of city, economy and environment: evidence from Hunan province, China. J Environ Manage 290:112554. https://doi.org/10.1016/j.jenvman.2021.112554 Article  PubMed  Google Scholar  * Hua

F, Wang X, Zheng X et al. (2016) Opportunities for biodiversity gains under the world’s largest reforestation programme. Nat Commun 7. https://doi.org/10.1038/ncomms12717 * Huang B, Wu B,

Barry M (2010) Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices. Int J Geogr Inf Sci 24:383–401.

https://doi.org/10.1080/13658810802672469 Article  Google Scholar  * Huang W (2022) The effects of education on health and mechanisms: evidence from China. Appl Econ 54:3582–3597 Article 

Google Scholar  * Jeffrey S, Schmidt-Traub G, Kroll C et al. (2021) Sustainable development report 2020: The sustainable development goals and Covid-19 includes the SDG index and dashboards.

Cambridge University Press * Kroll C, Warchold A, Pradhan P (2019) Sustainable development goals (SDGs): are we successful in turning trade-offs into synergies? Palgrave Commun 5:1–11.

https://doi.org/10.1057/s41599-019-0335-5 Article  Google Scholar  * Li H, Huang L (2009) Health, education, and economic growth in China: empirical findings and implications. China Econ Rev

20:374–387. https://doi.org/10.1016/j.chieco.2008.05.001 Article  Google Scholar  * Liu B, Wang T, Zhang J et al. (2021) Sustained sustainable development actions of China from 1986 to

2020. Sci Rep 11:1–10. https://doi.org/10.1038/s41598-021-87376-8 Article  CAS  Google Scholar  * Lu Y, Zhang Y, Cao X et al. (2019) Forty years of reform and opening up: China’s progress

toward a sustainable path. Sci Adv 5:1–10 Article  ADS  Google Scholar  * Lusseau D, Mancini F (2019) Income-based variation in Sustainable Development Goal interaction networks. Nat Sustain

2:242–247. https://doi.org/10.1038/s41893-019-0231-4 Article  Google Scholar  * Messerll P (2000) Use of sensitivity analysis to evaluate key factors for improving slash-and-burn

cultivation systems on the Eastern Escarpment of Madagascar. Mt Res Dev 20:32–41. https://doi.org/10.1659/0276-4741(2000)020[0032:uosate]2.0.co;2 Article  Google Scholar  * Mok KH, Xiong W,

Ke G et al. (2021) Impact of COVID-19 pandemic on international higher education and student mobility: Student perspectives from mainland China and Hong Kong. Int J Educ Res 105:101718.

https://doi.org/10.1016/j.ijer.2020.101718 Article  PubMed  Google Scholar  * Nilsson M, Chisholm E, Griggs D et al. (2018) Mapping interactions between the sustainable development goals:

lessons learned and ways forward. Sustain Sci 13:1489–1503. https://doi.org/10.1007/s11625-018-0604-z Article  PubMed  PubMed Central  Google Scholar  * Nilsson M, Griggs D, Visbeck M (2016)

Policy: map the interactions between sustainable development goals. Nature 534:320–322. https://doi.org/10.1038/534320a Article  ADS  PubMed  Google Scholar  * Ospina-Forero L, Castañeda G,

Guerrero OA (2022) Estimating networks of sustainable development goals. Inf Manag 59. https://doi.org/10.1016/j.im.2020.103342 * Pedercini M, Arquitt S, Collste D et al. (2019) Harvesting

synergy from sustainable development goal interactions. Proc Natl Acad Sci USA 116:23021–23038. https://doi.org/10.1073/pnas.1817276116 Article  ADS  CAS  PubMed  PubMed Central  Google

Scholar  * Pham-Truffert M, Metz F, Fischer M (2020) Interactions among sustainable development goals: knowledge for identifying multipliers and virtuous cycles. Sustain Dev 28:1236–1250.

https://doi.org/10.1002/sd.2073 Article  Google Scholar  * Pradhan P, Costa L, Rybski D et al. (2017) A systematic study of Sustainable Development Goal (SDG) interactions. Earth’s Futur

5:1169–1179. https://doi.org/10.1002/2017EF000632 Article  ADS  Google Scholar  * Pradhan P (2023) A threefold approach to rescue the 2030 Agenda from failing. Natl Sci Rev 10:1–3.

https://doi.org/10.1093/nsr/nwad015 Article  Google Scholar  * Qi X, Song H, Wu J et al. (2017) Eb&D: a new clustering approach for signed social networks based on both edge-betweenness

centrality and density of subgraphs. Phys A Stat Mech Appl 482:147–157. https://doi.org/10.1016/j.physa.2017.04.037 Article  MathSciNet  Google Scholar  * Salvia AL, Leal F et al. (2019)

Assessing research trends related to sustainable development goals: local and global issues J. Clean. Prod 208:841–849. https://doi.org/10.1016/j.jclepro.2018.09.242 Article  Google Scholar

  * Scharlemann JPW, Brock RC, Balfour N et al. (2020) Towards understanding interactions between sustainable development goals: the role of environment–human linkages. Sustain Sci

15:1573–1584. https://doi.org/10.1007/s11625-020-00799-6 Article  Google Scholar  * Sebestyén V, Bulla M, Rédey Á et al. (2019) Network model-based analysis of the goals, targets and

indicators of sustainable development for strategic environmental assessment. J Environ Manage 238:126–135. https://doi.org/10.1016/j.jenvman.2019.02.096 Article  PubMed  Google Scholar  *

Shi Y, Yuan X, Tang Y et al. (2022) Localized regional life cycle model research for the impacts of carbon dioxide on human health and ecosystem. Sustain Prod Consum 29:36–45.

https://doi.org/10.1016/j.spc.2021.09.019 Article  Google Scholar  * Ulrik B (2008) On variants of shortest-path betweenness centrality and their generic computation. Soc Networks 30:136–145

Article  ADS  Google Scholar  * United Nations (2015) Transforming our world: the 2030 Agenda for Sustainable Development * United Nations Statistics Division (2017) SDG Indicators.

https://unstats.un.org/sdgs/indicators/indicators-list/. (Accessed Apr 17 2023) * UN Department of Economic and Social Affairs (2012) SDG 3 Ensure healthy lives and promote well-being for

all at all ages. UN Department of Economic and Social Affairs. pp. 81–126 * Van Soest HL, Van Vuuren DP, Hilaire J et al. (2019) Analysing interactions among sustainable development goals

with integrated assessment models. Glob Transitions 1:210–225. https://doi.org/10.1016/j.glt.2019.10.004 Article  Google Scholar  * Vladimirova K, Le Blanc D (2016) Exploring links between

education and sustainable development goals through the lens of UN flagship reports. Sustain Dev 24:254–271. https://doi.org/10.1002/sd.1626 Article  Google Scholar  * Voulvoulis N,

Giakoumis T, Hunt C et al. (2022) Systems thinking as a paradigm shift for sustainability transformation. Glob Environ Chang 75:102544. https://doi.org/10.1016/j.gloenvcha.2022.102544

Article  Google Scholar  * Wang M, Janssen ABG, Bazin J et al. (2022) Accounting for interactions between sustainable development goals is essential for water pollution control in China. Nat

Commun 13:1–13. https://doi.org/10.1038/s41467-022-28351-3 Article  ADS  CAS  Google Scholar  * Wang X, Zhang J, Gao J et al. (2018) The new concept of water resources management in China:

ensuring water security in changing environment. Environ Dev Sustain 20:897–909. https://doi.org/10.1007/s10668-017-9918-8 Article  Google Scholar  * Wang Y, Yuan J, Lu Y (2020) Constructing

demonstration zones to promote the implementation of sustainable development goals. Geogr Sustain 1:18–24. https://doi.org/10.1016/j.geosus.2020.02.004 Article  Google Scholar  * Warchold

A, Pradhan P, Kropp JP et al. (2021) Variations in sustainable development goal interactions: population, regional, and income disaggregation. Sustain Dev 29:285–299.

https://doi.org/10.1002/sd.2145 Article  Google Scholar  * Warchold A, Pradhan P, Thapa P et al. (2022) Building a unified sustainable development goal database: Why does sustainable

development goal data selection matter? Sustain Dev 1–16. https://doi.org/10.1002/sd.2316 * Weitz N, Carlsen H, Nilsson M et al. (2018) Towards systemic and contextual priority setting for

implementing the 2030 agenda. Sustain Sci 13:531–548. https://doi.org/10.1007/s11625-017-0470-0 Article  PubMed  Google Scholar  * World Wide Fund for Nature (2018) Report: The role of SDG15

in underpinning the achievement of The 2030 Agenda Global Policy and Advocacy. World Wide Fund for Nature * Wu X, Fu B, Wang S et al. (2022) Decoupling of SDGs followed by re-coupling as

sustainable development progresses. Nat Sustain 5:452–459. https://doi.org/10.1038/s41893-022-00868-x Article  Google Scholar  * Zahoor Z, Khan I, Hou F (2022) Clean energy investment and

financial development as determinants of environment and sustainable economic growth: evidence from China. Environ Sci Pollut Res 29:16006–16016. https://doi.org/10.1007/s11356-021-16832-9

Article  Google Scholar  * Zhang J, Fu B, Stafford-Smith M et al. (2021a) Improve forest restoration initiatives to meet Sustainable Development Goal 15. Nat Ecol Evol 5:10–13.

https://doi.org/10.1038/s41559-020-01332-9 Article  CAS  PubMed  Google Scholar  * Zhang J, Wang S, Pradhan P et al. (2022a) Untangling the interactions among the sustainable development

goals in China. Sci Bull 67:977–984. https://doi.org/10.1016/j.scib.2022.01.006 Article  Google Scholar  * Zhang J, Wang S, Zhao W et al. (2022b) Finding pathways to synergistic development

of sustainable development goals in China. Humanit Soc Sci Commun 9:1–10. https://doi.org/10.1057/s41599-022-01036-4 Article  Google Scholar  * Zhang J, Wang S, Pradhan P et al. (2022c)

Mapping the complexity of the food-energy-water nexus from the lens of sustainable development goals in China. Resour Conserv Recycl 183. https://doi.org/10.1016/j.resconrec.2022.106357 *

Zhang X, Wang Z, Cheng M et al. (2021b) Long-term ambient SO2 concentration and its exposure risk across China inferred from OMI observations from 2005 to 2018. Atmos Res 247:105150.

https://doi.org/10.1016/j.atmosres.2020.105150 Article  CAS  Google Scholar  * Zhou G, Luo S (2018) Higher education input, technological innovation, and economic growth in China. Sustain

10:1–15. https://doi.org/10.3390/su10082615 Article  Google Scholar  * Xu Z, Chau SN, Chen X et al. (2020) Assessing progress towards sustainable development over space and time. Nature

577:74–78. https://doi.org/10.1038/s41586-019-1846-3 Article  ADS  CAS  PubMed  Google Scholar  Download references ACKNOWLEDGEMENTS This work was financially supported by the International

Research Center of Big Data for Sustainable Development Goals (CBAS2022GSP08), the National Natural Science Foundation of China (Grant No. 42271422 and 42201299) and the Key Project of the

National Natural Science Foundation of China (Grant number 41930648). AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Key Laboratory of Virtual Geographic Environment, Ministry of Education of

PRC, Nanjing Normal University, 210023, Nanjing, China Min Cao, Min Chen, Yue Li, Yuying Bai, Lijiao Chang & Guonian Lü * International Research Center of Big Data for Sustainable

Development Goals, 100094, Beijing, China Min Cao, Min Chen, Huadong Guo, Yu Chen & Zhongchang Sun * Jiangsu Center for Collaborative Innovation in Geographical Information Resource

Development and Application, 210023, Nanjing, China Min Cao, Min Chen & Guonian Lü * Chinese Academy of Sciences, State Key Laboratory of Urban and Regional Ecology, Research Center for

Eco-Environmental Sciences, 100085, Beijing, China Junze Zhang & Bojie Fu * Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, 14473, Potsdam,

Germany Prajal Pradhan * Bauhaus Earth, 10115, Berlin, Germany Prajal Pradhan * Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of

Sciences, 100194, Beijing, China Huadong Guo * State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, 100875,

Beijing, China Bojie Fu * Department of Geography, The University of Hong Kong, 999077, Hong Kong, China Zhenci Xu * Systems Science Department, Institute of High Performance Computing,

A*STAR, Singapore, 138632, Singapore Rui Zhu * Department of Environmental & Geographical Science, University of Cape Town, Rondebosch, 7700, South Africa Michael E. Meadows Authors *

Min Cao View author publications You can also search for this author inPubMed Google Scholar * Min Chen View author publications You can also search for this author inPubMed Google Scholar *

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You can also search for this author inPubMed Google Scholar * Guonian Lü View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS MC and MC

conceived the study and prepared the manuscript. YL, YB and LC performed the data analysis. JZ, YC, PP, HG, BF, ZS, ZX, RZ, MEM and GL contributed to the data interpretation and revision.

CORRESPONDING AUTHOR Correspondence to Min Chen. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ETHICAL APPROVAL This article does not contain any

studies with human participants performed by any of the authors. INFORMED CONSENT This article does not contain any studies with human participants performed by any of the authors.

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ARTICLE Cao, M., Chen, M., Zhang, J. _et al._ Spatio-temporal changes in the causal interactions among Sustainable Development Goals in China. _Humanit Soc Sci Commun_ 10, 450 (2023).

https://doi.org/10.1057/s41599-023-01952-z Download citation * Received: 13 December 2022 * Accepted: 18 July 2023 * Published: 29 July 2023 * DOI: https://doi.org/10.1057/s41599-023-01952-z

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