Global transboundary synergies and trade-offs among Sustainable Development Goals from an integrated sustainability perspective

Abstract

Domestic attempts to advance the Sustainable Development Goals (SDGs) in a country can have synergistic and/or trade-off effects on the advancement of SDGs in other countries. Transboundary SDG interactions can be delivered through various transmission channels (e.g., trade, river flow, ocean currents, and air flow). This study quantified the transboundary interactions through these channels between 768 pairs of SDG indicators. The results showed that although high income countries only comprised 14.18% of the global population, they contributed considerably to total SDG interactions worldwide (60.60%). Transboundary synergistic effects via international trade were 14.94% more pronounced with trade partners outside their immediate geographic vicinity than with neighbouring ones. Conversely, nature-caused flows (including river flow, ocean currents, and air flow) resulted in 39.29% stronger transboundary synergistic effects among neighboring countries compared to non-neighboring ones. To facilitate the achievement of SDGs worldwide, it is essential to enhance collaboration among countries and leverage transboundary synergies.

Full text

Article https://doi.org/10.1038/s41467-023-44679-w
Global transboundary synergies and trade-
offs among Sustainable Development Goals
from an integrated sustainability perspective
Huijuan Xiao
1,2
, Sheng Bao
3
, Jingzheng Ren
2,4,5
, Zhenci Xu
6,7
,
Song Xue
2
&JianguoLiu
8
Domestic attempts to advance the Sustainable Development Goals (SDGs) in a
country can have synergistic and/or trade-off effects on the advancement of
SDGs in other countries. Transboundary SDG interactions can be delivered
through various transmission channels (e.g., trade, river flow, ocean currents,
and air flow). This study quantified the transboundary interactions through
these channels between 768 pairs of SDG indicators. The results showed that
although high income countries only comprised 14.18% of the global popula-
tion, they contributed considerably to total SDG interactions worldwide
(60.60%). Transboundary synergistic effects via international trade were
14.94% more pronounced with trade partners outside their immediate geo-
graphic vicinity than with neighbouring ones. Conversely, nature-caused flows
(including river flow, ocean currents, and air flow) resulted in 39.29% stronger
transboundary synergistic effects among neighboring countries compared to
non-neighboring ones. To facilitate the achievement of SDGs worldwide, it is
essential to enhance collaboration among countries and leverage trans-
boundary synergies.
All United Nation (UN) member states have implemented 17 Sustain-
able Development Goals (SDGs) in pursuit of peace and prosperity for
all people and the planet1,2. The three main pillars of sustainability—
economy, society, and environment—encompassed these goals. Sus-
tainability is often approached from two perspectives: weak and
strong sustainability3. Weak sustainability posits that each of these
pillars holds equal weight and that the pillars are interchangeable3,4.
Strong sustainability prioritises the environmental pillar3,4. However,
recent research has introduced a new perspective on sustainable
development: integrated sustainability4–6. This concept extends
beyond traditional weak and strong sustainability perspectives and
incorporates the spillover effects generated by the transboundary
interactions across regions as a fourth pillar, alongside the original
three pillars6. These spillover effects represent the interplay of the
three original pillars ofsustainable development between two or more
regions5,7. In the current interconnected world, transboundary inter-
actions across countries may positively or negatively affect SDGs in
various other countries8. Global sustainable development cannot be
achieved by countries that act alone. Communication between coun-
tries can promote interdisciplinary programs and multilateral
Received: 11 May 2023
Accepted: 28 December 2023
Check for updates
1
Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
2
Department of Industrial
and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China.
3
Otto Poon C. F. Smart Cities Research Institute, Department of
Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China.
4
Research Center for Resources Engineering Towards
Carbon Neutrality, The Hong Kong Polytechnic University, Hong Kong SAR, China.
5
Department of Industrial and Systems Engineering, Research Institute for
Advanced Manufacturing, The Hong Kong Polytechnic University, Hong Kong SAR, China.
6
Department of Geography, The University of Hong Kong, Hong
Kong SAR, China.
7
Shenzhen Institute of Research and Innovation, The University of Hong Kong, Hong Kong SAR, China.
8
Center for Systems Integration and
Sustainability, Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI, USA. e-mail: jzhren@polyu.edu.hk;xuzhenci@hku.hk;
liuji@msu.edu
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collaborations, help policymakers formulate coherent plans and stra-
tegies, and effectively unlock transboundary SDG interaction
potential9.
Widespread interactions exist between SDGs across country
borders1,8,10–13, such as technological spillovers from multinational
corporations and profits from international trade. These transbound-
ary synergies may help receiving countries achieve their SDGs14.Con-
versely, there may also be transboundary trade-offs, such as
wastewater flow into transboundary rivers, which may hamper the
achievement of SDGs in countries receiving wastewater15–17. However,
little attention has been paid to determining the impacts of these
transboundary interactions on SDGs18. Quantifying transboundary
SDG interactions is challenging because countries are connected
through different transmission channels, and the outcomes of trans-
boundary SDG interactions can vary19–21. Regarding human-caused
flows, a common channel of transboundary SDG interactions is inter-
national trade22, which may have environmental and socioeconomic
impacts on trade partners owing to the water23,24,carbon
25, and labour
used to produce goods and embodied in trade26 (Fig. 1a). Additionally,
nature-caused flows connect many countries (Fig. 1b). For instance,
under uncontrolled pollution conditions, air and wind may transport
airborne pollutants into neighbouring countries27 or even to distant
countries through intercontinental transport21,28,29, thereby compro-
mising air quality and human health in receiving countries28.Further-
more, pollutants discarded in waterways affect local communities and
neighbouring countries.
This study investigates integrated sustainability in the context of
the SDGs to determine whether each SDG indicator exerts positive or
negative effects on the others and to quantify these effects. A con-
ceptual framework incorporating different channels of transboundary
SDG interactions was first proposed. This framework was built based
on metacoupling (e.g., human–nature interactions within and between
neighbouring and distant countries)30,asshowninFig.1.Thisstudy
classified these channels into two broad categories, each of which
interacted with the performance of the SDGs of other countries in
different ways: human-caused flows (e.g., international trade) and
nature-caused flows (e.g., river flow, ocean currents, and air flow)
(Fig. 1). Second, this study examined 768 pairs of SDG indicators to
evaluate how an individual SDG indicator of a country interacts with
other countries’indicators through different channels31–33.Thepairsof
indicators were identified as having causal relationships (e.g., energy
intensity and CO
2
emission intensity indicators as the interaction
generator and receiver, respectively), which can be derived from a
database about SDG interactions8(Supplementary information
Tables S1 and S2). Finally, this study proposed a spatial interaction
index to quantify the overall magnitude of transboundary interactions
between the performance of the SDGs in one country and those in
other countries. This index can be divided into transboundary syner-
gistic and trade-off effects and is a scorecard (score: 0–100) used to
indicate the magnitude of transboundary interactions. Based on
available data for 2010 to 2020, 121 countries werechosen for analysis
(Supplementary information Table S3). The findings of this study can
aid in improving the understanding, monitoring, and careful man-
agement of transboundary SDG interactions.
Results
Transboundary SDG interaction linkages across countries
Through the transmission channels of both international trade and
nature-caused flows (incorporating river flow, ocean currents, and air
flow), the transboundary synergistic linkages were more pronounced
than their trade-off counterparts. Specifically, amongst the trans-
boundary linkages, which include synergistic and trade-off linkages,
73.68% of the linkages resulting from international trade were syner-
gistic (Fig. 2a, b). Similarly, 81.82% of linkages originating form nature-
caused flows were synergistic (Fig. 2c, d). These results also highlight
that, compared with interaction linkages resulting from nature-caused
flows, linkages originating from international trade were generally more
susceptible to counterproductive effects, potentially undermining joint
efforts towards the SDGs. To provide further clarity, within the sphere
of international trade, trade-off linkages accounted for 26.32%
Fig. 1 | Conceptual framework of transboundary interactions of Sustainable
Development Goals (SDGs) across countries. a Human-caused flows. bNature-
caused flows. Two categories of channels can create SDG interactions between
countries: human-causedflows and na ture-caused flows. The receiving countries of
the interactions are classified as either neighbouring or non-neighbouring
countries.
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(calculated as 100–73.68%) of the total SDG interaction linkages (Fig. 2a,
b). This percentage is notably higher than the 18.18% (calculated as
100–81.82%) associated with nature-caused flows, as shown in Fig. 2c, d.
In the international trade channel, indicators related to target 7.1
(to ensure universal access to affordable, reliable, and modern energy
services) had the most (29) linkages with the SDG indicators in other
countries (Fig. 2a, b). These indicators were linked to various basic
human needs and the environment in other countries, such as basic
drinking water and sanitation services (four linkages with target 1.4),
agricultural productivity (two linkages with target 2.3), water-use effi-
ciency (two linkages with target 6.4), housing (three linkages with
target 11.1), and biodiversity (two linkages with target 15.5) (Fig. 2a, b).
For instance, via the channel of international trade, the spatial lag term
of target 7.1 proved to be both significant and positive in Table 1.This
implies that the achievement of target 6.4 in certain countries could be
promoted by synergistic effects stemming from the progress their
trade partners have made towards target 7.1. This extensive networkof
linkages may be primarily attributed to the fundamental role ofenergy
in many sectors. The production, distribution, and consumption of
energy through international trade can have far-reaching trans-
boundary impacts on various aspects of society and the environment.
In the channel of nature-caused flows, the SDG indicator that
affected the most SDG indicators in other countries was related to
target 6.6. (protect and restore water-related ecosystems) (Fig. 2c, d).
Attempts to improve the performance of target 6.6 in interconnected
countries may interact with the performance of 19 SDG indicators in
focal countries (Fig. 2c, d). For example, the spatial lag of term of
target 6.6 (river flow) is 0.094, with significance at the 1% level
(Table 1), suggesting the progress of SDG 1.4 of some countries can
be promoted by the other countries’actions towards achieving target
6.6 through the transboundary rivers. The actions of other countries
focused on protecting and restoring water-related ecosystems may
have transboundary SDG impacts, thereby creating numerous ben-
efits for focal countries. These benefits include promoting equitable
access to basic water and sanitation services (four linkages with tar-
get 1.4), contributing to the decoupling economic growth from
environmental degradation (two linkages with target 8.4), and sus-
tainable management and efficient use of natural resources (three
SDG targets as interaction generators
SDG targets as interaction generators
SDG targets as interaction receivers
SDG targets as interaction receivers
Interaction magnitude
SDG targets as interaction generators
SDG targets as interaction receivers
a b
c
SDG targets as interaction generators
d
SDG targets as interaction receivers
Interaction magnitude Interaction magnitude
Interaction magnitude
2
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18
0.0 0.4 0.8 1.2 1.6
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0.00.20.40.60.81.0
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0.00.20.40.60.81.0
Fig. 2 | Transboundary synergistic andtrade-off linkages acrossSDG indicators
among countries. a Transboundary synergistic linkage across SDG indicators via
international trade. bTransboundary trade-off linkage across SDG indicators via
international trade. cTransboundary synergistic linkages across SDG indicators
through nature-caused flows. dTransboundary trade-off linkages across SDG
indicators through nature-caused flows. For better demonstration, the SDG indi-
cators belonging to the same SDG target were grouped together basedon the UN
Global Indicator Framework for Sustainable Development Goals developed by the
Inter-Agency and Expert Group on SDG Indicators (IAEG-SDGs). The left and right
axes respectively denote the SDG targets belonging to SDG 1 to SDG 17, with the
formerserving as interaction generators and thelatter as interaction receivers. The
colour bars show the absolute values of spatial coefficients that are significant,
which serve to represent the magnitude of transboundary interactions. These
values were derived from 768 regression models based on spatial econometric
methods, elaborated further in the Methods section. A darker colour, corre-
sponding to a higher absolute value of the spatial coefficient, signifies stronger
transboundary interactions.
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linkages with target 12.2) (Fig. 2c, d). Furthermore, these actions can
promote the conservation, restoration, and sustainable use of ter-
restrial and inland freshwater ecosystems and their services in focal
countries (two linkages with target 15.1), as well as reduce the
degradation of natural habitats and biodiversity loss (two linkages
with target 15.5) (Fig. 2c, d). Fig. 2also reveals that a single SDG
indicator in other countries can influence both its counterpart and
various other SDG indicators in focal countries. For example, air
pollutants (target 11.6) in other countries, through air flow, can
profoundly impact both ambient air quality and 17 other indicators
across multiple SDGs in focal countries (Fig. 2c, d). This ripple effect
may influence health outcomes and economies—leading, for exam-
ple, to a potential reduction in work time and productivity, which
aligns with SDG 8 (promoting decent work and economic growth)
Table 1 | Empirical results of transboundary interactions based on a two-stage instrumental variable (2SIV) estimation of the
spatial econometric model
First stage estimation Explained variable: target 6.4 Explained variable: target 1.4
GDP 0.091*** (0.033) Spatial lag of target 7.1 (trade flow) 0.189*** (0.043) Spatial lag of target 6.6 (trade flow) 0.038*** (0.013)
Population 0.071** (0.035) Time lag 0.234*** (0.079) Spatial lag of target 6.6 (river flow) 0.094*** (0.013)
Governance 0.034 (0.030) Economy 0.020 (0.026) Time lag −0.003 (0.059)
Internet 0.028 (0.034) Education −0.029 (0.028) Economy −0.001 (0.007)
Export value 0.102*** (0.035) Technology 0.053** (0.024) Environment 0.017 (0.010)
Technology 0.038 (0.027) Governance 0.045 (0.028) Education 0.019* (0.010)
Agriculture −0.006 (0.025) Governance 0.018** (0.009)
Residual from the first stage 0.039* (0.023) Population −0.002 (0.009)
Residual from the first stage −0.018** (0.007)
The indicators chosen to represent SDG targets7.1, 6.4, 1.4, and 6.6 are proportion of populationwith primary relianceon clean fuels and technology,wateruseefficiency , proportion of pop ulation
usingbasic sanitationservices, andlakes and rivers seasonal waterarea (% of total land area),respectively. SupplementaryinformationTables S5 and S6 show thedetailed information regarding the
additional variables and the rationale behind their selection. Standard errors are provided in parentheses. Significance at the 1%, 5% and 10% levels is denoted by ***, ** and *, respectively.
60
40
20
a
b
spatial interaction index
c
0
5
10
15
20
Trade-off (Nature)
Synergy (Nature)
Trade-off (Trade)
Synergy (Trade)
SDG 1
SDG 2
SDG3
SDG 4
SDG 5
SDG 6
SDG7
SDG 8
SDG 9
SDG 10
SDG 11
SDG 12
SDG 13
SDG1
4
SDG 15
SDG 16
SDG 17
10 20 30 40 50
0
20
40
60
80
100
Interaction magnitude
Fig. 3 | Spatial interaction index and its components. a Spatial interaction index
of countries worldwide. bInteraction magnitude of 17 SDGs. cInteraction magni-
tude of 55 SDG indicators. Both the spatial interaction index and interaction
magnitude are dividedinto four components: synergistic effects via nature-caused
flows, trade-off effects via nature-caused flows, synergistic effects via international
trade, and trade-off effects via international trade. The numbers displayed on the
horizontal axis of crepresent the identifiers for the 55 SDG indicators included in
this study (Supplementary information Table S1).
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(Fig. 2c, d). Moreover, it can also impact biodiversity, as represented
by SDG 15 (life on land), by modifying habitats and harming wildlife
(Fig. 2c, d). This discovery underscores the complex and inter-
connected nature of SDG interactions mediated by natural flows.
Magnitude of transboundary SDG interactions of countries
worldwide
A spatial interaction index with a scale of 0 to 100 was devised to
quantify the overall magnitude of transboundary SDG interactions.
This index includes both synergistic and trade-off effects and con-
siders transmission channels via both human-caused and nature-
caused flows. A higher index indicates more substantial trans-
boundary SDG interactions with other countries, and the index
consists of four components, as shown in Fig. 3a. The aggregate of
two components, namely, the transboundary synergistic effects via
nature-caused flows and transboundary synergistic effects via inter-
national trade, denotes the total magnitude of the transboundary
synergistic effects. For the countries worldwide, this magnitude
represented 78.97% of the spatial interaction index (Fig. 3a) and was
3.76 times stronger than the total magnitude of the transboundary
trade-off effects. This finding indicates that transboundary SDG
interactions between countries can facilitate SDG accomplishment.
Compared to the transmission channel via nature-caused flows,
transboundary synergistic effects via international trade had a lower
share among the countries worldwide. Specifically, the contribution
of synergistic effects via nature-caused flows to the total trans-
boundary interactions via nature-caused flows of the countries
worldwide reached 90.71%, whereas the contribution of synergistic
effects via international trade to the total transboundary interactions
via international trade was 73.76% (Fig. 3a). Among the 17 SDGs, SDG
12 (responsible consumption and production) showed the most
potent transboundary synergistic effects, scoring 39.38, followed by
SDG 2 (zero hunger) and SDG 6 (clean water and sanitation), which
scored 32.13 and 36.49, respectively (Fig. 3b). Among the 55 SDG
indicators, target 6.6 (protect and restore water-related ecosystems)
received the strongest transboundary synergistic effects (88.74),
equivalent to the sum of transboundary synergistic effects through
international trade (55.45) and nature-caused flows (33.29) (Fig. 3c).
Considering the net effects of transboundary SDG interactions
(transboundary synergistic effects minus transboundary trade-off
effects), target 6.6 (protect and restore water-related ecosystems)
again ranked first, demonstrating the strongest net effects (77.49)
owing to its large transboundary synergistic effects (88.74) and small
transboundary trade-off effects (11.25). This finding suggests that
advancements in SDG indicators in other countries can significantly
advance target 6.6 in the focal countries (Fig. 3c).
Magnitude of transboundary SDG interactions by income group
This study divided 121 countries into four groups based on the World
Bank country classification (2022–2023). Compared to low, lower-
middle, and upper-middle income groups, high income countries bear
a greater responsibility for the influence of their domestic actions on
the achievement of the 17 SDGs in other countries, as the magnitude of
their transboundary SDG interactions accounted for the largest pro-
portion of the total transboundary SDG interactions of the four income
groups (sum of spatial interaction index), at 60.60% (Fig. 4a). High
income countries demonstrated strong transboundary interactions
with other countries; however, the population of high income coun-
tries over 2010–2020 accounted for an average of only 14.18% of the
global population, based on data from the World Bank. Despite
representing a relatively small fraction of the global population, high
income countries are often characterised by robust economies,
advanced technologies, and considerable political influence, which
may amplify their roles in SDG interactions. Furthermore, when ana-
lysing the components of transboundary SDG interactions, the trans-
boundary synergistic effects/trade-off effects of all high income
ab
High income Upper-middle income
Low incomeLower-middle income
High income
Upper-middle income
Lower-middle income
Low income
0
2
4
6
8
10
Synergy
Trade-off
Spatial interaction index
15.15%
84.85%
17.96%
82.04%
37.15%
62.85%
45.33%
54.67%
Fig. 4 | Magnitude and components of transboundary SDG interactions by
income group. a Spatial interaction index by income groups. bShare of the
components of spatial interaction index by income groups. The sum of synergistic
effects and trade-off effects equals the spatial interaction index. There are four
income groups: high, upper-middle, lower-middle, and low income groups (Sup-
plementary information Table S4). The blue in the pie charts represents the pro-
portion of transboundary synergistic effects.
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countries constituted a large percentage of those globally, at 64.95%
and 44.06%, respectively (Fig. 4a). High income countries showed the
largest share of transboundary synergistic effects in their total trans-
boundary SDG interactions (84.85%) compared to the other three
income groups (Fig. 4b), suggesting that transboundary SDG interac-
tions generated by high income countries may considerably promote
the achievement of SDGs in their connected countries.
Transboundary SDG interactions with neighbouring and non-
neighbouring countries
The intensity of the transboundary effects of the countries worldwide
differed depending on geographic proximity. Non-neighbouring
countries derived more benefits from transboundary interactions
facilitated by international trade; however, neighbouring countries
derived greater benefits from transboundary interactions via channels
of nature-caused flows (Fig. 5a). Specifically, the transboundary
synergistic effects were 14.94% more pronounced in interactions
between trade partners that did not share borders compared with their
neighbouring counterparts (Fig. 5a). Conversely, through the trans-
mission channel of nature-caused flows, neighbouring countries
showed transboundary synergistic effects that were 39.29% stronger
than those observed between non-neighbouring countries (Fig. 5a).
Transboundary trade-off effects were 17.81% stronger between neigh-
bouring trade partners than between non-neighbouring partners
(Fig. 5a). Through the nature-caused flow channel, non-neighbouring
countries exhibited transboundary trade-off effects that were 1.88%
stronger than those between neighbouring countries (Fig. 5a). The net
effects (i.e., synergistic effects minus trade-off effects) between non-
neighbouring countries through international trade were 35.46%
stronger than those between neighbouring nations (Fig. 5a). Contra-
rily, for the transmission channel of nature-caused flows, the net
effects between neighbouring countries were 45.59%more robust than
those between non-neighbouring countries (Fig. 5a).
An analysis of trade relationships revealed that all four income
groups—high, upper-middle, lower-middle, and low income—demon-
strated stronger synergistic effects than trade-off effects with both
neighbouring and non-neighbouring trade partners. The share of
synergistic effects among the total interactions (combining synergistic
and trade-off effects) varied by income group and by whether the
trading partners were neighbours. Specifically, the shares for high,
upper-middle, lower-middle, and low income countries with their
neighbouring partners were 72.83%, 72.46%, 72.96%, and 69.07%,
respectively (Fig. 5b). In contrast, their shares with non-neighbouring
partners were 86.07%, 73.61%, 56.76%, and 62.73%, respectively
(Fig. 5b). Interestingly, high income countries tended to establish
notably more intense synergistic relationships with trade partners
outside their immediate geographic vicinity than with neighbouring
trade partners (Fig. 5b). Specifically, high income countries showed
18.14% stronger synergistic effects with non-neighbouring trade part-
ners than with neighbouring ones (Fig. 5b). This can be attributed to
the extensive global practices and international influence of high
income countries. High income countries often have widespread net-
works of investments and trade relationships worldwide, facilitating
stronger interactions with non-neighbouring countries. Participation
in various international accords and organisations encourages these
countries to extend their relationships beyond their immediate geo-
graphic sphere, fostering more intensive interactions globally. More-
over, their relatively advanced technological infrastructure enables
efficient communication and transportation over long distances.
Discussion
This study quantifies transboundary interactions among 121 countries
in relation to 768 SDG indicator pairs from 2010 to 2020. This
assessment was conducted through various channels, including
international trade, river flow, ocean currents, and air flow, by
employing an integrated sustainability perspective4. This study makes
a key contribution by quantifying the magnitude and direction of the
fourth pillar ofintegrated sustainability: the spillover effects caused by
human–nature interactions. Therefore, sustainable development, as
considered in this study, rests on four pillars: (1) social, (2) environ-
mental, (3) economic, and (4) spillover effects4. These pillars correlate
with four key principles: (1) people, (2) planets, (3) prosperity, and (4)
peace and partnership4,6.
Some research credits globalisation and openness with benefiting
sustainability and economic development by invoking Ricardo’sthe-
ory of comparative advantage5,6,34. However, other scholars argue that
openness and globalisation run contrary to sustainability goals, based
on the pollution haven hypothesis6,35,36. While theories of comparative
advantage highlight the welfare gains of interconnectedness, the pol-
lution haven hypothesis introduces an important caveat regarding
ba
Trade Nature-caused
0
5
10
15
Transboundary interactions
High income
Upper-middle income
Lower-middle income
Low income
0
2
4
6
8
10
12
Synergy (neighbouring)
Trade-off (neighbouring)
Synergy (non-neighbouring)
Trade-off (non-neighbouring)
Trade-related
transboundary interactions
flows
Fig. 5 | Comparison of the transboundary SDG interactions with neighbouring
and non-neighbouring countries. a Magnitude of transboundary interactions
with neighbouring and non-neighbouring countries. bComparisons of the mag-
nitude of transboundary SDG interactions among high, upper-middle, lower-mid-
dle, and low income countries. The blue and red represent the transboundary
synergistic effects and transboundary trade-off effects, respectively. The light blue
and dark blue colours respectively indicate the synergistic effects with neigh-
bouring and non-neighbouring countries. In contrast, the light red and dark red
indicate the trade-off effects with neighbouring and non-neighbouring countries,
respectively.
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potential cross-border regulatory distortions and their impact on
sustainability outcomes in integrated economies at different devel-
opment levels. The study demonstrated that transboundary synergis-
tic effects through international trade were the dominant form of
interactions across borders, revealing the overall positive impact of
globalisation and openness on advancing global sustainability in an
interconnected world. While trade-offs exist in some issues, the pre-
dominance of cross-border coordination benefits underscores how
collective progress is enhanced through continued cooperation at a
global scale. Addressing a single SDG indicatorwithin one country can
automatically strengthen not only the same indicator but other indi-
cators across connected nations through transboundary synergistic
effects. Conversely, if these transboundary synergies develop in
negative ways, such as a country failing to progress on certain indi-
catorsor regressing, they mayresult in vicious cycles in which setbacks
are multiplied and transmitted to other countries8. This highlights the
risks and emphasizes the necessity to convert vicious inter-country
cycles into virtuous ones. Systemic interlinkages form either virtuous
or vicious cycles, indicating that transformations must be pursued
intentionally to initiate desirable co-benefits and multiplication effects
across borders. Pursuing progress in a coordinated manner across
countries could set the stage for mutually reinforcing advances in the
SDGs at a global scale.
Tobler’sfirst law of geography—that everything is related to
everything else, but near things are more related than distant ones—
serves as a foundational principle in numerous research fields,
including spatial analysis in epidemiology37, crime pattern analysis38,
economic development39,40, and environmental issues41. The continu-
ing relevance and applicability of this law are evidenced by the broad
range of methodologies and concepts that have been developed based
on it. In modern times, as sustainability issues increasingly intersect
with geographical considerations, there has been a increasing interest
in revisiting and further exploring Tobler’s law, especially with respect
to SDGs42.Whilethelaw’s core principle remains valid in many
instances, studies have illuminated the complex ways in which dis-
tance can shape interrelations, sometimes counterintuitively16.For
instance, some studies have highlighted habitat losses triggered by
distant consumption through international trade43,44. The negative
effects of distant activities on sustainable fisheries further challenge
this geographical principle45,46. The results of this study suggested that
the synergistic effects were 14.94% more pronounced in interactions
between trade partners that did not share borders compared with
those between neighbouring counterparts. Due to globalisation, dif-
ferent countries have become more connected andless geographically
limited through international trade. Globally, non-neighbouring
countries can benefit from comparative advantages by diversifying
their traded goods and services, allowing them to interact more with
each other than with neighbouring countries. While Tobler’slaw
remains valuable for understanding geographical influences, this
research has revealed the importanceof consideringa broader array of
factors, including non-proximate influences.
Transboundary SDG interactions are global issues that transcend
individual nations. Beyond traditional place-based governance
approaches with a focus on a country’s territory, it is significant to
adopt a flow-based perspective. This considers each country in the
context of its associations with others by identifying, monitoring, and
managing areas where key flows originate, progress between borders,
and ultimately terminate5,6,47. This study advocates that countries col-
laborate to find solutions through international organisations that act
as bridges to facilitate global policymaking and support the achieve-
ment of the 2030 Agenda. Some international organisations (e.g., the
UN and World Trade Organization) were formed to implement ade-
quatemeasurestoaddressglobalissues.Twomainmeasuresare
proposed to discourage transboundary trade-off effects and encou-
rage synergistic effects. One is to internalise the costs and benefits of
transboundary SDG interactions. Countries that generate trans-
boundary trade-off effects could be asked to provide adequate com-
pensation, discouraging activities that impose a cost on an unrelated
third party. Countries that generate transboundary synergistic effects
can internalise these benefits through subsidisation, which could
incentivise them to increase synergistic effects. This study evaluates
the magnitude and components of transboundary SDG interactions,
providing a foundation for countries worldwide to consider actions
that inadvertently generate trade-offs in other countries, while reaping
the benefits of synergies. The other measure is to establish a globally
tradable pollution permit that presents countries with legally accep-
table pollution limits. A tradable permit system has the substantial
advantage of allowing efficient exchange, which helps maintain the
overall level ofpollution by allowing one potential polluter to purchase
permits from another. A well-known example of this trading system is
the Emission Trading System of the European Union, established in
2005. Thus, addressingtransboundary SDG interactions requires more
effective transboundary solutions and multilateral governance to
achieve global sustainability.
In addition to international trade, river flow, ocean currents, and
air flow, other cross-border exchanges shape SDG interactions48,49.For
example, owing to the high-volume nature of seaborne freight, mar-
itime shipping is well-suited for transporting goods across interna-
tional bordersin regions with extensive coastlines50. Cargo vessels can
accommodate the bulk shipping of diverse goods across long dis-
tances in a relatively efficient manner compared with other forms of
international transport50. This strengthens economic cooperation and
trade opportunities between coastal countries50.Futurestudiesshould
further explore the impacts of various human-caused transboundary
flows, such as maritime transportation50, technology transfer, invest-
ment, knowledge sharing, human migration, disease dissemination,
and information diffusion, once data become available51.Theinfluence
of nature-caused flows, including animal migra tion, seed dispersal, and
disease spread, is also worth investigating52. Capturing these addi-
tional linkages may provide deeper insights into the complex inter-
connected relationships between countries’progress towards
achieving the SDGs. Modelling flows such as ocean currents and wind
patterns poses interesting methodological challenges given their
multidirectional, changing dynamics over varying temporaland spatial
scales. However, greater precision in characterising connectivity ten-
dencies may considerably enhance our understanding of sustainability
linkages. Future research should investigate novel approaches to sys-
tematically tracking variations in flow vectors—such as harnessing
remote sensing data—and integrating this directional flow of infor-
mation into spatial regression frameworks. This may entail simulating
transport processes or calibrating networks via hydrodynamic or
atmospheric modelling. Capturing the full complexity of flow regimes
may provide unprecedented insights into the causal relationships
among different countries.
Methods
SDG indicator selection and data sources
There are17 SDGs with 169 targetsand 231 unique indicators within the
global indicator framework1. This study chose the years 2010–2020
and 121 countries for analysis based on the best available data. A list of
the 121 countries was included in this study (Supplementary informa-
tion Table S3). This study included 55 indicators constructed using
robust data and applied to a broad range of countries (Supplementary
information Table S1). These 55 indicators were selected from the
Indicators and Monitoring Framework for the Sustainable Develop-
ment Goals developed by the UN Sustainable Development Solutions
Network, the UN Global Indicator Framework for Sustainable Devel-
opment Goals developed by the IAEG-SDGs, and some published
studies53,54. The values of these indicators ranged from 0 (worst per-
formance) to 100 (best performance)2,54.
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Transboundary SDG interactions across countries
This study proposed four interconnected steps to evaluate how SDG
indicators interact with indicators across countries through different
transmission channels.
Step 1: Match SDG indicator pairs with causal relationships
This study first determined whether there was a causal relationship
between the SDG indicator pairs. Based on the 55 SDG indicators, 768
SDG indicator pairs showing potential causal relationships were iden-
tified, derived from an interactive repository of SDG interactions8
(Supplementary information Table S2). By conducting a systematic
literature review (65 global scientific assessments and UN flagship
reports and 112 relevant scientific articles), this interactive repository
recorded causal relationships across SDG targets covering the 17
SDGs8. Based on the following steps, this study further quantitatively
verified whether the 768 SDG indicator pairs exhibited interactions
between different countries through a variety of channels.
Step 2: Construct spatial weight matrices for different channels
of transboundary SDG interactions
Many channels connect two or more countries. This study divided
these channels into human-caused and nature-caused flows (Fig. 1).
This study used a spatial weight matrix (W) with diagonal elements
equal to zero to represent each channel, as shown in the following
equation:
W=½wijN×N=
0w12 ... w1N
w21
.
.
.
wN1
0
.
.
.
wN2
... w2N
0.
.
.
 0
2
6
6
6
6
4
3
7
7
7
7
5,iand j=1,2,3,:::,Nð1Þ
where wij indicates the weight between country iand country j.N
represents the total number of countries. The channels of trans-
boundary SDG interactions with respect to international trade in year t
were constructed based on multiregional input–output (MRIO) tables
ðWtrad e,t=½wijt N×NÞ, and MRIO tables from years 2010 to 2020 were
obtained from the Eora26 database55,56. Several other MRIO databases
exist, including EXIOBase3, WIOD, and GTAP; however, this study
chose Eora26 because it has a higher country coverage (189 countries)
and can provide the most up-to-date MRIO tables55,56. The value-added
of international trade between one country and another is indicated in
each cell of the trade weight matrix (unit: USD).
This study divided the transboundary nature-caused flows into
three categories: river flow, ocean currents, and air flow, as follows:
(1) River flow channel ðWriver =½wij N×NÞ: When a transboundary
river crosses these countries, some water-related SDG indicators from
different countries can be linked through river flow. SDG 6 relates to
clean water and sanitation; therefore, 102 indicator pairs related to
SDG 6 were assumed to be connected via river flow channels (Sup-
plementary information Table S2). Global geographical information of
the rivers was derived from the HydroSHEDS database at a resolution
of 15 arc-seconds57,58. The database displays over eight million river
reaches worldwide, with more than 120,000 being the most down-
stream reaches of connected river basins57,58. These were used to
identify the entire river network belonging to this basin and determine
which countries share the entire river network57,58.Theresultsrevealed
2126 transboundary rivers worldwide. Subsequently, a weight matrix
was constructed, with each cell indicating the aggregation of river flow
between the two countries. To calculate this, this study added the
average long-term discharge estimates of all river reaches between the
two countries, which were obtained from the HydroSHEDS
database57,58. The unit of the matrix was cubic meters per second.
Greater river flow between the two countries indicates a stronger
connection.
(2) Ocean current channel ðWmaritime =½wij N×NÞ: Some SDG indi-
cators in one country may be influenced by some ocean-related SDG
indicators in countries with which they share sea areas. SDG 14 is
related to the preservation and sustainable exploitation of oceans,
seas, and their resources to foster sustainable development; thus, the
45 indicator pairs related to SDG 14 in coastal countries were assumed
to be linked via ocean current channels (Supplementary information
Table S2). Only coastal countries were included in the evaluation of
indicators under SDG 14 (life below water). To construct a weight
matrix for ocean currents across coastal countries, this study first
excluded inland countries based on the Central Intelligence Agency
World Factbook. Of the 121 countries, 91 were coastal and 30 were
inland (Supplementary information Table S7). Then, based on mar-
itime boundaries, this study identified each coastal country’sneigh-
bouring countries. The UN Conventions on the Law of the Sea defines
maritime boundaries as territorial waters and contiguous and exclu-
sive economic zones. Each matrix cell was filled with 1 or 0, indicating
whether or not the two countries were linked by ocean currents.
(3) Air flow channel ðWair =½wijN×NÞ: Some air-related indicators
(target 11.6: fine particulate matter) from various countries can influ-
ence certain SDG indicators of a particular country due to the move-
ment of air (Supplementary information Table S1). In this study, a
spatial weight matrix based on the inverse distance was constructed to
represent the air flow connections between countries. This study cal-
culated the distance between 121 countries based on their centroids.
The transboundary SDG interactions of 36 indicator pairs related to
the target 11.6 were estimated using this spatial weight matrix (Sup-
plementary information Table S2).
This study is grounded in the metacoupling framework, an inte-
grated conceptual construct examining the human–nature interplay
within a coupled human–nature system, adjacent to that system and
from distant locations16,30,52. This framework encompasses all flow types
relevant to human and natural systems. Determination of the spatial
weight matrix is guided by four key criteria: (1) Relevance: The chosen
transmission channel should mirror the real-world transmission
mechanism of the SDG indicator. For example, water-related SDG indi-
cators may be interlinked through transboundary rivers. Consequently,
the spatial weight matrix, represented by river flows across countries,
was utilised to examine the transboundary interactions of indicators
related to SDG 6 (clean water and sanitation). (2) Timeliness: Trans-
mission channels influenced by socio-economic conditions, such as
international trade, are dynamic and frequently change over time.
Consequently, data series must be updated regularly, published
promptly, and be made available for the most recent years to accurately
reflect these changes. (3) Coverage: The data must adequately define the
relationships between any two countries included in the study. They
should provide a comprehensive understanding of the interactions and
connections between these countries, offering a broad scope that does
not neglect critical relationships. (4) Data availability and quality: The
transmission channel data must represent the most accurate measure of
aspecific issue. They should be obtained from reliable national or
international sources to ensure credibility and reliability. Considering
these selection criteria, this study incorporated different flow types that
exist across countries: trade flows (human-caused flows), river flows,
ocean currents, and air flows (nature-caused flows).
Step 3: Construct row-standardised spatial weight matrices
Row standardisation suggests that each spatial weight in a matrix is
divided by its row sum, as shown below:
ws
ij =wij
PN
j=1 wij
ð2Þ
where wij and ws
ij indicate the weight between country iand country j,
and the weight after row standardisation, respectively.
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Step 4: Quantify transboundary SDG interactions
Spatial econometric models were used to explore the transboundary
SDG interactions of SDG indicator pairs under different transmission
channels. Endogeneity issues may arise in statistical analyses when an
explanatory variable is correlated with an error term, leading to biased
and inconsistent estimates. These complexities require specifictech-
niques to ensure accurate and reliable results59–63. Different techniques
can be used to address the endogeneity problem59–63.Todealwiththe
endogeneity issues caused by the elements of the spatial weight matrix
involving socioeconomic indicators64–68, this study applied the 2SIV
estimation method based on the control function method to explore
the spatial spillover effects of SDG indicators in a panel dataset64.The
first stage was estimated using the following regression model:64
lnFit =ηi+X1itγ+εit ,t=1,2,:::,Tð3Þ
Fit denotes the trade flow in country iat period t.γis a vector of
the coefficients of explanatory variables. X
1it
is a list variables mea-
suring the economy, population, government effectiveness, access to
the internet, performance of export sectors, and technological level of
country i69 (Supplementary information Table S5). Based on the resi-
dual c
εit from the first-step estimation, this study considered the fol-
lowing model for the second-stage estimation:64
lnSDGimt =ci+ρ1lnSDGim,t1+λ1X
j≠i
w1
ijt lnSDGjnt +X2itβ+^
δεit +vit ð4Þ
SDGimt and SDGjnt are a pair of SDG indicators that were deter-
mined in Step 1, which respectively indicate the interaction receiver m
and generator n.ρ1is the scale coefficient. λ1is the spatial coefficient
which can be used to measure the transboundary SDG interactions
under the transmission channel of international trade. w1
ijt is a spatial
weight matrix related to international trade between country iand
country jin year t.βis a vector of coefficient of the explanatory vari-
ables. X
2it
denotes the explanatory variables (Supplementary infor-
mation Table S6). The positive variables were transformed by taking
their natural logarithms in the spatial dynamic panel data model.
Multiple transmission channels could operate simultaneously70–73.
Indicators related to SDG 6 (clean water and sanitation), SDG 14 (life
below water), and SDG target 11.6 (fine particulate matter) could be
affected both through international trade and nature-caused flows
(transboundary river flow, ocean currents, and air flow). This study
employed higher-order spatial econometric models to account for
real-world complexity. These models can incorporate more than one
spatial weight matrix and, thus, characterise various types of spatial
dependence. Spatial weight matrix w2
ij was specifically utilised to
represent channels related to nature-caused flows. λ
2
is a spatial
coefficient used to evaluate transboundary SDG interactions under the
transmission channel of nature-caused flows.
lnSDGimt =ci+ρ1lnSDGim,t1+λ1X
j≠i
w1
ijt lnSDGjnt +λ2X
j≠i
w2
ijlnSDGjnt
+X2itβ+^
δεit +vit
ð5Þ
Creating the spatial interaction index and its decomposition
Domestic actions aimed at achieving SDGs may result in transbound-
ary interactions with other countries. This study proposed a spatial
interaction index to quantify the overall magnitude of transboundary
interactionsacross all transmission channels.As the spatial coefficients
(both λ1and λ2) of the same explained variable (SDG indicator) from
Step 4 werecomparable, this study summedthe absolute values of any
coefficients (both positive and negative coefficients) that were sig-
nificant at the 10% level or above. This provided a total impact measure
of transboundary interactions on a specific SDG indicator.
Subsequently, min–max normalisation was performed on each total
impactvaluetodeterminetheinteractionmagnitudeofeachSDG
indicator. This standardisation process scaled the values to a uniform
range of 0 to 100. Bringing comparable transboundary interactions
onto a unified scale allowed for an easy assessment of the relative
influence across different SDG indicators. Finally, the arithmetic
average of all the standardised values was calculated to derive the
overall spatial interaction index. Ranging from 0 to 100, a higher
spatial interaction index implied stronger transboundary interactions
between countries.
The spatial interaction index can be divided into four distinct
components: synergistic effects through nature-caused flows, trade-
off effects through nature-caused flows, synergistic effects via human-
caused flows, and trade-off effects via human-caused flows. First, the
interaction magnitudes of the four components for each SDG indicator
were determined. This was accomplished by multiplying the interac-
tion magnitude of each SDG indicator (on a scale of 0–100) by the
respective percentage shares of these components. We obtained these
percentage shares from the absolute values of coefficients that were
significant and calculated them as a proportion of the total sum.
Subsequently, the components of the spatial interaction index were
obtained by computing the arithmetic average of the interaction
magnitudes for each component across all SDG indicators. This
approach allows for a more nuanced understanding of the different
factors contributing to transboundary SDG interactions.
Using SDG indicator 7.1.1 (access to electricity) as an example, this
study analysed the magnitude and direction of impacts on a country’s
performance in achieving indicator 7.1.1 from progress on SDG indi-
cators in other countries. In Step 1, it was identified that the achieve-
ment of indicator 7.1.1 could be influenced by indicator 6.4.1(water use
efficiency) and indicator 7.3.1 (energy intensity). Spatial weight matri-
ces were constructed to represent connections between countries
through river basins and trade networks. Subsequently, this study row-
standardised the weight matrices. This normalisation process pre-
pared the data for spatial econometric modelling. The last step
involved using spatial econometric models to calculate the spatial
coefficients. These coefficients (both λ1and λ2) indicated the direction
and magnitude of transboundary interactions on indicator 7.1.1 out-
comes in the focal country, respectively. If both coefficients were
significant at least 10%, the study would sum their absolute values and
standardised this total into a single index from 0 to 100. This example
shows how the performance of indicator 7.1.1 in focal countries could
be influenced by other countries’progress on indicator 6.4.1, through
shared river flows powering hydropower, and indicator 7.3.1, through
energy used in internationally traded goods and services.
Transboundary interaction by income group
This study divided 121 countries into four groups based on the World
Bank country classification by income level (2022–2023) (Supple-
mentary information Table S4). Income level was measured as gross
national income per capita in current USD values. Subsequently, this
study compared the transboundary interactions exerted by each
income group. This was achieved by constructing an updated spatial
weight matrix. In this revised matrix, each row retained the value of
countries belonging to a specific income group, while all other values
were set to zero. Following this, Steps 3 and 4 were replicated by
employing the proposed spatial interaction index to contrast the
magnitude of SDG interactions across different income brackets. In
this analysis, the focus was strategically directed toward the trade-
related transboundary interactions of these four income groups rather
than interactions facilitated b y nature-caused flows.The unique nature
of the sparse spatial weight matrix of ocean currents and river flows
presents an intriguing challenge for quantifying transboundary inter-
actions using spatial econometric models, which has opened new
avenues for future exploration.
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Transboundary SDG interactions with neighbouring and non-
neighbouring countries
Transboundary SDG interactions between neighbouring and non-
neighbouring countries may vary16,47. In this study, neighbouring
countries refer to countries with a common vertex, land boundary, or
maritime boundary, whereas non-neighbouring countries indicate
countries without any common vertex, land boundary, or maritime
boundary16.Wneighbour =½wij M×Mis a spatial weight matrix related to
neighbouring countries. Each element of Wneighbour was filled with 1 or
0, indicating whether or not the two countries were neighbours. For
the common vertex or land boundary, neighbours were identified
based on a queen-contiguity-based spatial weight matrix
ðWqueen =½wijM×MÞ. For the common marine boundary, neighbours
were identified based on Wmarine =½wij M×M. The spatial weight matri-
ces related to non-neighbouring countries can be obtained after
excluding the neighbouring countries of each country. This study then
repeated Steps3 and 4 and used theproposed spatial interaction index
to compare the SDG interaction magnitude in non-neighbouring
countries to that in neighbouring countries.
It is essential to acknowledge that the definitions of neighbouring
and distant regions are subject to contextual variations, and there is no
universally recognised measure that unequivocally designates a region
as neighbouring or distant74. In certain research endeavours that seek to
investigate the impacts of channels closely related to distance, distant
regions can be identified through the application of various distance
thresholds, thereby facilitating a more comprehensive understanding of
the subject matter. To gain further insight, future studies should com-
pare the impacts of transboundary interactions using different distance
thresholds to distinguish between neighbouring and distant regions.
Data availability
The data generated in this study are available in the Supplementary
Information. Multiregional input-output (MRIO) tables can be
obtained from the Eora26 database (https://worldmrio.com/eora26/).
The geographical information of rivers globally can be derived from
the HydroSHEDS database (https://www.hydrosheds.org/). The data of
SDG indicators can be collected from SDG Global Database by UN
(https://unstats.un.org/sdgs/dataportal), World Bank (https://www.
worldbank.org/), and Our World in Data (https://ourworldindata.org/).
Code availability
All computer code used in conducting the analyses summarized in this
paper is available from the corresponding author upon request. The
code for the spatial econometric models can be accessed in the paper
with the https://doi.org/10.1111/ectj.12069.
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Article https://doi.org/10.1038/s41467-023-44679-w
Nature Communications | (2024) 15:500 11
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Acknowledgements
The work described in this paper was supported by a grant from
Research Institute for Advanced Manufacturing (RIAM), The Hong
Kong Polytechnic University (Project No. 1-CD4J, Project ID:
P0041367) (J.R.), a grant from Research Centre for Resources Engi-
neering towards Carbon Neutrality (RCRE), The Hong Kong Poly-
technic University (PolyU) (Project No.1-BBEC, Project ID: P0043023)
(J.R.), a grant from Research Grants Council of the Hong Kong Special
Administrative Region, China-General Research Fund (Project ID:
P0042030, Funding Body Ref. No: 15304222, Project No. B-Q97U)
(J.R.), U.S. National Science Foundation (Grants No. 1924111, 2033507
and 2118329), Michigan AgBioResearch (J.L.), National Natural Sci-
ence Foundation of China (grant #42101249), and the University of
Hong Kong HKU-100 Scholars Fund (Z.X.).
Author contributions
J.L., Z.X., and J.R. designed and supervised the study. H.X. performed
the analysis and prepared the manuscript. H.X. and S.B. established the
model and compiled the code. H.X. and S.X. collected the data. J.L., Z.X.,
J.R., and S.X. reviewed and revised the manuscript.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains
supplementary material available at
https://doi.org/10.1038/s41467-023-44679-w.
Correspondence and requests for materials should be addressed to
Jingzheng Ren, Zhenci Xu or Jianguo Liu.
Peer review information Nature Communications thanks Manuel
Fischer, Vahid Mohamad Taghvaee and the other, anonymous, review-
er(s) for their contribution to the peer review of this work. A peer review
file is available.
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