Polarizing and equalizing trends in international trade and Sustainable Development Goals

Abstract

The influence of international trade on the United Nations Sustainable Development Goals is multi-faceted. International trade can either promote or hinder progress, thus directly impacting people, economies and livelihoods. Here we explore the relevance of consumption-based proxies, which capture global demand for goods and services, to assess progress towards Sustainable Development Goals. We link these proxies to environmental and social issues for understanding trends in international outsourcing of resource and pollution-intensive production. We undertake a temporal assessment from 1990 to 2018 for the Global North and South to highlight polarizing trends that are affecting progress on achieving Sustainable Development Goals. We conclude that global trade can lead to both polarizing and equalizing trends that can influence a country’s ability to meet the 2030 Agenda for Sustainable Development.

Main

With six years left to achieve the United Nations (UN) 2030 Agenda for Sustainable Development Goals (SDGs), accurately measuring progress is essential for countries to understand what gaps and opportunities exist^1,2. A growing concern is that intersecting and compounding factors, such as geopolitical conflict (that is, the Russia–Ukraine conflict) and global disruptions (that is, the COVID-19 pandemic), impede SDG progress as their effects are acutely felt through international trade (Supplementary Information 1). Despite this growing concern, the 2023 UN Sustainable Development Report, which documents annual national progress towards achieving the SDGs³, broadly recognizes that international trade has both promoted SDG progress and caused adverse effects. However, probably because the 2030 Agenda and SDGs are nationally focused, the report did not measure the influence of international trade on SDG progress from a temporal perspective and for a range of environmental and social impacts (termed ‘spillovers’). This contrasts with another SDG Report by the UN Sustainable Development Solutions Network, which emphasizes the need to consider spillovers in determining SDG progress⁴.

Spillovers include effects (beneficial or detrimental) that result from a country’s demand for goods and services⁴. There are various ways of allocating responsibility for negative effects along value chains⁵. Here we focus on the consumption-based approach. Unanimously, across a range of social and environmental indicators, consumption levels in Global North countries lead to adverse outcomes in Global South countries. This has been shown in single-year assessments, such as for biodiversity threats and in temporal assessments of environmental and social consequences⁶, using a well-established macroeconomic modelling technique called multi-regional input–output (MRIO) analysis.

MRIO analysis is governed by UN standards⁷ and relies on global databases that depict information on national production structures and international imports and exports at a detailed country and sector level⁸. Over the past decade, multiple MRIO databases have been developed with varying regional and sectoral or product coverage^8,9,10. The analytical power of MRIO-based methods underpins various online assessment tools for analysing SDG-related effects. The Hotspot Analysis Tool for Sustainable Consumption and Production (SCP-HAT)¹¹, commissioned by the UN Environment Programme, applies the GLORIA MRIO database⁹ in combination with a series of environmental and socio-economic accounts to provide empirical evidence of ‘hotspots’ of unsustainable consumption and production practices.

Research quantifying supply-chain (also termed ‘indirect’) repercussions demonstrates the applicability and significance of MRIO analysis in measuring SDG progress^12,13,14,15. Of this research on SDGs, only SDG 12 (Responsible Production and Consumption) and SDG 8 (Decent Work and Economic Growth)^16,17 feature a consumption-based (or ‘footprint’) SDG indicator that takes account of supply-chain effects—that is, the ‘material footprint’¹⁷. There has also been research calculating consumer social risk footprints of nations’ imports¹⁴, however, none considers the influence of international trade on SDGs. Furthermore, prior work has only addressed one or a small number of indicators, has covered only one country¹⁸ or has not considered long-term trends.

Here we offer a systematic quantitative assessment of selected SDGs at a global level over three decades to assess the influence of international trade on SDG progress in terms of polarizing and equalizing trends over time, that is, towards either deterioration of economic disparities (polarization) or reduction of disparities (equalization). Such a quantitative assessment requires selecting environmental and social indicators representing the individual SDGs and most importantly, aligned to economic models that capture global trade linkages. This is challenging given the lack of relevant and timely measurement, especially for indicators of outsourcing and spillover effects¹. These effects were not considered in the conceptualization of the SDGs and the 2030 Agenda (as we explain in detail in ‘Indicator framework for Sustainable Development Goals’ in Methods). Therefore, to quantify spillovers, consumption-based proxy indicators/quantities (‘proxies’ in what follows) are matched to relevant SDGs, as there is currently only the material footprint stipulated in the SDG framework. These proxies form the basis of our assessment; they are listed in Supplementary Information 2.3 and explained in Methods.

Our findings highlight that performance regarding different SDGs has improved in some regions over the assessed period and has deteriorated in others. We show that this occurs because, generally, high-income (developed) countries outsource environmentally and socially detrimental production to low-income (developing) countries, resulting in shifting burdens. We find that this developed–developing divide, often referred to as the ‘Global North and Global South divide’ has worsened over time for some proxies (what we call polarizing trends) and that the rift between countries that outsource and countries that undertake adverse production has been widening. We quantify these polarizing trends, explaining them through several geopolitical circumstances, such as ecological and social races to the bottom, resource conflicts and civil strife, resource price developments and legal frameworks governing occupational health standards, drawing insights from the academic and grey literature. We conclude that consumption-based proxies are crucial for understanding the effects of international trade on SDG progress. We emphasize the value of accounting for spillover effects in trade agreements and strengthening international frameworks (for example, the Conference of Parties and the Global Biodiversity Framework) to reverse polarizing trends.

Results

Our results reveal two key perspectives: (1) global assessment of net importers and net exporters of environmental and social spillovers associated with international trade with an analysis of individual country trends towards import-trending or export-trending behaviour (Fig. 1 and Supplementary Information 3) and (2) specific examples of countries that demonstrate polarizing trends—net importers whose net-import position is increasing over time (‘import trending’, quadrant III); export-trending net exporters (quadrant II) and countries that demonstrate equalizing trends: export-trending net importers (quadrant I) and import-trending net exporters (quadrant IV) (Fig. 2 and Supplementary Information 3). The following sub-sections present detailed results for eight selected environmental proxies and four selected social proxies.

Fig. 1: Trend analysis of outsourcing for environmental and social proxies linked to SDGs in a four-quadrant perspective, 1990–2018.

There are 12 panels, each showing an environmental or social proxy linked to SDGs. The interpretation of all panels is as follows: quadrant I shows export-trending net importers; quadrant II shows export-trending net exporters; quadrant III shows import-trending net importers and quadrant IV shows import-trending net exporters. The x axis shows the time-average outsourced fraction $\overline{\overline{{\bf{s}}}}$ and the y axis shows the temporal change of the outsourced fraction $\dot{\bar{{\bf{s}}}}$. These fractions are calculated by considering the countries’ interactions according to the producer and consumer perspectives to quantify outsourcing over time—1990 to 2018 (Methods). The dotted line passing through the quadrants is the four-quadrant slope, a linear fit b^q, with countries in quadrants II and III representing overall polarizing trends and countries in quadrants I and IV overall equalizing trends. Uncertainty of the slope b^q is characterized as two bands: dark band showing one standard deviation and light band two standard deviations. Circle size represents impacts embodied in trade (for example, for SDG 13: emissions embodied in trade), circle fill/opacity represents per capita gross domestic product (GDP). Credit: icons from the UN Sustainable Development Goals (https://www.un.org/sustainabledevelopment). Full country names for abbreviations are provided in the supporting documentation of the GLORIA database (https://ielab.info/analyse/gloria).

Fig. 2: Country examples of trends of outsourcing for environmental and social proxies linked to SDGs, 1990–2018.

There are 12 panels, each showing an environmental or social proxy linked to SDGs. The interpretation of all panels is as follows: x axes show the analysed time period, y axes show territorial emissions ${\bar{Q}}_{t}^{q,p}$ (shaded line) and footprints ${\bar{F}}_{t}^{q,p}$ (dark line) with respect to time, in which F denotes footprint by consumer perspective, Q denotes territorial perspective, t refers to year of the assessment, p denotes the producing and consuming region, and q refers to the primary factor. For each proxy, there are two panels representing equalizing trends: panel I shows export-trending net importers, panel IV shows import-trending net exporters. There are two panels representing polarizing trends: panel III shows import-trending net importers and panel II shows export-trending net exporters. All proxies are defined as ‘bad’, as explained in Methods. Credit: icons from the UN Sustainable Development Goals (https://www.un.org/sustainabledevelopment).

Environmental proxies

Of all SDG consumption-based proxies examined (Methods and Supplementary Information 2.3 and 3), the energy–greenhouse gas emissions (GHG)–pollutant suite shows the strongest evidence of trends that have been exacerbating existing global outsourcing¹⁹ (Fig. 1). Energy resources are increasingly outsourced (Figs. 1 and 2), with wealthy countries such as Norway, Australia and Saudi Arabia assuming a net-exporting role. Some countries in the former Eastern Bloc have reduced outsourcing through a change in geopolitical circumstances (Fig. 2, air pollution panel I, IV), either of their trading partners (in the case of the Czech Republic²⁰, which opened up to the European Union after the dissolution of the Soviet Union) or of their domestic economy (in the case of Latvia²¹, which traded with Russia for longer, due to a notable proportion of their population having strong Russian ties). Equalizing trends are also observed for Mexico (Fig. 2, energy panel IV) and Indonesia (Fig. 2, GHG emissions panel IV). Almost all high-income economies such as the USA, Japan, the UK, Germany and France are increasingly importers of commodities embodying air pollutants and GHG emissions (Fig. 1). The GHG emissions proxy showed the starkest polarizing trend, which is expected due to the reliance on fossil fuel energy to produce goods. Demand for energy-intensive imports exacerbate local pollution in producing economies such as India and China. This polarizing trend indicates that in the short term, GHG emissions will continue to increase as Global South countries, such as India and China, rely on coal-fired power for producing manufactured goods, a part of which is consumed in the Global North.

The temporal change in water stress shows a high scatter (Fig. 1). However, there is a strong relationship between wealthy countries with either abundant rainfall or facing water scarcity themselves, for example, the European Union and Saudi Arabia, who are increasingly outsourcing production to water-stressed regions such as northern Africa (for example, Tunisia), Central Asia (for example, Turkmenistan), the Sahel (for example, Mali) and eastern Africa. Turkmenistan stands out as the most water-stressed region²², and the situation is predicted to worsen with the country demonstrating export-trending status. International trade relationships can exacerbate water stress in producing countries. For instance, France is a net importer of commodities such as vehicles, whose manufacturing process is water intensive (Fig. 2). Mali (panel I) and Australia (panel IV) demonstrate equalizing trends.

There is a balanced trend across polarizing and equalizing effects of material use (Fig. 1), which has recently plateaued globally, partly because of an increasing focus in China on domestic sourcing of manufacturing and construction inputs¹⁷. Canada’s position as a net exporter (Fig. 2, minerals panel II) of mineral resources is expected to strengthen over the coming years with the implementation of the minerals’ strategy. Canada’s key mineral trading partner is the USA, a net importer and trending towards net imports (Figs. 1 and 2), with over half of Canada’s mineral resources (54%) destined for the USA²³. Observed trends across Japan, South Korea and Saudi Arabia may have been influenced by external factors, particularly global financial crises (Supplementary Information 3.4). For example, Japan, an export-trending net importer of mineral products, primarily imports minerals from Australia²⁴ (Fig. 2, minerals panel I).

Results for land use and biodiversity, referred to as the use of land for cultivating annual crops, permanent crops, intensive forestry, extensive forestry and pastures, demonstrate polarizing trends. From 1990 to 2018, the temporal assessment of change in outsourcing shows that large EU nations maintained their status of net importers of land use but have started trending towards net exports (Fig. 1). For example, most of Germany’s agricultural imports come from Europe (Fig. 1, land quadrant I). The impact of land-use change dramatically impacts biodiversity, so its unsurprising trends in outsourcing reveal the influence of trade relationships on biodiversity loss. From 1995 to 2020, Brazil’s exports to China (Fig. 2, biodiversity panels II and III) have increased at an annual rate of 17% (ref. ²⁴) (Supplementary Information 3.5), increasing the potential of biodiversity loss due to the demand for soybeans grown in the Brazilian Cerrado²⁵. Outsourcing of production into biodiversity hotspots may increase over time and more resemble the pattern visible for GHG emissions when the mid-term impacts of climate change start to shift habitats.

Despite technological improvements, nitrogen emissions have increased over time (Fig. 1 and Supplementary Information 3.6) due to rising consumer demand²⁶. Developed nations, such as Norway, tend to be net importers of products that are associated with nitrogen emissions, which means that their demand has negative consequences for nitrogen emissions in developing countries, such as China, which is a key trade partner of Norway (Fig. 2, nitrogen emissions panel III). Net exporters of nitrogen emissions, such as Brazil, are experiencing polarizing trends (Fig. 2, nitrogen emissions panel II). Brazil is also export trending as it is heavily dependent on fertilizer imports applied to its key export crops, such as soybeans, corn and sugarcane²⁴. By contrast, net-exporting regions such as Egypt (Fig. 2, nitrogen emissions panel IV) are trending towards net importers with a growing trade with Ukraine—exports from Ukraine to Egypt have increased at a rate of about 13.9% per annum between 1995 and 2020; with key commodities including wheat and corn²⁴, both of which are produced using nitrogen-intensive inputs²⁷ (Supplementary Information 3.6).

Social proxies

Outsourcing activities regarding the participation of women, derived by comparing men’s and women’s participation in the labour force, has been intensifying (Fig. 1). This means that while women’s participation in the labour force has generally improved in wealthy countries (for example, Norway, New Zealand, Canada and the Netherlands), they are increasingly outsourcing production to countries such as Iran, South Sudan and Yemen, where women’s rights, and consequent participation in the local workforce, is low. In the case of some countries, such as the Central African Republic (Fig. 2, women’s participation panel I) Yemen (Fig. 2, women’s participation panel II), women’s participation is decreasing. Similarly, the Global North continues to increasingly outsource lower-skilled work to countries such as India and China (Fig. 1) and evolving skill needs in Japan’s labour market²⁸.

Concerning poverty trends, Zimbabwe stands out (Fig. 2, poverty panel II), where hyperinflation has led to an increase in poverty with inflation surging to a staggering 255% in 2019²⁹, impacting both rural and urban populations. In comparison, poverty in Bangladesh declined from 44.2% in 1991 to 13.8% in 2016/2017³⁰ and further declined from 7.57% in 2018 to 6.62% in 2019³¹, making the country an import-trending net exporter of commodities linked to poverty (Fig. 2, poverty panel IV), demonstrating an equalizing trend. Japan (Fig. 2, poverty panel I) is a net importer of products that are implicated with poverty, however, it has seen a decline in poverty associated with imports. This is largely due to reduced poverty in Japan’s key trading partners, such as China. Qatar (Fig. 2, poverty panel III) has increasingly started importing commodities from African countries with a high share of low-income population, such as Kenya and Sudan, where exports from these countries to Qatar have increased at an annual rate of about 26–27% (ref. ²⁴).

Developed economies are typically net importers of commodities associated with occupational accidents (Figs. 1 and 2), globally³² and particularly for the European Union¹². Cyprus and France are net importers (Fig. 2, occupational accidents panels I and III) of commodities that are associated with fatal and non-fatal occupational accidents in their trading partners. Sri Lanka and South Africa (Fig. 2, occupational accidents panel II and IV) are net exporters of occupational hazards. A cross-sectional study conducted in factories at a main Export Processing Industrial Zone in Sri Lanka reveals that high temperature, poor ventilation and overcrowding are commonly observed in many work settings³³. South Africa has seen a decline in occupational accidents during the years 2000–2015³⁴, however more needs to be done in gathering robust data to substantiate these observations. Overall, improvements in workplace practices with an equalizing trend are observed for this proxy.

Regional and sectoral outsourcing trends

By unravelling SDG footprints into tensor form (Methods), we can isolate trade relationships between source countries (bottom two rows in Fig. 3) locked into environmentally or socially adverse production, and outsourcing countries (top two rows in Fig. 3), characterized by relatively benign production environments. These examples demonstrate how foreign demand for specific commodities can lead to unsustainable outcomes in producing nations; thus, international trade can influence environmental, social and economic sustainability outcomes linked to the 2030 Agenda for Sustainable Development. Unsafe working conditions, polluted air from industrial development for supplying exports, availability of insufficient fresh water for drinking and poverty are interlinked with deteriorating health outcomes. These interactions can be influenced by international demand for products, resulting in polarizing effects in certain parts of the world (Fig. 4). As we illustrate below, a single demanding nation can have multiple wide-ranging repercussions across continents. The following provides examples of such circumstances based on selected country–commodity pairs.

Fig. 3: Regional and sectoral composition of outsourcing by SDG proxy.

There are eight selected proxies shown in this figure. For each proxy the interpretation is as follows: top two rows represent outsourcing regions and sectors, and bottom two rows represent source regions and sectors for selected SDG proxies. For each SDG proxy (each column) from bottom to top: production by industry in source country (${F}_{i\bullet ,t}^{\,q,{p}_{1}\bullet \bullet }$), exports from source country by destination (${F}_{\bullet \bullet ,t}^{\,q,{p}_{1}\bullet s}$), imports into outsourcing country by origin (${F}_{\bullet \bullet ,t}^{\,q,s\bullet {p}_{2}}$), production in outsourcing country by industry (${F}_{i\bullet ,t}^{\,q,{p}_{2}\bullet \bullet }$), in which i denotes producing (extracting, emitting) industries, and s refers to buying (consuming) regions. Some examples of traded products are provided in the second row. EECCA refers to Eastern Europe, Caucasus and Central Asia. Logical flow for each of the SDG proxies from bottom to top: adverse production increases in source countries, for the sake of providing exports, destined for imports in outsourcing countries with low and declining adverse production. For example, territorial biodiversity impacts in Burundi are increasing (bottom row) and also the embodiment of these impacts in Burundi’s exports are increasing. A commodity that often features as Burundi’s exports is tea, which eventually lands in countries worldwide (for example, China) for final consumption. China’s biodiversity impacts embodied in imports are increasing. Credit: icons from the UN Sustainable Development Goals (https://www.un.org/sustainabledevelopment).

Fig. 4: Regional outsourcing map of the USA for 2000 and 2018 for the energy proxy.

a, Year 2000. b, Year 2018. Arrows connect top trade partners with the USA, showing the change in pattern of international trade in terms of country of origin over time, with a focus on energy use embodied in the trade of goods and services. The thickness of the arrow represents the intensity of the flow.

The UK’s outsourcing of emissions-intensive production to countries such as China³⁵ has decreased slightly since 2010 but is still substantial when compared to its territorial emissions. Air pollution in the USA has nearly halved between 1990 and 2018; however, the USA has outsourced more than twice the amount of its domestic air pollution, mostly to India and China. China itself can be classified as an outsourcing country due to its tea imports from several regions, including from Burundi. In this African country, threats to rainforest species occur either directly through deforestation³⁶ or indirectly because of habitat loss through collection of firewood used in tea factories producing for export revenues that Burundi is dependent on. Canada contributes to water stress outside its borders, for example, in Tunisia, by importing olive oil and dried fruit such as dates and figs³⁷. Chile is affected by water and air pollution from its copper mines, which increasingly exports ores and alloys, for example, to South Korea for its copper plating factories.

Norway has a highly skilled workforce but imports commodities produced by low-skilled labour from around the world. Whereas Norway is associated with low-skilled labour, this relationship does not necessarily imply that reducing these imports effectively enhances skills in its trading partners. Similarly, the high participation of women in the workforce is a hallmark of New Zealand’s society, however, it imports from places such as the Middle East and Yemen where women do not have access to their full civil rights and are increasingly underrepresented in the workforce. Occupational accidents are embodied in Japan’s imports of Sri Lankan tea³⁸, where workers are exposed to long work hours and physically intensive work.

The USA has an extensive trade network that is increasingly monopolized in terms of trade value (Fig. 4). In 2020, the country imported US$2.26 trillion worth of products, with just four countries (China, Mexico, Canada and Japan) accounting for over half of this value³⁹. Many products the USA imports cause air pollution harmful to local environments and human health abroad⁴⁰. The domestic combustion of coal for generating electricity to produce products, such as computers, that the USA imports has driven air pollution in China, with particulate matter (PM_2.5) accounting for 60% of the total pollution⁴¹. The concentration of PM_2.5 is particularly concerning as it is one of the leading causes of cancer, heart disease and respiratory diseases⁴². Similarly, Mexico’s manufacturing sector relies on fossil fuel sources, causing PM_2.5 pollution. In comparison, Canada’s imports to the USA are dominated by petroleum oil, which accounts for 17.2% of its exports to the USA, mostly from tar oil sands extraction⁴³. Oil from tar sands generates environmental impacts and is one of the most polluting forms of oil extraction⁴⁴.

Global progress towards UN SDGs

Our results show that a consumption-based assessment reveals the influence of international trade on polarizing and equalizing trends in the context of the SDGs (Fig. 5). For the SDGs analysed in this study, considering the mean values and standard deviation estimates, clear equalizing trends are observed for only two SDGs: SDG 1: No Poverty and SDG 8: Decent Work and Economic Growth. The remaining SDGs show polarizing trends with some stronger than others (Fig. 5).

Fig. 5: Global polarizing and equalizing effects of international trade on SDGs.

There are 12 panels, each showing an environmental or social proxy linked to SDGs. The interpretation of all panels is as follows: polarizing means that international trade improves SDG performance of selected countries (for example, high-income countries) and deteriorates that of other countries (for example, low-income countries). Equalizing describes the opposite trend. The y axis shows the four-quadrant slope b^q (Methods), with the mean across countries indicated by a circle, the b^q + 1σ region (mean plus one standard deviation across countries) and b^q + 2σ region (mean plus two standard deviations across countries) as a box and whisker plot, respectively, and individual region observations as dots. Dashed (2σ) and solid (1σ) horizontal lines are added to show the results of the uncertainty analysis (Supplementary Information 4). Credit: icons from the UN Sustainable Development Goals (https://www.un.org/sustainabledevelopment).

Mitigating adverse outsourcing does not necessarily imply reducing trade, especially if the connection between trade and impact is not one of causation but of association. An example in this context is the Norwegian Generalized System of Preferences that allows lower or zero tariffs for imports from developing countries, with the aim of increasing their export revenues, in turn benefiting economic and social development. Textiles and clothing exports from Bangladesh have since dominated Norway’s imports from zero-tariff countries⁴⁵. New Zealand can support women in Middle Eastern nations by financing programmes focussed on enhancing education and skill attainment as it does for women in Pacific countries⁴⁶. Countries are able to take direct action, such as in the case of Norway where the government has funded the promotion of workers’ rights and labour relations in Bangladesh’s export-oriented ready-made garment and leather industries⁴⁷. On the contrary, unfortunately, a global ‘race to the bottom’ has been observed, where profit-seeking multinational corporations (MNCs) shift production from countries with strong environmental regulations to countries with weak regulations (typically in the Global South)⁴⁸, primarily to reduce costs and increase profits. As trade agreements only exist between economies, MNCs’ distribution of adverse impacts can go unabated, resulting in environmental and social cost shifting from the importing to the exporting country⁴⁹. Whereas many MNCs have defined their own private environmental and social standards, these standards can be exploited without overarching governance⁵⁰. Exacerbating the ‘race to the bottom’ is the concern of becoming ‘stuck at the bottom’ or being ‘locked into’ export dependence as developing countries succumb to competitive pressures, polarizing international environmental conditions between the Global South and North⁴⁹. Reversing this trend, or developing a ‘race to the top’ model, would involve reframing trade relations and implementing fair trade policies that go beyond implementation of environmental regulations but also consider fair work practices, with a focus on workers’ conditions, health and safety standards. This also goes in hand with implementation of enforcement measures should companies/organizations fail to implement fair work and fair trade practices.

Discussion

We show that if the Agenda 2030 is to be realized, countries must take into consideration their influence beyond the national borders. This study shows that in the absence of defined consumption-based indicators aligned to the SDG framework, proxies can be used for global, supply-chain-wide analyses of outsourcing trends. Such analyses are useful for understanding the influence of international trade on polarizing and equalizing effects at a global level. International trade can have both equalizing and polarizing effects, either in the form of positive outcomes such as economic growth leading to a reduction of poverty and raising of living standards or causing negative consequences such as pollution, resource depletion and social effects.

Our results have several implications. First, we show how consumption-based proxies can reveal how a country is progressing towards achieving SDGs. Progress towards achieving the UN SDGs is being tracked through the global indicator framework⁵¹, which was developed by the Inter-Agency and Expert Group on SDG Indicators and agreed upon in 2017. Its purpose is to provide the statistical basis for assessing the targets set out as part of the SDG framework. As such, each target has at least one indicator assigned. Thereby, one of the main challenges in putting together the indicator framework was to find indicators that would be meaningful and at the same time methodologically and statistically advanced enough, and by that means, applicable to any country in the world. This circumstance might explain to some extent why the Material Footprint featured under SDGs 8 and 12¹⁷ is the only indicator in the whole set that accounts for supply-chain-wide resource use. We show that meaningful consumption-based indicators for several SDGs provide useful basis for tracking a country’s impacts across borders.

Second, methods that reveal supply-chain impact hotspots, such as those employed in this research, are essential for informing strategies to reduce the impacts of trade. Carbon mitigation negotiations have acknowledged the importance of monitoring and correcting for outsourcing or ‘carbon leakage’⁵². For example, carbon taxes signal that carbon-intensive products must have a cost associated with them. The EU Carbon Border Adjustment Mechanism, which entered into a transitional phase in October 2023, puts a price on the emissions associated with the production of products entering the European Union⁵³. Still, the SDG indicator framework does not feature the carbon footprint as an explicit indicator. Indeed, whereas efforts to advance some SDGs, such as SDG 13, are being implemented (that is, carbon border taxes), integrating supply-chain-wide analyses into the SDG indicator framework is necessary for all SDGs and related indicators. For example, to address SDG 8 (Decent Work and Economic Growth), social consequences occurring in developing nations due to the demand for products by developed countries should be accounted for.

Finally, realizing the effects of international trade on SDG progress could have important implications for the ‘loss and damage’ debate. Loss and damage is typically understood as the effects of climate change that cannot be mitigated or avoided. Central to loss and damage are discussions on compensation of countries dealing with various impacts caused by climate change⁵⁴. The success of the envisaged loss and damage fund in responding to the human cost of climate change⁵⁵ will depend on critical questions of who is responsible for paying which amounts into the fund. As presented in our study, outsourcing analyses could provide empirical data to answer some of the questions around the influence of international trade on cross-border effects, such as the responsibility of some nations for increasing emissions in others based on their consumption patterns.

Integrating footprint measures into the SDG indicator set is of utmost importance to uncover how the implementation of policies and progress towards one goal in one country is likely to influence other countries. Our analyses substantiate evidence that trade integration has led to increased outsourcing of environmental and social repercussions, such as emissions and occupational hazards, from industrialized countries to developing countries, leading to observed polarizing and equalizing effects. Navigating these equalizing and polarizing effects requires striking the right balance by crafting policies promoting fair trade, environmental sustainability and adherence to labour standards, while encouraging international cooperation and agreements for addressing inequalities and the climate challenge. By measuring the effect of international trade, we can properly assess SDG progress and realize opportunities to reduce its negative impacts.

Methods

Indicator framework for SDGs

The UN SDGs are nationally focussed, thus countries determine how SDGs are incorporated into their planning processes and development strategies⁵⁶. The 193 signatories to the UN 2030 Agenda provide data on their SDG progress to the UN annually⁵⁷. Guidelines developed by the UN provide detail on what structure the SDG reports could take and what data are required⁵⁸. The regional UN offices provide region-specific information and resources regarding SDGs and their indicators. Despite efforts to improve and support national data collection and SDG reporting, many Global South countries still have limited resources for data collection and monitoring processes. The UN global indicator framework for the SDGs and targets of the 2030 Agenda for Sustainable Development provide a measurable way to progress towards each target of the SDGs⁵⁹.

Given the assumption that national efforts collectively add up to global progress on the SDGs, spillover effects (that is, impacts taking place outside countries’ borders) are not explicitly covered in the 2030 Agenda. Thus, nations are not required to report how their consumption patterns influence other countries’ SDG progress. However, recognizing the growing literature on burden shifting via global trade⁶, the Sustainable Development Solutions Network has developed a Spillover Index to shed light on how SDG progress in one region might hinder it in another⁶⁰. These spillover effects were not considered in the conceptualization of the SDGs and the 2030 Agenda⁶¹, thus a perfect alignment does not exist between the indicators often used for assessing spillover effects and those proposed to measure progress towards the SDGs. In this vein, this study develops consumption-based proxy quantities (‘proxies’) linked to specific SDG targets or different elements of the SDGs for undertaking a quantitative assessment to analyse the influence of international trade on polarizing and equalizing trends in the Global North and Global South.

Connecting SDGs to quantitative proxies

We express SDG performance using quantitative proxies that cover the environmental and social dimensions of sustainability (Supplementary Information 2.3). These proxies are related to several SDG targets; however, the majority of these (except for ‘material footprint’) are not part of the official global indicator framework for the SDGs⁵⁹ for reasons explained above. Hence, the selected environmental and social proxies are linked to 12 SDGs as representative consumption-based proxy quantities to capture consumption-based effects that are not otherwise considered in the SDG framework. Understandably, the proxies used in this study do not capture all the different dimensions encompassed by each of the SDGs (described by all their targets), however by being linked to specific single targets of the selected SDGs, our proxies show the extent to which international trade has contributed or not to some aspects of the SDGs in question.

In addition, an assessment of temporal change in outsourcing requires data on the selected proxies at a detailed country and sector level, hence their selection is determined by the choice of the economic global trade database used in the study. Thus, the selected proxies are linked to the GLORIA multi-region input–output (MRIO) framework¹⁷, with data on environmental proxies taken from the Sustainable Consumption and Production Hotspot Analysis Tool (SCP-HAT)¹¹ and on social proxies from a range of sources, as described below. When linked to the MRIO framework, the environmental and social proxies are called satellite accounts.

Outsourcing trends as assessed in this study for environmental and social proxy quantities can be understood as being associated with international trade⁶. For example, petroleum refining for meeting foreign demand of oil directly results in environmental repercussions, such as greenhouse gas (GHG) emissions—this is an example of the environmental effects of trade-related connections between producing and consuming countries. The connection between trade and social effects (for example, poverty, occupational accidents) is less direct due to inherent human dimensions. For example, poverty is a multi-faceted issue that depends on income, education, health, threat of violence⁶² and much more. MRIO analysis enables the tracking of production and consumption of commodities linked with workers below the poverty threshold; thus, connecting the demand for products and services with production in countries with a poorly paid population. We use environmental and social proxies to analyse SDG performance in terms of polarizing or equalizing outsourcing trends. Uncovering link of consumption with GHG emissions, water stress, land-use and biodiversity threats (which take place during the production of consumed products); and the link of consumption with the prevalence of poverty and occupational accidents contributes to the understanding of underlying relationships between production and consumption at a global level and also in understanding (using a time-series assessment) polarizing and equalizing effects of international trade on the SDGs.

MRIO analysis

MRIO analysis is based on statistical data that capture connections between sectors and regions in a global economy. Originally conceived by the Nobel Prize Laureate Wassily Leontief⁶³, the technique has been widely used for assessing environmental and social repercussions of international trade⁶. To date, multiple MRIO databases have been developed for assessments at local, national and global scales^8,9,10; for analysis of disasters⁶⁴; and for underscoring the use of the MRIO technique in implementing an SDG reporting and monitoring system¹⁷. Here we demonstrate the power of MRIO analysis in appraising temporal performance of countries over time in relation to the SDGs.

A MRIO system is represented by a set of matrices ${{{T}}}^{\,(P I)\times (R J)\times T}$, ${{{y}}}^{(R J)\times S\times T}{{,}}\,{{{Q}}}^{Q\times (P I)\times T}$ with data for $q=1,\ldots ,Q$ primary factors (such as labour, resources or pollution), $p=1,\ldots ,P$ producing (that is, extracting, employing, emitting) regions, $i=1,\ldots ,I$ producing (extracting, emitting) industries, $r=1,\ldots ,R$ selling (processing, manufacturing, trading) regions, $j=1,\ldots ,J$ sold (processed, manufactured) products and $s=1,\ldots ,S$ buying (consuming) regions, all for $t=1,\ldots ,T$ years. T is an intermediate transactions matrix, y is final demand and Q is a so-called satellite account showing the regions’ inventories in terms of the primary factors relating to the SDG proxies. There are two important derived quantities: the matrix ${{{q}}}^{Q\times (P I)\times T}$ holds proxy intensities with ${{q}}{{=}}{{Q}}{\widehat{\left({{T}}\,{{1}}{{+}}{{y}}{{1}}\right)}}^{{l{-}}1}$ and ${{{L}}}^{(P I)\times (R J)\times T}$ is the famous Leontief inverse with ${{L}}{{=}}{\left[{{I}}{{-}}{{T}}{\widehat{\left({{T}}\,{{1}}{{+}}{{y}}{{1}}\right)}}^{{{-}}1}\right]}^{{{-}}1}$. 1 is a suitable row summation operator, and the hat (^) accent denotes vector diagonalization. The footprint of proxy quantities contained in satellites Q are defined by their most general tensor form as ${F}_{{ij},t}^{\;q,{prs}}{{=}}{\left\{{{qLy}}\right\}}_{{ij},t}^{q,{prs}}{{=}}{q}_{i,t}^{q,p}{L}_{{ij},t}^{{pr}}{y}_{j,t}^{{rs}}$. Given Leontief’s national accounting identity ${\sum }_{{ij}}^{{rs}}{F}_{{ij},t}^{\;q,{prs}}={\sum }_{i}^{p}{Q}_{i,t}^{q,p}\forall q,t$, the quantities Q and F constitute dual perspectives on resource use (environmental and human) and pollution, one called the territorial or producers’ view, and the other the supply-chain or consumers’ view.⁶⁵

In line with the underlying MRIO database—GLORIA—the satellite accounts Q were established for multiple environmental and social proxies at a detail of 97 sectors in 164 countries.

Definitions and data sources for proxies

In the following we describe the 12 selected environmental and social proxies linked to the SDGs. For ease of interpretation, we define the proxies such that they are considered bad/detrimental for the accomplishment of the SDGs. In other words, if a downward trend is observed over the assessed time period for the proxies, then that can be interpreted as a desirable outcome. The selected proxies do not serve to comprehensively cover all targets and indicators in the 12 selected SDGs. Instead, the proxies are designed to link to relevant aspects of selected SDGs for assessing polarizing and equalizing international trade trends over time. Briefly, the proxies are (Supplementary Information 2.3):

• GHG emissions: we source data from the EDGAR v.5.0 database^66,67 to characterize region- and sector-specific anthropogenic GHG emissions. Construction of this proxy was done by implementing a ‘satellite account’ linked with the MRIO database, which required total sectoral output as additional data for allocating emissions to the MRIO database (supplementary information of Lenzen et al.⁶⁸ for details). This proxy is presented in the units of tonnes and has been linked to SDG 13 (Climate Action), target 13.2, indicator 13.2.2.

• Air pollutants: we cover three main air pollutants: sulfur dioxide (SO₂), particulate matter (PM_2.5) and nitrogen oxides (NO_x). This proxy is developed by applying characterization factors to emissions data from EDGAR v.5.0 database^66,67, expressed in disability-adjusted life years. This proxy is presented in the units of tonnes and has been linked to SDG 11 (Sustainable Cities and Communities), target 11.6, indicator 11.6.2

• Land: we cover six land-use classes: annual crops, permanent crops, pasture, extensive forestry, intensive forestry and urban, based on data from the Food and Agriculture Organization Corporate Statistical (FAOSTAT) Land Use domain. We map these land-use classes to the GLORIA MRIO table following the approach outlined in Annex VIII of the Sustainable Consumption and Production Hotspots Analysis Tool (SCP-HAT)–technical documentation⁶⁹. This proxy is presented in the units of hectares. This proxy has been linked to SDG 2 (Zero Hunger), considering that its target 2.4 argues for the need to ensure sustainable food production systems that increase productivity.

• Biodiversity: this proxy builds on the land-use proxy by applying characterization factors for global species loss on land-use categories to yield a measure of potentially disappeared fraction of species (that is, temporary loss of biodiversity from land occupation, refer to land-use-specific UN Environment Programme global guidance for Life Cycle Impact Assessment indicators⁷⁰ for characterization of biodiversity impacts from land use). This proxy is presented in the units of potentially disappeared fraction of species. This proxy has been linked to SDG 15 (Life on Land), target 15.5.

• Energy: we source data on primary energy production from the International Energy Agency⁷¹ to develop the energy proxy, which includes primary energy production from 21 energy products that are grouped into six broad groups: coal and peat; oil and natural gas; nuclear; solid biofuels; captured energy and heat. Annex XI in the SCP-HAT–technical documentation⁶⁹ describes the allocation of these sources to MRIO table. This proxy is presented in the units of joules. This proxy has been linked to SDG 7 (Affordable and Clean Energy), target 7.2.

• Materials: we source data on materials from the UN International Resource Panel Global Material Flows Database⁷², which presents direct material flows for 124 different material categories that can be categorized into four broad materials groups: biomass, metal ores, non-metallic minerals and fossil fuels¹⁷. This proxy is presented in the units of tonnes. This proxy has been linked to SDG 12 (Responsible Consumption and Production) in view of the material footprint being a UN proposed indicator for target 12.2 (that is, indicator 12.2.1) of SDG 12.

• Water stress: we compile this proxy based on data on water use⁷³ combined with the AWARE characterization factor⁷⁴. Development of this proxy is explained in detail in the SCP-HAT’s technical documentation⁶⁹. This proxy is presented in the units of m3 H₂O-equivalent. This proxy has been linked to SDG 6 (Clean Water and Sanitation) under the consideration that water stress is proposed by the UN as an indicator (indicator 6.4.2) for this goal’s target 6.4.

• Nitrogen emissions: we cover three categories of nitrogen emissions: nitrogen oxides (NO_x) and ammonia (NH₃) to air and nitrogen (N) leached to freshwater. We characterize nitrogen flows to water based on the FAOSTAT’s Climate Change Emissions domain⁷⁵. We then capture airborne nitrogen emissions based on the GHG emission and air pollution dataset as described above. We obtain nitrogen leaching information from the FAOSTAT Climate Change Emissions domain⁷⁵, encompassing data on nitrogen utilization and leaching to water. This proxy is presented in the units of tonnes. This proxy has been linked to SDG 14, target 14.1, indicator 14.1.1 (Life Below Water), given that nitrogen is a nutrient associated with eutrophication.

• Women’s participation: we distinguish employment by gender (male and female employment) based on data from the International Labour Organisation (ILO)⁷⁶, expressed in units of number of people. The ILO data feature more aggregated sectors than the GLORIA MRIO database, which are disaggregated using data on compensation to employees. The raw data for this proxy are then converted into a ratio by applying a weight of 0 to female employment and 1 to male employment, followed by normalization as described in Supplementary Information 2.3. Thus, this resultant proxy corresponds to women’s participation in the workforce, and it is expressed in the units of percentage of males employed. A negative trend where the percentage of males decreases over time is considered a positive outcome for this indicator. This proxy has been linked to SDG 5 (Gender Equality), as target 5.1 calls for the end of discrimination against women.

• Labour skills: we distinguish different levels of skills (high-, medium- and low-skilled) based on data from the ILO⁷⁶. Allocation of these data to the MRIO database follow the same approach as the women’s participation proxy described above, using data on compensation of employees (Supplementary Information 2.3 for details). This resultant proxy ‘labour skills’ is expressed in the units of percentage of low-skilled workforce and has been linked to SDG 10 (Reduced Inequalities), given that SDG 10’s target 10.1 argues for the income growth of the lower population percentiles. Because this proxy is based on employees’ compensation, a negative trend where the percentage of low-skilled workforce decreases over time is considered a positive outcome for this indicator.

• Poverty: we construct this proxy by considering individuals living below the poverty line (US$1.90 per person per day), with the daily income per person estimated as the ratio of total income to employment⁷⁶. The income data are obtained from the value-added block of the GLORIA database. This proxy has been linked to SDG 1 (No Poverty) in units of number of people, considering that its target 1.1 seeks to eradicate extreme poverty (people living below the international poverty line).

• Occupational accidents: this proxy covers fatal and non-fatal accidents⁷⁷ in connection with work. This proxy is presented in the units of cases and has been linked to SDG 8 (Decent Work and Economic Growth), taking into account that fatal and non-fatal injuries is the indicator (8.8.1) proposed by the UN to measure progress towards SDG 8’s target 8.8.

Outsourcing

Whereas the producers’ perspective reports primary factors of labour, resource extraction and emissions as attributes of the producing region, the consumers’ perspective re-allocates these to whoever ultimately consumes the product for which these factors have initially been expended. If a regions’ footprint (F, consumer perspective) is larger than its factor inventory (Q, producer perspective), the region is said to be a net importer of this factor and vice versa. A net-importing region is said to be outsourcing factor use to net-exporting regions, and the commodities it imports are said to embody the factor use while being traded.

The phenomenon of outsourcing is well known and has assumed various other connotations such as quantities that leak (for example, for carbon⁷⁸) or that are virtual (for example, for water⁷⁹). This work is concerned with whether and how outsourcing has changed over time, both in its direction and magnitude.

On the basis of the quantifications Q and F of the producer and consumer perspectives, we define outsourcing as a matrix ${\bar{{{S}}}}^{Q\times R\times T}$, with ${\bar{S}}_{t}^{q,p}{{=}}{\sum }_{i}{Q}_{i,t}^{q,p}{{-}}{\sum }_{{ij}}^{{sr}}{F}_{{ij},t}^{\;q,{srp}}={\bar{Q}}_{t}^{q,p}-{\bar{F}}_{t}^{\;q,p}$, or in matrix notation $\bar{{{S}}}{{=}}\bar{{{Q}}}{{-}}\bar{{{F}}}$, with the bar signifying summation over products. Note the reversal of the regional indices for the footprint tensor; in the summation above, p is taken as the producing and consuming region. ${\bar{S}}_{t}^{q,p} > 0$ indicates that in year t, region p is a net exporter of primary factor q and vice versa a net importer for ${\bar{S}}_{t}^{q,p} < 0$. We then normalize outsourcing so that we can compare across indicators and countries: we define the outsourced fraction $\bar{{{s}}}{{=}}\left(\bar{{{Q}}}{{-}}\bar{{{F}}}\right){{\oslash }}\bar{{{F}}}$, where ${{\oslash }}$ is element-wise division. This quantity describes what fraction of the regional footprint is outsourced (as opposed to of domestic origin). Similarly, ${\bar{s}}_{t}^{q,p} > 0$ indicates that in year t, region p is a net exporter of primary factor q and vice versa a net importer for ${\bar{s}}_{t}^{q,p} < 0$.

In our results, we use outsourcing averaged over time ($\overline{\overline{{{S}}}}$, denoted by a double bar), defined as $\overline{\overline{{{S}}}}=\overline{\overline{{{Q}}}}-\overline{\overline{{{F}}}}$, with ${\overline{\overline{Q}}}^{\;q,p}={\sum }_{t}{\bar{Q}}_{t}^{q,p}={\sum }_{it}{Q}_{i,t}^{q,p}$ and with ${\overline{\overline{F}}}^{\,q,p}=\sum _{{{t}}}{\bar{F}}_{t}^{\;q,p}=\sum _{it}{F}_{i,t}^{\;q,p}$. Similarly, we define $\overline{\overline{{{s}}}}=(\overline{\overline{{{Q}}}}-\overline{\overline{{{F}}}})\oslash \overline{\overline{{{F}}}}$ as the (time-)average outsourced fraction. As with the outsourced fraction, this quantity describes what fraction of the regional footprint is outsourced (as opposed to of domestic origin), but appraised over the entire study period.

Underlying data for constructing MRIO tables, such as GLORIA, vary in quality and resolution by country. Standard deviation estimates are published for GLORIA MRIO tables, which we use for performing an uncertainty assessment, as described in Supplementary Information 4.

Temporal change of outsourcing

As explained above, changes of outsourcing patterns over time are at the heart of this work. We plot outsourcing trends against net trade status and define four archetypes:

• i. Quadrant III: ${\overline{\overline{s}}}^{\,q,p} < 0$ and ${\bar{s}}^{\dot{q},p} < 0$; regions in this quadrant are (on average) net importers that have been boosting their net-importer status over time.

• ii. Quadrant II: ${\overline{\overline{s}}}^{\,q,p} > 0$ and ${\bar{s}}^{\dot{q},p} > 0$; regions in this quadrant are (on average) net exporters that have been boosting their net-exporter status over time.

• iii. Quadrant I: ${\overline{\overline{s}}}^{\,q,p} < 0$ and ${\bar{s}}^{\dot{q},p} > 0$; regions in this quadrant are (on average) net importers that have been diminishing their net-importer status over time (or have even become net exporters).

• iv. Quadrant IV: ${\overline{\overline{s}}}^{\,q,p} > 0$ and ${\bar{s}}^{\dot{q},p} < 0$; regions in this quadrant are (on average) net exporters that have been diminishing their net-exporter status over time (or have even become net importers).

Arranging these archetypes in a four-quadrant system allows us to identify situations where trends work to either further aggravate historical outsourcing (polarizing trends; quadrants II and III) or further alleviate historical outsourcing (equalizing trends; quadrants I and IV). Polarizing trends deteriorate existing disparities; equalizing trends mitigate them.

To this end, we regress ${\bar{S}}_{t}^{q,p}={\bar{S}}^{q,p}\left(t\right)={m}^{q,p}t+{\bar{S}}_{t=0}^{q,p}\forall p$ by following the weighted least squares approach by taking country footprints as weights. The regression slope m_r is the average temporal trend ${m}^{q,p}=\frac{\partial {\bar{S}}^{q,p}(t)}{\partial t}$ of the outsourcing of region p in terms of primary factor q. We use ${m}^{q,p}$ to determine the temporal change of the outsourced fraction $\bar{{{s}}}$, calculated as ${\bar{s}}^{\dot{q},p}=\frac{\partial {\bar{s}}^{q,p}(t)}{\partial t}=\frac{\partial [{\bar{S}}^{q,p}(t)/{\bar{F}}_{t}^{\;q,p}]}{\partial t}\approx \frac{\partial {\bar{S}}^{q,p}(t)/\partial t}{{\sum }_{t}{\bar{F}}_{t}^{\;q,p}/T}=\frac{{m}^{q,p}}{{\overline{\overline{F}}}^{\;p}/T}$, with the dot (·) accent denoting the temporal derivative. Regions with ${m}^{q,p} < 0$ have been increasingly outsourcing and vice versa.

Finally, regressing a regional four-quadrant cloud of outsourced fractions $\overline{\overline{{{s}}}}$ and their temporal changes $\dot{\bar{{{s}}}}$ yields four-quadrant slopes ${b}^{q}=\partial \dot{\bar{{{s}}}}/\partial /\overline{\overline{{{s}}}}$; Fig. 1). Regions in quadrants II and III are part of a polarizing trend (${b}^{q} > 0$) and regions in quadrants I and IV are part of an equalizing trend (${b}^{q} < 0$).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.