The data visualization tool shows bilateral spillover effects that one country has on another. To this end, the data visualization uses five metrics of environmental and social harms, listed below, in addition to the spillover index score from the Sustainable Development Report 2019. For the below metrics, the higher the value, the larger the harmful spillover effects one country has on another. For the spillover index score (0-100), 100 denotes a perfect score of no spillovers, while smaller values denote a worsening score and larger spillover effects.
Greenhouse gas emissions: Measures the CO2 equivalent of the following greenhouse gases: Carbon Dioxide, Methane, the Fluorinated gases, and Nitrous Oxide. The units are kilograms for the absolute emissions and kilograms/100,000 population for the per capita emissions.
Nitrogen: Measures the total emissions of reactive nitrogen potentially exportable to water bodies and ammonia. Nitrogen emissions stem mostly from crop fertilizer and harm human health notably via aquifer contamination and the environment, notably through eutrophication. The units are kilograms for the absolute emissions and kilograms/100,000 population for the per capita emissions.
Nitrogen Oxides: Measures the emissions from Nitrogen Oxides (NOx). Nitrogen oxides stem mostly from the combustion of fossil fuels and are harmful air pollutants that also contribute to acid rain. The units are kilograms for the absolute emissions and kilograms/100,000 population for the per capita emissions.
Water depletion: Measures water embodied into imports (virtual water) weighted by the water scarcity of the region where the imports were produced. The units are Mm3 world water equivalents for the absolute measure and Mm3 world water equivalents per 100,000 population for the per capita measure. Fatal Accidents at work : Measures fatal work-related accidents embodied into imported goods. The indicator attributes fatal accidents in countries with low labor standards to importing countries based on the monetary value of the goods by sector. The units are the number of deaths for the absolute measure and deaths/100,000 population for the per capita measure.
In an increasingly interdependent world, countries’ actions can have positive or negative effects on other countries’ ability to achieve the SDGs. Such international “spillovers” are pervasive and have been growing fast as a result of globalization and growth in trade, exceeding the growth in production and in world gross domestic product (Fischer-Kowalski et al., 2015) (Table 1). Rapid population growth, political instability and weak institutions in some regions of the world have also contributed to the growth in spillover effects (e.g. trade in illicit arms exports). These spillovers have been classified into four broad types: trade, people, finance, and biophysical (Davis et al. 2016).
The EII focuses on trade-related spillovers, i.e. mainly environmental footprints that occur along international supply chains. Examples include countries’ excessive use of fossil fuels, resulting in high levels of CO2 emissions, for this is driving global warming, resulting in polar melt and rising seas which threaten to submerge many countries and coastal communities around the world.
International demand for palm oil /or products that are produced with palm oil in one country, may fuel deforestation in another country, where the palm oil is grown (Valin et al., 2015; Lustgarten, 2018). In our data vizualisation tool, we also included one socio-economic spillover effects since tolerance for poor labor standards in international supply chains harms the poor, and women in particular, in many developing countries (ILO, 2014).
Positive and negative spillovers must be understood, measured, and carefully managed since countries cannot achieve the SDGs if spillovers from other countries actions counteract their efforts. The 2017 SDG Index Report issued by the SDSN and Bertelsmann Stiftung (Sachs et al., 2017) provided a first systematic assessment of international spillover effects, further developed in its 2019 edition (Sachs et al., 2019). It showed that negative effects tend to flow from rich to poor countries. Most trans-national companies that control international supply chains are also located in high-income countries, so these countries bear a special responsibility to identify and tackle international spillover effects.
Overall, data and information on cross-border spillover effects tends to be sparse and incomplete. To some extent this is due to the lack of a clear conceptual framework, but more importantly the increasing length and complexity of supply chains slows down the assessments of trade-related spillovers. Other challenges are that national and international databases are often inconsistent and that national statistical offices are rarely mandated to measure international spillovers. Even if data are available and consistent, it remains challenging to clearly assign responsibility for negative externalities to an individual country along the supply chain. The work of international organizations in this area is furthermore hampered by political sensitivities among member states on the measurement of spillover effects and on the difficulties of clearly assigning responsibility for negative externalities to one particular country.
Methods for assessing international trade-related spillovers fall into three broad categories:
Different research groups have applied these three methods to different impacts (Figure 1). The focus so far has been primarily on environmental and less on socio-economic impacts. Spillovers relating to economy, finance, and governance or security spillovers, cannot yet be estimated with these methods.
Several teams develop hybrid approaches that seek to combine advantages of the different methods and to overcome individual constraints.
For details on these indicators and the methodology for producing them, please see the references below:
Lenzen M, Kanemoto K; Moran D, and Geschke A (2012) Mapping the structure of the world economy. Environmental Science & Technology 46(15) pp 8374–8381. DOI: 10.1021/es300171x
Lenzen, M., Moran, D., Kanemoto, K., Geschke, A. (2013) Building Eora: A Global Multi-regional Input-Output Database at High Country and Sector Resolution. Economic Systems Research, 25:1, 20-49, DOI:10.1080/09535314.2013.769938
Kanemoto, K., D. Moran, Hertwitch, E. (2016) Mapping the Carbon Footprint of Nations. Environmental Science and Technology 10.1021/acs.est.6b03227
Lenzen, M, D. Moran, Bhaduri, A., Kanemoto, K., Bekchanov, M., Geschke, A., Foran, B. (2013) International Trade of Scarce Water. Ecological Economics 10.1016/j.ecolecon.2013.06.018
Oita, A., Malik, A., Kanemoto, K., Geschke, A., Nishijima, A., Lenzen, M.(2016). Substantial nitrogen pollution embedded in international trade. Nature Geoscience, 9(2), 111–115. 10.1038/ngeo2635
Alsamawi, A., J. Murray, M. Lenzen, and R. C. Reyes. 2017. Trade in occupational safety and health: Tracing the embodied human and economic harm in labour along the global supply chain. Journal of Cleaner Production 147: 187-196.