GLOBALCOMMONSSTEWARDSHIPINDEXTACKLING
ENVIRONMENTAL
SPILLOVERS

2022

Technical
AppendixAcknowledgements
This Technical Appendix was prepared by the Sustainable Development Solutions Network (SDSN),
Paris; the Yale Center for Environmental Law & Policy, New Haven, CT; and the Center for Global
Commons at the University of Tokyo. The analysis and report production were undertaken by T. Reed
Miller, Zachary A. Wendling, Salma Dahir, Eamon Drumm, and Akiyuki Kawasaki under the direction of
Guillaume Lafortune, Daniel C. Esty, and Naoko Ishii.
Please cite this Technical Appendix as:
SDSN, Yale Center for Environmental Law & Policy, and Center for Global Commons at the
University of Tokyo. 2023. Global Commons Stewardship Index 2022: Technical Appendix. Paris; New
Haven, CT; and Tokyo.
We are grateful for statistical support, guidance, and inputs provided by researchers from the following
organizations:
SDSN

Grayson Fuller, Max Gruber, Sara Kasim, and Ruben Andino

University of Tokyo

Zhao Han

Sea Around Us

Maria L.D. Palomares

Global Forest Watch

James MacCarthy

UN Environment Programme World Conservation Monitoring Centre
Jessica Vitale, Kelly Malsch, and Katia Sanchez-Ortiz
The report benefited from numerous comments and inputs provided during an online public consultationin October 2022. The authors are grateful for contributions and feedback provided by the Yale Center for
Environmental Law & Policy, SYSTEMIQ, the World Resources Institute, the Potsdam Institute for
Climate Impact Research, and the Institute for Future Initiatives at the University of Tokyo. The Center
for Global Commons at the University of Tokyo provided financial support for this work.
The views expressed in this report do not reflect the views of any organizations, agencies, or programs of
the United Nations.

1

Table of Contents
Abbreviations .............................................................................................................................................. 3
1. Conceptual framework........................................................................................................................... 4
1.1 Pillars.................................................................................................................................................. 5
1.2 Sub-pillars .......................................................................................................................................... 5
2. Spillover calculations .............................................................................................................................. 6
2.1 Environmental accounting ............................................................................................................. 6
2.2 Impact matrices ................................................................................................................................ 8
2.3 Matrix calculations ......................................................................................................................... 11
3. Data selection ........................................................................................................................................ 14
3.1 Inclusion criteria............................................................................................................................. 14
3.2 Indicators......................................................................................................................................... 14
4. Methods for indicator development .................................................................................................. 23
4.1 GHG emissions.............................................................................................................................. 24
4.2 Terrestrial and marine biodiversity loss ...................................................................................... 25
4.3 Nutrient cycle disruptions ............................................................................................................ 30
4.4 Water cycle disruptions ................................................................................................................. 31
5. Country coverage .................................................................................................................................. 33
6. Indicator construction ......................................................................................................................... 34
6.1 Standardization ............................................................................................................................... 34
6.2 Rescaling .......................................................................................................................................... 34
6.3 Transformation............................................................................................................................... 35
7. Weighting & Aggregation .................................................................................................................... 38
8. Missing data ........................................................................................................................................... 38
9. Dashboards ............................................................................................................................................ 39
10. Trajectories .......................................................................................................................................... 39
References .................................................................................................................................................. 402

Abbreviations
AR5
AWARE
BHI
CBA
CITES
CO2
CSIRO
EDGAR
EE
EEZ
FAO
FE
FUBC
GCS
GFW
GHG
GLORIAGWP100
IFA
IPCC
KBA
ML
MRIO
N2O
NOX
ODS
OECD
PBA
PDF

Fifth Assessment Report of the IPCC
Available WAter Remaining
Biodiversity Habitat Index
Consumption-Based Accounting
Convention on International Trade in Endangered Species of Wild Fauna and Flora
Carbon dioxide
Commonwealth Scientific and Industrial Research Organisation (Australia)
Emissions Database for Global Atmospheric Research
environmentally extended
Exclusive Economic Zone
Food and Agriculture Organization of the United Nations
Fishing Entity
Fertilizer Use By Crop
Global Commons Stewardship
Global Forest Watch
Greenhouse gas
Global Resource Input-Output Assessment
Global Warming Potential (100-year)
International Fertilizer Association
Intergovernmental Panel on Climate Change
Key Biodiversity Area
megaliter
Multi-Regional Input-Output
Nitrous oxide
Nitrogen oxides
Ozone Depleting Substances
Organisation for Economic Co-operation and Development
Production-Based Accounting
Potentially Disappeared Fraction of species

SAU
SCPHAT
SDG
SDSN
SO2
UNEP
WCMC
WOE

Sea Around Us
Sustainable Consumption and Production Hotspot Analysis Tool
Sustainable Development Goals
Sustainable Development Solutions Network
Sulfur dioxide
United Nations Environment Programme
World Conservation Monitoring Centre (UNEP)
Whole Organism Equivalent3This Technical Appendix contains the details behind the methods and assumptions used in the2022 Global Commons Stewardship (GCS) Index. Nardo et al. (2008) describe the generaltechniques of composite indexing, and we follow the best practices of this field. Here, weelaborate on our data and other choices in the construction of the 2022 Index and subsequent
analyses. In doing so, we invite feedback and critiques from researchers and other stakeholders.
Future iterations of this Index will incorporate constructive suggestions for improvement.

1. Conceptual framework
Measuring countries’ impacts on the Global Commons requires many indicators, and building a
coherent narrative requires organizing these indicators. The GCS Index is a composite index,
with a hierarchy of indicators, sub-pillars, and pillars within the overall Index (see Figure 1). Thissection explains the logic of the Index hierarchy.

Global Commons
Stewardship Index
Domestic

SpilloverGHG
emissionsAerosolemissionsGHG
emissionsAerosolemissions

Terrestrial
biodiversity loss

Marinebiodiversity loss

Terrestrial
biodiversity loss

Marinebiodiversity loss

Nutrient cycle
disruptions

Water cycle
disruptions

Nutrient cycle
disruptions

Water cycle
disruptions

Figure 1. Conceptual framework of pillars and sub-pillars within the 2022 Global
Commons Stewardship Index.

41.1 Pillars
Focusing on the Global Commons means contrasting domestic environmental impacts with theextra-territorial environmental impacts resulting from transboundary issues, especially impacts
embodied in traded goods and services. We therefore divide the GCS Index into two pillars asshown in Figure 1: Domestic, which covers impacts to the Global Commons that occur withincountries’ territorial boundaries, and Spillover, which covers environmental impacts that occur
beyond territorial boundaries.
1.2 Sub-pillars
As discussed in Section 4.4 of the 2022 GCS Index Report, the Index seeks to categorize
available data on impacts to the Global Commons in thematically coherent sub-pillars. These
categories reflect Earth system science as well as levers for policymaking. Measuring countries’
impacts on these commons often entails crossing causal pathways. Emissions of CO2, forexample, contribute to both climate change and ocean acidification. Our sub-pillars also reflect
the current state of data availability. Despite the importance of the ozone layer, we could identify
virtually no indicators on impacts to stratospheric ozone that satisfy our inclusion criteria.
Alternative categorizations are possible, especially if future research closes important gaps in theavailable data. Our scheme, however, is meant to provide a recognizable and useful framework
for a broad audience. We have grouped our indicators into six sub-pillars: Aerosols, GHG
Emissions, Terrestrial and Marine Biodiversity Loss, and disruptions to the Nutrient and Water
Cycles.

5

2. Spillover calculations
2.1 Environmental accounting
Two major accounting methods (Peters & Hertwich, 2008) exist for attributing environmental
impacts across countries: production-based accounting (PBA) and consumption-based accounting (CBA). PBA examines the domestic emissions and impacts which take place within a country
due to production and use of products. CBA accounts for impacts that occur along the global
supply chains of final products in order to satisfy a country’s final demand. Final demand
includes final consumption (household and government expenditures) as well as investment in
fixed capital assets. There are several ways to approach these calculations; one useful calculationis shown below.
Production = domestic production for domestic final demand +domestic production for exports + use phase
Consumption = domestic production for domestic final demand + imports embodied
in domestic final demand + use phase
As illustrated in Figure 2, both methods include use-phase emissions associated with households
and government consumption, e.g., tailpipe emissions from driving personal vehicles or
combustion emissions from home heating and cooking. The “imports embodied in domestic
final demand” dimension refers to the creation of goods and services in foreign countries along
the supply chain for a final product consumed domestically. For instance, the environmental
impacts of producing bananas in India for export to Iran would be attributed to Iran. This metric also captures more complex situations, such as attributing to the US the impacts of creatingtires in Mexico that are imported by the US and installed on cars sold to consumers in the US.
Production-BasedAccounting (PBA)

Domestic
production
exported forforeign final
demandConsumption-BasedAccounting (CBA)

Domestic
production
for domestic
final demand+ use phase

Imports
embodiedin domestic
final
demandDomestic Pillar

Domestic
production+ use phase

Spillover Pillar

Imports
embodiedin domestic
final
demandFigure 2. Production- and Consumption-based accounting (left) versus GCS Index
Domestic and Spillover accounting (right).
6

Figure 2 shows how the two accounting frameworks overlap: There is typically a high level ofcorrelation between impacts measured by CBA and PBA. To avoid double counting the portionof impacts labeled, “domestic production for domestic final demand and use phase,” the GCS
Index does not use straightforward CBA estimates. Instead, while the Domestic pillar is equivalent to PBA, the Spillover pillar isolates “imports embodied in domestic final demand,” making
importing countries accountable for negative environmental impacts generated abroad. Overall,at the country-level, the most significant component is domestic production for domestic final
demand, which accounted for roughly 74% of global GHG emissions in 2015.
PBA is the most commonly used framework. Under the Paris Climate Change Agreement, themethods used to track the evolution of GHG emissions as part of the National Inventory
Report of the Intergovernmental Panel on Climate Change (IPCC) and the UN Framework
Convention on Climate Change focus, for practical reasons, on PBA (Afionis et al., 2017).
Methods used to generate PBA estimates of CO2 emissions (or other types of impacts) arerather straightforward. By contrast, CBA relies on more complex input-output matrices andsophisticated modeling techniques, and, therefore, it is generally more subject to debates amongexperts than PBA.
Yet there is a crucial need to better integrate CBA within monitoring and policy frameworks,
including in tracking and reducing GHG emissions (Kander et al., 2015). CBA incorporates carbon leakages and attributes them to the countries that externalize CO2 emissions. While PBA
rightfully emphasizes the principle of “product liability,” which states that producers are responsible for the quality and safety of their products, CBA emphasizes the responsibility of consumers and international trade policies and agreements. In the contexts of the SDGs and Agenda
2030, domestic decarbonization should not be achieved by outsourcing certain high-emitting sectors to other countries, such as cement or steel, and then re-importing the final production(Sachs et al., 2017; Schmidt-Traub et al., 2019).
The GCS Index uses both accounting methods. The Domestic pillar makes use of indicators calculated using PBA or related approaches. Doing so underlines the need for countries to take domestic actions in order to clean their industries and implement effective strategies to curb negative impacts on the Global Commons. The Spillover pillar, by contrast, uses indicators calculatedusing CBA and attributes negative impacts to importing countries. Poor scores on the Spillover
pillar highlight areas where countries need to take further actions related to consumption andalso to closely monitor trade relationships that might generate negative impacts abroad.

7

2.2 Impact matrices
Calculations using MRIO models can provide us with a matrix of impacts. Along the rows ofthese matrices are the countries where the impacts occurred, in which, for instance, a factoryreleased pollution while making a part. Along the columns are the countries which purchased thefinal goods and services, for instance, where a homeowner purchased a laptop incorporating apart made abroad. The arrows in the matrix indicate the flow from the Country of Impact to theCountry of Final Demand. Note that there may be several steps along the supply chain between
the first Country of Impact and the Country of Final Demand; the arrow does not necessarily
represent direct imports. Consider a world with three countries trading with each other: A partmade in a factory in Country A could be assembled into a laptop in Country B before it isimported into Country C.
We can evaluate this matrix from several perspectives. The boxes along the diagonal of thematrix represent “domestic production for domestic final demand + use phase.” The off-diagonal boxes could be considered from the perspective of the producer (Country of Impact) or the
final product importer (Country of Final Demand).

8

In the first perspective, we can calculate CBA impacts. The CBA impacts are the column sums,
representing the impacts that are driven by final demand. These impacts include the “domestic
production for domestic final demand + use phase” and “imports embodied in domestic final
demand.” Therefore, the sum of A → A, B → A, and C → A is CBAA, as shown in Table 1.
Table 1. Matrix of impacts, CBA perspective.
Country of Final Demand
A
B
C
A→A

A→B

A→C

B

B→A

B→B

B→C

C

C→A

C→B

C→C

Sum CBAA

CBAB

CBAC

Country of Impact

A

Legend:Domestic production for domestic final demand + use phase
Imports embodied in domestic final demand
Domestic production of exports
Domestic Pillar
Spillover Pillar

9The PBA impacts are the sums across the rows, representing the impacts that occur within thatcountry due to production and use of products. Taking the perspective of the exporter (theCountry of Impact), the off-diagonal boxes represent “domestic production of exports.” The

row sum of A → A, A → B, and A → C is PBAA, as shown in Table 2. The Domestic pillar in the
GCS Index is equivalent to PBA for the indicators calculated using MRIO models.
Table 2. Matrix of impacts, PBA and Domestic Pillar perspective.

Country of Impact

Country of Final Demand
A
B
C

Sum

A

A→A

A→B

A→C

DomesticA
=
PBAA

B

B→A

B→B

B→C

DomesticB
=
PBAB

C→C

DomesticC
=
PBAC

C

C→A

C→B

Lastly, we can focus on the off-diagonal boxes to calculate the Spillover pillar. In this perspective,
the Country of Final Demand is said to drive the impacts which occurred elsewhere. Thus, thepurchase of a laptop in Country C results in spillover impacts in Country A where a part wasmade and in Country B where the laptop was assembled. Here, the sum of just B → A and C →A is SpilloverA, as shown in Table 3.

Table 3. Matrix of impacts, Spillover perspective.

Country of Impact

Country of Final Demand
A
B
C
A

A→B

B

B→A

C

C→A

C→B

Sum SpilloverA

SpilloverB

A→C

B→C

SpilloverC

10

2.3 Matrix calculations
There are several key matrices in MRIO analysis. Data needed to compile the monetary MRIO
matrices are derived from national statistical offices and trade statistics. Balancing techniques arethen applied to misaligned data so that the model is internally consistent. The 2022 Index uses
Release 055 of the GLORIA global environmentally extended MRIO database (Lenzen et al.,2022), constructed in the Global MRIO Lab (Lenzen et al., 2017) at the University of Sydney, forspillover indicators not calculated by other research groups.
The square intermediate demand matrix, 𝑻𝑻, shows the expenditures between and within productsectors in a given year. The rows of 𝑻𝑻 list products, 𝑖𝑖, from countries, 𝑟𝑟, that are used to makeproducts, 𝑗𝑗, in countries, 𝑠𝑠, across the columns. For instance, 𝑻𝑻 estimates that the UK cereal
products sector purchased $2.24 million of US wheat products in 2018.
The final demand matrix, 𝒀𝒀, includes components, d of the expenditures on final products byhouseholds and governments, as well as investments in equipment, structures, and intellectual
property products by households (real estate), businesses, and governments. The matrix 𝒀𝒀 estimates that Brazilians purchased $16.24 billion of Brazilian beef in 2018. We find the total final
demand per for each Country of Final Demand, s, summing over the components d.
𝒀𝒀𝑠𝑠 = � 𝒀𝒀𝑠𝑠,𝑑𝑑
𝑑𝑑

The combined row sums of the intermediate 𝑻𝑻 and final demand 𝒀𝒀 matrices is total output, 𝒙𝒙.
𝒙𝒙 = 𝑻𝑻 + 𝒀𝒀

The intermediate demand matrix 𝑻𝑻 is normalized by the total output 𝒙𝒙 to create 𝑨𝑨, the direct
requirements matrix. The columns in 𝑨𝑨 have technical coefficients, which are effectively ‘recipes’
showing how many units of an input product 𝑖𝑖 from region 𝑟𝑟 are directly needed to produce one
unit of output of a final product 𝑗𝑗 in region 𝑠𝑠. For instance, producing 1 dollar of basic copper
in Japan directly requires 0.195 dollars of Chilean copper ores, 0.063 dollars of Indonesian
copper ores, 0.007 dollars of Japanese electricity, and so on.
�−𝟏𝟏𝑨𝑨 = 𝑻𝑻𝒙𝒙

11

The Leontief inverse, 𝑳𝑳, or total requirements matrix, takes another step and quantifies theindirect inputs needed across the entire supply chain. For instance, while 𝑨𝑨 shows how much
petroleum is a direct input into truck transportation, 𝑳𝑳 would additionally include the petroleum
needed along the supply chain to create other goods and services used by truck transportation.
Note that total output 𝒙𝒙 = 𝑳𝑳𝑳𝑳.
𝑳𝑳 = (𝑰𝑰 − 𝑨𝑨)−𝟏𝟏

Extensions with direct environmental impact data, 𝑸𝑸, are appended to MRIO tables, making
them “environmentally extended” (EE-MRIO). These data could be derived from national statistics or third-party databases. These data may be very disaggregated, and therefore characterization factors, 𝑪𝑪, are applied. For instance, raw values of GHG emissions can all be converted to
global warming potentials by multiplying a matrix of CO2 equivalencies, e.g., 1 kg of methane
becomes 28 kg CO2-eq. (following IPCC AR5).
The subset 𝑸𝑸𝑇𝑇 are impacts from production and the subset 𝑸𝑸𝑌𝑌 are impacts from use of final
products. Summing both per Country of Impact, 𝑟𝑟, is the PBA approach, which is equivalent to
the Domestic Pillar.
𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑟𝑟 = 𝑪𝑪𝑪𝑪𝑇𝑇 𝑟𝑟 + 𝑪𝑪𝑪𝑪𝑌𝑌 𝑟𝑟

To derive spillover estimates, it is critical that the extension data distinguish the production ofwhich product i in region r directly creates the environmental impact. A common challenge isthat the classification of products in environmental data differs from the classification in MRIO
tables, and therefore allocation procedures must be applied to distribute the impacts across the
products.
The impact per unit output, 𝒒𝒒, is found by normalizing 𝑸𝑸𝑇𝑇 by the total output, 𝒙𝒙, just as wasdone before with matrices 𝑻𝑻 and 𝑨𝑨. Note that the use phase impacts are not included in theSpillover pillar, because they do not happen abroad, but instead happen domestically in theCountry of Final Demand.
�−𝟏𝟏
𝑪𝑪𝑪𝑪 = 𝑪𝑪𝑸𝑸𝑇𝑇 𝒙𝒙

In order to distinguish by Country of Impact for spillover analyses, the impacts are diagonalized.
It is necessary to perform this step separately for each impact, c, considered, creating an array ofmatrices. The columns of 𝒀𝒀 already distinguish the Country of Final Demand. The total footprint matrix, 𝑭𝑭𝑐𝑐 , is found by multiplying the impact per output of products made in the Country� 𝑐𝑐 , by the total output needed to satisfy final demand for those products, 𝑳𝑳𝒀𝒀𝑠𝑠 .
of Impact, 𝑪𝑪𝑪𝑪
� 𝑐𝑐 𝑳𝑳𝒀𝒀𝑠𝑠
𝑭𝑭𝑐𝑐 = 𝑪𝑪𝑪𝑪

12

In order to separate the spillover footprint from the total footprint, the domestic production fordomestic final demand is set to zero. This is done by element-wise multiplying the total footprint
with a matrix of 1s with 0s across the diagonal, since domestic impacts for domestic final
demand are in the diagonal.
𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑐𝑐,𝑟𝑟,𝑠𝑠,𝑖𝑖,𝑗𝑗 = 𝑭𝑭𝑐𝑐 × (𝟏𝟏 − 𝑰𝑰)

Finally, the spillover impacts per impact, c, Country of Impact, r, and Country of Final Demand,s, are found by taking sums over products i and j.
𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑐𝑐,𝑟𝑟,𝑠𝑠 = � 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑐𝑐,𝑟𝑟,𝑠𝑠,𝑖𝑖,𝑗𝑗
𝑖𝑖,𝑗𝑗

13

3. Data selection
3.1 Inclusion criteriaWith a wide variety of environmental data on impacts to the Global Commons, the GCS Indexrequires some criteria for selecting appropriate indicators for a composite index. The data we use
come from a variety of sources, including international agencies, academia, and non-governmental organizations. The indicator selection will evolve over time as new data and statistics become
available.
We selected data for inclusion based on five selection criteria:
1. Global relevance and applicability to a broad range of country settings: The indicators should be relevant and allow for direct comparison of impacts across countries. In
particular, they should allow for the definition of quantitative thresholds that signify goal
achievement.
2. Statistical adequacy: The indicators selected should represent valid and reliablemeasures.
3. Timeliness: The indicators selected should be up-to-date and published on a reasonably
prompt schedule.
4. Data quality: Data series should represent the best available measure for a specific issue
and be collected according to methods either peer-reviewed by the academic community
or endorsed by an international organization or other reputable sources.
5. Country coverage: Data should be available for a large range of countries.
3.2 Indicators
After careful review of available sources, we identified 39 indicators that met our inclusion criteria and could be assembled with the necessary expedience (see Table 4). We also acknowledge
that there are gaps in how we measure impacts to the Global Commons, either because there aredata sources unknown to us or because further research is needed. We welcome suggestions for
additional datasets that meet our inclusion criteria for incorporation into future versions of the
GCS Index.

14

Table 4. Indicators included in the 2022 Global Commons Stewardship Index.
Sub-pillar
Aerosols

Indicator
SO2 emissions
NOX emissions

GHG Emissions
Terrestrial
Biodiversity

Black Carbon emissions
Greenhouse Gas emissions

CO2 emissions embodied in fossil fuel exports
Unprotected freshwater Key Biodiversity Areas
Freshwater biodiversity threats
Deforestation

Red List Index of species survival

CITES-listed terrestrial organisms
Biodiversity Habitat Index

Unprotected marine Key Biodiversity Areas
Marine biodiversity threats

CITES-listed marine organisms
Fish caught from vulnerable taxa
Fish caught from overexploited or collapsed fish stocks
Nutrient Cycles

✔
✔
✔
✔
✔
✔
✔

Fish caught by trawling

Sustainable Nitrogen Management Index
Nitrogen surplus

Water Cycle

✔
✔
✔
✔

Unprotected terrestrial Key Biodiversity Areas
Land use biodiversity loss

MarineBiodiversity

Spillover

Phosphorus fertilizer
Scarce water consumption
Water stress

✔
✔
✔
✔

Note: All indicators listed are included in the Domestic pillar; only those indicators with a ✔
are included in the Spillover pillar.

15

3.2.1 Domestic indicators
For the Domestic pillar we present 24 indicators that meet our selection criteria (Table 5).
Aerosol indicators consist of emissions of sulfur dioxide (SO2), nitrogen oxides (NOX), andblack carbon. GHG Emissions are a combination of carbon dioxide (CO2), methane (CH4),
nitrous oxide (N2O), and fluorinated gases (F-gases) – the latter three converted to CO2-equivalents. We also include a measure of GHG emissions embodied in exported fossil fuels.
Terrestrial Biodiversity Loss is our most expansive sub-pillar, with indicators on terrestrial andfreshwater protection, as measured by the percentage of a country’s Key Biodiversity Areas(KBA) (BirdLife International, 2022) that are not in protected areas. These KBA indicators are
the only measures in the current indicator set that track policy intentions and not outcomes.
Human activities also impact species in terrestrial and freshwater ecosystems whose conservation
status is threatened. As a biome of critical importance for many ecosystem services, we include ameasure of deforestation. The 2022 Index also includes a pilot indicator of domestic export of
CITES-protected terrestrial animals. Two additional indicators provide a more holistic picture of
biodiversity loss: the Red List Index (IUCN, 2022) and the Biodiversity Habitat Index (Ferrier etal., 2022; Hoskins et al., 2020; Mokany et al., 2020).
For Marine Biodiversity Loss, we also include measures of unprotected KBAs and threats to species. As a classic example of the commons, we include measurements of fish stocks, with a status indicator of percentage of catches that come from over-exploited or collapsed stocks and ameasure of percentage of catches that use trawling or longlines, especially destructive fishinggear. These indicator are based on data from the Sea Around Us using methods described by
Pauly et al. (2020). Two pilot indicators measure domestic export of CITES-protected marine
animals and fisheries catch from vulnerable taxa. Impacts to the Nutrient Cycles are largely
driven by agriculture. We use the Sustainable Nitrogen Management Index (Zhang et al., 2022), a
measure of nitrogen surplus, and a measure of phosphorus used as fertilizer. Water Cycle
disruptions include consumption of scarce water resources and water stress due to crops.
3.2.2 Spillover indicators
The 15 indicators in the Spillover pillar (Table 6) are derived from CBA (isolating the imported
for final demand dimension) using MRIO tables that link traded goods with environmental andbiodiversity impacts. These derivations are only possible for those datasets in which impacts canbe mapped onto the economic sectors described in the MRIO models. The Spillover indicators
include all of the Aerosol emissions; GHG emissions; threats to terrestrial, freshwater, andmarine species; deforestation; trade in CITES-protected organisms (does not use MRIO tables);vulnerable marine taxa; nitrogen surplus and phosphorus fertilizer; and scarce water
consumption and water stress due to crops.

16Table 5. Domestic indicators in the 2022 GCS Index.
Indicator

Description

Input Data Sources

SectionDomestic SO2 emissions

SO₂ emissions embodied in domestic production of goods and services fordomestic consumption and export.

Domestic NOX emissions

Nitrogen oxides (NOX) emissions embodied in domestic production of goods
and commodities for domestic consumption and export.

EDGAR 6.1 (European
Commission Joint
Research Centre, 2019)

Domestic black carbon
emissionsBlack carbon emissions embodied in domestic production of goods andservices for domestic consumption and export.

Domestic GHG emissions

Greenhouse gas emissions (CO2, CH4, N2O, F-Gasses [HFCs, PFCs, SF6]) in
CO₂-equivalent embodied in domestic production for domestic consumption
and exports.

EDGAR 7.0 (European
Commission Joint
Research Centre, 2022)

CO2 emissions embodied infossil fuel exports

CO₂ emissions embodied in the exports of coal, gas, and oil. Calculated using
a 5-year average of fossil fuel exports and converting exports into theirequivalent CO₂ emissions. Exports for each fossil fuel are capped at thecountry's level of production.

UN Comtrade (UNDepartment of Economic
and Social Affairs, 2022)

Unprotected terrestrial
biodiversity sites

The mean percentage area of terrestrial Key Biodiversity Areas that is notcovered by protected areas and remains at risk.

Birdlife International(2022)

Unprotected freshwater
biodiversity sites

The mean percentage area of freshwater Key Biodiversity Areas that is notcovered by protected areas and remains at risk.

Birdlife International(2022)

Domestic land use related
biodiversity loss

Fraction of global species that are committed to extinction as a result ofdomestic anthropogenic land use for crops, pasture, and forestry, fordomestic consumption and export.

GLORIA

4.2.1

Domestic freshwater
biodiversity threats

Number of freshwater species threatened as a result of domestic productionof goods and services for domestic consumption and export.

Peterson et al. (2020)

4.2.2

Domestic deforestation

Three-year average tree cover loss due to urbanization, commodity
production, the forestry sector, and certain types of small-scale agriculture. It
does not include temporary loss due to wildfires.

The Sustainability
Consortium et al. (2022)

4.2.3

4.1

17

Indicator

Description

Input Data Sources

SectionDomestic export of CITESlisted terrestrial animals

Direct export of CITES-listed terrestrial and freshwater species, converted toWhole Organism Equivalents.

CITES Trade Database(UNEP-WCMC, 2022)

4.2.4

Red List Index of species
survival

The change in aggregate extinction risk across groups of species. The index isbased on genuine changes in the number of species in each category of
extinction risk on The IUCN Red List of Threatened Species.

IUCN (2022)

Biodiversity Habitat Index

Estimates the effects of habitat loss, degradation, and fragmentation on the
expected retention of terrestrial biodiversity. CSIRO calculates the BHI fromremote sensing data and other studies of ecological diversity. A score of 100
indicates that a country has experienced no habitat loss or degradation, and ascore of 0 indicates complete habitat loss.

CSIRO

Unprotected marine
biodiversity sites

The mean percentage area of marine Key Biodiversity Areas that is notcovered by protected areas and remains at risk.

Birdlife International(2022)

Domestic marine
biodiversity threats

Number of marine species threatened as a result of domestic production of
goods and services for domestic consumption and export.

Peterson et al. (2020)

4.2.2

Domestic export of CITESlisted marine animals

Direct export of CITES-listed marine species, converted to Whole Organism
Equivalents.

CITES Trade Database(UNEP-WCMC 2022)

4.2.4

Domestic vulnerable marine
animals

Catch of marine species within a country's EEZ classified as vulnerable (orunable to be classified due to insufficient reporting).

Sea Around Us

4.2.5

Fish caught fromoverexploited or collapsed
stocksThe percentage of a country’s total catch, within its EEZ, that is comprised of
species that are overexploited or collapsed.

Sea Around UsFish caught by trawling

The percentage of a country's total fish catch caught by trawling.

Sea Around Us

Sustainable Nitrogen
Management Index

The Sustainable Nitrogen Management Index is a one-dimensional ranking
score that combines two efficiency measures in crop production: Nitrogen
Use Efficiency and land use efficiency (crop yield).

Zhang Lab at Universityof Maryland

18

Indicator

Description

Input Data Sources

SectionDomestic nitrogen surplus

Excess nitrogen embodied in domestic production of crops for domestic
consumption and export

Vishwakarma et al.
(2022)

4.3.1

Domestic phosphorus
fertilizer

Phosphorus fertilizer applied to erodible soils embodied in domestic
production of goods and services for domestic consumption and export.

FAO (2022), IFA (2021)

4.3.2

Domestic scarce water
consumption

Volume of scarce water embodied in domestic production of goods andservices for domestic consumption and export.

GLORIA

4.4.1

Domestic water stress

Volume of water stress-weighted blue water use embodied in domestic
production of crops for domestic consumption and export.

GLORIA

4.4.2

19

Table 6. Spillover indicators in the 2022 GCS Index.
Indicator

Description

Input Data Sources

SectionSpillover SO2 emissions

SO₂ emissions occurring in foreign countries and embodied in domestic final
demand.

Spillover NOX emissions

Nitrogen oxides (NOX) emissions occurring in foreign countries and embodied
in domestic final demand.

EDGAR 5.0 (European
Commission Joint
Research Centre, 2016b)

Spillover black carbon
emissionsBlack carbon emissions occurring in foreign countries and embodied indomestic final demand.

Spillover GHG emissions

Greenhouse gas emissions (CO2, CH4, N2O, F-Gasses [HFCs, PFCs, SF6]) in
CO₂-equivalent occurring in foreign countries and embodied in domestic final
demand.

EDGAR 6.0 (European
Commission Joint
Research Centre, 2016a)

4.1

Spillover land use related
biodiversity loss

Fraction of global species that are committed to extinction as a result ofanthropogenic land use for crops, pasture and forestry in foreign countries,
embodied in domestic final demand.

GLORIA

4.2.1

Spillover freshwater
biodiversity threats

Number of freshwater species threatened as a result of imports of final
products for domestic final demand.

Peterson et al. (2020)

4.2.2

Spillover deforestation

Tree loss occurring in foreign countries and embodied in domestic final
demand (excluding wildfires and urbanization).

The Sustainability
Consortium et al. (2022)

4.2.3

Spillover CITES-listed
terrestrial animals

Final import of CITES-listed terrestrial and freshwater species, converted toWhole Organism Equivalents.

CITES Trade Database(UNEP-WCMC 2022)

4.2.4

Spillover marine
biodiversity threats

Number of marine species threatened as a result of imports of final products
for domestic final demand.

Peterson et al. (2020)

4.2.2

20

Indicator

Description

Input Data Sources

SectionSpillover CITES-listed
marine animals

Final import of CITES-listed marine species, converted to Whole Organism
Equivalents.

CITES Trade Database(UNEP-WCMC 2022)

4.2.4

Spillover vulnerable marine
animals

Catch of marine species by foreign fishing entities embodied in domestic final
demand and classified as vulnerable (or unable to be classified due toinsufficient reporting).

Sea Around Us

4.2.5

Spillover nitrogen surplus

Excess nitrogen from crop production occurring in foreign countries andembodied in domestic final demand.

Vishwakarma et al.
(2022)

4.3.1

Spillover phosphorus
fertilizer

Phosphorus fertilizer applied to erodible soils in foreign countries andembodied in domestic final demand.

FAO (2022), IFA (2021)

4.3.2

Spillover scarce water
consumption

Volume of scarce water occurring in foreign countries and embodied indomestic final demand.

GLORIA

4.4.1

Spillover water stress

Volume of water stress-weighted blue water use occurring in foreigncountries and embodied in domestic final demand.

GLORIA

4.4.2

21

3.2.3 Data gaps
Despite an extensive data search and expert consultations, there are many impacts to the Global
Commons for which appropriate metrics are unknown or unavailable to the research team. Some
datasets are excluded by our inclusion criteria (see Section 3.1 of this Technical Appendix), andsome are yet to be developed by scientific researchers. These persistent data gaps limit thecomprehensiveness of the 2022 GCS Index, and our results should be interpreted in light ofthese limitations. Future versions of the Index will work with the scientific community andnational and international organizations to close these gaps, with the following being of specialnote:● GHG Emissions○ CO2 fluxes from anthropogenic land use change, including those fluxes embodied in trade● Terrestrial Biodiversity Loss
○ Functional biodiversity loss
○ Loss of intact areas and wilderness, including those losses attributable to trade● Marine Biodiversity Loss
○ Fish stock depletion embodied in trade, including overfishing in international
waters○ Coastal pollution, especially of plastics, including those releases embodied intrade● Nutrient Cycles○ Hypoxia attributable to sources, including eutrophication embodied in trade● Water Cycle○ Water use disaggregated at the basin level○ Groundwater depletion, including embodied in trade● Stratospheric Ozone Depletion○ Unreported or illegal production of ozone depleting substances (ODS), including
those ODS embodied in trade○ Mitigation of ODS in existing products or temporary storage● Novel entities○ Toxic pesticides, including those embodied in trade● Physical flows of pollutants across country boundaries in air and water.

22

4. Methods for indicator development
While Tables 5 & 6 provide basic descriptions of the indicators and the sources for input data,
this section provides additional information about the calculation of metrics, including citationsof relevant documentation or additional steps needed to replicate our results.
For most of the Spillover indicators, we calculated the indicators using GLORIA Release 055(Lenzen et al., 2017, 2022) model following the methods described above in Section 2. Spillover
calculations. The technical documentation for the Sustainable Consumption and Production
Hotspots Analysis Tool (SCP-HAT Version 2.0) (Piñero et al., 2021) is rich in additional details
about data sources and how those datasets were applied to GLORIA.
A handful of Spillover indicators derived from other trade data:••

Nitrogen surplus, Freshwater biodiversity threats, and Marine biodiversity threats were
calculated with the Eora MRIO, and the values used in the 2022 GCS Index are the sameas those used in previous versions.
Indicators about CITES-listed animals did not involve an MRIO model.

234.1 GHG emissions
Our indicator for GHG emissions is expressed in CO2-eq. based on GWP100 using the IPCC
AR5 factors (Myhre et al., 2013). Table 7 provides the characterization factors, C (see Section 2.3
Matrix calculations), used in the conversion, with the first two columns corresponding to the
GLORIA satellite accounts.
Table 7. Characterization factors for Greenhouse gases based on 100-year Global
Warming Potential.
Lfd_Nr
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25

Sat_indicator
C4F8
C2F6
C3F8
C4F10
C5F12
C6F14
C7F16
CF4
HFC_23
HFC_32
HFC_43
HFC_125
HFC_134A
HFC_143A
HFC_152A
HFC_227EA
HFC_236FA
HFC_245FA
HFC_365MFC
NF3SF6
CO2 excluding short cycle
organic C
CO2 short cycle organic C
N2O
CH4

CO2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1

CH4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

N 2O
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

F-Gas
9540
11100
8900
9200
8550
7910
7820
6630
12690
705
1470
3450
1360
5080
148
3140
7680
880
810
15750
23500
0

Total
9540
11100
8900
9200
8550
7910
7820
6630
12690
705
1470
3450
1360
5080
148
3140
7680
880
810
15750
23500
1

1
0
0

0
0
28

0
265
0

0
0
0

1
265
28

24

4.2 Terrestrial and marine biodiversity loss
4.2.1 Land use biodiversity loss
We derive the domestic and spillover land use biodiversity loss indicators using the GLORIA
Release 055 model and the provided satellite extension. These data were based on modeled land
use per sector and the estimated potential species loss associated with each class of land use.
According to the satellite extension documentation for the Sustainable Consumption and
Production Hotspot Analysis Tool (SCP-HAT) (Piñero et al., 2021):
Following UNEP’s recommendations, country-level average characterization
factors for global species loss from Chaudhary et al. (2015) are used in the
SCP-HAT, aggregated over five taxa (mammals, reptiles, birds, amphibians,
and vascular plants) for each of the six land use categories. The unit of thisindicator is PDF*year which stands for the Potentially Disappeared Fractionof species for the duration of a year. Land use impact modelling assumes thatonce an activity (land use) stops, the system will slowly return to its naturalstate. The indicator therefore does not reflect full extinction of species but atemporary decline in biodiversity. (12)4.2.2 Freshwater & marine biodiversity threats
The domestic and spillover Freshwater and Marine biodiversity threats indicators were provided
as custom calculations by the authors of “The Ecological Cost of Consumption”, based on the
Eora MRIO (Peterson et al., 2020).
4.2.3 Deforestation (Pilot Indicator)
In the 2021 GCS Index we recognized ‘Forest cover loss embodied in trade’ as a data gap that
limited the comprehensiveness of our report. For the 2022 Index, we seek to close this gap by
developing a pilot spillover indicator and updating the domestic deforestation indicatoraccordingly. Global Forest Watch (GFW) uses satellite data to create spatial models of global tree
cover loss (Hansen et al., 2013; Weisse & Potapov, 2021). GFW states that “This data set
indicates the dominant driver of tree cover loss for 10 km grid cells, based on nearly 5,000
sample points and a model built on data for tree cover, tree cover loss, tree cover gain,
population density, land cover, and fires” (GFW, 2023). GFW attributes the tree cover loss toseveral drivers, following Curtis et al. (2018). GFW provided a dataset of tree cover loss bydominant drivers (The Sustainability Consortium et al., 2022). We are grateful to James
MacCarthy at GFW for his expert guidance on this indicator.
Table 8 describes the drivers of tree cover loss in the GFW data and different approaches toindicator construction. Only two of these drivers are considered permanent: commodity-driven
deforestation and urbanization. We did not include urbanization in our Spillover indicator due todifficulty in assigning it to economic sectors within GLORIA. Following Hoang & Kanemoto(2021), we also exclude wildfires from both versions of our indicators due to the difficulty in25

attributing fire to human activity (rather than natural causes) by Curtis et al. (2018). Shiftingagriculture and forestry are included in our indicator construction despite not always being
permanent. Hoang & Kanemoto (2021) argue that shifting-agriculture is the dominant driver inmuch of Africa as well as Central and South America, so excluding it would result in anunderestimate of those impacts. They also argue for including forestry since regrowth of
harvested forests in the long-term could pose challenges for climate neutrality targets on shortertime horizons.
Table 8. Drivers of tree cover loss.
DriverCommoditydriven
Urbanization
Shiftingagriculture

Forestry

Wildfire

GFW definition“Large-scale deforestation
linked primarily tocommercial agricultural
expansion.”“Deforestation for
expansion of urban
centers”“Temporary loss orpermanent deforestation
due to small- andmedium-scale agriculture”“Temporary loss fromplantation and naturalforest harvesting, withsome deforestation ofprimary forests.”“Temporary loss, does notinclude fire clearing for
agriculture.”

Hoang &
Kanemoto(2021)

Included in
Domestic
Indicator?

Included in
Spillover
Indicator?

X

X

X

X

X

X

X

X

X

X

X

For the spillover indicator, we assigned annual tree cover loss data from select drivers toeconomic sectors within GLORIA. Forestry-driven tree cover loss mapped to the “Forestry &Logging” sector. We summed tree cover loss attributed to commodities and shifting agriculture
then attributed this loss to GLORIA agricultural sectors in proportion to agricultural land use ineach country. This proportional allocation is a proxy for the actual allocation of tree cover loss
by economic sectors, and more detailed data about commodity-specific drivers could improve
the accuracy of this indicator. Further, commodity-driven tree cover loss also includes nonagricultural commodities, such as energy and mining, but these may be considered minor uses ofland. The implication of our mappings is that tree cover loss has been embodied in the goods
traded captured by the GLORIA sectors.
26

4.2.4 CITES-Protected animals (Pilot Indicators)
In the 2021 GCS Index we recognized trade in endangered species as a data gap that limited thecomprehensiveness of our report. For the 2022 Index, we seek to close this gap by developing
four pilot indicators (domestic and spillover, terrestrial and marine). In light of the repercussionsof global trade on biodiversity, the Convention on International Trade in Endangered Species ofWild Fauna and Flora (CITES) collects international trade data on endangered species and aims
to protect them from extinction, since trade is a threat to species survival (Wilcove, 2022).
CITES came into force in 1975 and became the largest multilateral agreement on regulating andconserving international trade for over 40,900 species and currently has a membership of 183
countries and the EU (CITES, 2023). We are grateful to Jessica Vitale, Kelly Malsch, and Katia
Sanchez-Ortiz at the UN Environment Programme World Conservation Monitoring Centre for
their expert guidance on these pilot indicators.
The CITES parties annually report volume of trade per species; countries of origin, export, andimport; and the source of traded organisms and their purposes at the destination. We assigned
Domestic impacts to the country of origin and Spillover impacts to the country of finaldestination. Note that this is unlike other Spillover indicators, which were analyzed through the
GLORIA MRIO model.
In the CITES database, wild animals (alive or dead) are included, while plants are excluded. The
basic unit of trade volume for these animals is Whole Organism Equivalents (WOE). According
to Harfoot et al. (2018), for example, one head, two ears, or four feet (from dead animals) would
each be counted as one WOE. The database only includes the following purposes of these
traded WOE: commercial purposes (Code T), personal possessions (Code P), hunting trophies(Code H), and unknown. We excluded animals sourced from captivity (not wild, Codes A, C, F,
and D).
Tracing the flows of WOE across countries required us to adjust reported data for cases of reexportation when there were three trade partners. We did not assign trade volumes from reexporters in our spillover indicator, as they are passthrough countries. To distinguish between
terrestrial and marine indicators (placed in different sub-pillars), we classified species in the
CITES database as terrestrial, marine, or both (50/50 split) using the World Register of Marine
Organisms (Ahyong et al., 2023) and Wikipedia.org. Landlocked countries were not considered
to be possible origins of marine species.
As pilot indicators, we are aware of several limitations in our approach to constructing metrics
around endangered species. Beyond the restriction to only covering animals and not plants, thedomestic indicator does not include endangered species that are sourced and purchased entirely
within the same country, i.e., not traded. Since the CITES database also includes data reported byboth importers and exporters, there is an opportunity for substantial disagreement between trade
partners. In cases where trade volumes did not agree, we used the maximum value. Traderelationships that involve more than three trade partners, i.e., multiple re-exportation, alsopresent a challenge when constructing our indicators, and further refinements could account for27

these situations. Accounting for all nodes in the supply chains for WOE is further frustrated by
the fact that the EU is a party to CITES and reports data as a bloc. Intra-EU trade, therefore, is
not fully captured in the database and is underreported. For the purposes of our spillover
indicator, we consider EU countries that import WOE as the final destination, even though thesecountries may re-export elsewhere in the EU. Additional limitations include reported trade that
could not be converted into WOE, and so were excluded, and the trade of products derived
from animals, such as skins for shoes, was also not able to be captured. Despite these problems,
we feel that our pilot indicators are still useful for understanding the negative environmental
impacts of trade in endangered species and point the way for future work.
4.2.5 Vulnerable marine animals (Pilot Indicators)
For the 2022 Index, we developed pilot domestic and spillover indicators for vulnerable marine
animals. Global data on the health of marine life and harvests come from the Sea Around Us(SAU) using methods described by Pauly et al. (2020). For these indicators, we focus on thosemarine animals (fish, crustaceans, and mollusks) that could be considered vulnerable by at least
one published criterion. Each country sets the catch policies within their EEZs and, theoretically,
could take action to reduce the catch of vulnerable taxa. We also include catches reported at sucha high taxonomic level that the vulnerability could not be determined. A country can also
improve its reporting practices to record fish catches at more granular taxonomic levels, which
would enable more accurate assessments of the vulnerability of those catches. We are grateful to
Maria L.D. Palomares for her expert guidance on these indicators.
We requested SAU data on the mass fish catch disaggregated by taxa, fishing entity (FE),exclusive economic zone (EEZ), and fishing category. The FEs and EEZs match with countries
and regions within GLORIA, and we assigned the taxa to either the “Fishing” or “Crustaceans &mollusks” GLORIA sectors. We limited our analysis to only fish catches classified by SAU as“Industrial” and “Artisanal.”
Developing these indicators also requires assigning a vulnerability status to each taxon. SAU
provided vulnerability criteria from FISHBASE and other sources merged with marine catch
data. Ultimately, we relied on four vulnerability metrics from the scientific literature and experts.

28

Resilience: “The capacity of a system to tolerate impacts without irreversible change in its
outputs or structure. In species or populations often understood as the capacity to withstandexploitation” (Froese et al., 2017)•••

Range:
Vulnerable:
Not Vulnerable:

Very Low – HighVery Low & Low
Medium & HighIUCN Red List: “Established in 1964, the International Union for Conservation of Nature’s
Red List of Threatened Species has evolved to become the world’s most comprehensive
information source on the global extinction risk status of animal, fungus and plant species”(IUCN, 2022)•••

Range:
Vulnerable:
Not Vulnerable:

1–8>1
Least Concern (1) or Data Deficient

Vulnerability to fishing: “[F]uzzy expert system that integrates life history and ecologicalcharacteristics of marine fishes to estimate their intrinsic vulnerability to fishing.” (Cheung et al.,
2005)
•
•
•

Range:
Vulnerable:
Not Vulnerable:

10–90
≥25
<25

Vulnerability to climate change: “[S]pecies-specific estimates of exposure, and ecological andbiological traits to undertake an assessment of vulnerability (sensitivity and adaptive capacity)and risk of impacts (combining exposure to hazards and vulnerability) of climate change(including ocean acidification) for global marine fishes and invertebrates.” (Jones & Cheung,
2018)
•
•
•

Range:
Vulnerable:
Not Vulnerable:

0–100
≥25
<25

If vulnerability had been assessed, we classified a taxon as ‘vulnerable’ if it met any of thevulnerability criteria and ‘not vulnerable’ if it met none of the criteria. If vulnerability for a taxon
had not been assessed – of if the level of taxonomic reporting was too high to clearly identify
the vulnerability – we created a complex decision tree to assign ‘vulnerable’ or ‘not vulnerable’
status based on the taxonomic level reported and whether the vulnerability status could be
inferred from other species in the genus or family. We chose to double the impact of the catch
reported at the highest taxonomic level, such as the generic “marine species not elsewhere
specified,” as a penalty for under-reporting.
29

With these data, we created domestic and spillover indicators. The Domestic indicator is a sumof the mass of vulnerable taxa caught within each country’s EEZs. The Spillover indicator is thesum of the mass of vulnerable taxa caught due to final demand in a country, caught by otherFEs. Unlike other data on fisheries, the proportional versions of these indicators aredenominated on a per capita basis.
As pilot indicator, we are aware of the limitations of our metrics and opportunities for further
refinement. Our data do not include catches on the high seas (only those within EEZs), bycatch,or discards. Vulnerability criteria are not available for all marine species; funding should beallocated for research to enable these vulnerability assessments. Additional vulnerability criteria
also may be appropriate for assessing taxa, such as sustainability yield level, though such criteria
may be more complicated to apply due to spatial variability. An alternative perspective on thedefinition of the Spillover indicator would be to distinguish catch occurring in other countries’
EEZs to satisfy a country’s final demand, regardless of the FE. Future work on these indicators
will yield more useful and accurate metrics to guide the protection of marine commons.
4.3 Nutrient cycle disruptions
4.3.1 Nitrogen surplus
Disaggregated nitrogen surplus (N surplus, or Soil Nutrient Balance) data were provided by theauthors of Vishwakarma et al. (2022), which covered 218 countries, 170 crop types, and 55 years
through 2015. Negative values in this dataset represent extraction of N from the soil; We set
these negative values to zero in our calculations so as not to incidentally give a country a benefit
for this activity. Crop types in these data were mapped to the crop sectors in Eora, which vary bycountry. The domestic indicator is simply the sum of N surplus across all crop types. The
spillover indicator derive from using the N surplus per country – crop sector and running theEora MRIO model. Given the wide variation of annual estimates for N surplus across datasets(Zhang et al., 2021), we elected to not update these indicators from the 2021 GCS Index versions
until high quality disaggregated updates are available.
4.3.2 Phosphorus fertilizer
Phosphorus (P) fertilizer entering waterways can cause freshwater eutrophication and ocean
anoxia. These impacts point to the recognition of disruptions to the phosphorus cycle as aPlanetary Boundary, especially through the mining of phosphorus to fertilize soils (Steffen et al.,2015).

30

Four datasets provide the foundation for estimating the annual P2O5 fertilizer applied per
country:
1. UN Food and Agriculture Organization (FAO) data on annual P2O5 consumption per
country (FAO, 2022)
2. International Fertilizer Association (IFA) data on annual P2O5 consumption per country(IFA, 2021)
3. IFA timeseries data on fertilizer use by crop (FUBC) (Ludemann et al., 2022)
4. GLORIA satellite extension on land use per sector
Our domestic indicator is a simple composite of the annual P2O5 consumption per country, as
found in either Source (1) or, if missing, Source (2). For the spillover indicator, we distributed
the country-level consumption of phosphorus by mapping the crop groups in Source (3) acrossthe economic sectors in Source (4). Over the period 2014–2018, we calculated the sum of P2O5
per GLORIA crop sector. We allocated any fertilizer use for “Grassland” to the GLORIA
sectors based on the proportion of “Pasture land” in the GLORIA land use satellite extension
for each country. We then normalized the fertilizer use across all GLORIA sectors by dividing by
the IFA FUBC total to find the percent of P2O5 per GLORIA sector, country, and year. We
interpolated and extrapolated data linearly for missing years. We assigned the mean percent foreach GLORIA sector per ISO subregion to countries not present in the IFA FUBC dataset.
Finally, we multiplied these percents by the annual P2O5 consumption per country to arrive at theannual P2O5 consumption per country and GLORIA sector.
4.4 Water cycle disruptions
Both water cycle disruption indicators are based on the modeled ‘Blue water consumption’satellite extension data provided with GLORIA Release 055. The indicators use the sum of‘Agriculture’ and ‘Non-Agriculture’ blue water consumption. According to the satellite extension
documentation for the Sustainable Consumption and Production Hotspot Analysis Tool (SCPHAT) (Piñero et al., 2021):
Blue water is defined as water stemming from surface water sources (e.g.,rivers or lakes) or groundwater bodies. Water consumption is defined as thedifference between overall water withdrawals and direct return flows. Bluewater consumption hence encompasses water withdrawn from surface water
sources or groundwater bodies that is either incorporated into products orevaporated during the growth period of a crop or the production process of agood. (15)The difference between the two sets of indicators described below is the choice of country-level
characterization factors applied to convert from consumption to scarce water or water stress. It
is important to multiply these characterization factors to each country’s blue water consumption
data prior to performing the spillover MRIO calculations.
31

4.4.1 Scarce water consumption
These indicators were derived mimicking the approach established by Lenzen et al. (2013):
In order to incorporate water scarcity into the virtual water flow calculus weconstruct a new satellite account where water use entries are weighted so thatthey reflect the scarcity of the water being used. As a weight we choose ameasure of water withdrawals as a percentage of the existing local renewablefreshwater resources. We use the Water Scarcity Index for converting total
water use into scarce water use. Global data for this measure are provided by
the [FAO AQUASTAT]. According to the FAO, “this parameter is anindication of the pressure on the renewable water resources”. Note that we
use resource and scarcity information only as an input into a weighting
procedure, and that we do not determine water stress or water scarcity as aresult of our calculations. (80)
The characterization factors here were based on the equivalent FAO AQUASTAT indicator forpressure on water resources: “MDG 7.5 Freshwater withdrawal as percent of total renewable
water resources” (FAO, 2020) since the Water Scarcity Index data is no longer available. Data
were linearly interpolated for countries lacking data points for specific years.
4.4.2 Water stress
The Water stress indicators were derived using the GLORIA Release 055 model and its
precalculated satellite extensions (Piñero et al., 2021). These satellite extensions incorporate the
Available WAter Remaining (AWARE) water stress characterization factors (Boulay et al., 2018),
which “represent the relative Available WAter REmaining per area in a watershed, after thedemand of humans and aquatic ecosystems has been met.”32

5. Country coverage
The GCS Index seeks to provide information on the widest set of countries for which anassessment of impacts to the Global Commons would be useful. The major constraint on
country coverage is data availability, especially the trade data that underpins our spillover
indicators. GLORIA Release 055 expands the coverage in the 2022 GCS Index to 145 countries
and the European Union, for a total of 146 entities.
Albania

Colombia

Hungary

Moldova

Singapore

Algeria

Congo, Dem. Rep.

IcelandMongolia

Slovakia

AngolaCongo, Rep.

India

MoroccoSlovenia

Argentina

Costa Rica

Indonesia

MozambiqueSomalia

Armenia

Cote d'Ivoire

Iran

MyanmarSouth Africa

Australia

Croatia

Iraq

Namibia

Spain

Austria

Cuba

Ireland

Nepal

Sri Lanka

Azerbaijan

CyprusIsraelNetherlands

SwedenBahrainCzechia

Italy

New Zealand

Switzerland

Bangladesh

Denmark

Jamaica

Nicaragua

Tajikistan

Belarus

Dom. Rep.

Japan

Niger

Tanzania

Belgium

EcuadorJordanNigeria

Thailand

BelizeEgypt

Kazakhstan

N. Macedonia

Togo

Benin

El Salvador

Kenya

NorwayTunisia

BhutanEritreaSouth Korea

Oman

TurkeyBolivia

Estonia

KuwaitPakistan

UgandaBosnia & Herz.

Ethiopia

Kyrgyzstan

PanamaUkraineBotswana

EU27

Laos

Papua New Guinea

UAE

BrazilFinland

LatviaParaguay

United Kingdom

BruneiFranceLebanonPeru

United States

Bulgaria

Gabon

Liberia

Philippines

Uruguay

Burkina Faso

GambiaLithuania

PolandUzbekistan

BurundiGeorgia

Luxembourg

Portugal

Venezuela

Cambodia

Germany

Madagascar

Qatar

VietnamCameroon

Ghana

MalawiRomania

ZambiaCanadaGreeceMalaysia

RussiaZimbabwe

Cen. Afr. Rep.

GuatemalaMali

RwandaChad

GuineaMalta

Saudi Arabia

Chile

Haiti

Mauritania

SenegalChina

Honduras

MexicoSierra Leone33

6. Indicator construction
6.1 Standardization
We present the indicators in two forms: proportional and absolute. Proportional indicators are
standardized to allow cross-country comparison, regardless of country size. We standardize mostmetrics by population rather than GDP. Population sizes tend to be more stable over time, andthe MRIO databases from which the CBA indicators are calculated with GDP as a denominator.
Absolute indicators present unstandardized metrics of environmental impacts. While the proportional indicators emphasize that governments and citizens in small countries can strengthen policies and actions for sustainable development, the absolute indicators emphasize the efforts andleadership needed from large countries who have the greatest global impacts. This two-track
approach reflects the growing trend in the field of industrial ecology, where researchers tend topresent both per capita and absolute results in peer-reviewed papers (e.g., Lenzen et al., 2018).
6.2 Rescaling
To make the data comparable across indicators, we rescale each variable between 1 and 100, with
1 being the lowest bound denoting worst impacts and 100 denoting thresholds met or surpassed.
We truncate each dataset so that all countries exceeding the threshold score no more than 100
and all countries falling below the lowest bound score 1.
We select the sustainability thresholds, or upper bounds, using a decision tree reflecting theapproach used by the SDSN (Sachs et al., 2023) and the OECD (2019, Table 3.1) to compute
distance to SDG targets (see Figure 3). Optimally, sustainability thresholds set for each indicator
should be based on international agreements such as the SDGs and Paris Climate Change
Agreement. When such a target is not available, we rely on scientific input and expert judgment.
Finally, if neither of these two options are available, the upper bound is based on the average of
top performing countries. A public consultation was organized in October 2022 to collect feedback from experts on the indicator selection, the quality of the data, the methodological choices
and sustainability thresholds assigned, and the potential uses of this work. We welcome further
comments and feedback at GCSIndex@unsdsn.org.
International
Agreements• 1stScientific
Research• 2nd
Empirical
Performance• 3rd

Figure 3. Hierarchy of sources for thresholds.
34

Three indicators illustrate this decision tree for threshold selection. First, international agreements, especially the Paris Climate Change Agreement, seek to limit global warming to below
1.5°C. Meeting this goal requires limiting per capita emissions of GHG to 2.0–2.5 tonnes CO2eq. by 2030 (UNEP, 2020). Considering the upper end of that range as a generous target, we set
our 2050 threshold two decades later at the lower end: 2 tonnes CO2-eq. per capita. Second, theSustainable Nitrogen Management Index (Zhang et al., 2022) is a relatively novel indicator, andtherefore unlikely to have yet been incorporated into international agreements. Based upon the
logic of the underlying scientific research, the optimal value on a unitless scale is zero. Third, neither international agreements nor scientific research offers strict guidance on the target for water
stress from crops. For this indicator, we selected the 2.5th-percentile of all observed values as thethreshold.
We rescale all indicators using a distance-to-target technique described by the following equation.
Indicator Score = (X – L) / (U – L) × 99 + 1
where X is a raw data value and U and L denote the upper and lower bounds, respectively. Our
selection of bounds ensures that for all rescaled variables, higher values indicate better mitigationof impacts on the global commons (Table 9). Thus, a country that scores 50 on an indicator ishalfway toward achieving the optimum value. A country with a score of 75 has covered threequarters of the distance from the lower to the upper bound.
6.3 Transformation
One of the best practices in composite indexing is inspecting metrics for skewness and mitigating its effects. Skewed data are distributed with many countries at one end of the spectrum ofvalues and fewer countries spread into the opposite end. Such datasets pose problems on both
empirical and theoretical grounds. When thresholds are selected by percentiles (see previous section), outliers can bias the scale by drawing thresholds into extreme ranges (see Nardo et al.,2008). Aggregating skewed datasets can also give undue emphasis to indicators with relatively
small variation in scores among the clustered observations. On theoretical grounds, skewness
also obscures differentiation between nations, reducing the usefulness of the indicator (seeWendling et al., 2020, pp. 171–172). Based upon statistical examination of our underlying datasets, it is warranted to transform certain indicators through a natural logarithmic normalization,
namely, all indicators used in the GHG Emissions and Water Cycle sub-pillars, within both theDomestic and Spillover pillars. Data were transformed prior to scaling, and the thresholds were
likewise also transformed.

35

Table 9. Thresholds used to score indicators in the 2022 GCS Index on a 1–100 scale.

Lower
Bound

Unit

Absolute
Upper
Bound

0.87 a64.39 eGg

1.10 a7,551 fkg/capita

0.72 a26.04 fGg

8.33 a2,079 fDomestic NOX emissions

kg/capita

4.89 a53.27 eGg15.37 a9,768 fSpillover NOX emissions

kg/capita

0.634 a26.36 dGg

7.67 a2,311 fDomestic black carbon emissions

kg/capita

0.097 a1.19 cGg

0.13 a233.76 f

Spillover black carbon emissions

kg/capita

0.0279 a

1.00 fGg

0.24 a100.91 f

Domestic GHG emissionst CO2e/capita

2 h25.66 eTg

3.84 a3,722 fSpillover GHG emissionst CO2e/capita

0.41 a13.87 fTg

4.91 a1,614 f

CO2 emissions embodied in fossil fuel exports

t CO2e/capita

8.09E-09 i

43.15 eTg

1.55E-07 a

960.60 f

Unprotected terrestrial biodiversity sites%

2.33 a100 j%

2.33 a100 j

Unprotected freshwater biodiversity sites%

4.17 a100 j%

4.17 a100 j

Domestic land use related biodiversity loss

global PDF/capita 1.82E-14 a

7.44E-11 e

global PDF

3.53E-08 a

4.73E-03 f

Spillover land use related biodiversity loss

global PDF/capita 5.12E-13 a

1.70E-11 e

global PDF

6.52E-06 a

3.43E-03 f

Domestic freshwater biodiversity threats

per million people 2.00E-03 a

5.36 dspecies

5.57E-03 a

256.62 f

Spillover freshwater biodiversity threats

per million people 2.21E-03 a

0.85 especies

1.18E-02 a

120.55 f

Domestic deforestation%

9.86E-06 i

1.32 ehectares0.39 i

1,333,792 f

Spillover deforestation

ha/capita

1.45E-04 a

6.89E-03 e

hectares2,111.82 a

996,935 f

Red List Index of species survival

scale 0–1

0.9885 g

0.67 bscale 0–1

0.99 g0.67 b

Biodiversity Habitat Index

scale 0–1

0.9885 g

0.29 ascale 0–1

0.99 g0.29 aDomestic export of CITES terrestrial animals

WOE/million

7.74E-09 i

9.50E-03 f

WOE

0.33 i

762,807 f

Spillover CITES terrestrial animals

WOE/capita

3.66E-09 i

8.46E-03 e

WOE

2 i

852,233 f

Indicator

Unit

Domestic SO2 emissions

kg/capita

Spillover SO2 emissions

Proportional
Upper
Bound

Lower
Bound

36

Proportional
Upper
Bound

Lower
Bound

Unit

Absolute
Upper
Bound

Lower
Bound

Indicator

Unit

Domestic export of CITES marine animals

WOE/million

9.50E-08 i

2.91E-03 f

WOE

5 i

194,728 f

Spillover CITES marine animals

WOE/capita

1.74E-08 i

1.55E-03 f

WOE

1 i

110,924 f

Unprotected marine biodiversity sites%

3.40E-04 i

100 j

%

3.40E-04 i

100 j

Domestic marine biodiversity threats

per million people 6.11E-03 a

8.06 fspecies

2.94E-02 a

272.66 f

Spillover marine biodiversity threats

per million people 2.79E-04 a

1.01 fspecies

5.21E-03 a

122.50 f

Fish caught from overexploited or collapsed stocks %0.04 i62.0 e%0.04 i62.0 eFish caught by trawling%

0.17 i60.5 e%0.17 i60.5 eDomestic vulnerable fisheries catch

tonnes/capita

0.09 a169.07 eTg

0.0008 a

12.48 fSpillover vulnerable fisheries catch

tonnes/capita

0.19 a73.12 etonnes

0.0056 a

6.04 fSustainable Nitrogen Management Index

scale 0–1.4

0.0115 i

1.15 fscale 0–1.4

0.0115 i

1.15 fDomestic nitrogen surplus

kg/capita

0.48 b

34.84 dGg

0.22 a7,376 fSpillover nitrogen surplus

kg/capita

0.12 a22.35 eTg

2.54 a1,672 fDomestic phosphorus fertilizer

kg/capita

0.75 b30.49 dkt

6.10 a6,968 fSpillover phosphorus fertilizer

g/capita

0.37 a9.06 ekt

3.03 a1,222 fDomestic scarce water consumption

m3/capita

0.035 a233.29 e

Mm³

0.019 a40,822 f

Spillover scarce water consumption

m3/capita

5.06 a227.22 f

Mm³

34.33 a14,145 f

Domestic water stressML/capita

0.0072 a

15.51 eBm³

0.0063 a4,231 fSpillover water stressm3/capita

0.199 a9.14 eMm³

1.91 a564.13 f

Note: PDF = potentially disappeared fraction of species; WOE = whole organism equivalent
Rationale for bounds: a = 2.5th-percentile, b = 5th-percentile, c = 90th-percentile, d = 92.5th-percentile, e = 95th-percentile, f = 97.5th-percentile, g= expert judgment, h = international target, i = minimum non-zero observation, j = technical bound (all percentiles adjusted for outliers).

37

7. Weighting & Aggregation
Aggregating individual indicator scores to the levels of sub-pillars and pillars requires two
choices. The first choice is to select how to weight each subcomponent. Within the sub-pillars,
we weight each indicator equally, for the sake of simplicity. Within each pillar, we give thegreatest weight, 75% of the total, to GHG Emissions, in recognition of the urgent importanceof this impact on the Global Commons. The balance of the weight is distributed equally acrossthe remaining five sub-pillars.
The second choice is the method of aggregation. While we used arithmetic means for the Pilot
GCS Index, our subsequent analysis (Wendling et al., 2021) showed that this method allows
countries to balance low scores on some indicators with high scores in others. This effect would
be warranted if the indicators are compensatory, but on both theoretical and empirical grounds,
this assumption is not warranted. Therefore, we use geometric means in the 2022 GCS Index, amethod that results in a steeper penalty for low scores in any of the indicators.

8. Missing data
Data for our indicators may be missing for two reasons. First, data may not be applicable for acountry due to its geography. For example, landlocked countries will have no indicators on
marine resources. In these cases, no score is recorded, and the indicator and sub-pillar will
receive no weight in the aggregation step. For spillover scores, however, even landlocked countries may import environmental impacts to Marine Biodiversity Loss, and so no such materiality
filter is applied to these spillover indicators. Second, our data sources may fail to report relevant
data. In these cases, missing values are imputed using the population-weighted means of theother countries in a given region. The only exception is the European Union: When observations
are not available from, or computable from, original datasets, missing values are calculated fromthe population-weighted averages of the member states. We note here that there are someindicators for which we do not yet have absolute values. In these cases, the proportional values
are used in both the absolute and proportional versions.

38

9. Dashboards
For ease of communicating the results, we provide a dashboard color for each score and foroverall impacts. These colors classify countries on an ordinal scale by how seriously they are
impacting the Global Commons (see Table 10).
Table 10. Legend for dashboard categories and trajectories in the 2022 GCS Index.
Dashboard Impacts on the
GlobalScore
Commons
95–100None or limited

Arrow Meaning

90–95

Low

↑

Projected to meet 2050 Threshold

80–90

Medium-low↗

Projected to meet only 2030 Threshold

70–80

Medium-high→Insufficient progress toward threshold

50–70

High

↓

Trajectory headed in wrong direction

30–50

Very High

0–30

Extreme10. Trajectories
Because the 2022 GCS Index uses time-bound thresholds, this report includes an assessment of
the trajectories of countries’ impacts. The scores provide a snapshot of the level of impacts
based on the most recent year of data, but we also calculate an annual average growth rate overthe past five years of data. Projecting these growth rates into the future, we can determine
whether countries are on- or off-track to meet sustainability threshold. We classify impacts into
four categories (Table 10).
Interim thresholds are based on aggregate global impacts in proportional terms. Given the mostrecent levels of impact, we calculate the average annual growth rate for the world as a whole tomeet the 2050 threshold. The interim threshold is then the projected value in the year 2030,
which coïncides with the target date for many of the SDGs.
Even if a country is currently meeting the interim or 2050 threshold, we categorize theirtrajectory as being off-track if it is trending in the wrong direction. This would allow a country
with a “green” dashboard to still receive a ↓ trajectory. We aggregate trajectories from indicators
to higher levels using the methods described in Sachs et al. (Sachs et al., 2019, pp. 46–47).

39

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