Methodology

In the SFT Index formulation, data from international sources are used to generate internationally comparable scores and thus the ranks. For SFT analysis, users can select appropriate comparators sharing similar features and challenges. 

Twenty indicators are selected from international data sources to cover the three pillars of the SFT (See the table below  for further information). These indicators are aggregated into overall scores and the sustainability scores in three pillars of SFT (i.e., economic, social, and environmental pillars). 

Six steps in the process of creating the SFT Index
  1. Remove economies that have data for less than or equal to 12 indicators (60 per cent of all indicators). After the removal, a certain number of economies are included in the calculation below.
  2. Some indicators (e.g., labour productivity) are converted to a logarithmic scale. This conversion can be justified by the existence of diminishing returns to scale of these indicators for sustainability objectives.
  3. For some indicators (e.g., time in ports), extreme values are censored, with lower and upper bounds determined by 5th and 95th percentiles. All values below the lower bound are replaced by the lower bound, and values above the upper bound are replaced by the upper bound.
  4. As the twenty indicators have different units and scales, they are rescaled to ensure comparability across indicators, using min-max normalization, i.e., x^'=(x-min⁡(x))/(max⁡(x)-min⁡(x)). The normalized indicators take values from 0 (i.e., the lowest value in the world) to 1 (i.e., the highest value in the world). 
  5. For some indicators (e.g., road injury death rate), the normalized indicators are inverted, i.e., x^''=1-x^', to ensure that low values indicate poor performance and that high values mean good performance. 
  6. The normalized indicators are aggregated by the following three steps: 
        i. Normalized indicators are aggregated within SFT categories (e.g., infrastructure, transport productivity) with equal weight on each indicator. This gives scores at the SFT category level; 
        ii. The scores at the SFT category level are aggregated within the SFT three pillars (i.e., economic, social, and environmental) with equal weight on each SFT category. This gives scores at the SFT pillar level. Before the aggregation, if scores at the SFT category are missing for a particular economy, the missing values are replaced by the regional average;
        iii. The scores at the SFT pillar level are aggregated to the overall score. 
Limitations

As indicated by the list of indicators below, the coverage of the internationally comparable indicators is relatively limited. Such indicators do not exist for many social and environmental pillars. Further, some indicators are only available for the entire transport sector (e.g., labour productivity in the transport and storage sector). Also, some other indicators only cover a specific transport mode (e.g., road injury death rate). 

To overcome these limitations, information from local sources such as existing policy documents and studies as well as stakeholder surveys and interviews will be relied upon to complement the SFT Index in UNCTAD SFT rapid assessment.

SFT categoriesIndicatorsData sourceReference yearTransformationAvailable economies
Economic pillar
InfrastructureRoad density (km/km2 of land area)International Road Federation, World Road Statistics Data WarehouseLatest available year from 2015-2021log156
Road paved ratio (%) 128
LPI – Infrastructure scoreWorld Bank, Logistics Performance Index2023 139
Transport productivityLabour productivity of the transport and storage sector (value added per employment)UNSD, National Accounts Official Country Data; UNSD, National Accounts Main Aggregates Database; OECD, Detailed Tables of National Accounts; ILO, Labour Force Statistics Database;Average over 2015-2021log118
Labour productivity of road freight transport (ton-kilometres per employment)UNSD, SDG Global Database – Indicator 9.1.2 Freight volume by mode of transport; ILO, Labour Force Statistics DatabaseAverage over 2016-2019log, censored106
Time in ports (all ships)UNCTAD, Port call and performance statistics2019inverse, censored180
Quality and reliabilityLPI - Timeliness scoreWorld Bank, Logistics Performance Index2023 139
LPI – Logistics competence and quality score 
Transport costsTransport cost from warehouse to ports or land borderWorld Bank, Doing Business (legacy) - Trading across borders - Domestic transport2020log, inverse, censored187
Transport cost from ports or land border to warehouse
LPI – International shipments scoreWorld Bank, Logistics Performance Index2023 139
ConnectivityLiner Shipping Connectivity Index (LSCI)UNCTAD, Liner Shipping Connectivity Index2023log, censored176
Social pillar
SafetyRoad injury death rateIHME, Global Health Data2019log, inverse, censored203
  Exchange – Global Burden of Disease Study 2019   
AccessibilityRural access index (RAI)World Bank, Rural access index2019censored204
EmploymentAverage wage (i.e., monthly earnings of employees) in the transport and storage sector (purchasing power parity)ILO, Wages and Working Time Statistics DatabaseAverage over 2016-2019log, censored114
Gender equalityFemale employment share in the transport and storage sector (female employment / total employment)ILO, Labour Force Statistics DatabaseAverage over 2016-2019censored152
 Female wage (i.e., monthly earnings of employees) ratio in the transport and storage sector (female wage / male wage)ILO, Wages and Working Time Statistics DatabaseAverage over 2016-2019log, censored106
Noise pollutionNoise levelNoise-Planet, NoiseCapture (1)Average over 2016-2023inverse, censored211
Environmental pillar
Climate mitigationGHG emission intensity from the transport sector (GHG emission/value added)Emissions Database for Global Atmospheric Research (EDGAR) v7.0 (2) ; UNSD, National Accounts Main Aggregates DatabaseAverage over 2016-2021inverse, censored193
Air pollutionPM2.5 emission intensity from the transport sector (PM 2.5 emission/value added)Emissions Database for Global Atmospheric Research (EDGAR) v6.1, Global Air Pollutant Emissions (3) ; UNSD, National Accounts Main Aggregates DatabaseAverage over 2016-2018inverse, censored192

Source: UNCTAD Secretariat based on respective data sources indicated in the table.
Note: In the transformation column, “log” indicates log transformation on the original series and “censored” indicates censoring of extreme values. “Inverse” means rescaled scores are inverted.

(1) Bocher E, Petit G, Picaut J, Fortin N and Guillaume G (2017). Collaborative noise data collected from smartphones. Data in Brief. 14498–503.
 
(2) European Commission, Joint Research Centre (JRC) and International Energy Agency (IEA) (2022a). EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database (a collaboration between the European Commission, Joint Research Centre (JRC), the International Energy Agency (IEA), and comprising IEA-EDGAR CO2, EDGAR CH4, EDGAR N2O, EDGAR F-GASES version 7.0. Available at https://edgar.jrc.ec.europa.eu/dataset_ghg70.

(3) European Commission, Joint Research Centre (JRC) and International Energy Agency (IEA) (2022b). EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database (a collaboration between the European Commission, Joint Research Centre (JRC), the International Energy Agency (IEA), and comprising IEA-EDGAR CO2, EDGAR CH4, EDGAR N2O, EDGAR F-GASES version 6.1 October. Available at https://edgar.jrc.ec.europa.eu/dataset_ap61.