Redefining Development through Logistics performance and ESG metrics

This dataset was created to empirically test the hypothesis that logistics performance, environmental sustainability, governance quality, and sustainable development progress are interdependent drivers of economic output, specifically GDP per capita. The data is used in the paper “Redefining Development through Logistics Performance and ESG Metrics.”

Research Hypothesis:
ESG-related metrics—specifically the Environmental Performance Index (EPI), Sustainable Development Goals Index (SDG), and Worldwide Governance Indicators (WGI)—have a significant and positive influence on national logistics performance (LPI).

Logistics performance, when combined with ESG indicators, explains a significant portion of cross-country variation in GDP per capita.

Data Contents:
The dataset includes normalized, cross-sectional data from 123 countries, covering the following variables:

Logistics Performance Index (LPI) and its six components: customs (EFF), infrastructure (QUAL), international shipments (EASE), logistics quality (COM), tracking (TR), and timeliness (FREQ).

Environmental Performance Index (EPI) – 2024 version from Yale University

Sustainable Development Goals Index (SDG) – 2024 global dataset

Worldwide Governance Indicators (WGI) – mean and six sub-indicators from the World Bank (2023)

GDP per capita (in current USD) and its natural log transformation (lnGDP)

All variables were normalized to a common scale (0–100) for comparability. WGI indicators were rescaled from their original –2.5 to +2.5 range.

Notable Findings:
All ESG indicators were significantly and positively associated with logistics performance (LPI), with WGI exerting the strongest influence.

The combination of LPI, EPI, SDG, and WGI explained 81.7% of the variance in GDP per capita across countries.

Governance quality (WGI) was the strongest predictor of both LPI and GDP.

Fuzzy Cognitive Mapping (FCM) simulations showed that improving governance and environmental metrics creates positive feedback loops in logistics and economic outcomes.

Data Collection Sources:
World Bank (2023): LPI and WGI data

Yale & Columbia University (2024): Environmental Performance Index (EPI)

Dublin University (Sachs, Lafortune, Fuller) (2024): SDG Index

World Bank Open Data: GDP per capita (2023)

Use and Interpretation:
Variables are standardized (0–100); higher values always indicate better performance.

The data can be used for replication, policy simulation, comparative country analysis, and machine learning on development outcomes.

Analysts can perform linear regression, correlation, or fuzzy systems modeling using this dataset.

Recommended tools: SPSS, R, Python (Pandas), or Mental Modeler (for FCM simulation