Degree

Master of Science in Economics

Faculty / School

School of Economics and Social Sciences (SESS)

Department

Department of Economics

Date of Submission

2026-09-30

Supervisor

Dr. Heman Das Lohano, Professor and Program Director BS (Economics and EM), Department of Economics

Project Type

MSECO Research Project

Access Type

Restricted Access

Keywords

Technical efficiency, Climate justice, Data Envelopment Analysis, Renewable  energy, Panel data analysis

Abstract

This study examines the technical efficiency of countries in converting key economic inputs into output. Using a balanced panel of 134 countries for the period 2000–2019 in the DEA stage, the analysis applies Data Envelopment Analysis (DEA) under a Variable Returns to Scale (VRS) framework for estimating technical efficiency scores, incorporating CO2-equivalent emissions as an undesirable by-product. The results reveal notable variation in technical efficiency across countries and country groups, reflecting differences in resource utilization, economic structure, and environmental performance. A comparative assessment across income, economies, and geopolitical classifications highlights systematic differences in technical efficiency levels and output-emissions performance. To further examine these patterns, panel data regression methods are used to examine the association between technical efficiency and selected economic, human development, institutional, environmental, and demographic variables. The findings indicate that GDP per capita, Human Development Index, government effectiveness, renewable energy consumption share, and energy use per capita are positively associated with variations in technical efficiency across countries. In contrast, urbanization rate and land area per capita show a negative association with technical efficiency. Overall, the study provides a consistent empirical assessment of cross-country technical efficiency and its determinants, offering evidence on how countries differ in their ability to utilize resources efficiently in the presence of environmental constraints. The second-stage regressions use an unbalanced sample of 130 countries because some explanatory variables are not reported for every country-year observation.

Pages

x, 59

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