Benchmarking international performance on climate change mitigation
: an application of Data Envelopment Analysis (DEA)

  • Andreia Filipa Lima Borges (Student)

Student thesis: Master's Thesis


Since the Industrial Revolution, large amounts of greenhouse gases (GHGs) have been released to the atmosphere which led to global warming and climate change. Despite the efforts from nations to limit the temperature rise to 1.5 °C, as defined in the Paris Agreement (2015), if emissions do not half until 2030, it is likely to achieve a global warming of 2.7 °C by the end of the century. Thus, the assessment of environmental performance become crucial. The objective of this thesis is, then, to measure and compare the environmental efficiency at the country level, over the period 2000-2018, being its main contribution to overcome the lack of literature studies with a global scope. To answer the research questions (How can countries be ranked in terms of their performance? What have been the best and worst performing over time?), a DEA methodology (additive model) was employed. DEA has become a wellestablished tool to judge the relative efficiency in the environmental field. A clustering analysis was also carried out to distinguish countries based on their proximity-to-target value (%), in 2018. The DEA model includes three inputs (population, energy use and GHGs emissions) and two outputs (GDP and renewables). The population and GDP are non-discretionary variables. Regarding the main findings, globally, countries have become more efficient over time. Bhutan, Kiribati, Norway, Nepal and Iceland have been the efficient countries that appear more times in the reference set of other countries, being an example of best practices. In 2018, the poorest 5 performing countries were Russia, followed by Iran, Saudi Korea, Saudi Arabia, and South Africa, being all inefficient since 2000. Despite being inefficient during most of the years, China, United States and India significantly improved their performance which was mainly explained by their higher consumption of renewables.
Date of Award13 Jul 2022
Original languageEnglish
Awarding Institution
  • Universidade Católica Portuguesa
SupervisorMaria Silva (Supervisor) & Alexandra Leitão (Co-Supervisor)


  • Climate change
  • Environmental performance
  • Data envelopment analysis (DEA)
  • Additive models
  • Efficiency analysis
  • Benchmarking
  • Clustering analysis


  • Mestrado em Gestão

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