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

Authors: Borges, Andreia Filipa Lima;

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

Abstract

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. Desde a Revolução Industrial, elevadas quantidades de gases de efeito de estufa (GEE) têm sido libertados para a atmosfera, levando ao aquecimento global e às alterações climáticas. Apesar dos esforços das nações para limitar o aumento das temperaturas em 1.5 °C, como definido no acordo de Paris (2015), se as emissões não forem reduzidas para metade até 2030, é provável atingir um aquecimento global de 2.7 °C até ao final do século. Assim, a avaliação do desempenho ambiental tornou-se crucial. O objetivo desta tese é, desta forma, medir e comparar a eficiência ambiental ao nível dos países, durante o período 2000-2018, sendo a sua principal contribuição ultrapassar a falta de estudos na literatura com um foco global. Para responder às questões de pesquisa (Como é que os países podem ser ordenados em termo do seu desempenho? Quais têm sido os países com melhores e piores desempenhos, ao longo do tempo?), a metodologia DEA (modelo aditivo) foi aplicada. O DEA tornou-se numa ferramenta bem estabelecida em avaliar a eficiência relativa no campo ambiental. A análise de clusters foi, também, desenvolvida para distinguir os países em termos da sua proximidade ao target (%), em 2018. O modelo DEA inclui três inputs (população, uso de energia, emissões GEE) e dois outputs (PIB e renováveis). A população e o PIB são variáveis não discricionárias. Face aos principais resultados, globalmente, os países têm-se tornado mais eficientes ao longo do tempo. Butão, Kiribati, Noruega, Nepal e Islândia têm sido os países eficientes que mais vezes têm aparecido como referência para os outros, sendo exemplos de melhores práticas. Em 2018, os 5 países com pior desempenho foram a Rússia, seguida pelo Irão, Coreia do Sul, Arábia Saudita e Africa do Sul, sendo todos ineficientes desde 2000. Apesar de terem sido ineficientes na maioria dos anos, a China, os Estados Unidos e a India melhoraram significativamente o seu desempenho, explicado sobretudo pelo maior consumo de renováveis.

Country
Portugal
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Keywords

Modelo aditivo, Análise de clusters, Efficiency analysis, Alterações climáticas, :Ciências Sociais::Economia e Gestão [Domínio/Área Científica], Environmental performance, Análise de eficiência, Benchmarking, Clustering analysis, Climate change, Desempenho ambiental, Additive models, Data envelopment analysis (DEA)

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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