Research Portfolio Post #7: Qualitative Data Sources

My research question is “what explains variation in implementation of global climate initiatives?” In a small-n neopositivist analysis of my research topic, my dependent variable would be the success or failure of the Kyoto Protocol. I have chosen the Kyoto Protocol over other global climate initiatives as it sets binding greenhouse gas emissions reductions targets, rather than other UN climate initiatives such as the Paris Agreement which also aims to reduce emissions, but is not legally binding.[1]

One source I will consult to decide whether the Kyoto Protocol succeeded or failed in certain countries is the World Development Indicators dataset from the World Bank.[2] From this dataset, I would look at the percentage change in greenhouse gas emissions from 1990. The reason for the year 1990 is that it is around that time that climate change became a significant issue on the international level, specifically within the United Nations.[3] The way I will operationalize my variable in accordance with this data source to decide whether or not the Kyoto Protocol was a success in a certain country is if they met their set emissions targets. For example, Croatia’s 2008 goal was a five percent reduction of greenhouse gas emissions by 2012, and according to the World Development Indicators dataset, they reduced their emissions by 7.4%.[4] Because of this, I would consider that Kyoto Protocol to be a success in Croatia. This measure of success gauges more than just efficacy of the initiative itself, but also shows how much, if any, a country is implementing global climate policy.

Another mechanism of the Kyoto Protocol is the Clean Development Mechanism (CDM) that allows developed countries to develop emission-reduction projects in developing countries.[5] Through an analysis of these projects, I can see how developed countries are making efforts not just domestically, but also collective international efforts to combat climate change, thus playing a role into whether implementation of the Kyoto Protocol is a success or failure. I also plan to incorporate discourse analysis of statements submitted by member countries of the UN during UNFCC meetings in my research, to assess support or opposition to climate policy. Through usage of both quantitative and qualitative sources, I hope to develop a more complex and multi-faceted dependent variable suitable for small-n neopositivist research.

[1]“Climate Change,” United Nations January 11, 2016, <> (Accessed: 26 October 2019).

[2]“World Development Indicators DataBank,” <> (Accessed: 26 October 2019).

[3] Ibid.

[4] “Kyoto Protocol – Targets for the First Commitment Period | UNFCCC,” <> (Accessed: 27 October 2019). See Also: “World Development Indicators Databank.”

[5] “The Clean Development Mechanism | UNFCCC,” <> (Accessed: 27 October 2019).

Research Portfolio Post #6: Quantitative Data Sources

I am studying global climate initiatives because I want to find out what explains the variation in implementation of these initiatives in order to help my reader understand whether or not international action to combat climate change is effective and how solutions can be crafted to create equality in burden sharing and make significant environmental progress.

In a large-N statistical analysis of my project, my dependent variable would be how much a country is lowering their greenhouse gas emissions. According to the Environmental Protection Agency, the four main greenhouse gases are carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and fluorinated gases.[1] In measuring my dependent variable, I would use the ‘total greenhouse gas emissions’ statistic measured in kilotons of carbon dioxide equivalent from World Bank’s DataBank, which includes the four main greenhouse gases as listed by the EPA, and is sourced from the European Commission’s Emission Database for Global Atmospheric Research.[2] This database gives emissions levels by year, which I could use as interval data. This would be my dependent variable because many global climate initiatives, such as the Kyoto Protocol and the Paris Agreement focus on slashing greenhouse gas emissions or even creating binding emissions reduction targets.[3] I could also narrow down this variable into specific gas emissions, such as Perfluorocarbon which is a byproduct of certain manufacturing processes, or narrow it down to specific sectors of the economy, such as agricultural emissions or emissions from transportation.[4] The cases I would study with this dependent variable are all 264 countries and regions that the World Bank’s World Development Indicators dataset has information on.

The limitations of this data set are that it does not have data on certain countries for every year. For example, there is no emissions data for Afghanistan from the year 2013 to present day, due to conflict in the region.[5] Another limitation is that the data excludes emissions from some biomass burning, like the incineration of agricultural waste, which is a large source of greenhouse gas emissions, as well as other gases which are not included in the main four greenhouse gases.[6]

[1] “Overview of Greenhouse Gases,” Overviews and Factsheets, US EPA, December 23, 2015,

[2] “World Development Indicators | DataBank,” accessed October 11, 2019,

[3] Hiroki Iwata and Keisuke Okada, “Greenhouse Gas Emissions and the Role of the Kyoto Protocol,” Environmental Economics & Policy Studies 16, no. 4 (October 2014): 325,; Jana Lippelt and Lea Mayer, “After the Paris Agreement – What’s Next? Worldwide Implementation,” CESifo Forum; München 18, no. 4 (Winter 2017): 43.

[4] “World Development Indicators | DataBank.”

[5] Ibid.

[6] “Air Pollution | Partnership for Policy Integrity,” accessed October 11, 2019,