Abstract
The mitigation of CO2 emissions requires a global effort with common but differentiated responsibilities. In this paper, we identify clusters of CO2 emissions across 72 countries. First, using the stochastic version of the IPAT and employing the dynamic common correlated effects technique, we identify three key determinants affecting CO2 emissions (non-renewables, population, and real GDP). In the second step, both hierarchical and non-hierarchical clustering methods are considered to identify the optimal number of clusters. We identify two to four clusters with different member countries, and in particular establish that in most cases, a 2-cluster solution appears to be optimal. The contents of clusters vary slightly according to the clustering methods for each period. The clustering results from using only the overall CO2 emissions indicate that the countries we consider form three clusters, with China and the USA each within a single member cluster. The remaining 70 countries form the third cluster. Our findings reflect the prominent roles of China and the USA in overall CO2 emissions. Analyses with sub-period and largest emitters reflect a different clustering structure. Some policy recommendations in setting emission reductions are made, considering different clusters across countries.
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Notes
CO2 emissions is considered here as the primary source of GHG.
We consider CO2 emissions, as this emission is the primary mitigating factor for climate change in the global context. Other studies include Awaworyi Churchill et al. (2018); Cai et al. (2018); Churchill et al. (2018); Mikayilov et al. (2018); Mrabet et al. (2017); and Wu et al. (2018); Zhou et al. (2018).
See Schandl et al. (2016) for further discussion.
Impact (I) = Population (P) × Affluence (A) × Technology (T).
Features are the input variables used for clustering.
Non-renewable energy consumption (NRE) is the sum of coal, gas and oil. Renewable energy consumption (RNE) is the sum of hydro, wind, solar, geothermal, marine, waste, and solid, liquid and gaseous biofuel-derived energy. Renewable (combustibles) and non-renewable (fossil fuel) energy intensity measures are in kilograms of oil equivalent per GDP (in US dollar).
The data is from the World Bank (2015).
In implementing our clustering procedures, we use the Euclidean and Manhattan distances on normalised data.
In practice in order to ensure that different seed choices do not produce different results and the k-means procedure converges, the k-means algorithm is run with multiple repetitions. In our implementation in MATLAB, we use 1000 repetitions.
For all models, dendrograms are shown and referred to in the Online Supplementary Material (Figures A1–A8).
For all models, tables with the Adjusted Rand Indices are shown in the Online Supplementary Material (Table A5–A12).
For all models, cluster solutions based on CO2 features are shown and referred to in the online Supplementary Material (Tables A1–A4).
For the 1970–2015 period the mean and standard deviation of China’s CO2 emissions are 2574324.35 metric tons and 2053792.29 metric tons, respectively.
For the 1970–2015 period the mean and standard deviation of USA’s CO2 emissions are 4682367.57 metric tons and 790581.60 metric tons, respectively.
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All authors contributed to the development of ideas and methods. JI collected the data and wrote the code in STATA for the generation output of the models; EAM wrote the code in MATLAB for the generation of the cluster analysis output; all authors contributed to the writing the paper and editing it to arrive at the final draft.
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Inekwe, J., Maharaj, E.A. & Bhattacharya, M. Drivers of carbon dioxide emissions: an empirical investigation using hierarchical and non-hierarchical clustering methods. Environ Ecol Stat 27, 1–40 (2020). https://doi.org/10.1007/s10651-019-00433-4
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DOI: https://doi.org/10.1007/s10651-019-00433-4