Implication of the cluster analysis using greenhouse gas emissions of Asian countries to climate change mitigation

  • Yongbum Kwon
  • Hyeji Lee
  • Heekwan Lee
Original Article


Climate change caused by excessive emission of greenhouse gases (GHGs) into the atmosphere has gained serious attention from the global community for a long time. More and more countries have decided to propose their goals such as Paris agreements, to reduce emitting these heat trapping compounds for sustainability. The Asian region houses dramatic changes with diverse religions and cultures, large populations as well as a rapidly changing socio-economic situations all of which are contributing to generating a mammoth amount of GHGs; hence, they require calls for related studies on climate change strategies. After pre-filtering of GHG emission information, 24 Asian countries have been selected as primary target countries. Hierarchical cluster analysis method using complete linkage technique was successfully applied for appropriate grouping. Six groups were categorized through GHG emission properties with major and minor emission sectors based on the GHG inventory covering energy, industrial processes, agriculture, waste, land use change, and forestry and bunker fuels. Assigning six groups using cluster analysis finally implied that the approach to establish GHG emission boundaries was meaningful to develop further mitigation strategies. Following the outcome of this study, calculating amount of reduction potential in suitable sectors as well as determining best practice, technology, and regulatory framework can be improved by policy makers, environmental scientists, and planners at the different levels. Therefore, this work on reviewing a wide range of GHG emission history and establishing boundaries of emission characteristics would provide further direction of effective climate change mitigation for sustainability and resilience in Asia.


GHG inventory Cluster analysis Asian countries Climate change mitigation 



This work is financially supported by Ministry of Environment (MOE), South Korea as 「Knowledge-based environmental service Human resource development Project」. Furthermore, we appreciate World Resources Institute (WRI) for sharing country GHG emission data across the world through the Climate Access Indicators Tool (CAIT,


  1. Aaheim A, Amundsen H, Dokken T, Wei T (2012) Impacts and adaptation to climate change in European economies. Glob Environ Chang 22(4):959–968. CrossRefGoogle Scholar
  2. Abdul-Wahab SA, Charabi Y, Al-Maamari R, Al-Rawas GA, Gastli A, Chan K (2015) CO 2 greenhouse emissions in Oman over the last forty-two years. Renew Sust Energ Rev 52:1702–1712. CrossRefGoogle Scholar
  3. Al-Nuaimy W, Huang Y, Nakhkash M, Fang M, Nguyen V, Eriksen A (2000) Automatic detection of buried utilities and solid objects with GPR using neural networks and pattern recognition. J Appl Geophys 43(2-4):157–165. CrossRefGoogle Scholar
  4. An F, Sauer A (2004) Comparison of passenger vehicle fuel economy and greenhouse gas emission standards around the world Pew Center on Global Climate Change 25Google Scholar
  5. Backman CA, Verbeke A, Schulz RA (2017) The drivers of corporate climate change strategies and public policy: a new resource-based view perspective. Business & Society 56(4):545–575. CrossRefGoogle Scholar
  6. Bajracharya SR, Mool PK, Shrestha BR (2007) Impact of climate change on Himalayan glaciers and glacial lakes: case studies on GLOF and associated hazards in Nepal and Bhutan. Int Centre Integr Mt Dev KathmanduGoogle Scholar
  7. Bensassi S, Márquez-Ramos L, Martínez-Zarzoso I, Zitouna H (2011) The geography of trade and the environment: the case of CO2 emissions. In: Economic Research Forum Working Papers 635Google Scholar
  8. Botzen WJ, Gowdy JM, van den Bergh JC (2008) Cumulative CO2 emissions: shifting international responsibilities for climate debt. Clim Pol 8(6):569–576. CrossRefGoogle Scholar
  9. Bouguettaya A, Yu Q, Liu X, Zhou X, Song A (2015) Efficient agglomerative hierarchical clustering. Expert Syst Appl 42(5):2785–2797. CrossRefGoogle Scholar
  10. Chang CC (2002) The potential impact of climate change on Taiwan's agriculture. Agric Econ 27(1):51–64. CrossRefGoogle Scholar
  11. Clarke L, Edmonds J, Jacoby H, Pitcher H, Reilly J, Richels R (2007) Scenarios of greenhouse gas emissions and atmospheric concentrations US Department of Energy Publications:6Google Scholar
  12. Dagvadorj D, Natsagadorj L, Dorjpurev J, Namkhainyam B (2009) MARCC 2009: Mongolia assessment report on climate change 2009Google Scholar
  13. Dulal HB, Akbar S (2013) Greenhouse gas emission reduction options for cities: finding the “coincidence of agendas” between local priorities and climate change mitigation objectives. Habitat International 38:100–105. CrossRefGoogle Scholar
  14. Falkner R (2016) The Paris agreement and the new logic of international climate politics. Int Aff 92(5):1107–1125. CrossRefGoogle Scholar
  15. Fearnside PM (2000) Global warming and tropical land-use change: greenhouse gas emissions from biomass burning, decomposition and soils in forest conversion, shifting cultivation and secondary vegetation. Clim Chang 46(1/2):115–158. CrossRefGoogle Scholar
  16. Ferrari DG, De Castro LN (2015) Clustering algorithm selection by meta-learning systems: a new distance-based problem characterization and ranking combination methods. Inf Sci 301:181–194. CrossRefGoogle Scholar
  17. Garg A, Shukla P, Kankal B, Mahapatra D (2017) CO2 emission in India: trends and management at sectoral, sub-regional and plant levels. Carbon Management 8(2):111–123. CrossRefGoogle Scholar
  18. Gielen D, Moriguchi Y (2002) CO 2 in the iron and steel industry: an analysis of Japanese emission reduction potentials. Energy policy 30(10):849–863. CrossRefGoogle Scholar
  19. Guiteras R (2009) The impact of climate change on Indian agriculture manuscript. University of Maryland, College Park, Maryland, Department of EconomicsGoogle Scholar
  20. Huq S (2001) Climate change and Bangladesh science 294:1617-1617Google Scholar
  21. Jain AK, Duin RPW, Mao J (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22(1):4–37. CrossRefGoogle Scholar
  22. Jiang X, Mira D, Cluff D (2016) The combustion mitigation of methane as a non-CO 2 greenhouse gas progress in energy and combustion scienceGoogle Scholar
  23. Juaidi A, Montoya FG, Gázquez JA, Manzano-Agugliaro F (2016) An overview of energy balance compared to sustainable energy in United Arab Emirates. Renew Sust Energ Rev 55:1195–1209. CrossRefGoogle Scholar
  24. Kafle S, Parajuli R, Bhattarai S, Euh SH, Kim DH (2017) A review on energy systems and GHG emissions reduction plan and policy of the Republic of Korea: past, present, and future. Renew Sust Energ Rev 73:1123–1130. CrossRefGoogle Scholar
  25. Kasneci E, Kasneci G, Schiefer U, Rosenstiel W (2014) Rule-based Classification of visual field defects. In: HEALTHINF, pp 34–42Google Scholar
  26. Kaufman L, Rousseeuw PJ (2009) Finding groups in data: an introduction to cluster analysis vol 344. John Wiley & SonsGoogle Scholar
  27. Kim Y, Worrell E (2002) International comparison of CO 2 emission trends in the iron and steel industry. Energy policy 30(10):827–838. CrossRefGoogle Scholar
  28. Knox J, Hess T, Daccache A, Wheeler T (2012) Climate change impacts on crop productivity in Africa and South Asia. Environ Res Lett 7(3):034032. CrossRefGoogle Scholar
  29. Kuramochi T (2016) Assessment of midterm CO 2 emissions reduction potential in the iron and steel industry: a case of Japan. J Clean Prod 132:81–97. CrossRefGoogle Scholar
  30. Kurukulasuriya P, Ajwad MI (2007) Application of the Ricardian technique to estimate the impact of climate change on smallholder farming in Sri Lanka. Clim Chang 81(1):39–59. CrossRefGoogle Scholar
  31. Lansigan F, De los Santos W, Coladilla J (2000) Agronomic impacts of climate variability on rice production in the Philippines. Agric Ecosyst Environ 82(1-3):129–137. CrossRefGoogle Scholar
  32. Lasco RD, Pulhin FB (2000) Forest land use change in the Philippines and climate change mitigation Mitigation and adaptation strategies for global change 5(1):81–97, DOI:
  33. Lee H, Matsuura H, Sohn I (2016) Symbiosis of steel, energy, and CO2 evolution in Korea. Metallurgical and Materials Transactions E 3(3):171–178. CrossRefGoogle Scholar
  34. Li L, Hong X, Tang D, Na M (2016) GHG emissions, economic growth and urbanization: a spatial approach. Sustainability 8(5):462. CrossRefGoogle Scholar
  35. Li M, Deng S, Wang L, Feng S, Fan J (2014) Hierarchical clustering algorithm for categorical data using a probabilistic rough set model. Knowl-Based Syst 65:60–71CrossRefGoogle Scholar
  36. Liu et al. (2016) Uncovering driving forces on greenhouse gas emissions in China’aluminum industry from the perspective of life cycle analysis. Appl Energy 166:253–263CrossRefGoogle Scholar
  37. Liu GY, Lindner S, Guan D (2012) Uncovering China’s greenhouse gas emission from regional and sectoral perspectives. Energy 45(1):1059–1068. CrossRefGoogle Scholar
  38. Marcotullio PJ, Sarzynski A, Albrecht J, Schulz N (2012) The geography of urban greenhouse gas emissions in Asia: a regional analysis. Glob Environ Chang 22(4):944–958. CrossRefGoogle Scholar
  39. Martinez WL, Martinez AR (2007) Computational statistics handbook with MATLAB vol 22. CRC pressGoogle Scholar
  40. Miles L, Kapos V (2008) Reducing greenhouse gas emissions from deforestation and forest degradation: global land-use implications. Science 320(5882):1454–1455. CrossRefGoogle Scholar
  41. Mirasgedis S, Sarafidis Y, Georgopoulou E, Lalas D, Papastavros C (2004) Mitigation policies for energy related greenhouse gas emissions in Cyprus: the potential role of natural gas imports. Energy Policy 32:1001–1011CrossRefGoogle Scholar
  42. Mohajan H (2013) Greenhouse gas emissions of China journal of environmental treatment techniques 1:190-202Google Scholar
  43. Montzka SA, Dlugokencky EJ, Butler JH (2011) Non-CO2 greenhouse gases and climate change. Nature 476(7358):43–50. CrossRefGoogle Scholar
  44. Mottet A, Henderson B, Opio C, Falcucci A, Tempio G, Silvestri S, Chesterman S, Gerber PJ (2017) Climate change mitigation and productivity gains in livestock supply chains: insights from regional case studies. Reg Environ Chang 17(1):129–141. CrossRefGoogle Scholar
  45. Murdiyarso D, Lebel L (2007) Local to global perspectives on forest and land fires in Southeast Asia Mitigation and Adaptation Strategies for Global Change 12:3–11Google Scholar
  46. Murtagh F, Legendre P (2014) Ward’s hierarchical agglomerative clustering method: which algorithms implement Ward’s criterion? J Classif 31(3):274–295. CrossRefGoogle Scholar
  47. Oh I, Wehrmeyer W, Mulugetta Y (2010) Decomposition analysis and mitigation strategies of CO 2 emissions from energy consumption in South Korea. Energy Policy 38(1):364–377. CrossRefGoogle Scholar
  48. Qader MR (2009) Electricity consumption and GHG emissions in GCC countries. Energies 2(4):1201–1213. CrossRefGoogle Scholar
  49. Rhodes CJ (2016) The 2015 Paris climate change conference: COP21. Sci Prog 99(1):97–104. CrossRefGoogle Scholar
  50. Rodoulis N (2010) Evaluation of Cyprus’ electricity generation planning using mean-variance portfolio theory Cyprus economic. Pol Rev 4:25–42Google Scholar
  51. Ryberg M (2015) Molecular operational taxonomic units as approximations of species in the light of evolutionary models and empirical data from fungi. Mol Ecol 24(23):5770–5777. CrossRefGoogle Scholar
  52. Rypdal K, Winiwarter W (2001) Uncertainties in greenhouse gas emission inventories—evaluation, comparability and implications. Environ Sci Pol 4(2-3):107–116. CrossRefGoogle Scholar
  53. Sasaki N (2006) Carbon emissions due to land-use change and logging in Cambodia: a modeling approach. J For Res 11(6):397–403. CrossRefGoogle Scholar
  54. Schneider EN, Riley R, Espey E, Mishra SI, Singh RH (2017) Nitrous oxide for pain management during in-office hysteroscopic sterilization: a randomized controlled trial. Contraception 95(3):239–244. CrossRefGoogle Scholar
  55. Searchinger T, Heimlich R, Houghton RA, Dong F, Elobeid A, Fabiosa J, Tokgoz S, Hayes D, Yu TH (2008) Use of US croplands for biofuels increases greenhouse gases through emissions from land-use change. Science 319(5867):1238–1240. CrossRefGoogle Scholar
  56. Shrestha AB, Aryal R (2011) Climate change in Nepal and its impact on Himalayan glaciers. Reg Environ Chang 11(S1):65–77. CrossRefGoogle Scholar
  57. Sibley KM, Voth J, Munce SE, Straus SE, Jaglal SB (2014) Chronic disease and falls in community-dwelling Canadians over 65 years old: a population-based study exploring associations with number and pattern of chronic conditions. BMC Geriatr 14(22).
  58. Solanki PS, Mallela VS, Zhou C (2013) Estimation and diminution of co2 emissions by clean development mechanism option at power sector in Oman international journal of energy and environment 4:641-652Google Scholar
  59. Sulbaek Andersen MP, Kyte M, Andersen ST, Nielsen CJ, Nielsen OJ (2017) Atmospheric chemistry of (CF3)2CF-C≡N: a replacement compound for the most potent industrial greenhouse gas, SF6. Environmental science & technology 51(3):1321–1329. CrossRefGoogle Scholar
  60. Sultana H, Ali N, Iqbal MM, Khan AM (2009) Vulnerability and adaptability of wheat production in different climatic zones of Pakistan under climate change scenarios. Clim Chang 94(1-2):123–142. CrossRefGoogle Scholar
  61. Timilsina GR, Shrestha A (2009) Transport sector CO 2 emissions growth in Asia: underlying factors and policy options. Energy Policy 37(11):4523–4539. CrossRefGoogle Scholar
  62. UNFCCC Report of the Conference of the Parties on its twenty-first session, held in Paris from 30 November to 13 December 2015. In: Addendum. Part Two: Action taken by the Conference of the Parties at its twenty-first session, 2015Google Scholar
  63. Van der Hoeven M (2012) World energy outlook 2012 Paris: international energy agencyGoogle Scholar
  64. Verchot LV et al. (2010) Reducing forestry emissions in IndonesiaGoogle Scholar
  65. Vogel H, Flerus B, Stoffner F, Friedrich B (2017) Reducing greenhouse gas emission from the neodymium oxide electrolysis. Part I: analysis of the anodic gas formation. Journal of Sustainable Metallurgy 3(1):99–107. CrossRefGoogle Scholar
  66. Wang J, Mendelsohn R, Dinar A, Huang J, Rozelle S, Zhang L (2009) The impact of climate change on China's agriculture. Agric Econ 40(3):323–337. CrossRefGoogle Scholar
  67. Webb AR (2003) Statistical pattern recognition. John Wiley & SonsGoogle Scholar
  68. Weisser D (2007) A guide to life-cycle greenhouse gas (GHG) emissions from electric supply technologies. Energy 32(9):1543–1559. CrossRefGoogle Scholar
  69. Wigand C, Ardito T, Chaffee C, Ferguson W, Paton S, Raposa K, Vandemoer C, Watson E (2017) A climate change adaptation strategy for management of coastal marsh systems. Estuar Coasts 40(3):682–693. CrossRefGoogle Scholar
  70. Woodcock J, Edwards P, Tonne C, Armstrong BG, Ashiru O, Banister D, Beevers S, Chalabi Z, Chowdhury Z, Cohen A, Franco OH, Haines A, Hickman R, Lindsay G, Mittal I, Mohan D, Tiwari G, Woodward A, Roberts I (2009) Public health benefits of strategies to reduce greenhouse-gas emissions: urban land transport. Lancet 374(9705):1930–1943. CrossRefGoogle Scholar
  71. Wright J, Ma Y, Mairal J, Sapiro G, Huang TS, Yan S (2010) Sparse representation for computer vision and pattern recognition. Proc IEEE 98(6):1031–1044. CrossRefGoogle Scholar
  72. Xiao X, Boles S, Frolking S, Li C, Babu JY, Salas W, Moore B (2006) Mapping paddy rice agriculture in south and Southeast Asia using multi-temporal MODIS images. Remote Sens Environ 100:95–113CrossRefGoogle Scholar
  73. Yu W et al (2010) Climate change risks and food security in Bangladesh. RoutledgeGoogle Scholar
  74. Yuksel I, Kaygusuz K (2011) Renewable energy sources for clean and sustainable energy policies in Turkey. Renew Sust Energ Rev 15(8):4132–4144. CrossRefGoogle Scholar
  75. Zadegan SMR, Mirzaie M, Sadoughi F (2013) Ranked k-medoids: a fast and accurate rank-based partitioning algorithm for clustering large datasets. Knowl-Based Syst 39:133–143CrossRefGoogle Scholar
  76. Zhang CZ, Qiao H, Chen B, Hayat T, Alsaedi A (2015) China's non-CO 2 greenhouse gas emissions: inventory and input–output analysis. Ecological Informatics 26:101–110. CrossRefGoogle Scholar
  77. Zhang Q, Zhao X, Lu H, Ni T, Li Y (2017) Waste energy recovery and energy efficiency improvement in China’s iron and steel industry. Appl Energy 191:502–520. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Environmental EngineeringIncheon National UniversityIncheonRepublic of Korea
  2. 2.International Institute for Applied Systems AnalysisLaxenburgAustria

Personalised recommendations