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

Abstract

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.

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References

  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. https://doi.org/10.1016/j.gloenvcha.2012.06.005

    Article  Google 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. https://doi.org/10.1016/j.rser.2015.07.193

    Article  Google 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. https://doi.org/10.1016/S0926-9851(99)00055-5

    Article  Google 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 25

  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. https://doi.org/10.1177/0007650315578450

    Article  Google 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 Kathmandu

  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 635

  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. https://doi.org/10.3763/cpol.2008.0539

    Article  Google Scholar 

  9. Bouguettaya A, Yu Q, Liu X, Zhou X, Song A (2015) Efficient agglomerative hierarchical clustering. Expert Syst Appl 42(5):2785–2797. https://doi.org/10.1016/j.eswa.2014.09.054

    Article  Google Scholar 

  10. Chang CC (2002) The potential impact of climate change on Taiwan's agriculture. Agric Econ 27(1):51–64. https://doi.org/10.1111/j.1574-0862.2002.tb00104.x

    Article  Google 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:6

  12. Dagvadorj D, Natsagadorj L, Dorjpurev J, Namkhainyam B (2009) MARCC 2009: Mongolia assessment report on climate change 2009

  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. https://doi.org/10.1016/j.habitatint.2012.05.001

    Article  Google Scholar 

  14. Falkner R (2016) The Paris agreement and the new logic of international climate politics. Int Aff 92(5):1107–1125. https://doi.org/10.1111/1468-2346.12708

    Article  Google 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. https://doi.org/10.1023/A:1005569915357

    Article  Google 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. https://doi.org/10.1016/j.ins.2014.12.044

    Article  Google 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. https://doi.org/10.1080/17583004.2017.1306406

    Article  Google 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. https://doi.org/10.1016/S0301-4215(01)00143-4

    Article  Google Scholar 

  19. Guiteras R (2009) The impact of climate change on Indian agriculture manuscript. University of Maryland, College Park, Maryland, Department of Economics

    Google Scholar 

  20. Huq S (2001) Climate change and Bangladesh science 294:1617-1617

    Article  Google Scholar 

  21. Jain AK, Duin RPW, Mao J (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22(1):4–37. https://doi.org/10.1109/34.824819

    Article  Google 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 science

  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. https://doi.org/10.1016/j.rser.2015.07.024

    Article  Google 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. https://doi.org/10.1016/j.rser.2017.01.180

    Article  Google Scholar 

  25. Kasneci E, Kasneci G, Schiefer U, Rosenstiel W (2014) Rule-based Classification of visual field defects. In: HEALTHINF, pp 34–42

  26. Kaufman L, Rousseeuw PJ (2009) Finding groups in data: an introduction to cluster analysis vol 344. John Wiley & Sons

  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. https://doi.org/10.1016/S0301-4215(01)00130-6

    Article  Google 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. https://doi.org/10.1088/1748-9326/7/3/034032

    Article  Google 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. https://doi.org/10.1016/j.jclepro.2015.02.055

    Article  Google 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. https://doi.org/10.1007/s10584-005-9021-2

    Article  Google 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. https://doi.org/10.1016/S0167-8809(00)00222-X

    Article  Google 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: https://doi.org/10.1023/A:1009629220978

    Article  Google Scholar 

  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. https://doi.org/10.1007/s40553-016-0084-y

    Article  Google Scholar 

  34. Li L, Hong X, Tang D, Na M (2016) GHG emissions, economic growth and urbanization: a spatial approach. Sustainability 8(5):462. https://doi.org/10.3390/su8050462

    Article  Google 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 65:60–71

    Article  Google 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 166:253–263

    Article  Google 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. https://doi.org/10.1016/j.energy.2012.06.007

    Article  Google 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. https://doi.org/10.1016/j.gloenvcha.2012.07.002

    Article  Google Scholar 

  39. Martinez WL, Martinez AR (2007) Computational statistics handbook with MATLAB vol 22. CRC press

  40. Miles L, Kapos V (2008) Reducing greenhouse gas emissions from deforestation and forest degradation: global land-use implications. Science 320(5882):1454–1455. https://doi.org/10.1126/science.1155358

    Article  Google 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 32:1001–1011

    Article  Google Scholar 

  42. Mohajan H (2013) Greenhouse gas emissions of China journal of environmental treatment techniques 1:190-202

  43. Montzka SA, Dlugokencky EJ, Butler JH (2011) Non-CO2 greenhouse gases and climate change. Nature 476(7358):43–50. https://doi.org/10.1038/nature10322

    Article  Google 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. https://doi.org/10.1007/s10113-016-0986-3

    Article  Google 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 12:3–11

    Article  Google 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. https://doi.org/10.1007/s00357-014-9161-z

    Article  Google 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. https://doi.org/10.1016/j.enpol.2009.09.027

    Article  Google Scholar 

  48. Qader MR (2009) Electricity consumption and GHG emissions in GCC countries. Energies 2(4):1201–1213. https://doi.org/10.3390/en20401201

    Article  Google Scholar 

  49. Rhodes CJ (2016) The 2015 Paris climate change conference: COP21. Sci Prog 99(1):97–104. https://doi.org/10.3184/003685016X14528569315192

    Article  Google Scholar 

  50. Rodoulis N (2010) Evaluation of Cyprus’ electricity generation planning using mean-variance portfolio theory Cyprus economic. Pol Rev 4:25–42

    Google 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. https://doi.org/10.1111/mec.13444

    Article  Google 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. https://doi.org/10.1016/S1462-9011(00)00113-1

    Article  Google 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. https://doi.org/10.1007/s10310-006-0228-5

    Article  Google 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. https://doi.org/10.1016/j.contraception.2016.09.006

    Article  Google 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. https://doi.org/10.1126/science.1151861

    Article  Google Scholar 

  56. Shrestha AB, Aryal R (2011) Climate change in Nepal and its impact on Himalayan glaciers. Reg Environ Chang 11(S1):65–77. https://doi.org/10.1007/s10113-010-0174-9

    Article  Google 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). https://doi.org/10.1186/1471-2318-14-22

  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 4:641-652

  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. https://doi.org/10.1021/acs.est.6b03758

    Article  Google 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. https://doi.org/10.1007/s10584-009-9559-5

    Article  Google 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. https://doi.org/10.1016/j.enpol.2009.06.009

    Article  Google 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, 2015

  63. Van der Hoeven M (2012) World energy outlook 2012 Paris: international energy agency

  64. Verchot LV et al. (2010) Reducing forestry emissions in Indonesia

  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. https://doi.org/10.1007/s40831-016-0086-0

    Article  Google 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. https://doi.org/10.1111/j.1574-0862.2009.00379.x

    Article  Google Scholar 

  67. Webb AR (2003) Statistical pattern recognition. John Wiley & Sons

  68. Weisser D (2007) A guide to life-cycle greenhouse gas (GHG) emissions from electric supply technologies. Energy 32(9):1543–1559. https://doi.org/10.1016/j.energy.2007.01.008

    Article  Google 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. https://doi.org/10.1007/s12237-015-0003-y

    Article  Google 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. https://doi.org/10.1016/S0140-6736(09)61714-1

    Article  Google 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. https://doi.org/10.1109/JPROC.2010.2044470

    Article  Google 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 100:95–113

    Article  Google Scholar 

  73. Yu W et al (2010) Climate change risks and food security in Bangladesh. Routledge

  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. https://doi.org/10.1016/j.rser.2011.07.007

    Article  Google 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 39:133–143

    Article  Google 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. https://doi.org/10.1016/j.ecoinf.2014.01.009

    Article  Google 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. https://doi.org/10.1016/j.apenergy.2017.01.072

    Article  Google Scholar 

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Acknowledgements

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, http://cait.wri.org/).

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Correspondence to Heekwan Lee.

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Kwon, Y., Lee, H. & Lee, H. Implication of the cluster analysis using greenhouse gas emissions of Asian countries to climate change mitigation. Mitig Adapt Strateg Glob Change 23, 1225–1249 (2018). https://doi.org/10.1007/s11027-018-9782-3

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Keywords

  • GHG inventory
  • Cluster analysis
  • Asian countries
  • Climate change mitigation