Allocation of National Energy Saving Target to Provinces for Vietnam Using Cluster Analysis Method
Energy saving allocation
DOI:
https://doi.org/10.13052/spee1048-5236.4346Keywords:
Allocation target, clustering analysis, energy saving target, energy efficiency, province levelAbstract
Vietnam government has adopted a series solutions and policies to improve energy efficiency to meet the national energy efficiency target of period 2019–2030. The Vietnam’s provinces will act as main actor for the national achievement of energy efficiency. Thus, understanding the province’s potentiality of energy efficiency is useful for the harmonious and sustainable development between the economy and energy systems as well as for finding the best way of allocating energy saving target to each province. Based on the difference of GDP development and energy consumption levels, the target of energy efficiency for each province through clustering is set. The provinces of Vietnam clustered into 7 groups by using clustering method. The results show that 33 provinces included in the cluster 1, 2, 3, 4 and 6 should burden a heavy contribution of energy saving. Among them, the provinces in the cluster 2 and 3 need to focus on improvement of energy saving in the industry sector. The cluster 7 included the under-developed provinces can learn development’s experiences of the provinces in the cluster 1, 2, 3 and 4 to find the best way of their future development. The objective of this study is to clarify and provide a comprehensive target for energy saving allocation at provincial level of Vietnam.
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References
Minh, N.D. and D.T. Kien, Assessment of the impact of managing large energy-using users on national energy efficiency of Vietnam. International Journal of Energy Economics and Policy, 2021. 11(5): p. 519–530.
Le-Anh, T., et al., Energy saving intention and behavior under behavioral reasoning perspectives. Energy Efficiency, 2023. 16(2): p. 1–19.
Tan, C.-S., H.-Y. Ooi, and Y.-N. Goh, A moral extension of the theory of planned behavior to predict consumers’ purchase intention for energy-efficient household appliances in Malaysia. Energy Policy, 2017. 107: p. 459–471.
Gao, L., et al., Application of the extended theory of planned behavior to understand individual’s energy saving behavior in workplaces. Resources, Conservation and Recycling, 2017. 127: p. 107–113.
Prime minister, Decision 280/QÐ-TTg on Approval of the National Energy Efficiency Programme (VNEEP) for the Period 2019–2030. 2019.
Nong, D., C. Wang, and A.Q. Al-Amin, A critical review of energy resources, policies and scientific studies towards a cleaner and more sustainable economy in Vietnam. Renewable Sustainable Energy Reviews, 2020. 134: p. 110117.
Luong, N.D., A critical review on energy efficiency and conservation policies and programs in Vietnam. Renewable Sustainable Energy Reviews, 2015. 52: p. 623–634.
Nguyen, X.P., et al., Mission, challenges, and prospects of renewable energy development in Vietnam. Energy Sources, Part A: Recovery, Utilization, Environmental Effects, 2021: p. 1–13.
Prime minister, Decision 79/QÐ-TTg on Approval of the National Energy Efficiency Programme (VNEEP) for the Period 2006–2015. 2006.
The National Assembly of Vietnam, Law No. 50/2010/QH12 on Economical and Efficient Use of Energy. 2010.
Luong, N.D.J.R. and S.E. Reviews, A critical review on energy efficiency and conservation policies and programs in Vietnam. 2015. 52: p. 623–634.
Zhou, P. and M. Wang, Carbon dioxide emissions allocation: A review. Ecological economics, 2016. 125: p. 47–59.
Zhang, P., et al., China’s energy intensity target allocation needs improvement! Lessons from the convergence analysis of energy intensity across Chinese Provinces. Journal of cleaner production, 2019. 223: p. 610–619.
Foo, D.C., R.R. Tan, and D.K. Ng, Carbon and footprint-constrained energy planning using cascade analysis technique. Energy, 2008. 33(10): p. 1480–1488.
Xiong, S., et al., Allocation of energy consumption among provinces in China: A Weighted ZSG-DEA Model. Sustainability, 2017. 9(11): p. 2115.
Zhang, Y.-J. and J.-F. Hao, The allocation of carbon emission intensity reduction target by 2020 among provinces in China. Natural Hazards, 2015. 79: p. 921–937.
Wang, X., et al., Optimal strategies for carbon reduction at dual levels in China. Journal of Cleaner Production, 2014. 88: p. 185–193.
Guo, J., X. Zheng, and C. Wei, Disaggregating energy use cap among China’s provinces. Journal of Cleaner Production, 2016. 127: p. 374–386.
Wu, J., Q. Zhu, and L. Liang, CO2
emissions and energy intensity reduction allocation over provincial industrial sectors in China. Applied Energy, 2016. 166: p. 282–291.
Khatwani, G. and A.K. Kar, Improving the Cosine Consistency Index for the analytic hierarchy process for solving multi-criteria decision making problems. Applied computing informatics, 2017. 13(2): p. 118–129.
Yuan, Y., et al., Regional allocation of CO2
intensity reduction targets based on cluster analysis. Advances in climate change research, 2012. 3(4): p. 220–228.
Plossky, A., et al. Cluster analysis method usage for implementation of regional approach to digital dividend allocation. in 2013 International Symposium on Electromagnetic Compatibility. 2013. IEEE.
China, K.J.C.K.A., China’s 12th five-year plan: Overview. 2011.
Sun, J., et al., Optimizing the provincial target allocation scheme of renewable portfolio standards in China. Energy, 2022. 250: p. 123699.
Jiang, M., et al., Allocating provincial CO2
quotas for the Chinese national carbon program. Australian Journal of Agricultural Resource Economics, 2018. 62(3): p. 457–479.
Casey, J. and K. Koleski, Backgrounder: China’s 12th five-year plan. 2011: US-China Economic and Security Review Commission Washington, DC, USA.
Blashfield, R.K. and M.S. Aldenderfer, The literature on cluster analysis. Multivariate behavioral research, 1978. 13(3): p. 271–295.
Punj, G. and D.W. Stewart, Cluster analysis in marketing research: Review and suggestions for application. Journal of marketing research, 1983. 20(2): p. 134–148.
Hempel, C.G., Fundamentals of concept formation in empirical science. 1952: University of Chicago Press.
Tiwari, M. and B. Misra, Application of Cluster Analysis In Agriculture - A Review Article. International Journal of Computer Applications, 2011. 36(4): p. 43–7.
Arabie, P. and L. Hubert, Advances in cluster analysis relevant to marketing research, in From Data to Knowledge: Theoretical and Practical Aspects of Classification, Data Analysis, and Knowledge Organization. 1996, Springer. p. 3–19.
Ližbetinová, L., et al., Application of cluster analysis in marketing communications in small and medium-sized enterprises: An empirical study in the Slovak Republic. Sustainability, 2019. 11(8): p. 2302.
Lorentz, H., et al., Cluster analysis application for understanding SME manufacturing strategies. Expert Systems with Applications, 2016. 66: p. 176–188.
Shelly, D.R., et al., A new strategy for earthquake focal mechanisms using waveform-correlation-derived relative polarities and cluster analysis: Application to the 2014 Long Valley Caldera earthquake swarm. Journal of Geophysical Research: Solid Earth, 2016. 121(12): p. 8622–8641.
Marrone, P., et al., Energy benchmarking in educational buildings through cluster analysis of energy retrofitting. Energies, 2018. 11(3): p. 649.
Walsh, A., D. Cóstola, and L.C. Labaki, Performance-based climatic zoning method for building energy efficiency applications using cluster analysis. Energy, 2022. 255: p. 124477.
Parobek, J., et al., Energy Utilization of Renewable Resources in the European Union – Cluster Analysis Approach. BioResources, 2016. 11(1): p. 984–995.
Arbolino, R., R. Boffardi, and G. Ioppolo, The effectiveness of European energy policy on the Italian system: Regional evidences from a hierarchical cluster analysis approach. Energy Policy, 2019. 132: p. 47–61.
Pickton, D.W. and S. Wright, What’s swot in strategic analysis? Strategic change, 1998. 7(2): p. 101–109.
Kotler, P., E. Roberto, and H. Hugo, Social marketing. 1991: Econ-Verlag.
Punj, G. and D.W. Stewart, Cluster aanlysis in marketing reseach: Review and suggestions for application. Journal of Marketing Research, 1983. 20(2): p. 134–148.
Soares, J.O., M. M. L. Marques, and C. M. F. Monteiro, A multivariate methodology to uncover regional disparities: A contribution to improve European Union and governmental decisions. European Journal of Operational Research,, 2003. 145(1): p. 121–135.
Del Campo, C., C. Monteiro, and J. O. Soares, The European regional policy and the socio-economic diversity of European regions: A multivariate analysis. European Journal of Operational Research, 2008. 187(2): p. 600–612.
Ward, J.H., Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 1963. 58(301): p. 236–244.
Lloyd, S.P., Least squares quantization in PCM. IEEE Transactions on Information Theory, 2007. 28(2): p. 129–137.
Zhang, L., Y. Feng, and B. Zhao, Disaggregation of energy-saving targets for China’s provinces: modeling results and real choices. Journal of Cleaner Production, 2015. 103: p. 837–846.
Roser, H.R.a.M. Vietnam: Energy Country Profile. 2023; Available from: https://ourworldindata.org/energy/country/vietnam.
Lorenz, M.O., Methods of measuring the concentration of wealth. Publications of the American Statistical Association, 1905. 9: p. 209–219.
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