To further grasp the energy security situation in Latin America and explore the main influencing factors. Based on the data of 525 energy evaluation indicators in Latin America, the global time-series principal component analysis combined with the entropy weight method is used to analyze the energy security situation in the region. The results of the study show that the energy security scores obtained from the 525 samples range from -0.012 to 0.61, indicating that there are significant differences between countries in terms of energy security. Upper-middle-income countries have a more stable energy security situation relative to high-income countries, while lower-middle-income countries need to take measures to improve their energy security situation. Energy use, energy intensity levels, and renewable energy output are the main influencing factors of energy security in the region. The research methodology is able to objectively assess the energy security score and reflect the energy security situation in Latin America. Finally, conclusions are summarized and recommendations are made with a view to informing the opportunities and challenges facing energy security in Latin America, as well as providing lessons for maintaining and focusing on energy security in our country.

Lijuan Wu. Study on Entropy Weighting Method of Quantitative Evaluation of Energy Security: The Case of Latin America. Academic Journal of Business & Management (2024) Vol. 6, Issue 4: 254-261. https://doi.org/10.25236/AJBM.2024.060437.

[1] Bazilian M, Bradshaw M, Goldthau A, et al. Model and manage the changing geopolitics of energy. Nature, 2019, 569(7754): 29-31.

[2] Yang Y, Zheng X, Sun Z. Coal Resource Security Assessment in China: A Study Using Entropy-Weight-Based TOPSIS and BP Neural Network. Sustainability, 2020, 12(6): 2294.

[3] Iqbal W, Fatima A, Yumei H, et al. Oil supply risk and affecting parameters associated with oil supplementation and disruption. Journal of Cleaner Production, 2020, 255: 120187.

[4] Tao Ran, Cai Yunze, Lou Zhenfei, et al. Comprehensive research of domestic and foreign energy forecast model and energy safety appraisal systems. Shanghai Energy Conservation, 2012(1): 16-21.

[5] Huang B, Zhang L, Ma L, et al. Multi-criteria decision analysis of China’s energy security from 2008 to 2017 based on Fuzzy BWM-DEA-AR model and Malmquist Productivity Index. Energy, 2021, 228: 120481.

[6] Wu TH, Chung YF, Huang SW. Evaluating global energy security performances using an integrated PCA/DEA-AR technique. Sustainable Energy Technologies and Assessments, 2021, 45: 101041.

[7] Shittu W, Adedoyin FF, Shah MI, et al. An investigation of the nexus between natural resources, environmental performance, energy security and environmental degradation: Evidence from Asia. Resources Policy, 2021, 73: 102227.

[8] Meng Chao,Hu Jian.A research on China' s coal mine safety evaluation based on BP neural network.Science Research Management,2016,37(8):153-160.

[9] Su Jun, Wang Yongxun, Wang Qiang. Pattern evolution of global energy security and the geopolitical game. Journal of Natural Resources, 2020, 35(11): 2613-2628.

[10] Liang Jinqiang, Liu Danzhu, Xu Shuliang, et al. Quantitative evaluation method of energy security under dual carbon target. Chemical Industry and Engineering Progress, 2022, 41(3): 1622-1633.

[11] Ang BW, Choong WL, Ng TS. Energy security: Definitions, dimensions and indexes. Renewable and Sustainable Energy Reviews, 2015, 42: 1077-1093.

[12] Šprajc P, Bjegović M, Vasić B. Energy security in decision making and governance-Methodological analysis of energy trilemma index. Renewable and Sustainable Energy Reviews, 2019, 114: 109341.

[13] Shi, D, Xue, Q. Influencing Factors, Evaluation and Outlook of Primary Energy Security in China. Econ. Rev,2021, 1, 31–45.

[14] Mahlknecht J, González-Bravo R, Loge FJ. Water-energy-food security: A Nexus perspective of the current situation in Latin America and the Caribbean. Energy,2020, 194: 116824.

[15] Balza L, Espinasa R, Serebrisky T. Lights On?: Energy Needs in Latin America and the Caribbean to 2040. 2016.

[16] Le TH, Chang Y, Taghizadeh-Hesary F, et al. Energy insecurity in Asia: A multi-dimensional analysis. Economic Modelling, 2019, 83: 84-95.

[17] Alemzero DA, Sun H, Mohsin M, et al. Assessing energy security in Africa based on multi-dimensional approach of principal composite analysis. Environmental Science and Pollution Research, 2021, 28(2): 2158-2171.

[18] Abdullah FB, Iqbal R, Hyder SI, et al. Energy security indicators for Pakistan: An integrated approach. Renewable and Sustainable Energy Reviews, 2020, 133: 110122.

[19] Hock D, Kappes M, Ghita B. Entropy-Based Metrics for Occupancy Detection Using Energy Demand. Entropy, 2020, 22(7): 731.

[20] TIAN Shuicheng,SHEN Zhangjin. Safety evaluation of coal mine flooding based on entropy method and catastrophe theory. Journal of Xi’an University of Science and Technology,2022,42( 6) : 1064-1070.

[21] Tian Fujin, Ma Qingshan, Zhang Ming, Tang Zhimin. Evaluation of water quality in Xin'anjiang River Basin based on principal component analysis and entropy weight method. Geology in China, 2023, 50(2): 495-505.