Data-Driven Insights into Climate Change and Technological Levels: Data Mining Visualization and TOPSIS Approach

Abstract

The 2024 Global Risks Report identifies misinformation and disinformation under the technology category as the foremost short-term global risk, followed closely by the risk of extreme weather events categorized under environmental concerns. In a longer-term perspective, the prominence of extreme weather events escalates to the top position, with misinformation and disinformation remaining significant, and adverse outcomes from AI technologies emerging as the seventh most critical risk. This delineation underscores the preeminent challenges posed by environmental and technological factors to global stability. These risks are not confined to the realm of environmental scientists or technologists; rather, they impact humanity as a whole. Recognizing that each individual holds inherent responsibilities, it is crucial to approach these issues through a multidisciplinary academic lens. This study, therefore, concurrently addresses climate change and technological impacts, investigating the interconnections and sub-indicators of these issues on a national scale. Countries were assessed based on these dimensions, compared, and visually represented, culminating in a comprehensive ranking. To facilitate these analyses, methodologies such as exploratory data analysis, principal component analysis, multidimensional scaling, and the TOPSIS were employed. The findings reveal a negative correlation between energy consumption and technology metrics, and a positive correlation between renewable energy indicators and technology. This study provides a nuanced understanding of how countries align with these global risks, offering a ranked evaluation starting from the most to the least affected.

Keywords

• Data mining
• visualization
• TOPSIS
• climate change
• technology level

References

1. Aydın, N., & Yalçın, E. (2017). The Comparison of Satisfaction Levels of Southeastern Anatolian Provinces by MultidimensionalScaling Analysis.Alphanumeric Journal,5(1), 15. https://doi.org/10.17093/alphanumeric.301604
2. Buja, A., Swayne, D. F., Littman, M. L., Dean, N., Hofmann, H., & Chen, L. (2008). Data Visualization With Multidimensional Scaling.Journal of Computational and Graphical Statistics,17(2), 444–472. https://doi.org/10.1198/106186008x318440
3. Burck, J., Uhlich, T., Bals, C., Höhne, N., & Nascrimiento, L. (2024).Climate Change Performance Index 2024.https://ccpi.org/download/climate-change-performance-index-2024/
4. Bánhidi, Z., & Dobos, I. (2024). Measuring digital development: ranking using data envelopment analysis (DEA) and networkreadiness index (NRI).Central European Journal of Operations Research. https://doi.org/10.1007/s10100-024-00919-y
5. Caglar, A. E., Avci, S. B., Ahmed, Z., & Gökçe, N. (2024). Assessing the role of green investments and green innovation in ecologicalsustainability: From a climate action perspective on European countries.Science of the Total Environment,928, 172527.https://doi.org/10.1016/j.scitotenv.2024.172527
6. Chakraborty, S. (2022). TOPSIS and Modified TOPSIS: A comparative analysis.Decision Analytics Journal,2, 100021. https://doi.org/10.1016/j.dajour.2021.100021
7. Demirbaş, F., & Elmaslar Özbaş, E. (2024). Review on the use of artificial neural networks to determine the relationship betweenclimate change and the occupancy rates of dams.Environmental Research and Technology,7(1), 140–147. https://doi.org/10.35208/ert.1340030
8. Doğruel Anuşlu, M., & Fırat, S. Ü. (2020). Ülkelerin Endüstri 4.0 seviyesinin sürdürülebilir kalkınma düzeylerine etkisinin analizi.Endüstri Mühendisliği,31, 44–58.

9. Dutta, S., & Lanvin, B. (2024).Network Readiness Index 2024: Building a Digital Tomorrow: Public-Private Partnerships for DigitalReadiness.https://networkreadinessindex.org/
10. Fernández Fernández, Y., Fernández López, M., & Olmedillas Blanco, B. (2018). Innovation for sustainability: The impact ofR&D spending on CO2 emissions.Journal of Cleaner Production,172, 3459–3467. https://doi.org/10.1016/j.jclepro.2017.11.001
11. Han, F., Alkhawaji, R. N., & Shafieezadeh, M. M. (2024). Evaluating sustainable water management strategies using TOPSIS andfuzzy TOPSIS methods.Applied Water Science,15(1). https://doi.org/10.1007/s13201-024-02336-7
12. Karacaoğlu, B., & Akbaba, M. F. (2024). Multidisciplinary perspective: A review of the importance of communication in managingclimate change challenges.Environmental Research and Technology,7(3), 457–470. https://doi.org/10.35208/ert.1461700
13. Karaman, E., & Kazan, H. (2015). Performance Evaluation In Family Physician: The Application of TOPSIS Multi-Criteria DecisionMaking Method.Alphanumeric Journal,3(2). https://doi.org/10.17093/aj.2015.3.2.5000139457
14. Komorowski, M., Marshall, D. C., Salciccioli, J. D., & Crutain, Y. (2016). Exploratory Data Analysis. InSecondary Analysis of ElectronicHealth Records(pp. 185–203). Springer International Publishing. https://doi.org/10.1007/978-3-319-43742-2_15
15. Manly, B. F., & Navarro Alberto, J. A. (2016).Multivariate Statistical Methods. Chapman, Hall/CRC. https://doi.org/10.1201/9781315382135
16. Martin, V. (2025). Climate Change Indicator Analysis.Journal of Central Banking Theory and Practice,14(1), 75–94. https://doi.org/10.2478/jcbtp-2025-0005
17. Masnan, M. J., Zakaria, A., Shakaff, A. Y. M., Mahat, N. I., Hamid, H., Subari, N., & Saleh, J. M. (2012). Principal Component Analysis– A Realization of Classification Success in Multi Sensor Data Fusion. In S. Parinya (Ed.),Principal Component Analysis:Engineering Applications(pp. 1–24). ınTech.
18. Mathaisel, D. (2024). Using data visualizations as information communication tools during a crisis: a critical review.Journal ofInformation, Communication and Ethics in Society,22(1), 103–123. https://doi.org/10.1108/jices-06-2023-0080
19. Miranda-Calle, J. D., Reddy C., V., Dhawan, P., & Churi, P. (2021). Exploratory data analysis for cybersecurity.World Journal ofEngineering,18(5), 734–749. https://doi.org/10.1108/wje-11-2020-0560
20. Muhammad, A., & Hazelton, J. (2024). An analysis of the use of 3D game engine technology in visualising sustainability data.Meditari Accountancy Research,33(2), 442–471. https://doi.org/10.1108/medar-02-2024-2381
21. Mukhiya, S. K., & Ahmed, U. (2020).Hands-on exploratory data analysis with Python: perform EDA techniques to understand,summarize, and investigate your data. Packt.
22. NetworkReadinessIndex2024. (2024, ).Complete Dataset.https://networkreadinessindex.org/
23. Nielsen, S. (2022). Management accounting and the concepts of exploratory data analysis and unsupervised machine learning:a literature study and future directions.Journal of Accounting & Organizational Change,18(5), 811–853. https://doi.org/10.1108/jaoc-08-2020-0107
24. Peterfreund, E., & Gavish, M. (2021). Multidimensional scaling of noisy high dimensional data.Applied and ComputationalHarmonic Analysis,51, 333–373. https://doi.org/10.1016/j.acha.2020.11.006
25. Puertas, R., & Marti, L. (2021). International ranking of climate change action: An analysis using the indicators from the ClimateChange Performance Index.Renewable and Sustainable Energy Reviews,148, 111316. https://doi.org/10.1016/j.rser.2021.111316
26. Qazi, A. (2025). Fostering economic development and sustainability: insights into the role of regulatory quality and e-commercelegislation in future technologies.International Journal of Innovation Science. https://doi.org/10.1108/ijis-04-2024-0091
27. Saeed, N., Nam, H., Haq, M. I. U., & Muhammad Saqib, D. B. (2018). A Survey on Multidimensional Scaling.ACM Computing Surveys,51(3), 1–25. https://doi.org/10.1145/3178155
28. Saka, C., Oshika, T., & Jimichi, M. (2019). Visualization of tax avoidance and tax rate convergence: Exploratory analysis of world-scale accounting data.Meditari Accountancy Research,27(5), 695–724. https://doi.org/10.1108/medar-02-2018-0298
29. Sevim, C. (2011). Enerji Teknolojilerindeki Anlayış Model Değişimi ve Hızlı İklim Değişikliği.Yaşar Üniversitesi E-Dergisi,6(21),3515–3522. https://doi.org/10.19168/jyu.41266
30. Shen, H., Bhamidi, S., & Liu, Y. (2023). Statistical Significance of Clustering with Multidimensional Scaling.Journal of Computational and Graphical Statistics,33(1), 219–230. https://doi.org/10.1080/10618600.2023.2219708
31. Silva, D. S., Yamashita, G. H., Cortimiglia, M. N., Brust-Renck, P. G., & Caten, C. S. ten. (2022). Are we ready to assess digitalreadiness? Exploring digital implications for social progress from the Network Readiness Index.Technology in Society,68,101875. https://doi.org/10.1016/j.techsoc.2022.101875
32. Tanious, R., & Manolov, R. (2022). Violin plots as visual tools in the meta-analysis of Single-Case Experimental Designs.Methodology,18(3), 221–238. https://doi.org/10.5964/meth.9209
33. WEForum. (2024).The Global Risks Report 2024. World Economic Forum.https://www.weforum.org/reports/global-risks-report-2024alphanumeric13(1), 13–3635Data-Driven Insights into Climate Change and Technological Levels: Data Mining Visualization and TOPSIS Approach|Doğruel, 2025
34. Yu, C., Leng, Y., Li, J., & Yu, J. (2020). Blockchain crowdfunding projects evaluation using GRA-TOPSIS.Kybernetes,50(11), 3017–3036. https://doi.org/10.1108/k-02-2020-0067alphanumeric13(1), 13–3636