Measuring Climate–Labor Vulnerability in the Platform Economy Through a Climate-Informed Flexibility Vulnerability Index for Indonesia
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Issue
Vol. 21 No. 1 (2026)
Keywords:
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Platform economy, Digital labour, Climate vulnerability, Gig work, Algorithmic management
Abstract
This study develops the Climate-Informed Flexibility Vulnerability Index (CAFVI) to measure multidimensional climate–labor vulnerability among platform workers in Jakarta, Indonesia. The index integrates four dimensions: economic vulnerability, institutional vulnerability, demographic sensitivity, and environmental exposure. Using secondary data from Fairwork Indonesia, BPS Sakernas, and BMKG, the study applies variable normalization, equal weighting, and principal component analysis to produce comparable vulnerability scores across age groups and platform providers. The findings show a clear U-shaped pattern of vulnerability, with the highest scores observed among younger and older workers and the lowest among prime-age workers. At the platform level, vulnerability is shaped primarily by the interaction between institutional protection and income conditions, while environmental exposure functions as a shared structural baseline. Principal component analysis confirms the internal coherence of the index, with the first component explaining most of the total variance and all loadings remaining positive. The results indicate that climate stress does not operate independently but intensifies existing labor precarity where worker protection and income stability are weak. This study contributes by extending platform labor analysis beyond conventional fairness assessments and by offering a transparent, replicable framework for evaluating climate-sensitive labor vulnerability in data-constrained urban settings, especially in the Global South and comparable emerging economies.
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Benlian, A., Wiener, M., Cram, W. A., Krasnova, H., Maedche, A., Möhlmann, M., Recker, J., & Remus, U. (2022). Algorithmic management. Business and Information Systems Engineering, 64(6), 825–839. https://doi.org/10.1007/s12599-022-00764-w
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Duggan, J., Carbery, R., McDonnell, A., & Sherman, U. (2023). Algorithmic HRM control in the gig economy: The app-worker perspective. Human Resource Management, 62(6), 883–899. https://doi.org/10.1002/hrm.22168
Dwiputrianti, S., Kotchegura, A., & Lee, H. K. (2025). Gig economy or digital inequality? Lessons for Indonesia’s labour future. Jurnal Ketenagakerjaan, 20(3), 3–12. https://doi.org/10.47198/jnaker.v20i3.616
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Fieseler, C., Bucher, E., & Hoffmann, C. P. (2019). Unfairness by design? The perceived fairness of digital labor on crowdworking platforms. Journal of Business Ethics, 156(4), 987–1005. https://doi.org/10.1007/s10551-017-3607-2
Garcia Ferrari, S., Crane De Narváez, S., Castro Mera, W. E., Velásquez, C., & Bain, A. A. (2022). Collective action towards risk management in informal urban areas in Medellín: Covid-19 lessons for reducing vulnerability and inequality. Frontiers in Environmental Science, 9(February), 1–17. https://doi.org/10.3389/fenvs.2021.726867
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He, C., Li, A., Li, D., & Yu, J. (2022). Does Digital Inclusive Finance Mitigate the Negative Effect of Climate Variation on Rural Residents’ Income Growth in China? International Journal of Environmental Research and Public Health, 19(14). https://doi.org/10.3390/ijerph19148280
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Hope, D., & Martelli, A. (2019). The transition to the knowledge economy, labor market institutions, and income inequality in advanced democracies. World Politics, 71(2), 236–288. https://doi.org/10.1017/S0043887118000333
Hyder, A. A., Puvanachandra, P., & Morrow, R. H. (2012). Measuring the health of populations: Explaining composite indicators. Journal of Public Health Research, 1(3). https://doi.org/10.4081/jphr.2012.e35
Izzati, N. R., & Kwang, E. N. (2025). Regulating platform work in Indonesia: Exploring legal options for workers’ protection. Jurnal Ketenagakerjaan, 20(3). https://doi.org/10.47198/jnaker.v20i3.622
Kashif, M., Garg, H., Shajar, S. N., & David, A. (2026). Climate-related risks in emerging markets: Understanding the landscape, challenges and strategic solutions. In A. Rahman, S. Akhtar, M. Alam, & M. Khan (Eds.), Climate Risk and Emerging Markets: Valuation, Volatility, and Portfolio Strategies (p. 0). Emerald Publishing Limited. https://doi.org/10.1108/978-1-83708-088-520261009
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Kloppenburg, S., Gupta, A., Kruk, S. R. L., Makris, S., Bergsvik, R., Korenhof, P., Solman, H., & Toonen, H. M. (2022). Scrutinizing environmental governance in a digital age: New ways of seeing, participating, and intervening. One Earth, 5(3), 232–241. https://doi.org/10.1016/j.oneear.2022.02.004
Lajeunesse, S. (2025). On the concepts of human security, dignity and vulnerability: Understanding the mechanisms of being ‘at risk.’ The International Journal of Human Rights, 0(0), 1–26. https://doi.org/10.1080/13642987.2025.2555324
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Mekonen, A. A., & Berlie, A. B. (2021). Rural households’ livelihood vulnerability to climate variability and extremes: A livelihood zone-based approach in the northeastern highlands of Ethiopia. Ecological Processes, 10(1). https://doi.org/10.1186/s13717-021-00313-5
Miao, D., Gillani, S., Abbas, H. S. M., & Zhan, H. (2023). Do institutional governance and state fragility affect institutional quality in Asian economies? Heliyon, 9(4), e15467. https://doi.org/10.1016/j.heliyon.2023.e15467
N.M, Q. (2022). A method for measuring women climate vulnerability: a case study in Vietnam’s Mekong Delta. International Journal of Climate Change Strategies and Management, 14(2), 101–124. https://doi.org/10.1108/IJCCSM-05-2021-0047
Navajas-Romero, V., Muñoz-Benito, R., & Sánchez-Rodríguez, M. I. (2026). Rethinking job dimensions: The value of riders and gig economy workers. Technological Forecasting and Social Change, 222(May 2023). https://doi.org/10.1016/j.techfore.2025.124379
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Odonkor, S. T., & Adams, S. (2022). Climate change-mediated heat stress vulnerability and adaptation strategies among outdoor workers. Climate and Development, 14(7), 591–599. https://doi.org/10.1080/17565529.2021.1954867
Okoye, K., & Hosseini, S. (2024). Correlation Tests in R: Pearson Cor, Kendall’s Tau, and Spearman’s Rho. In R Programming: Statistical Data Analysis in Research (pp. 247–277). Springer Nature Singapore. https://doi.org/10.1007/978-981-97-3385-9_12
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Authors
Hutabarat, J. S. and Hamise, V. T. . (2026) “Measuring Climate–Labor Vulnerability in the Platform Economy Through a Climate-Informed Flexibility Vulnerability Index for Indonesia”, Jurnal Ketenagakerjaan, 21(1). doi: 10.47198/jnaker.v21i1.614.
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