A global panel analysis comparing carbon emissions across levels of economic development
Keywords:
environmental Kuznets curve; panel data; robust random effects; carbon emission
Abstract
This study compares carbon emissions across panels of high-income countries (HICs) and low- and middle-income countries (LMICs). Using the Environmental Kuznets Curve (EKC) hypothesis as a theoretical framework, this study observes the curve for each income panel using random effects panel regression, controlling for the scale, composition, technological, and pollution outsourcing effects. With a dataset ranging 30 years from 1990 to 2019 and a panel of 18 HICs and 20 LMICs, the regression results validate the presence of an EKC-like relationship between emissions and income per capita for both panels. Key findings show that LMICs are on a path of growth that emits fewer emissions than HICs at the same income level due to access to less emission-intensive technologies. This suggests that, in contrast to previous theoretical understanding, the effects observed in the EKC occur simultaneously rather than sequentially and may be leveraged to dominate at any point on the curve. In practice, LMICs are urged to dismiss the “grow now, clean later” ethos and instead, adopt cleaner production methods through energy efficiency initiatives, technological transfers, and technological leapfrogging to manage economic growth without a corresponding growth in emissions.
Keywords: environmental Kuznets curve; panel data; robust random effects; carbon emission
JEL Classification: Q56; O44
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[2] As-syakur, A. R., Ariastina, W. G., Kumara, N. I., Antara, I. M., Osawa, T., & Cahyani, D. A. (2021). Impact of Covid-19 Pandemic on Electricity Consumption and Nighttime Lights Based on NPP-VIIRS DNB Image Products. 2021 International Conference on Smart-Green Technology in Electrical and Information Systems (ICSGTEIS) (pp. 161-164). IEEE.
[3] Awirya, A., Novita, D., Irawadi, C., & Wilandari, R. (2022). Workers Adaptation during Pandemic, Evidence from Bali Province as Main Tourist Destination in Indonesia. e-Journal Ekonomi Bisnis dan Akuntansi, 14-22.
[4] Box, G., Jenkins, G., Reinsel, G., & Ljung, G. (2015). Time series analysis: forecasting and control. John Wiley and Sons.
[5] BPS. (2022). Banyaknya Wisatawan Mancanegara ke Bali Menurut Kawasan. Retrieved from https://bali.bps.go.id/indicator/16/129/1/banyaknya-wisatawan-mancanegara-ke-bali-menurut-kawasan.html
[6] Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Lawrence Erlbaum Associates.
[7] Devkota, B., Miyazaki, H., Witayangkurn, A., & Kim, S. (2019). Using Volunteered Geographic Information and Nighttime Light Remote Sensing Data to Identify Tourism Areas of Interest. Sustainability, 11(17), 4718.
[8] Elvidge, C., Baugh, K., Zhizhin, M., Hsu, F., & Tilottama, G. (2017). VIIRS Night-Time Lights. International Journal of Remote Sensing, 5860-5879.
[9] Field, A. (2018). Discovering statistics using IBM SPSS statistics. Sage publications.
[10] Gorelick, N., Hancher, M., Dixon, M., Ilyuschenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 18-27. Retrieved from https://www.sciencedirect.com/science/article/pii/S0034425717302900
[11] Haavelmo, T., & Hansen, S. (1991). On the Strategy of Trying to Reduce Economic Inequality by Expanding the Scale of Human Activity. In Environmentally Sustainable Economic Development: Building on Brundtland (pp. 41-49). Retrieved from https://documents1.worldbank.org/curated/en/332821467989482335/pdf/multi-page.pdf
[12] Han, G., Zhou, T., Sun, Y., & Zhu, S. (2022). The relationship between night-time light and socioeconomic factors in China and India. PLOS ONE (17)1: e0262503.
[13] Hyndman, R. J., & Khandakar, Y. (2008). Automatic Time Series Forecasting: The forecast Package for R. Journal of Statistical Software, 27(3), 1–22.
[14] Karimah, I. D., & Yudhistira, M. H. (2020). Does small-scale port investment affect local economic activity? Evidence from small-port development in Indonesia. Economic of Transportation 23.
[15] Kopp, T. J., Thomas, W., Heidinger, A. K., Botambekov, D., Frey, R. A., Hutchinson, K. D., . . . Reed, B. (2014). The VIIRS Cloud Mask: Progress in the first year of S-NPP toward a
common cloud detection scheme. JGR Atmosphere, 2441-2456. doi:https://doi.org/10.1002/2013JD020458
[16] Levin, N., Kyba, C., Zhang, Q., de Miguel, A., Roman, M., Xi, L., & Elvidge, C. (2020). Remote sensing of night lights: a review and an outlook for the future. Remote Sensing of Environment, 111443. Retrieved from https://www.sciencedirect.com/science/article/abs/pii/S0034425719304626?via%3Dihub
[17] Li, X., Li, D., Xu, H., & Wu, C. (2017). Intercalibration between DMSP/OLS and VIIRS night-time light images to evaluate city light dynamics of Syria’s major human settlement during Syrian Civil War. International Journal of Remote Sensing, 5934-5951.
[18] Makridakis, S. G., Wheelwright, S. C., & Hyndman, R. J. (1997). Forecasting Methods and Applications Third Edition. Hoboken: Wiley.
[19] Makridakis, S., Wheelwright, S., & Hyndman, R. (1997). Forecasting Methods and Applications (Third Edition ed.). Hoboken: Wiley.
[20] Mardika, I. M., Putra, I. B., & Wisnumurti, A. A. (2020). Pemberdayaan Pecalang Desa Adat Sumerta dalam Penanggulangan Covid-19. Postgraduated Community Service Journal, 1 (2), 37-42.
[21] Mellander, C., Lobo, J., Stolarick, K., & Matheson, Z. (2015). Night-Time Light Data: A Good Proxy Measure for Economic Activity? PLOS ONE 10(10): e0139779.
[22] Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to Time Series Analysis and Forecasting Second Edition. New Jersey: Wiley.
[23] Montgomery, D., Jennings, C., & Kulahci, M. (2015). Introduction to Time Series Analysis and Forecasting (Second Edition ed.). New Jersey: Wiley.
[24] Pankratz, A. (1983). Forecasting with Univariate Box-Jenkins Models: Concepts and Cases. Greencastle: John Wiley and Sons.
[25] Pesaresi, M., Corbane, C., Julea, A., Florczyk, A. J., Syrris, V., & Soille, P. (2016). Assessment of the Added-Value of Sentinel-2 for Detecting Built-up Areas. Remote Sensing, 299. Retrieved from https://www.mdpi.com/2072-4292/8/4/299
[26] Pesaresi, M., Ehrlich, D., Ferry, S., Florczyk, A. J., Freire, S., Halkia, M., . . . Syrris, V. (2016). Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014. Italy: European Commission.
[27] Pesaresi, M., Ehrlich, D., Ferry, S., Florczyk, A., Freire, S., Halkia, M., & Syrris, V. (2016). Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014. Italy: European Commission.
[28] Shmueli, G., & Lichtendahl Jr., K. (2016). Practical Time Series Forecasting with R: A Hands-on Guide. Axelrod Schnall.
[29] WHO. (2020). Novel Coronavirus (2019-ncov) Situation Report - 1. Retrieved from https://apps.who.int/iris/bitstream/handle/10665/330760/nCoVsitrep21Jan2020-eng.pdf?sequence=3'isAllowed=y
[30] World Bank. (n.d.). 1. Time Series Charts. Retrieved from Open Night Light: https://worldbank.github.io/OpenNightLights/tutorials/mod4_1_time_series_charts.html
[31] Ye, J., Xiao, C., Esteves, R., & Rong, C. (2015). Time series similarity evaluation based on
Spearman’s correlation coefficients and distance measures. Cloud Computing and Big
[32] Data: Second International Conference, CloudCom-Asia 2015, Huangshan, China, June 17-19, 2015, Revised Selected Papers 2 (pp. 319-331). Springer International Publishing.
[33] Zhao, N., Liu, Y., Hsu, F., Samson, E., Letu, H., Liang, D., & Cao, G. (2020). Time series analysis of VIIRS-DNB nighttime lights imagery for change detection in urban areas: A case study of devastation in Puerto Rico from hurricanes Irma and Maria. ScienceDirect
Published
2025-04-24
How to Cite
Tiono, W., Mulia, N., Kadarusman, Y., & Ramadhanti, F. (2025). A global panel analysis comparing carbon emissions across levels of economic development. Jurnal Ekonomi Indonesia, 14(1), 24-37. https://doi.org/10.52813/jei.v14i1.562
Section
Articles
