ICT, carbon emissions, climate change, and energy demand nexus: the potential benefit of digitalization in Taiwan

Adha, Rishan and Hong, Cheng-Yih and Agrawal, Somya and Li, Li-Hua (2022): ICT, carbon emissions, climate change, and energy demand nexus: the potential benefit of digitalization in Taiwan. Published in: Energy & Environment journal No. 0958305X221093458 (13 April 2022)

Abstract

The global rise in energy consumption makes managing energy demands a priority. Here, the potential of Information and Communication Technology (ICT) in controlling energy consumption is still debated. Within this context, the main objective of the current study is to measure the impact of ICT, its potential benefit, and environmental factors on household electricity demand in Taiwan. A panel of data from 20 cities in Taiwan was collected during the period 2004-2018. We adopted PMG estimation and applied the DH-causality test for analysis. The estimation results show that ICT, carbon emissions, and climate change will drive household electricity demand in Taiwan in the long term. However, ICT has a higher potential to reduce electricity demand in the short-term period. In addition, the results of the causality test reveal a two-way interrelationship between ICT and electricity demand. Our study also found that climate change indirectly affects the use of electricity through household appliances. We also presented several policy implications at the end of this paper.

• Item Type: MPRA Paper
• Language: English
• Keywords: energy demand, ICT, carbon emissions, climate change, dynamic panel data model
• Subjects: C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C33 - Panel Data Models ; Spatio-temporal Models; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q43 - Energy and the Macroeconomy

References

References

Adha, R., & Hong, C.-Y. (2021). How Large the Direct Rebound Effect for Residential Electricity Consumption When the Artificial Neural Network Takes on the Role? A Taiwan Case Study of Household Electricity Consumption. International Journal of Energy Economics and Policy, 11(3), 354-364. doi:https://doi.org/10.32479/ijeep.9834

Adha, R., Hong, C.-Y., Firmansyah, M., & Paranata, A. (2021). Rebound effect with energy efficiency determinants: a two-stage analysis of residential electricity consumption in Indonesia. Sustainable Production and Consumption, 28, 556-565. doi:https://doi.org/10.1016/j.spc.2021.06.019

Al-Mulali, U., Sheau-Ting, L., & Ozturk, I. (2015). The global move toward Internet shopping and its influence on pollution: an empirical analysis. Environmental Science and Pollution Research, 22(13), 9717-9727. doi:10.1007/s11356-015-4142-2

Alsaleh, M., & Abdul-Rahim, A. S. (2021a). The pathway toward bioenergy growth: does information and communication technology development make a difference in EU economies? Biomass Conversion and Biorefinery. doi:10.1007/s13399-021-01933-9

Alsaleh, M., & Abdul-Rahim, A. S. (2021b). The pathway toward pollution mitigation in EU28 region: does bioenergy growth make a difference? Management of Environmental Quality: An International Journal, 32(3), 560-574. doi:10.1108/MEQ-08-2020-0177

Alsaleh, M., & Abdul-Rahim, A. S. (2022). The pathway toward pollution mitigation in EU28 region: Does hydropower growth make a difference? Renewable Energy, 185, 291-301. doi:https://doi.org/10.1016/j.renene.2021.12.045

Amasawa, E., Ihara, T., & Hanaki, K. (2018). Role of e-reader adoption in life cycle greenhouse gas emissions of book reading activities. The International Journal of Life Cycle Assessment, 23(9), 1874-1887. doi:10.1007/s11367-017-1417-5

Anda, M., & Temmen, J. (2014). Smart metering for residential energy efficiency: The use of community based social marketing for behavioural change and smart grid introduction. Renewable Energy, 67, 119-127. doi:https://doi.org/10.1016/j.renene.2013.11.020

Andrae, A. S. G., & Edler, T. (2015). On Global Electricity Usage of Communication Technology: Trends to 2030. Challenges, 6(1). doi:10.3390/challe6010117

Azam, A., Rafiq, M., Shafique, M., & Yuan, J. (2021). An empirical analysis of the non-linear effects of natural gas, nuclear energy, renewable energy and ICT-Trade in leading CO2 emitter countries: Policy towards CO2 mitigation and economic sustainability. Journal of Environmental Management, 286, 112232. doi:https://doi.org/10.1016/j.jenvman.2021.112232

Belkhir, L., & Elmeligi, A. (2018). Assessing ICT global emissions footprint: Trends to 2040 & recommendations. Journal of Cleaner Production, 177, 448-463. doi:https://doi.org/10.1016/j.jclepro.2017.12.239

Blasch, J., Boogen, N., Filippini, M., & Kumar, N. (2017). Explaining electricity demand and the role of energy and investment literacy on end-use efficiency of Swiss households. Energy Economics, 68, 89-102. doi:https://doi.org/10.1016/j.eneco.2017.12.004

BOE. (2020). Bureau of Energy: Energy Statistical annual Reports. Retrieved from Taiwan: https://www.moeaboe.gov.tw/ECW/english/content/ContentLink.aspx?menu_id=1540

Caglar, A. E., Mert, M., & Boluk, G. (2021). Testing the role of information and communication technologies and renewable energy consumption in ecological footprint quality: Evidence from world top 10 pollutant footprint countries. Journal of Cleaner Production, 298, 126784. doi:https://doi.org/10.1016/j.jclepro.2021.126784

De Hoyos, R., & Sarafidis, V. (2006). Testing for cross-sectional dependence in panel-data models. Stata Journal, 6(4), 482-496. Retrieved from https://EconPapers.repec.org/RePEc:tsj:stataj:v:6:y:2006:i:4:p:482-496

Dumitrescu, E.-I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling, 29(4), 1450-1460. doi:https://doi.org/10.1016/j.econmod.2012.02.014

EDGAR. (2020). Emission Database for Global Atmospheric Research: Global GHG and CO2 Emissions. Retrieved from: https://edgar.jrc.ec.europa.eu/overview.php?v=booklet2020

Emir, F., & Bekun, F. V. (2018). Energy intensity, carbon emissions, renewable energy, and economic growth nexus: New insights from Romania. Energy & Environment, 30(3), 427-443. doi:10.1177/0958305X18793108

Ferry, T. (2015). Taiwan’s Energy Dilemma: Emission Reductions vs. Dwindling Supply. Industry Focus. Retrieved from https://topics.amcham.com.tw/2015/09/carbon-abatement-and-energy-supply/

Filippini, M., & Hunt, L. C. (2011). Energy Demand and Energy Efficiency in the OECD Countries: A Stochastic Demand Frontier Approach. The Energy Journal, Volume 32(2), 59-80. doi:https://doi.org/10.5547/ISSN0195-6574-EJ-Vol32-No2-3

Filippini, M., & Hunt, L. C. (2012). US residential energy demand and energy efficiency: A stochastic demand frontier approach. Energy Economics, 34(5), 1484-1491. doi:https://doi.org/10.1016/j.eneco.2012.06.013

Filippini, M., & Hunt, L. C. (2016). Measuring persistent and transient energy efficiency in the US. Energy Efficiency, 9(3), 663-675. doi:https://doi.org/10.1007/s12053-015-9388-5

Filippini, M., & Zhang, L. (2016). Estimation of the energy efficiency in Chinese provinces. Energy Efficiency, 9(6), 1315-1328. doi:https://doi.org/10.1007/s12053-016-9425-z

Frees, E. W. (1995). Assessing cross-sectional correlation in panel data. Journal of Econometrics, 69(2), 393-414. Retrieved from https://EconPapers.repec.org/RePEc:eee:econom:v:69:y:1995:i:2:p:393-414

Friedman, M. (1937). The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance. Journal of the American Statistical Association, 32(200), 675-701. doi:10.2307/2279372

Guerhardt, F., Silva, T. A., Gamarra, F. M., Ribeiro Júnior, S. E., Llanos, S. A., Quispe, A. P., . . . Maria Vanalle, R. (2020). A Smart Grid System for Reducing Energy Consumption and Energy Cost in Buildings in São Paulo, Brazil. energies, 13(15). doi:10.3390/en13153874

Haftu, G. G. (2019). Information communications technology and economic growth in Sub-Saharan Africa: A panel data approach. Telecommunications Policy, 43(1), 88-99. doi:https://doi.org/10.1016/j.telpol.2018.03.010

Haseeb, A., Xia, E., Saud, S., Ahmad, A., & Khurshid, H. (2019). Does information and communication technologies improve environmental quality in the era of globalization? An empirical analysis. Environmental Science and Pollution Research, 26(9), 8594-8608. doi:10.1007/s11356-019-04296-x

Hilty, L., & Bieser, J. (2017). Opportunities and risks of digitalization for climate protection in Switzerland. doi:https://doi.org/10.5167/uzh-141128

Hofman, A., Aravena, C., & Aliaga, V. (2016). Information and communication technologies and their impact in the economic growth of Latin America, 1990–2013. Telecommunications Policy, 40(5), 485-501. doi:https://doi.org/10.1016/j.telpol.2016.02.002

Ishida, H. (2015). The effect of ICT development on economic growth and energy consumption in Japan. Telematics and Informatics, 32(1), 79-88. doi:https://doi.org/10.1016/j.tele.2014.04.003

Jin, L., Duan, K., & Tang, X. (2018). What Is the Relationship between Technological Innovation and Energy Consumption? Empirical Analysis Based on Provincial Panel Data from China. Sustainability, 10(1). doi:10.3390/su10010145

Kao, C. (1999). Spurious regression and residual-based tests for cointegration in panel data. Journal of Econometrics, 90(1), 1-44. doi:https://doi.org/10.1016/S0304-4076(98)00023-2

Kuppusamy, M., Raman, M., & Lee, G. (2009). Whose ICT Investment Matters to Economic Growth: Private or Public? The Malaysian Perspective. The Electronic Journal of Information Systems in Developing Countries, 37(1), 1-19. doi:https://doi.org/10.1002/j.1681-4835.2009.tb00262.x

Lange, S., Pohl, J., & Santarius, T. (2020). Digitalization and energy consumption. Does ICT reduce energy demand? Ecological Economics, 176, 106760. doi:https://doi.org/10.1016/j.ecolecon.2020.106760

Lean, H., & Smith, R. (2009). CO2 emissions, energy consumption and output in ASEAN. Business and Economics, Development Research Unit Discussion Paper DEVDP, 09-13.

Lee, J. W., & Brahmasrene, T. (2014). ICT, CO2 Emissions and Economic Growth: Evidence from a Panel of ASEAN. Global Economic Review, 43(2), 93-109. doi:10.1080/1226508X.2014.917803

Lopez, L., & Weber, S. (2017). Testing for Granger Causality in Panel Data. The Stata Journal, 17(4), 972-984. doi:10.1177/1536867x1801700412

Lu, W.-C. (2018). The impacts of information and communication technology, energy consumption, financial development, and economic growth on carbon dioxide emissions in 12 Asian countries. Mitigation and Adaptation Strategies for Global Change, 23(8), 1351-1365. doi:10.1007/s11027-018-9787-y

Majeed, M. T., & Ayub, T. (2018). Information and communication technology (ICT) and economic growth nexus: A comparative global analysis. Pakistan Journal of Commerce and Social Sciences (PJCSS), 12(2), 443-476.

Malmodin, J., & Lundén, D. (2018). The Energy and Carbon Footprint of the Global ICT and E&M Sectors 2010–2015. Sustainability, 10(9). doi:10.3390/su10093027

Mayers, K., Koomey, J., Hall, R., Bauer, M., France, C., & Webb, A. (2015). The Carbon Footprint of Games Distribution. Journal of Industrial Ecology, 19(3), 402-415. doi:https://doi.org/10.1111/jiec.12181

Menegaki, A. N. (2019). The ARDL Method in the Energy-Growth Nexus Field; Best Implementation Strategies. Economies, 7(4), 105. doi:doi:10.3390/economies7040105

Menyah, K., & Wolde-Rufael, Y. (2010). Energy consumption, pollutant emissions and economic growth in South Africa. Energy Economics, 32(6), 1374-1382.

Mickoleit, A. (2010). Greener and Smarter. OECD Green Growth Papers 2010/01. doi:doi:https://doi.org/10.1787/5k9h3635kdbt-en

Orea, L., Llorca, M., & Filippini, M. (2015). A new approach to measuring the rebound effect associated to energy efficiency improvements: An application to the US residential energy demand. Energy Economics, 49, 599-609. doi:https://doi.org/10.1016/j.eneco.2015.03.016

Osman, M., Gachino, G., & Hoque, A. (2016). Electricity consumption and economic growth in the GCC countries: Panel data analysis. Energy Policy, 98, 318-327. doi:https://doi.org/10.1016/j.enpol.2016.07.050

Otsuka, A. (2017). Determinants of efficiency in residential electricity demand: stochastic frontier analysis on Japan. Energy, Sustainability and Society, 7(1), 31. doi:https://doi.org/10.1186/s13705-017-0135-y

Ozcan, B., & Apergis, N. (2018). The impact of internet use on air pollution: Evidence from emerging countries. Environmental Science and Pollution Research, 25(5), 4174-4189. doi:10.1007/s11356-017-0825-1

Pedroni, P. (1999). Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors. Oxford Bulletin of Economics and Statistics, 61(S1), 653-670. doi:https://doi.org/10.1111/1468-0084.0610s1653

Pedroni, P. (2004). Panel cointegration: Asymptotic and finite sample properties of pooled time series tests with an application to the ppp hypothesis. Econometric Theory, 20(3), 597-625. doi:10.1017/S0266466604203073

Peng, G. C. (2013). Green ICT: A Strategy for Sustainable Development of China's Electronic Information Industry. China: An International Journal, 11(3), 68-86. Retrieved from https://www.worldscientific.com/worldscinet/cij

Pesaran, M. (2004). General Diagnostic Tests for Cross Section Dependence in Panels. Retrieved from https://EconPapers.repec.org/RePEc:cam:camdae:0435

Pesaran, M. (2006). Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure. Econometrica, 74(4), 967-1012. doi:https://doi.org/10.1111/j.1468-0262.2006.00692.x

Pesaran, M. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22(2), 265-312. doi:https://doi.org/10.1002/jae.951

Pesaran, M., Shin, Y., & Smith, R. P. (1999). Pooled Mean Group Estimation of Dynamic Heterogeneous Panels. Journal of the American Statistical Association, 94(446), 621-634. doi:10.1080/01621459.1999.10474156

Pesaran, M., & Smith, R. (1995). Estimating long-run relationships from dynamic heterogeneous panels. Journal of Econometrics, 68(1), 79-113. doi:https://doi.org/10.1016/0304-4076(94)01644-F

Sadorsky, P. (2012). Information communication technology and electricity consumption in emerging economies. Energy Policy, 48, 130-136. doi:https://doi.org/10.1016/j.enpol.2012.04.064

Saidi, K., & Hammami, S. (2015). The impact of CO2 emissions and economic growth on energy consumption in 58 countries. Energy Reports, 1, 62-70. doi:https://doi.org/10.1016/j.egyr.2015.01.003

Salahuddin, M., & Alam, K. (2015). Internet usage, electricity consumption and economic growth in Australia: A time series evidence. Telematics and Informatics, 32(4), 862-878. doi:https://doi.org/10.1016/j.tele.2015.04.011

Salahuddin, M., & Alam, K. (2016). Information and Communication Technology, electricity consumption and economic growth in OECD countries: A panel data analysis. International Journal of Electrical Power & Energy Systems, 76, 185-193. doi:https://doi.org/10.1016/j.ijepes.2015.11.005

Schulte, P., Welsch, H., & Rexhäuser, S. (2016). ICT and the Demand for Energy: Evidence from OECD Countries. Environmental and Resource Economics, 63(1), 119-146. doi:10.1007/s10640-014-9844-2

Shiau, Y.-H., Yang, S.-F., Adha, R., & Muzayyanah, S. (2022). Modeling Industrial Energy Demand in Relation to Subsector Manufacturing Output and Climate Change: Artificial Neural Network Insights. Sustainability, 14(5). doi:10.3390/su14052896

Usman, A., Ozturk, I., Hassan, A., Maria Zafar, S., & Ullah, S. (2021). The effect of ICT on energy consumption and economic growth in South Asian economies: An empirical analysis. Telematics and Informatics, 58, 101537. doi:https://doi.org/10.1016/j.tele.2020.101537

Van Heddeghem, W., Lambert, S., Lannoo, B., Colle, D., Pickavet, M., & Demeester, P. (2014). Trends in worldwide ICT electricity consumption from 2007 to 2012. Computer Communications, 50, 64-76. doi:https://doi.org/10.1016/j.comcom.2014.02.008

van Loon, P., Deketele, L., Dewaele, J., McKinnon, A., & Rutherford, C. (2015). A comparative analysis of carbon emissions from online retailing of fast moving consumer goods. Journal of Cleaner Production, 106, 478-486. doi:https://doi.org/10.1016/j.jclepro.2014.06.060

Westerlund, J. (2007). Testing for Error Correction in Panel Data*. Oxford Bulletin of Economics and Statistics, 69(6), 709-748. doi:https://doi.org/10.1111/j.1468-0084.2007.00477.x

Yan, Z., Shi, R., & Yang, Z. (2018). ICT Development and Sustainable Energy Consumption: A Perspective of Energy Productivity. Sustainability, 10(7). doi:10.3390/su10072568

Yu-Chen, Y., Cheng-Yih, H., Muzayyanah, S., & Adha, R. (2020). Decomposition of Growth Factors in High-tech Industries and CO2 Emissions: After the World Financial Crisis in 2008. International Journal of Energy Economics and Policy, 10(4), 500. doi:https://doi.org/10.32479/ijeep.9411

Zaharia, A., Diaconeasa, M. C., Brad, L., Lădaru, G.-R., & Ioanăș, C. (2019). Factors Influencing Energy Consumption in the Context of Sustainable Development. Sustainability, 11(15), 4147. Retrieved from https://www.mdpi.com/2071-1050/11/15/4147

Zhang, C., & Liu, C. (2015). The impact of ICT industry on CO2 emissions: A regional analysis in China. Renewable and Sustainable Energy Reviews, 44, 12-19. doi:https://doi.org/10.1016/j.rser.2014.12.011