New Quality Productive Forces in Taiwan Evidence from the Three New Economy Framework
DOI:
https://doi.org/10.59976/jebin.v2i1.241Keywords:
New quality productive forces; Three New Economy; Malmquist index; regional disparity; spatial Markov chain analysis.Abstract
This study aims to measure the development of New Quality Productive Forces (NQPF) in Taiwan and explain their regional growth patterns, disparities, and spatial coordination dynamics under the Three New Economy framework. The study analyzes 19 Taiwanese administrative units from 2016 to 2022 using the super-efficiency MinDW Malmquist index, Dagum Gini decomposition, spatial conditional kernel density estimation, and spatial Markov chain analysis. Taiwan’s NQPF increased moderately, with a national average index of 1.02, but growth remained uneven. Six regional growth patterns were identified, ranging from synchronous growth in major innovation corridors to incubation patterns in structurally constrained areas. Regional inequality widened after 2019 and formed a North high, East low spatial gradient, with between-region disparity contributing the largest share of total inequality. Spatial analysis further shows that backwash effects constrain low-tier regions, whereas medium-high regions such as Taichung and Kaohsiung function as potential spread transmitters. This study extends NQPF literature by operationalizing the Three New Economy concept within an efficiency-based productivity framework and by integrating productivity, inequality, and spatial mobility analysis in a single empirical design. The findings suggest the need for innovation corridors, relay-region strengthening, and location-specific transformation strategies. The study is limited to available regional data for 2016–2022; future research should incorporate firm-level data and longer post-pandemic observations to refine causal interpretation.
References
Chang, J., Lan, Q., Tang, W., Chen, H., Liu, J., & Duan, Y. (2023). Research on the impact of digital economy on manufacturing total factor productivity. Sustainability, 15(7), 5683. https://doi.org/10.3390/su15075683
Chen, Y., Hu, S., & Wu, H. (2023). The digital economy, green technology innovation, and agricultural green total factor productivity. Agriculture, 13(10), 1961. https://doi.org/10.3390/agriculture13101961
Chen, Y. S., & Lin, M. J. (2021). Technology innovation spillovers and regional productivity convergence: Evidence from Taiwan’s manufacturing sector. Asia Pacific Journal of Regional Science, 5(3), 781–808.
Directorate General of Budget Executive Yuan, Taiwan, A. and S. (2022). National accounts and regional economic statistics 2016 to 2022. Directorate General of Budget, Accounting and Statistics, Executive Yuan, Taiwan.
Fan, H., Zhang, N., & Su, H. (2023). The effects of smart city construction on urban green total factor productivity: Evidence from China. Economic Research-Ekonomska Istraživanja, 36(1), 2181840. https://doi.org/10.1080/1331677X.2023.2181840
Fang, L., Zhao, B., Li, W., Tao, L., He, L., Zhang, J., & Wen, C. (2023). Impact of digital finance on industrial green transformation: Evidence from the Yangtze River Economic Belt. Sustainability, 15(17), 12799. https://doi.org/10.3390/su151712799
Guo, B., Wang, Y., Zhang, H., Liang, C., Feng, Y., & Hu, F. (2023). Impact of the digital economy on high-quality urban economic development: Evidence from Chinese cities. Economic Modelling, 120, 106194. https://doi.org/10.1016/j.econmod.2023.106194
Hu, X., & Guo, P. (2022). A spatial effect study on the digital economy affecting the green total factor productivity in the Yangtze River Economic Belt. Environmental Science and Pollution Research, 29, 90868–90886. https://doi.org/10.1007/s11356-022-22168-9
Lee, C.-C., & Lee, C.-C. (2022). How does green finance affect green total factor productivity? Evidence from China. Energy Economics, 107, 105863. https://doi.org/10.1016/j.eneco.2022.105863
Li, T., Han, D., Ding, Y., & Shi, Z. (2020). How does the development of the Internet affect green total factor productivity? Evidence from China. IEEE Access, 8, 216477–216490. https://doi.org/10.1109/ACCESS.2020.3041511
Lin, W. C., & Wang, S. H. (2022). Capital stock estimation for Taiwan’s regional industries: A perpetual inventory approach with sectoral deflators. Taiwan Economic Review, 50(2), 145–188.
Liu, H. J., Qiao, L. C., & Sun, S. H. (2020). Spatial pattern and dynamic evolution of water use efficiency in the Yellow River basin. Resources Science, 42(1), 57–68.
Liu, H. J., & Sun, Y. N. (2023). Quantitative analysis of economic data: Methods, tools and applications. Higher Education Press.
Liu, Y., Yang, Y., Li, H., & Zhong, K. (2022). Digital economy development, industrial structure upgrading and green total factor productivity: Empirical evidence from China’s cities. International Journal of Environmental Research and Public Health, 19(4), 2414. https://doi.org/10.3390/ijerph19042414
Luo, S., Yimamu, N., Li, Y., Wu, H., Irfan, M., & Hao, Y. (2023). Digitalization and sustainable development: How could digital economy development improve green innovation in China? Business Strategy and the Environment, 32(4), 1847–1871. https://doi.org/10.1002/bse.3223
Lyu, Y., Wang, W., Wu, Y., & Zhang, J. (2023). How does digital economy affect green total factor productivity? Evidence from China. Science of the Total Environment, 857, 159428. https://doi.org/10.1016/j.scitotenv.2022.159428
Meng, F., & Zhao, Y. (2022). How does digital economy affect green total factor productivity at the industry level in China: From a perspective of global value chain. Environmental Science and Pollution Research, 29(52), 79497–79515. https://doi.org/10.1007/s11356-022-21434-0
National Science and Technology Council, T. (2022). Science and technology statistical abstract 2022. National Science and Technology Council, Taiwan.
Office, T. I. P. (2023). Annual report on Taiwan patent statistics 2022. Taiwan Intellectual Property Office.
Pan, W., Xie, T., Wang, Z., & Ma, L. (2022). Digital economy: An innovation driver for total factor productivity. Journal of Business Research, 139, 303–311. https://doi.org/10.1016/j.jbusres.2021.09.061
Sun, X., Jiang, K., Cui, Z., Xu, J., & Zhao, X. (2023). Exploring the impact of the digital economy on green total factor productivity in China: A spatial econometric perspective. Frontiers in Environmental Science, 10, 1097944. https://doi.org/10.3389/fenvs.2022.1097944
Wang, C., & Wang, L. (2023). Green credit and industrial green total factor productivity: The impact mechanism and threshold effect tests. Journal of Environmental Management, 331, 117266. https://doi.org/10.1016/j.jenvman.2023.117266
Wang, H., Cui, H., & Zhao, Q. (2021). Effect of green technology innovation on green total factor productivity in China: Evidence from spatial Durbin model analysis. Journal of Cleaner Production, 288, 125624. https://doi.org/10.1016/j.jclepro.2020.125624
Wang, J., & Guo, D. (2023). Siphon and radiation effects of ICT agglomeration on green total factor productivity: Evidence from a spatial Durbin model. Energy Economics, 126, 106953. https://doi.org/10.1016/j.eneco.2023.106953
Wang, J., Liu, Y., Wang, W., & Wu, H. (2023). How does digital transformation drive green total factor productivity? Evidence from Chinese listed enterprises. Journal of Cleaner Production, 406, 136954. https://doi.org/10.1016/j.jclepro.2023.136954
Wu, P. C., & Chen, S. C. (2021). Regional total factor productivity in Taiwan: Estimation and spatial dependence. Journal of the Asia Pacific Economy, 26(4), 653–678.
Xu, X. C., & Zhang, M. H. (2020). Measuring China’s digital economy scale: An international comparative perspective. China Industrial Economics, 5, 23–41.
Yu, W., Dong, P., & Lei, N. (2023). Does national civilized city selection improve the green total factor productivity? Based on quasi-natural experiment in China. Environmental Impact Assessment Review, 99, 106983. https://doi.org/10.1016/j.eiar.2022.106983
Zhao, P., Gao, Y., & Sun, X. (2022). How does artificial intelligence affect green economic growth? Evidence from China. Science of the Total Environment, 834, 155306. https://doi.org/10.1016/j.scitotenv.2022.155306
Zhao, X., Nakonieczny, J., Jabeen, F., Shahzad, U., & Jia, W. (2022). Does green innovation induce green total factor productivity? Novel findings from Chinese city level data. Technological Forecasting and Social Change, 185, 122021. https://doi.org/10.1016/j.techfore.2022.122021
Zilibotti, F. (2017). Growing and slowing down like China. Journal of the European Economic Association, 15(5), 943–988.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Ling-Chih Chen, Yung-Shan Su, Wen-Tha Hsu, Tai-San Hu, Chiang Kao
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
