Research on Digital Trade Measurement and Green Economy Spatial Econometrics Based on DEA-SBM
Downloads
In the process of China's economic transformation toward high-quality development, green development has become the core path to breaking through resource constraints and environmental pressures. Against this backdrop, computer technology has demonstrated significant advantages in addressing the complex network data in areas such as digital trade and the efficiency bottlenecks of traditional models in big data scenarios. This study develops a digital trade scale measurement system based on data envelopment analysis (DEA) and slacks-based measure (SBM) in an attempt to investigate the coordinated development path of digital trade and the green economy. Digital trade is measured using the DEA-SBM-GA model embedded with genetic algorithm (GA). Each province's degree of development in digital trade is assessed using the entropy value technique. The level of green economic development is gauged by the super-efficiency SBM model. Spatial spillover effects are examined using the spatial Durbin model. The results indicated that DEA-SBM-GA was significantly more efficient than traditional models p<0.001), reducing single iteration time by 87.3%. For every 1-unit increase in artificial intelligence-enabled digital trade, local green economic growth increased by 0.129 (p<0.05), with an indirect effect of 0.112 (p<0.05) and a total effect of 0.246 (p<0.05). Technological innovation had a dual threshold (0.314/0.721), and after crossing the high threshold, the impact coefficient increased from 0.063 to 0.247 (p<0.01). There was a positive geographical spillover impact and notable regional heterogeneity. The Moran's I index for the green economy was 0.304, with a Z-value of 4.82 (p<0.001), indicating significant spatial clustering. The study addresses the shortcomings of earlier research by offering a theoretical framework and policy suggestions for the coordinated growth of the green economy and digital trade. This work has significant academic and practical value.
Downloads
[1] Suh, J., & Roh, J. (2023). The effects of digital trade policies on digital trade. World Economy, 46(8), 2383–2407. doi:10.1111/twec.13407.
[2] Li, Z., Lee, G., Raghu, T. S., & Shi, Z. (2025). Impact of the General Data Protection Regulation on the Global Mobile App Market: Digital Trade Implications of Data Protection and Privacy Regulations. Information Systems Research, 36(2), 669–689. doi:10.1287/isre.2022.0421.
[3] Peters, M. A. (2023). Digital trade, digital economy and the digital economy partnership agreement (DEPA). Educational Philosophy and Theory, 55(7), 747–755. doi:10.1080/00131857.2022.2041413.
[4] Wu, Z., Zhao, Y., & Zhang, N. (2023). A Literature Survey of Green and Low-Carbon Economics Using Natural Experiment Approaches in Top Field Journal. Green and Low-Carbon Economy, 1(1), 2–14. doi:10.47852/bonviewglce3202827.
[5] Osma, A. N. (2025). Measuring Digital Trade Development in ASEAN: A Mixed-Method Framework. Electronic Journal of Business Research Methods, 23(1), 35–52. doi:10.34190/ejbrm.23.1.3982.
[6] Hao, X., Wang, X., Wu, H., & Hao, Y. (2023). Path to sustainable development: Does digital economy matter in manufacturing green total factor productivity? Sustainable Development, 31(1), 360–378. doi:10.1002/sd.2397.
[7] Xu, G., Lu, T., Chen, X., & Liu, Y. (2022). The convergence level and influencing factors of China’s digital economy and real economy based on grey model and PLS-SEM. Journal of Intelligent and Fuzzy Systems, 42(3), 1575–1605. doi:10.3233/JIFS-210981.
[8] Yang, X., & Zhang, J. (2025). In the Metaverse: Empowering Rural Revitalization through International Trade. Financial Economics Research, 2(1), 18–26. doi:10.70267/q1hnpe42.
[9] Xu, Y., Chen, Y., & Shi, X. (2023). Does the digital economy empower the green development of foreign trade? Environmental Science and Pollution Research, 30(51), 110395–110416. doi:10.1007/s11356-023-30076-9.
[10] Ren, S., Li, L., Han, Y., Hao, Y., & Wu, H. (2022). The emerging driving force of inclusive green growth: Does digital economy agglomeration work? Business Strategy and the Environment, 31(4), 1656–1678. doi:10.1002/bse.2975.
[11] 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. doi:10.1002/bse.3223.
[12] Peng, L., & Anthony Maria Das, P. (2025). Investigating the spatial spillover impact of digital economy with research and development investment on environmental sustainability. Management and Sustainability, 4(4), 748–773. doi:10.1108/MSAR-07-2024-0063.
[13] Chen, Y., & Liu, J. (2025). Digital Economy and Carbon Emissions: Spatial Spillover Effect and Industrial Structure Mediation Effect in China. Technological and Economic Development of Economy, 31(3), 793–818. doi:10.3846/tede.2025.22955.
[14] Yu, H., Wang, J., Xu, J., & Ding, B. (2025). Does digital economy agglomeration promote green economy efficiency? A spatial spillover and spatial heterogeneity perspective. Environment, Development and Sustainability, 27(3), 7379–7406. doi:10.1007/s10668-023-04197-7.
[15] Xiao, Y., & Abula, B. (2023). Examining the Impact of Digital Economy on Agricultural Trade Efficiency in RCEP Region: A Perspective Based on Spatial Spillover Effects. Journal of the Knowledge Economy, 15(3), 9907–9934. doi:10.1007/s13132-023-01484-6.
[16] Li, Z., & Meng, H. (2025). Effects of digital transformation of logistics on urban innovation in China: a spatial econometric analysis. Applied Economics, 57(51), 8450–8470. doi:10.1080/00036846.2024.2399819.
[17] Wang, X., Zhu, Y., Ren, X., & Gozgor, G. (2023). The impact of digital inclusive finance on the spatial convergence of the green total factor productivity in the Chinese cities. Applied Economics, 55(42), 4871–4889. doi:10.1080/00036846.2022.2131721.
[18] Hong, M., Tian, M., & Wang, J. (2023). The impact of digital economy on green development of agriculture and its spatial spillover effect. China Agricultural Economic Review, 15(4), 708–726. doi:10.1108/CAER-01-2023-0004.
[19] Zhao, S., Peng, D., Wen, H., & Wu, Y. (2023). Nonlinear and spatial spillover effects of the digital economy on green total factor energy efficiency: evidence from 281 cities in China. Environmental Science and Pollution Research, 30(34), 81896–81916. doi:10.1007/s11356-022-22694-6.
[20] Liu, Y., Ma, C., & Huang, Z. (2023). Can the digital economy improve green total factor productivity? An empirical study based on Chinese urban data. Mathematical Biosciences and Engineering, 20(4), 6866–6893. doi:10.3934/mbe.2023296.
[21] Zhang, Z., Cheng, Y., & Zhang, J. (2025). Spatial spillover and threshold effects of digital economy on green innovation efficiency–based on provincial level data in China. Environment, Development and Sustainability, 27(3), 6733–6755. doi:10.1007/s10668-023-04164-2.
[22] Yang, X., Xu, Y., Razzaq, A., Wu, D., Cao, J., & Ran, Q. (2024). Roadmap to achieving sustainable development: Does digital economy matter in industrial green transformation? Sustainable Development, 32(3), 2583–2599. doi:10.1002/sd.2781.
[23] Liu, Y., Xie, Y., & Zhong, K. (2024). Impact of digital economy on urban sustainable development: Evidence from Chinese cities. Sustainable Development, 32(1), 307–324. doi:10.1002/sd.2656.
[24] Gu, R., Li, C., Yang, Y., Zhang, J., & Liu, K. (2023). Impact of digital economy development on carbon emission intensity in the Beijing-Tianjin-Hebei region: a mechanism analysis based on industrial structure optimization and green innovation. Environmental Science and Pollution Research, 30(14), 41644–41664. doi:10.1007/s11356-023-25140-3.
[25] Yuan, D., Du, J., Pan, Y., & Li, C. (2025). The collaborative agglomeration of industries and the realization of the digital economy on the green high-quality development of cities. Business Process Management Journal, 31(4), 1222–1245. doi:10.1108/BPMJ-11-2023-0881.
[26] Yuan, R., Wang, C., Masron, T. A., & Ibrahim, H. (2025). Digital economy empowerment on carbon emission reduction: an analysis of spatial spillover effects based on temporal heterogeneity. Applied Economics, 57(35), 5359–5373. doi:10.1080/00036846.2024.2364924.
[27] Lin, X., & Baskaran, A. (2025). Regional economic growth, digital economy and tax competition in China: mechanism and spatial assessment. Journal of the Asia Pacific Economy, 30(3), 886–912. doi:10.1080/13547860.2024.2325251.
[28] Chen, S., Yang, Y., & Wu, T. (2023). Digital economy and green total factor productivity—based on the empirical research on the resource-based cities. Environmental Science and Pollution Research, 30(16), 47394–47407. doi:10.1007/s11356-023-25547-y.
[29] Yu, H., & Zhu, Q. (2023). Impact and mechanism of digital economy on China’s carbon emissions: from the perspective of spatial heterogeneity. Environmental Science and Pollution Research, 30(4), 9642–9657. doi:10.1007/s11356-022-22552-5.
[30] Yan, X., Deng, Y., Peng, L., & Jiang, Z. (2023). Study on the impact of digital economy development on carbon emission intensity of urban agglomerations and its mechanism. Environmental Science and Pollution Research, 30(12), 33142–33159. doi:10.1007/s11356-022-24557-6.
[31] Qian, Y., Liu, J., Shi, L., Forrest, J. Y. L., & Yang, Z. (2023). Can artificial intelligence improve green economic growth? Evidence from China. Environmental Science and Pollution Research, 30(6), 16418–16437. doi:10.1007/s11356-022-23320-1.
[32] Chang, K., Zhang, H., & Li, B. (2024). The Impact of Digital Economy and Industrial Agglomeration on the Changes of Industrial Structure in the Yangtze River Delta. Journal of the Knowledge Economy, 15(2), 9207–9227. doi:10.1007/s13132-023-01448-w.
[33] Zhang, Y. Q. (2023). Impact of green finance and environmental protection on green economic recovery in South Asian economies: mediating role of FinTech. Economic Change and Restructuring, 56(3), 2069–2086. doi:10.1007/s10644-023-09500-0.
[34] Shang, Y., Pu, Y., Yu, Y., Gao, N., & Lu, Y. (2023). Role of the e-exhibition industry in the green growth of businesses and recovery. Economic Change and Restructuring, 56(3), 2003–2020. doi:10.1007/s10644-023-09502-y.
[35] Stater, M., & Hoag, C. (2023). Including the Instruments in the Regression Is the Hausman Test. Annals of Economics and Statistics, 152(152), 43–64. doi:10.2307/48754784.
[36] Tian, H., Qin, J., Cheng, C., Javeed, S. A., & Chu, T. (2024). Towards low-carbon sustainable development under Industry 4.0: The influence of industrial intelligence on China’s carbon mitigation. Sustainable Development, 32(1), 455–480. doi:10.1002/sd.2664.
- This work (including HTML and PDF Files) is licensed under a Creative Commons Attribution 4.0 International License.
