在氣候變遷與能源轉型壓力日益升高的國際情勢下，如何兼顧經濟發展與能源使用效率已成為政策核心課題。台灣作為高度工業化且以出口導向為核心的經濟體，工業部門長期為能源消耗主體，工業用電密集度之變動不僅反映技術升級與結構調整，亦影響能源永續發展目標之達成。近年來，在智慧製造政策推動及中美貿易戰等外部衝擊影響下，台灣出口部門與製造業結構均出現顯著調整，智慧製造技術導入所產生的節能效果與出口結構轉型所帶來的能源效率改善，亦逐漸受到重視。
本研究以2009年1月至2024年12月之台灣月資料為樣本，採用自我迴歸分配遞延模型（Autoregressive Distributed Lag, ARDL），探討出口成長率、智慧製造技術導入、出口產業結構轉型(虛擬變數)及其交互項對工業用電密集度之短期與長期影響，並納入電價與重工業產值占比作為控制變數，以掌握能源效率變動的關鍵驅動因素。
研究結果顯示，出口成長與工業用電密集度在長期呈顯著正向關聯，反映我國出口成長仍有一定比例集中於高耗能產業；智慧製造技術導入在長期可有效降低用電密集度，惟短期因轉型初期設備調整反使電力需求暫時上升；出口產業結構轉型(虛擬變數)及其與出口成長的交互項在長期皆顯著為負，顯示產業結構優化有助於調節出口成長對能源所造成之壓力；控制變數方面，重工業產值占比與電價皆對用電密集度產生動態遞延效果。
研究結果亦顯示，我國工業用電密集度之變動為出口規模、產業結構、智慧製造導入與價格誘因等多重因素交互作用之結果，提升能源效率須同時考量出口導向政策、產業結構調整與技術導入策略，方能達成經濟發展與能源永續雙重目標。

Balancing economic growth with energy efficiency and long-term energy sustainability has become a critical policy issue for highly industrialized, export-driven economies such as Taiwan. These issues are of particular salient amid global climate and energy transition pressures, the structural shift toward smart manufacturing and automation, and external shocks, such as the US–China trade war.
This study investigated the short- and long-term effects of export growth, smart manufacturing, and changes in the export structure on electricity intensity in the industrial sector, using an autoregressive distributed lag (ARDL) model with monthly data from 2009 to 2024. Control variables include electricity prices and the share of heavy industry output.
Our results indicate that the transition to smart manufacturing will reduce electricity intensity over the short-term; however, export growth is likely to increase intensity over the long term, reflecting persistent reliance on energy-intensive exports. By contrast, the export structure (a dummy variable) and its export growth indicate significantly negative long-run effects, suggesting that structural transformation mitigates the energy burden of export expansion. Control variables also exhibit lagged effects.
Overall, Taiwan’s industrial electricity intensity is shaped by export scale, industrial structure, technological adoption, and price mechanisms. Enhancing energy efficiency will require integrated policies that promote structural upgrading, the early adoption of smart manufacturing, and incentives that align with long-term sustainability goals.

1. Akaike, H. (1974). A new look at the statistical model identification, IEEE T. Automat. Contr., 19, 716–723.
2. Andrews, D. W. (1991). Heteroskedasticity and autocorrelation consistent covariance matrix estimation. Econometrica: Journal of the Econometric Society, 817-858.
3. Breusch, T. S. (1978). Testing for autocorrelation in dynamic linear models. Australian Economic Papers, 17(31).
4. Breusch, T. S., & Pagan, A. R. (1979). A simple test for heteroscedasticity and random coefficient variation. Econometrica: Journal of the Econometric Society, 1287-1294.
5. Brown, R. L., Durbin, J., & Evans, J. M. (1975). Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society Series B: Statistical Methodology, 37(2), 149-163.
6. Chen, J., Lin, H. Y., & Lien, Y. T. (2021). Taiwan’s shifting role in the global supply chain in the US-China trade war. Joint US-Korea Academic Studies, 316-329.
7. Chen, K. H., Yang, H. Y., Lee, J. M., & Chi, C. F. (2016). The impact of energy prices on energy consumption and energy efficiency: evidence from Taiwan. Energy Efficiency, 9(6), 1329-1349.
8. Chou, K. T., Walther, D., & Liou, H. M. (2019). The conundrums of sustainability: Carbon emissions and electricity consumption in the electronics and petrochemical industries in Taiwan. Sustainability, 11(20), 5664.
9. Dempsey. (2025). Hitachi Energy says AI power spikes threaten to destabilise global supply. https://www.ft.com/content/2789d048-7791-4f9a-8dbe-229c45f8083b
10. Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427-431.
11. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987-1007.
12. Freitas, I. M. B., Jacob, J., Wang, L., & Li, Z. (2023). Energy use and exporting: an analysis of Chinese firms. Journal of Evolutionary Economics, 33(1),179-207.
13. Graetz, G., & Michaels, G. (2018). Robots at work. Review of Economics and Statistics, 100(5), 753-768.
14. International Federation of Robotics. (2012). History of Industrial Robots. https://s1b0d3d77136c1679.jimcontent.com/download/version/1485405362/module/12883910330/name/History%20of%20Industrial%20Robots%20by%20IFR%202012.pdf
15. International Federation of Robotics. (2018). Executive summary – World robotics 2018 Industrial robots. https://ifr.org/downloads/press2018/Executive_Summary_WR_2018_Industrial_Robots.pdf
16. International Federation of Robotics. (2024). Global robot density in factories doubled in seven years. https://ifr.org/ifr-press-releases/news/global-robot-density-in-factories-doubled-in-seven-years
17. International Federation of Robotics. (2017). Robots: China breaks historic records in automation. https://ifr.org/news/robots-china-breaks-historic-records-in-automation/
18. Jinji, N., & Sakamoto, H. (2015). Does exporting improve firms' CO2 emissions intensity and energy intensity? Evidence from Japanese manufacturing. Research Institute for Economy, Trade and Industry Discussion Paper, 1-27.
19. Kwiatkowski, D., Phillips, P. C., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?. Journal of Econometrics, 54 (1-3), 159-178.
20. Lastauskas, P., Ding, Z., & Douch, M. (2024). The Heterogeneous Impacts of Firm Upgrading on Energy Intensity.
21. Lee, C. C., & Ho, S. J. (2022). Impacts of export diversification on energy in tensity, renewable energy, and waste energy in 121 countries: Do environmental regulations matter?. Renewable Energy, 199, 1510-1522.
22. Lin, B., & Xu, C. (2024). The effects of industrial robots on firm energy intensity: From the perspective of technological innovation and electrification. Technological Forecasting and Social Change, 203, 123373.
23. Lu, Y., Tian, J., & Ma, M. (2023). The effect of automation on firms’ carbon dioxide emissions of China. Digital Economy and Sustainable Development, 1(1), 8.
24. Luan, B., Zou, H., Chen, S., & Huang, J. (2021). The effect of industrial structure adjustment on China’s energy intensity: Evidence from linear and nonlinear analysis. Energy, 218, 119517.
25. Newey, W. K., & West, K. D. (1994). Automatic lag selection in covariance matrix estimation. The Review of Economic Studies, 61(4), 631-653.
26. Pesaran, M. H., & Shin, Y. (1995). An autoregressive distributed lag modelling approach to cointegration analysis (Vol. 9514, pp. 371-413). Cambridge, UK: Department of Applied Economics, University of Cambridge.
27. Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3),289-326.
28. Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346.
29. Rahman, M. M., Khan, Z., Khan, S., & Tariq, M. (2023). How is energy intensity affected by industrialisation, trade openness and financial development? A dynamic analysis for the panel of newly industrialized countries. Energy Strategy Reviews, 49, 101182.
30. Ramsey, J. B. (1969). Tests for specification errors in classical linear least-squares regression analysis. Journal of the Royal Statistical Society Series B: Statistical Methodology, 31(2), 350-371.
31. Roy, J., & Yasar, M. (2015). Energy efficiency and exporting: Evidence from firm level data. Energy Economics, 52, 127-135.
32. Wang, E. Z., Lee, C. C., & Li, Y. (2022). Assessing the impact of industrial robots on manufacturing energy intensity in 38 countries. Energy Economics, 105, 105748.
33. Yang, C. H., & Hayakawa, K. (2023). The Substitution Effect of US‐China Trade War on Taiwanese Trade. The Developing Economies, 61(4), 324-341.
34. Yang, Z., Cai, J., Lu, Y., & Zhang, B. (2022). The impact of economic growth, industrial transition, and energy intensity on carbon dioxide emissions in China. Sustainability, 14(9), 4884.
35. Zaidi, S. A. H., Dessoulavy-Śliwiński, B., Ashraf, R. U., & ul Haq, M. A. (2024). Determinants of energy intensity in emerging economies: a comprehensive review of the last three decades. Journal of Modern Science, 56(2), 663-693.
36. Zhang, B., Bai, S., & Ning, Y. (2021). Embodied energy in export flows along global value chain: a case study of China’s export trade. Frontiers in Energy Research, 9, 649163.
37. 主計協進社(2019)，主計月刊第757期。
38. 洪御豪(2021)，中美貿易戰對台灣出口貿易的影響，台灣博碩士論文知識加值系統，https://hdl.handle.net/11296/46h2h9。
39. 海峽交流基金會(2019)，兩岸經貿月刊第331期，https://www.sef.org.tw/list-1-131?q=%2663686172423035%3D6394。