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Top1. Introduction And Literature Review
China's electricity consumption growth has decoupled from economic growth, e.g. Zhang et al. (2019), Lin and Liu (2016), Wu et al. (2018), Lin and Wang (2019) and among others. While the economy continues to expand, the rate of electricity consumption growth has slowed, suggesting a shift towards more efficient energy use and a transition to a service-oriented economy. This decoupling is attributed to various factors, including improvements in energy efficiency, structural changes in the economy, and increased reliance on renewable energy sources, indicating a move towards sustainable development while maintaining economic growth. However, in 2023, China's electricity generation growth unexpectedly exceeded the overall economic growth rate. China's GDP increased by around 5.2% in 2023, while total electricity generation surged to 9,090.9 billion kilowatt-hours, reflecting a year-on-year average monthly growth of 5.17% and even peaking at 8.4% in November 2023 (and all these data can be accessed through Wind).
Additionally, during this period, there has been a surge in anticipated power demand driven by the rapid growth of general artificial intelligence (AI) models, like ChatGPT, which require substantial computational energy. Understanding these dynamics is crucial for energy economics research, as it highlights the evolving relationship between energy production and economic performance in China. The implications of these trends could guide future policy decisions and investment strategies within both the energy sector and the broader economy. This paper utilizes a Bayesian Structural Vector Autoregression (BSVAR) to analyze the semiconductor cycle and China's coal-electricity market, aiming to determine whether the recent unexpected increase in China's electricity production differs from historical observations.
BSVARs greatly enhance the analytical framework, providing valuable insights into the evolving dynamics of the semiconductor and coal-electricity markets. In the studies by Baumeister and Hamilton (2015, 2019), BSVARs integrate prior information while accounting for its uncertainty, allowing for more precise characterization of uncertainty in the shock transmission process. This approach effectively disentangles concurrent shocks within a system and assesses the cumulative effects of historical shocks. SVARs are adept at estimating relationships among multiple time series variables, making them particularly suitable for capturing the complex dynamics of energy economic systems, as highlighted by Kilian (2013) and Kilian and Murphy (2014), reviewed by Baumeister and Hamilton (2024). Additionally, BSVARs have successfully evaluated the impact of various shocks on electricity consumption and production globally, as shown in studies by Esmaeili and Rafei (2021), Paschen (2016), Ahelegbey et al. (2024), and others.
In the literature, several comparative methods have been explored for this type of analysis, including unrestricted VAR, Vector Error Correction Models (VECM), local projections, and calibration models such as Computable General Equilibrium (CGE) and Dynamic Stochastic General Equilibrium (DSGE). However, BSVAR consistently emerges as the suitable choice due to its flexibility and robustness. Traditional VAR and VECM models often face limitations in managing structural complexities and the uncertainties of economic time series data, potentially leading to less reliable results, see textbooks Lütkepohl (2005) and Kilian and Lütkepohl (2017). Calibration models, though effective for refining predictions, can impose restrictive assumptions that overlook the dynamic interactions within complex economic systems. In contrast, VAR not only provides a framework for real-time adjustments and responsiveness to market fluctuations but also fosters a deeper understanding of causal relationships in the interconnected domains of semiconductor production and energy markets, for example Stock and Watson (2001). This adaptability positions BSVAR as the preferred methodology for interpreting the intricate dynamics between these sectors.