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Relationship between Urban Innovation Capability and Energy Utilization Efficiency: An Empirical Study of 281 Prefecture‐Level Cities in China

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  • Wanshu Wu
  • Kai Zhao

Abstract

Following a dynamic nonlinear perspective, this study explores the relationship between urban innovation capability and energy utilization efficiency by employing the Panel Vector Autoregression (PVAR) and Dynamic Panel Threshold Regression (DPTR) methods. Using the 2003–2020 panel data of 281 prefecture‐level cities in China, this study confirms that energy utilization efficiency improves owing to the improvement of urban innovation capability. Depending on the characteristics of the city, such as population density, industrial structure, and environmental pollution, high energy utilization efficiency in the early stages of city development may help or hinder the improvement of energy utilization efficiency in the later stages. The enhancement in urban innovation capability has failed to improve energy utilization efficiency and has adversely affected cities with a low population density or weak secondary industrial foundation. However, in cities with a high population density or proportion of secondary industry, the improvement in innovation capability significantly increases the efficiency of energy utilization. In addition, the positive effect that urban innovation capability has on energy utilization efficiency is higher in low‐pollution cities than in high‐pollution cities.

Suggested Citation

  • Wanshu Wu & Kai Zhao, 2022. "Relationship between Urban Innovation Capability and Energy Utilization Efficiency: An Empirical Study of 281 Prefecture‐Level Cities in China," Complexity, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:complx:v:2022:y:2022:i:1:n:8765949
    DOI: 10.1155/2022/8765949
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    1. Hansen, Bruce E., 1999. "Threshold effects in non-dynamic panels: Estimation, testing, and inference," Journal of Econometrics, Elsevier, vol. 93(2), pages 345-368, December.
    2. Hansen, Bruce E, 1996. "Inference When a Nuisance Parameter Is Not Identified under the Null Hypothesis," Econometrica, Econometric Society, vol. 64(2), pages 413-430, March.
    3. Che, Xiao-Jing & Zhou, P. & Chai, Kah-Hin, 2022. "Regional policy effect on photovoltaic (PV) technology innovation: Findings from 260 cities in China," Energy Policy, Elsevier, vol. 162(C).
    4. Ai, Hongshan & Wang, Mengyuan & Zhang, Yue-Jun & Zhu, Tian-Tian, 2022. "How does air pollution affect urban innovation capability? Evidence from 281 cities in China," Structural Change and Economic Dynamics, Elsevier, vol. 61(C), pages 166-178.
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