RESUMO
The transformation of regional energy structures plays a pivotal role in enhancing ecological quality and advancing national green development efficiency. However, this transformation is characterized by dual externalities, necessitating governmental intervention to rectify market failures. Therefore, this study focuses on the critical influence and mechanisms through which local governments affect regional energy structure transformation, particularly within the context of China's unique fiscal decentralization. This study explores the impact of fiscal decentralization on the transformation of China's regional energy structures and its spatial correlations. Employing a bidirectional fixed-effect model and a spatial Durbin model, we analyze Chinese provincial panel data from 2002 to 2021 to explore the effects of fiscal decentralization on regional energy structure transformation and its spatial spillover. Findings reveal that fiscal decentralization significantly enhances the efficiency of regional energy structure transformation, with an effect coefficient of 0.127. Additionally, the fiscal decentralization mechanism positively influences spatial spillover effects on regional energy transformation, with these effects becoming more pronounced alongside higher degrees of economic agglomeration. Moreover, local governments are found to foster regional scientific and technological innovation, thereby facilitating adjustments in the regional industrial structure and promoting energy structure transformation. The intermediary effect test highlights the role of local governments in promoting scientific and technological advancements that drive industrial restructuring and energy transformation. The study recommends for a long-term strategy to balance central and local financial development, aiming to enhance the efficiency of regional energy structure transformation. These findings offer a theoretical foundation and policy recommendations for optimizing and transforming energy structures in pursuit of China's green and high-quality development goals. These findings offer globally relevant insights into leveraging fiscal decentralization for energy structure transformation, supporting sustainable development and green growth worldwide.
RESUMO
1D nanowire networks, sharing similarities of structure, information transfer, and computation with biological neural networks, have emerged as a promising platform for neuromorphic systems. Based on brain-like structures of 1D nanowire networks, neuromorphic synaptic devices can overcome the von Neumann bottleneck, achieving intelligent high-efficient sensing and computing function with high information processing rates and low power consumption. Here, high-temperature neuromorphic synaptic devices based on SiC@NiO core-shell nanowire networks optoelectronic memristors (NNOMs) are developed. Experimental results demonstrate that NNOMs attain synaptic short/long-term plasticity and modulation plasticity under both electrical and optical stimulation, and exhibit advanced functions such as short/long-term memory and "learning-forgetting-relearning" under optical stimulation at both room temperature and 200 °C. Based on the advanced functions under light stimulus, the constructed 5 × 3 optoelectronic synaptic array devices exhibit a stable visual memory function up to 200 °C, which can be utilized to develop artificial visual systems. Additionally, when exposed to multiple electronic or optical stimuli, the NNOMs effectively replicate the principles of Pavlovian classical conditioning, achieving visual heterologous synaptic functionality and refining neural networks. Overall, with abundant synaptic characteristics and high-temperature thermal stability, these neuromorphic synaptic devices offer a promising route for advancing neuromorphic computing and visual systems.