Spatio-temporal dynamic relationship of renewable energy penetration and CO2 emissions in China: An econometric analysis using PMG-ARDL and ESTDA models

Yasi Yang Wensheng Wang ^*

• China University of Mining and Technology, Beijing, Beijing, China

This study examines the complex relationship between renewable energy penetration (REP) and CO2 emissions in China, aiming to provide insights into deep decarbonization strategies. Using the advanced and robust pooled mean group-autoregressive distributed lag (PMG-ARDL) estimation, this paper analyzes the long-run equilibrium and short-run dynamics of REP and CO2 emissions based on panel data from 30 Chinese provinces (1997-2021). Wavelet coherence analysis (WCA) and exploratory spatio-temporal data analysis (ESTDA) are also employed to explore timefrequency relationships and spatial correlation dynamics. The findings indicate that: (1) A 1% increase in REP reduces long-term and short-term carbon emissions by 0.05% and 0.26%, respectively. (2) A more pronounced coherence in the 1-2 year time-frequency band. (3) REP and CO2 exhibit opposing spatial distribution patterns (-0.224), with distinct north-south clustering, dominated by low-high and low and high clustering, respectively. (4) Most provinces' LISA time paths demonstrate strong spatiotemporal stability, while Central, South, and Southwest China exhibit a more complex spatial variability process, and the spatiotemporal transitions, predominantly of Type IV, reflect strong locking effects. This paper offers policy suggestions for China's low-carbon energy system and lessons for other economies.