The digital economy plays a pivotal role in advancing green productivity; however, the specific configurations driving this relationship remain underexplored. Employing the TOE theoretical framework alongside k-means clustering and fuzzy-set qualitative comparative analysis (fsQCA), we systematically examine the heterogeneous pathways through which digital economy configurations enhance green productivity in China’s Beijing–Tianjin–Hebei region. The results reveal that (1) green productivity exhibits distinct temporal evolution phases and spatial distribution patterns; (2) five characteristic digital economy city clusters emerge from the clustering analysis; (3) improvements in green productivity require specific synergistic combinations of technological, organizational, and environmental factors; and (4) antecedent conditions demonstrate complex substitution patterns across different development stages. These findings offer a configurational perspective on how digital economy architectures differentially influence regional green productivity development.
Against the backdrop of global digitalization and green development, digital innovation ecosystems have emerged as key drivers for advancing regional green innovation cooperation and achieving sustainable development goals. This study constructs a theoretical analytical framework…
Amidst booming digital economy and tightening climate governance, enhancing green total-factor energy efficiency has become pivotal for socioeconomic transformation. Whether digital economy drives urban green energy transition remains unresolved, particularly regarding factor mob…
In the digital economy era, digitalization and intelligent technologies have profoundly influenced regional green development. This study uses data from 277 prefecture-level and above cities in China spanning the years 2011 to 2022 and employs a two-way fixed effects model along…
New quality productive forces (NQPFs) are a key driver for sustainable and high-quality development, where digital innovation (DI) plays a crucial role in promoting the evolution of NQPFs. Based on this, this paper takes 2740 A-share listed companies from 2011 to 2022 as research…
This study evaluated the spatio-temporal dynamics of vegetation cover in the Capellanía wetland (Bogotá, Colombia) between 2013 and 2032 through spectral indices, machine learning, and spatial simulation. A multitemporal Random Forest model (R2 = 0.991; RMSE = 0.0214; MAE = 0.012…
This study intends to portray how varying degrees of environmental policy stringency and the growing pressure of global competition reflect on high-tech (HT) sectors’ cost rationalization strategies and lead to environmental consequences in 15 G20 countries (1992–2019). Moreover,…