Digital enterprise distribution and green total factor productivity: A spatial agglomeration perspective

Environmental Impact Assessment Review, Год журнала: 2025, Номер 112, С. 107832 - 107832

Опубликована: Янв. 22, 2025

Язык: Английский

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Spatial–temporal evolution, drivers, and pathways of the synergistic effects of digital transformation on pollution and carbon reduction in heavily polluting enterprises

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 8, 2025

Abstract Under the “dual carbon” goals, heavily polluting enterprises face dual pressures to reduce both pollution and carbon emissions, necessitating urgent exploration of effective pathways for coordinated emission reductions. This study investigates potential digital transformation in achieve synergistic First, entropy method is employed measure enterprise digitalization pollutant levels, spatial–temporal evolution characteristics regional reductions are analyzed. Subsequently, using panel data from Yangtze River Economic Belt, examines impact on reduction, its underlying mechanisms, moderating effects environmental policies these relationships. Robustness tests confirm synergy between emissions. The findings reveal that contributes reduction emissions enterprises, primarily through two pathways: integration internal innovation resources collaborative engagement external networks. Furthermore, air control low-carbon city initiatives significantly enhance digitalization. Interestingly, located downstream regions River, those with smaller operational scales, or facing strong financing constraints, demonstrate more pronounced transformation. Based conclusions, we recommend governments focus strengthening either “pollution reduction” “carbon policies, as alone can yield benefits. Additionally, tailoring local conditions maximize economic

Язык: Английский

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Perception of Hybrid Learning Platform Self-Efficacy

Advances in computational intelligence and robotics book series, Год журнала: 2025, Номер unknown, С. 237 - 258

Опубликована: Апрель 18, 2025

In the rapidly evolving landscape of higher education, hybrid learning platforms have become a central mode instruction, combining traditional in-person teaching with online learning. . This study investigates role platforms, self-efficacy, and technological readiness in shaping student satisfaction, emotional engagement serving as mediating factor. Using quantitative research approach, survey data was collected from students environments. Structural equation modeling (SEM) employed to assess direct indirect relationships between constructs. Results indicate that both self-efficacy positively affect engagement, which, turn, significantly influences satisfaction.This underscores need for educational institutions focus on enhancing students' well ensuring fostering maximize satisfaction

Язык: Английский

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Feature fusion improves performance and interpretability of machine learning models in identifying soil pollution of potentially contaminated sites

Ecotoxicology and Environmental Safety, Год журнала: 2023, Номер 259, С. 115052 - 115052

Опубликована: Май 23, 2023

Owing to the rapid development of big data technology, use machine learning methods identify soil pollution potentially contaminated sites (PCS) at regional scales and in different industries has become a research hot spot. However, due difficulty obtaining key indexes site sources pathways, current have problems such as low accuracy model predictions insufficient scientific basis. In this study, we collected environmental 199 PCS 6 typical involving heavy metal organic pollution. Then, 21 based on basic information, potential for from product raw material, control level, migration capacity pollutants were used established identification index system. We fused original into new feature subset with 11 through method consolidation calculation. The was then train models random forest (RF), support vector (SVM), multilayer perceptron (MLP), tested determine whether it improved precision pollination models. results correlation analysis showed that four created by fusion is similar indexes. accuracies precisions three trained 67.4%− 72.9% 72.0%− 74.7%, which 2.1%− 2.5% 0.3%− 5.7% higher than these indexes, respectively. When divided according enterprise industries, two datasets identifying significantly improve approximately 80%. imbalance positive negative samples prediction pollution, 58%− 72.5%, lower their accuracies. According factors interpretability SHAP, most level had degrees impact least effect classification task PCS. Among traces industrial utilization years/start-up time, risk scores scale having greatest effects mean SHAP values 0.17–0.36, reflected contribution rate could help optimize scoring technical regulation This study provides methods, addition providing reference basis management

Язык: Английский

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Mechanism and evolution trend of digital green fusion in China's regional advanced manufacturing industry

Journal of Cleaner Production, Год журнала: 2023, Номер 427, С. 139264 - 139264

Опубликована: Окт. 16, 2023

Язык: Английский

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