Economic Change and Restructuring, Год журнала: 2024, Номер 57(4)
Опубликована: Июль 3, 2024
Язык: Английский
Energy Economics, Год журнала: 2024, Номер 142, С. 108159 - 108159
Опубликована: Дек. 25, 2024
Язык: Английский
Процитировано
5
Energy, Год журнала: 2024, Номер 308, С. 132953 - 132953
Опубликована: Авг. 23, 2024
Язык: Английский
Процитировано
4
Sustainability, Год журнала: 2025, Номер 17(1), С. 370 - 370
Опубликована: Янв. 6, 2025
The realization of intelligent transformation is an important path for the industry to move towards low-carbon development. Based on panel data from 30 provinces in China, this study utilizes intermediate effect model and spatial econometric analyze influence industrial intelligence carbon emissions. research reveals that helps with reduction, result still valid after undergoing various tests. Industrial relies green technological innovation, structure upgrading, energy intensity realize reduction. There a spillover role emissions, which has positive reduction local adjoining regions. emissions exhibits heterogeneity regional dimension, time level dimension. provides empirical evidence implications using artificial achieve
Язык: Английский
Процитировано
0
Sustainability, Год журнала: 2025, Номер 17(3), С. 990 - 990
Опубликована: Янв. 25, 2025
Countries worldwide are focusing on energy efficiency, economic sustainability, and responsible resource management to address climate change meet sustainable development goals (SDGs). This study investigates how factors such as artificial intelligence, renewable energy, green human capital, geopolitical risk, natural rent, information communication technology influenced CO2 emissions in 36 countries between 2000 2021. The also explores institutional quality moderates these relationships. We employed advanced econometric techniques this gap, including panel-correlated standard errors (PCSE) the Driscoll–Kraay estimations (DKSE) models. A two-step system GMM approach was used strengthen robustness of our findings. findings reveal that consumption, can significantly reduce emissions. Conversely, contribute increased Institutional enhances positive impact capital emission reduction. However, it has opposite effect leading an even greater increase These underscore importance policies achieving goals. recommend policymakers prioritize investing clean while strengthening effectively mitigate carbon SDGs. They regulate AI ICT footprints risks through diversification international cooperation.
Язык: Английский
Процитировано
0
Environmental Technology, Год журнала: 2025, Номер unknown, С. 1 - 12
Опубликована: Март 4, 2025
The purpose of this study is to investigate and compare the ICT sector's carbon emission responsibility under production-based (PBA), consumption-based (CBA), income-based accounting principle (IBA) shared-responsibility approach (SRA), focusing on case China. We utilise environmentally extended multiregional input-output (EE-MRIO) model based China's 2012 2017 provincial MRIO table. empirical finding demonstrate that responsibilities assigned sector CBA greater than those SRA, IBA PBA. Regional emissions are highly concentrated PBA IBA. absolute amount increased all method, but increase in national share varied significantly. inter-sectoral transfer pattern, shows exhibits dual lock-in effects, demonstrates strong supply-chain dependencies, upstream procurement anchored energy-intensive sectors (S23, S14, S13), while downstream consumption path-dependent concentration S23, S29. Inter-regional significant regional heterogeneity. In economically developed provinces like Guangdong, Beijing Zhejiang, has a downstream-pushing effect notable upstream-pulling other regions. Conversely, less northeastern northwestern provinces, sector, mainly serving local consumption, leads minimal effect. These results provide supportive references for China develop more integrated policies, supporting common differentiated reduction targets.
Язык: Английский
Процитировано
0
Energy Economics, Год журнала: 2025, Номер unknown, С. 108349 - 108349
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
0
Journal of Environmental Management, Год журнала: 2025, Номер 378, С. 124686 - 124686
Опубликована: Март 5, 2025
Язык: Английский
Процитировано
0
Environmental Impact Assessment Review, Год журнала: 2025, Номер 114, С. 107911 - 107911
Опубликована: Март 11, 2025
Язык: Английский
Процитировано
0
Advances in computational intelligence and robotics book series, Год журнала: 2025, Номер unknown, С. 355 - 362
Опубликована: Апрель 8, 2025
Deep in the depths of analytics and big data, AI can play a vital role understanding impacts climate change. This digital world is increasingly using technology to collect analyze environmental sift through this data highly granular way, seeking uncover trends patterns that help guide change efforts. In context challenge, scientists engineers are working together design powerful predictive models enable analysis future scenarios. advance necessary step towards better determining how humans adapt provide effective solutions.
Язык: Английский
Процитировано
0
Sustainable Cities and Society, Год журнала: 2025, Номер unknown, С. 106372 - 106372
Опубликована: Апрель 1, 2025
Язык: Английский
Процитировано
0