Unfavorable Weather, Favorable Insights: Exploring the Impact of Extreme Climate on Green Total Factor Productivity

Economic Analysis and Policy, Год журнала: 2024, Номер 85, С. 626 - 640

Опубликована: Дек. 25, 2024

Язык: Английский

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World crude oil price volatility impacts on domestic fuel-imports and carbon emissions: short and long-run evidence using ARDL

Environment Development and Sustainability, Год журнала: 2025, Номер unknown

Опубликована: Янв. 11, 2025

Язык: Английский

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Energy structural change towards net-zero economy: What can we learn from carbon finance initiatives in China?

Energy Economics, Год журнала: 2025, Номер 144, С. 108321 - 108321

Опубликована: Фев. 18, 2025

Язык: Английский

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Impact of AI Applications on Corporate Green Innovation

International Review of Economics & Finance, Год журнала: 2025, Номер unknown, С. 104007 - 104007

Опубликована: Фев. 1, 2025

Язык: Английский

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Accounting carbon emission responsibility on China's ICT sector under different principles based on the EE-MRIO model

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

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Artificial intelligence and enterprise pollution emissions: From the perspective of energy transition

Energy Economics, Год журнала: 2025, Номер unknown, С. 108349 - 108349

Опубликована: Март 1, 2025

Язык: Английский

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Environmental governance shock and industrial intelligence upgrading: Insights from machine-labor substitution

International Review of Financial Analysis, Год журнала: 2025, Номер unknown, С. 104102 - 104102

Опубликована: Март 1, 2025

Язык: Английский

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Artificial intelligence and green total factor energy efficiency: evidence from non-linear models

Applied Economics, Год журнала: 2025, Номер unknown, С. 1 - 17

Опубликована: Март 25, 2025

This research examines how Artificial Intelligence (AI) affects Green Total Factor Energy Efficiency (GTFEE) in China, emphasizing regional differences and their impacts on sustainable urban growth. By introducing a non-linear analytical approach, the study offers fresh insights into AI influences GTFEE diverse regions areas. It provides valuable information for policymakers planners by exploring spatial variations influence of industrial structure as moderating factor. Key findings include: (1) pattern, showing initial increases, subsequent declines, eventual growth; (2) Yangtze River Economic Belt specific clusters experience most significant effects; (3) while enhances AI's marginal effect GTFEE, this weakens some areas; (4) development stages play crucial role shaping AI-GTFEE relationship. contributes to understanding dynamics, broadens existing literature energy efficiency, detailed framework enhance sustainability.

Язык: Английский

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Suppliers’ AI adoption and customers’ carbon emissions: firm-level evidence from China

Applied Economics, Год журнала: 2025, Номер unknown, С. 1 - 15

Опубликована: Март 27, 2025

Using panel data of Chinese listed firms from 2010 to 2021, we investigate whether and how suppliers' artificial intelligence (AI) adoption affects their customers' carbon emissions. We find that increased AI by supplier reduces emissions, this result is robust various tests. The main mechanisms are the innovation chain (measured green patents) capital (based on trade credit). Cross-sectional analyses reveal negative impact more pronounced for customers boasting higher ESG score, better absorptive capacity, lower resource endowments, or stronger coordination with suppliers. also show as adopt AI, own emissions rise, but downstream across multiple tiers fall. Our findings suggest a firm's position in supply determines positively negatively impacts its

Язык: Английский

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How does supply chain digitalization enhance energy resilience? Empirical evidence from listed companies in China

Energy Economics, Год журнала: 2025, Номер unknown, С. 108447 - 108447

Опубликована: Март 1, 2025

Язык: Английский

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