Can digitalization alleviate China's energy poverty? Empirical investigation and mechanism analysis

Environmental Impact Assessment Review, Journal Year: 2024, Volume and Issue: 109, P. 107634 - 107634

Published: Aug. 23, 2024

Language: Английский

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Smart Cities and Greener Futures: Evidence from a Quasi-Natural Experiment in China’s Smart City Construction

Sustainability, Journal Year: 2024, Volume and Issue: 16(2), P. 929 - 929

Published: Jan. 22, 2024

As the digital economy becomes new engine of economic growth, China has introduced a series smart city policies aimed at promoting high-quality and sustainable urban development. This paper aims to evaluate green development effects China’s “Smart City Pilot” policy explore heterogeneity across different types cities. Using panel data from 283 prefecture-level cities in 2006 2020, this study examines relationship between construction efficiency using total factor productivity (GTFP). We employ Causal Forest mediation effect models estimate impact pilot on GTFP underlying mechanisms. The main results are: (1) significantly enhances GTFP, finding consistent diverse evaluation approaches. (2) influence varies among cities, such is effectively captured by Forest. (3) varied primarily stems location factors inherent characteristics. Notably, Eastern outpaces that other regions. yields greater benefits with financial medical capital rises, but excessive government public expenditure curtails its positive influence. (4) mechanisms through which promotes exhibit certain differences “high-effect group” “low-effect group”. former predominantly leverages innovation-driven agglomeration effects, while latter chiefly relies industrial structural advancement rationalization.

Language: Английский

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Driving effect of digital government policy on synergy in corporate pollution reduction, carbon reduction, and green expansion

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 369, P. 122301 - 122301

Published: Aug. 30, 2024

Language: Английский

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Reinforcement Learning-Based Optimization for Sustainable and Lean Production within the Context of Industry 4.0

Algorithms, Journal Year: 2024, Volume and Issue: 17(3), P. 98 - 98

Published: Feb. 23, 2024

The manufacturing industry often faces challenges related to customer satisfaction, system degradation, product sustainability, inventory, and operation management. If not addressed, these can be substantially harmful costly for the sustainability of plants. Paradigms, e.g., Industry 4.0 smart manufacturing, provide effective innovative solutions, aiming at managing operations, controlling quality completed goods offered customers. Aiming that end, this paper endeavors mitigate described in a multi-stage degrading manufacturing/remanufacturing through implementation an intelligent machine learning-based decision-making mechanism. To carry out decision-making, reinforcement learning is coupled with lean green manufacturing. scope creation sustainable production environment has minimal environmental impact. Considering latter, effort made reduce material consumption extend lifecycle manufactured products using pull production, predictive maintenance, circular economy strategies. validate this, well-defined experimental analysis meticulously investigates behavior performance proposed Results obtained by support presented learning/ad hoc control mechanism’s capability competence achieving both high enhanced reuse.

Language: Английский

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Digital Technology Innovation as a Catalyst for Real Economy Enhancement: A Chinese Perspective

Journal of the Knowledge Economy, Journal Year: 2024, Volume and Issue: unknown

Published: June 25, 2024

Language: Английский

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