The impacts of improvements in the unified economic and environmental efficiency of transportation infrastructure on industrial structure transformation and upgrade from the perspective of resource factors

Wang, Yijiao

doi:10.1371/journal.pone.0278722

PLoS ONE

2022

DOI: 10.1371/journal.pone.0278722

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The impacts of improvements in the unified economic and environmental efficiency of transportation infrastructure on industrial structure transformation and upgrade from the perspective of resource factors

Yijiao Wang

Abstract: As an important part of a modern economic system, a modern industrial system is the key to promoting high-quality economic development. China’s modern industrial system construction focuses on industrial restructuring. At present, in order to strengthen the support and leading role of transportation in the modern economic system, China is actively promoting the construction of a competitive transportation power. Therefore, it is necessary to study whether large-scale investment in transportation infrastructure… Show more

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2024

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Cited by 2 publications

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A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering

Yaghoubi,

Khamees

et al. 2024

Neural Comput & Applic

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Artificial neural networks (ANN), machine learning (ML), deep learning (DL), and ensemble learning (EL) are four outstanding approaches that enable algorithms to extract information from data and make predictions or decisions autonomously without the need for direct instructions. ANN, ML, DL, and EL models have found extensive application in predicting geotechnical and geoenvironmental parameters. This research aims to provide a comprehensive assessment of the applications of ANN, ML, DL, and EL in addressing forecasting within the field related to geotechnical engineering, including soil mechanics, foundation engineering, rock mechanics, environmental geotechnics, and transportation geotechnics. Previous studies have not collectively examined all four algorithms—ANN, ML, DL, and EL—and have not explored their advantages and disadvantages in the field of geotechnical engineering. This research aims to categorize and address this gap in the existing literature systematically. An extensive dataset of relevant research studies was gathered from the Web of Science and subjected to an analysis based on their approach, primary focus and objectives, year of publication, geographical distribution, and results. Additionally, this study included a co-occurrence keyword analysis that covered ANN, ML, DL, and EL techniques, systematic reviews, geotechnical engineering, and review articles that the data, sourced from the Scopus database through the Elsevier Journal, were then visualized using VOS Viewer for further examination. The results demonstrated that ANN is widely utilized despite the proven potential of ML, DL, and EL methods in geotechnical engineering due to the need for real-world laboratory data that civil and geotechnical engineers often encounter. However, when it comes to predicting behavior in geotechnical scenarios, EL techniques outperform all three other methods. Additionally, the techniques discussed here assist geotechnical engineering in understanding the benefits and disadvantages of ANN, ML, DL, and EL within the geo techniques area. This understanding enables geotechnical practitioners to select the most suitable techniques for creating a certainty and resilient ecosystem.

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A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering

Yaghoubi,

Khamees

et al. 2024

Neural Comput & Applic

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Do public-private partnerships (PPPs) promote the sustainable development of infrastructure (SDI)? Evidence from China

Ren,

Huang,

Xia

et al. 2024

ECAM

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PurposeTo address challenges such as inadequate funding and inefficiency in public infrastructure construction, PPPs have gained significant global traction. This study aims to comprehensively assess the impacts and mechanisms of PPPs on the SDI and to provide rational policy recommendations based on the findings.Design/methodology/approachWe collated a dataset from 30 Chinese provinces covering the years 2005–2020 as our research sample. The study’s hypotheses are tested using a double fixed-effects model, a chained mediated-effects model and a multidimensional heterogeneity analysis.FindingsOur findings indicate that PPPs have a facilitating effect on SDI in general. This boost usually lags behind policy implementation and is cyclical in the time dimension. In the spatial dimension, PPPs contribute significantly to SDI in the eastern and western regions, but not in the central region. From the perspective of the dynamics of economic, social and industrial development, PPPs in economically backward areas are difficult to promote SDI, promote it the most in economically medium regions and are slightly less in economically developed regions than in medium regions. This promotion effect has an inverted U-shaped relationship with social development and diminishes with industrial structure upgrading. Finally, due to the negative relationship between PPPs and social development and between social development and SDI, PPPs are shown to contribute to SDI and are identified as critical paths. However, PPPs suppress SDI by inhibiting economic and industrial development.Originality/valueThis study makes three novel contributions to the existing body of knowledge: (1) we innovatively introduce the United Nations Sustainable Development Goals (SDGs) into the field of infrastructure research, offering fresh perspectives on SDI enhancement; (2) revealing the mechanisms by which PPPs affect SDI through the three dimensions of economic, social and industrial development enabling policymakers to better understand and optimize resource allocation and improve planning, design and management of PPP projects for sustainable infrastructure and (3) we assess the spatiotemporal variances of PPPs’ effects on SDI and the diversity across regions at different social, economic and industrial structures developmental stages, offering critical insights to global decision-makers to devise tailored policy measures.

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The impacts of improvements in the unified economic and environmental efficiency of transportation infrastructure on industrial structure transformation and upgrade from the perspective of resource factors

Cited by 2 publications

References 21 publications

A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering

A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering

Do public-private partnerships (PPPs) promote the sustainable development of infrastructure (SDI)? Evidence from China

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