Maritime transport in the French economy and its impact on air pollution: An input-output analysis

Bagoulla, Corinne; Guillotreau, Patrice

doi:10.1016/j.marpol.2020.103818

Cited by 48 publications

(24 citation statements)

References 15 publications

(53 reference statements)

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“…Over the last decades, with globalization expanding, the maritime sector becomes more and more important. The maritime industry already accounts for 90% of international trade [73]. The fish market is regulated because the supply of most fish species depends primarily on catch limits and the potential of the fishing fleet [74].…”

Section: Fish Processing Wastes and Methods Of Its Utilizationmentioning

confidence: 99%

Use of Non-Conventional Yeast Yarrowia lipolytica in Treatment or Upgradation of Hydrophobic Industry Wastes

Wierzchowska

Zieniuk

Fabiszewska

2021

Waste Biomass Valor

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The review aims to summarize the current knowledge on the possibility of using non-conventional yeast species Yarrowia lipolytica in the treatment and upgradation of industry wastes. Importantly Y. lipolytica yeast is argued as generally recognized as safe species, what indicates the high application potential of the reviewed technologies. Special emphasis in the paper was given on microbial processing of the food industry wastes, including fish and animals’ wastes utilization. Yarrowia-based processing of waste cooking oil or oil-bearing plants wastewaters, such as palm oil mill effluents or olive mill wastewater was reviewed. Recent advances in biosynthesis of valuable metabolites (e.g. lipases or microbial oil) with simultaneous wastes utilization by Y. lipolytica are additionally discussed. The broad implications of the present paper are a part of sustainable development policy. Graphic Abstract

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Section: Fish Processing Wastes and Methods Of Its Utilizationmentioning

confidence: 99%

Use of Non-Conventional Yeast Yarrowia lipolytica in Treatment or Upgradation of Hydrophobic Industry Wastes

Wierzchowska

Zieniuk

Fabiszewska

2021

Waste Biomass Valor

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“…Oxford Economics (2017) researched the Croatian shipping industry role that is not directly classified to transport, but for which performance is strongly related to water transport services. Bagoulla and Guillotreau (2020) analyzed the impact of maritime transport in France on the domestic economy, providing a different perspective by assessing the environmental impact of shipping on direct and indirect gas emissions. Yu et al (2019) constructed the China non-competitive constant price IO model comprising the transport and storage sector.…”

Section: Literature Reviewmentioning

confidence: 99%

The Impact of Transportation on the Croatian Economy: The Input–Output Approach

2021

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The aim of this paper was to determine the economic impact of the transportation sector on the Croatian economy by using input–output analysis. According to the input–output tables for the Croatian economy for 2004, 2010, 2013, and 2015, output and gross value-added multipliers were calculated. The results of the conducted analysis indicated that the multiplicative effects of the transportation sector in Croatia were significant in the observed period, especially for the air transport sector. Furthermore, comparative multiplier analysis with selected European Union countries was performed to assess the Croatian transportation industry position from an international perspective. Lower output and gross value-added multipliers for the Croatian transportation sector imply that old European Union member states capitalized the transportation sector more for growth and development. The Croatian transportation sector recorded lower imported intermediate inputs, average domestic inputs, and higher value-added multipliers similar to new European Union members. Simulations based on multiplicative effects show that restrictions on movements and human contacts, imposed due to the COVID-19 pandemic, could induce a strong reduction in the economic activity of transport and other sectors that are included in the value-added chain of the transport industry.

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“…We tried to determine whether there was a lag between the weekly freight volumes in order to judge whether there was a guide-follow relationship between the different ports. We set the window size at 20 weeks and then used windowed time-lagged crosscorrelations (WTLCC) based on Equation (2) to analyze the weekly freight volume of the target port and that of the other ports every month, as shown in Figure 7. The freight volume correlations between these three ports and Wuhan Port were different.…”

Section: Was There a Lag Between The Weekly Freight Volumes Of Different Ports?mentioning

confidence: 99%

“…According to statistics from the United Nations Conference on Trade and Development, about 80% of global trade is transported by shipping. There is an obvious relationship between the development of maritime transport and the economic growth of countries, especially landlocked countries [1,2]. Although the COVID-19 pandemic may slow down this increasing trend, maritime transportation flows and container demand have continued to grow [3].…”

Section: Introductionmentioning

confidence: 99%

Is the Spatial-Temporal Dependence Model Reliable for the Short-Term Freight Volume Forecast of Inland Ports? A Case Study of the Yangtze River, China

Liu

Zhang

Chen

et al. 2021

JMSE

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The purpose of this study is to investigate whether spatial-temporal dependence models can improve the prediction performance of short-term freight volume forecasts in inland ports. To evaluate the effectiveness of spatial-temporal dependence forecasting, the basic time series forecasting models for use in our comparison were first built based on an autoregression integrated moving average model (ARIMA), a back-propagation neural network (BPNN), and support vector regression (SVR). Subsequently, combining a gradient boosting decision tree (GBDT) with SVR, an SVR-GBDT model for spatial-temporal dependence forecast was constructed. The SVR model was only used to build a spatial-temporal dependence forecasting model, which does not distinguish spatial and temporal information but instead takes them as data features. Taking inland ports in the Yangtze River as an example, the results indicated that the ports’ weekly freight volumes had a higher autocorrelation with the previous 1–3 weeks, and the Pearson correlation values of the ports’ weekly cargo volume were mainly located in the interval (0.2–0.5). In addition, the weekly freight volumes of the inland ports were higher depending on their past data, and the spatial-temporal dependence model improved the performance of the weekly freight volume forecasts for the inland river. This study may help to (1) reveal the significance of spatial correlation factors in ports’ short-term freight volume predictions, (2) develop prediction models for inland ports, and (3) improve the planning and operation of port entities.

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Maritime transport in the French economy and its impact on air pollution: An input-output analysis

Cited by 48 publications

References 15 publications

Use of Non-Conventional Yeast Yarrowia lipolytica in Treatment or Upgradation of Hydrophobic Industry Wastes

Use of Non-Conventional Yeast Yarrowia lipolytica in Treatment or Upgradation of Hydrophobic Industry Wastes

The Impact of Transportation on the Croatian Economy: The Input–Output Approach

Is the Spatial-Temporal Dependence Model Reliable for the Short-Term Freight Volume Forecast of Inland Ports? A Case Study of the Yangtze River, China

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