![]() Using rail transport emitted higher particulate emissions, and trucks with higher capacity reduced the particulate emissions by 55%. Using 80% train transportation mode by suppliers would reduce emissions cost by 58%. Results showed that a combination of truck and train transport with increasing size and capacity of container leads to a reduction of around 60% gaseous pollutant emissions. The developed model is tested through a case study in Victoria, Australia. Scenario analysis is conducted to measure the particle-gaseous pollutant emissions and transportation costs of woody BSC. The two main forms of land freight, train and truck, with three sizes of capacity (light, medium, and heavy) are considered for the study. This study aims to develop an integrated Geographical Information System and Agent-based simulation modeling tool to fill this knowledge gap by investigating the particle-gaseous pollutant emissions and transportation cost of woody BSC. Geographical scale, distribution, and the type of transportation of waste wood cause considerable challenges to meet the economic and environmental sustainability of the woody biomass supply chain (BSC). In this regard, an important challenge is employing woody biomass effectively and considering the lower environmental emissions, transportation, and emissions cost of the supply chain and activities needed to convert biomass into a valued energy source. Wood based bioenergy industry is growing rapidly due to climate change, challenging environmental and economic conditions, progress in energy conversion, and development of renewable energy policies. Using 80% train transportation mode by suppliers would reduce emissions by 58%. Finally, the sensitivity analysis results revealed that using 100% truck transportation leads to the highest emission cost for BSC. Changing the capacity of the containers from light to medium and heavy would lead to reducing the cumulative emissions by an averagely of 46% and 59%, respectively. Results showed that a combination of truck and train transport with increasing size and capacity of container leads to decreasing near 60% gaseous pollutant emissions. ![]() The developed model is tested on the case study in Victoria-Australia. This work aims at analyzing and quantifying the environmental emissions and economic cost of woody waste BSC transportation design. Given these research challenges, this work contributes to the existing literature by developing an integrated Geographical Information System (GIS) and Agent-based simulation modeling to assess the effect of intermodal transportation (truck and train) technology developments (capacity and size) of the woody biomass supply chain (BSC) on the environmental emissions and cost. The logistic costs of collecting and transporting waste wood cause considerable challenges regarding scale, geography of supplier location, sustainability of supply chain, etc. Apart from all benefits it has to human beings this process is associated with some problems such as air pollution and high transportation cost. This paper helps scholars to understand the evolution of AI&RE research from a bibliometric perspective and inspires them to think about the field through multiple aspects.Įnergy generation refers to a process through which energy (in two forms of heat and electricity) is extracted from waste wood. In addition, future research trends are discussed. The study reveals that AI-related technologies can effectively solve issues related to integrating renewable energy with power system, such as solar and wind forecasting, power system frequency analysis and control, and transient stability assessment. The analysis results show that China is the most productive and influential country/region, with the widest range of collaborative partners. This paper uses VOS viewer, CiteSpace, and Bibliometrix to perform bibliometric analysis and science mapping. ![]() This study is performed based on the Web of Science Core Collection Database, and a dataset of 469 publications have been retrieved. In this paper, we provide a comprehensive bibliometric analysis to better understand the evolution of Artificial Intelligence in Renewable Energy (AI&RE) research from 2006 to 2022. In recent years, artificial intelligence methods have been widely applied to solve issues related to renewable energy because of their ability to solve nonlinear and complex data structures.
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