To alleviate the geographical mismatch between supply and demand of water resources, virtual water trade had attracted extensive attention. Many studies had estimated the virtual water flow and measured the virtual water inequality using Environmental Input-Output (EIO) model. However, EIO model ignores the feedback effect in the trade, which may lead an overestimation or underestimation of virtual water transfer. Moreover, while considering the relation between economic benefits and environmental costs, the studies of virtual water inequality are still limited in both number and methodology. Here, to address these gaps, we recalibrated the virtual water and value-added transfer in China's 30 provinces in 2017 using a new Environmental Spillover-Feedback Effects (ESFEs) model, and then measured the inequality between virtual water transfer and the resource endowments taking the value-added into account. Our results show that the virtual water transfer of half of provinces changed exceeding 50 %, with a maximum of 428 %. The ratio of net virtual water outflow to one-way virtual water inflow (which is called virtual water plunder index in this study) in Xinjiang is up to 935 %, which directly contributing to the inequality among regions. Moreover, the virtual water transfer in different regions is not compensated equally from the perspective of economy. As a result, some regions are getting both water resources and economic benefits, while others are getting the opposite. Our study highlights the importance of considering both the pressure on water resources and economic benefits when measuring the virtual water inequality. Our findings support policymakers in developing adequate responses, i.e., clarifying regional responsibilities of virtual water trade, building a whole industrial chain, and balancing the transfer of value-added and virtual water.@en

This paper presents a method for determining the deployment of one-way electric carsharing services within a designated region that maximizes the total profit of the operator. A mixed integer non-linear program model is built, with a strategic planning level that decides the fleet size and the station capacity and an operational level that decides on the required relocation operations. The state of charge (SOC) of the vehicles parked in one station is assumed to follow a continuous distribution. A rolling horizon method is used to optimize the operational decisions over the course of a day, considering demand fluctuations and the limited battery capacity of the vehicles. A golden section line search method and a shadow price algorithm are developed to optimize the fleet size and station capacity, with the results feeding back to the carsharing operations. To demonstrate the applicability of the formulated models and solution algorithms, a large-scale case study is conducted for Suzhou Industrial Park, China as the region of operation. A two-step verification method that combines an optimization model via tracking of individual vehicle SOC and a discrete event simulation, demonstrates the accuracy of the SOC distribution model. Managerial insights from the application are also presented.@en

This paper studies the demand-supply imbalance problem for one-way carsharing systems under a combination of pricing strategy, relocations and access trips considering stochastic demand. A novel concept of a virtual zone is utilized to capture vehicle relocation range and client walking or biking distance constraints in one-way carsharing systems. The vehicle imbalance problem is further addressed by combining a long-term pricing strategy and real-time vehicle relocations in a two-stage stochastic programming model. In the first stage, the tactical decisions including fleet size and trip price are optimized, while anticipating the operational costs from the second stage. The second stage optimizes operational decisions under uncertain demand including vehicle relocations conditional on the tactical decisions in stage one. The model aims to maximize the profit of a carsharing company considering the fleet costs calculated in stage one and the expected operational costs and revenue obtained in stage two. A dedicated gradient search algorithm is developed to solve the two-stage stochastic programming and results are compared to a genetic algorithm and an iterated local search algorithm. The proposed model and corresponding solution approach are applied to a large-scale network with 50 zones and over 1000 vehicles in Suzhou, China. The application allows us to attain additional operational insight. Results suggest that increased prices for high demand stations during peak hours reduce demand while maintaining profitability of the system. It is also found that the real-time vehicle relocations and flexibility of clients to pick up vehicles at farther stations can increase demand service rate by as much as 10%.@en