Addressing water scarcity requires significant attention to reducing water footprint (WF) related to food consumption. Since individuals' dietary behavior is largely influenced by their demographic and anthropometric attributes, it is crucial to identify individuals who have a high dietary WF and prioritize them as the focus of policies. Several studies analyzing the driving factors behind dietary WF exist but have multiple limitations. These include the statistical models with rather modest performances, lack of rigorous sensitivity analysis/feature importance (FI) analysis, and lack of generalization ability. Here, we developed a novel ML-based framework for analyzing the driving forces behind dietary WF. The framework incorporated three machine learning (ML) models (Extra-Trees (ET), Histogram-based Gradient Boosting (HGB), and eXtreme Gradient Boosting (XGB)) and an ML explanation approach Shapley Additive exPlanations (SHAP). This framework was applied to a case study on Chinese inhabitants. The derived results validated the proposed framework and demonstrated ML's superiority over conventional statistical methods. XGB was identified as the optimal model as it effectively captured the variability in the data and showed good generalization performance. The FI analysis for XGB revealed the most influential features on dietary WF, with income level, urbanization level, education level, and gender emerging as the top four features in descending order. Through the subsequent SHAP dependence analysis, the priority groups for dietary WF reduction interventions were identified as high-income residents, urban residents, highly educated residents, and male residents. In light of these findings and their underlying causes, the paper concluded with a set of policy recommendations.

Multiple parameter environment monitoring via wireless Internet of Thing sensors is growing rapidly, thanks to low power techniques of the node. More importantly, the ever more complex and highly efficient energy harvesting systems enable long-term continuous monitoring in inaccessible environments without needing to change the battery. This paper reviews existing energy harvesting modalities, including photovoltaic, piezoelectric, pyroelectric, electromagnetic, and vibration, together with circuit techniques of interfacing power management circuits for energy harvesters. Moreover, techniques used to interface with multiple mode energy harvesters to obtain a stable output power with optimal power efficiency are discussed as an emerging direction. The state-of-the-art energy harvesting systems together with future development trends are provided.

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.

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%.

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.

One-way station-based carsharing systems allow users to return a rented car to any designated station, which could be different from the origin station. Existing research has been mainly focused on the vehicle relocation problem to deal with the travel demand fluctuation over time and demand imbalance in space. However, the strategic planning of the stations’ location and their capacity for one-way carsharing systems has not been well studied yet, especially when considering vehicle relocations simultaneously. This paper presents a Mixed-integer Non-linear Programming (MINLP) model to solve the carsharing station location and capacity problem with vehicle relocations. This entails considering several important components which are for the first time integrated in the same model. Firstly, relocation operations and corresponding relocation costs are taken into consideration to address the imbalance between trip requests and vehicle availability. Secondly, the flexible travel demand at various time steps is taken as the input to the model avoiding deterministic requests. Thirdly, a logit model is constructed to represent the non-linear demand rate by using the ratio of carsharing utility and private car utility. To solve the MINLP model, a customized gradient algorithm is proposed. The application to the SIP network in Suzhou, China, demonstrates that the algorithm can solve a real world large scale problem in reasonable time. The results identify the pricing and parking space rental costs as the key factors influencing the profitability of carsharing operators. Also, the carsharing station location and fleet size impact the vehicle relocation and carsharing patronage.