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.

Examining boiler failure causes is crucial for thermal power plant safety and profitability. However, traditional approaches are complex and expensive, lacking precise operational insights. Although data-driven approaches hold substantial potential in addressing these challenges, there is a gap in systematic approaches for investigating failure root causes with unlabeled data. Therefore, we proffered a novel framework rooted in data mining methodologies to probe the accountable operational variables for boiler failures. The primary objective was to furnish precise guidance for future operations to proactively prevent similar failures. The framework was centered on two data mining approaches, Principal Component Analysis (PCA) + K-means and Deep Embedded Clustering (DEC), with PCA + K-means serving as the baseline against which the performance of DEC was evaluated. To demonstrate the framework’s specifics, a case study was performed using datasets obtained from a waste-to-energy plant in Sweden. The results showed the following: (1) The clustering outcomes of DEC consistently surpass those of PCA + K-means across nearly every dimension. (2) The operational temperature variables T-BSH3rm, T-BSH2l, T-BSH3r, T-BSH1l, T-SbSH3, and T-BSH1r emerged as the most significant contributors to the failures. It is advisable to maintain the operational levels of T-BSH3rm, T-BSH2l, T-BSH3r, T-BSH1l, T-SbSH3, and T-BSH1r around 527 °C, 432 °C, 482 °C, 338 °C, 313 °C, and 343 °C respectively. Moreover, it is crucial to prevent these values from reaching or exceeding 594 °C, 471 °C, 537 °C, 355 °C, 340 °C, and 359 °C for prolonged durations. The findings offer the opportunity to improve future operational conditions, thereby extending the overall service life of the boiler. Consequently, operators can address faulty tubes during scheduled annual maintenance without encountering failures and disrupting production.

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.