Xiaohui Yang

Clustered microgrids, capable of hosting high-penetration of renewable generation, are increasingly viewed as building blocks of an intelligent, low-carbon Energy Internet. However, their collective potential is undermined when operating in isolation. Furthermore, segregated markets for power, carbon allowances, tradable green certificates, and ancillary services (P-C-T-A) fragment price signals, causing inefficient dispatch, value loss, and weak cross-stakeholder coordination. To coordinate multi-stakeholder microgrid clusters in a joint P-C-T-A market, a local sharing framework embedded with the carbon responsibility sharing concept is developed, formulated as a bilevel optimization that captures strategic interactions between self-interested microgrids and the joint market center, while ensuring carbon responsibility's fair allocation. At the lower-level market, P-C-T-A products are jointly cleared via a continuous double auction mechanism. To solve this complex equilibrium, the Multi-Agent Deep Deterministic Policy Gradient algorithm is employed to empower microgrids to learn optimal bidding strategies under incomplete information. Results from a case study of heterogeneous microgrids in Dalian, China, demonstrate that the proposed framework outperforms alternative market architectures and carbon responsibility allocation schemes, yielding minimum cost and emission reductions of 19.00% and 9.40%, respectively, while enhancing distributive fairness and coordination efficiency. The reliability and robustness of the framework are systematically validated via multidimensional tests targeting parameter sensitivity, structural validity, and algorithmic stability. This research provides a practical blueprint for the market design and engineering management of community- and park-level microgrids, while contributing a novel theoretical paradigm for coordinated governance in multi-stakeholder Energy Internet systems.

Water and energy are mutually reinforcing elements in production and consumption activities, forming an inseparable relationship. This study, conducted at the provincial level, investigates the intricate connections between energy water footprint flow, water shortage, and water pollution. It aims to elucidate the vulnerability of provincial energy sectors to water use, fostering regional coordinated management for sustainable energy and water development. Utilizing a multi-regional input–output model, the research analyzes both the quantity and quality of water resources. The water footprint of China’s provincial energy sector is quantified and scrutinized. Furthermore, an evaluation index system is constructed based on the water footprint concept. The entropy weight TOPSIS method is then applied to assess the vulnerability of inter-provincial energy sector water footprints. The key findings are as follows: (1) The energy water footprint has shifted from water resource-deficient areas, such as the northwest, North China, and northeast, to economically developed regions with relatively abundant water resources, such as East China and South China. This reveals that the transfer of water footprints in economic activities does not entirely alleviate water shortages in China and may even exacerbate shortages in certain water-deficient areas. (2) The graywater footprint of the energy sector is significantly larger than the blue water footprint and water shortage footprint. (3) The production and supply departments of power and heat emerge as the largest contributors to the transfer of energy and water footprints from water shortage areas to water-rich areas, followed by coal mining and dressing departments. (4) Top 10 Vulnerable Provinces: Tibet, Zhejiang, Ningxia, Tianjin, Jiangsu, Henan, Hunan, Beijing, Chongqing, and Hebei are identified as the top 10 provinces vulnerable to water footprints in the energy sector. This vulnerability closely correlates with the energy sector’s dependence on water footprints and production leverage. Recommendations include optimizing the energy mix and establishing stable external water footprint supply channels for provinces with high water footprint dependence. Provinces with high production leverage are advised to accelerate innovation and application of water-saving technologies in key energy sectors, increase the utilization of clean energy, and enhance resource recycling. National government departments should focus on controlling and reducing water pollutants in the energy sector across regions, addressing water shortages by purifying water bodies and improving water quality.

Introduction: The hierarchical healthcare delivery system is an important measure to improve the allocation of medical resources and promote equitable distribution of basic medical and health services. It is one of the key factors in the success or failure of China’s medical reform. This study aims to analyze the factors influencing patients’ healthcare-seeking behaviors, including socioeconomic and clinical outcomes, under China’s hierarchical healthcare delivery system, and to provide potential solutions.Methods: Patients receiving outpatient treatment in the past 14 days and inpatient care in the past 1 year were investigated. The multivariate logistic regression was used to analyze the influencing factors of patient’s medical treatment behavior selection, and to compare whether the clinical outcomes of primary medical institutions and grade A hospitals are the same.Results: Nine thousand and ninety-eight person-times were included in the study. Of these, 4,538 patients were outpatients, 68.27% of patients were treated in primary medical institutions; 4,560 patients were hospitalized, 58.53% chose to be hospitalized in grade A hospitals. Provinces and cities, urban and rural areas, occupation, education level, medical insurance type, income, whether there are comorbid diseases, and doctors’ medical behavior are the factors affecting the choice of medical treatment behavior. Patients who choose primary medical institutions and grade A hospitals have different control levels and control rate for the blood pressure, blood lipids, blood glucose.Conclusion: Under the hierarchical diagnosis and treatment system, the patients’ choice of hospital is mainly affected by their level of education, medical insurance types, and the inpatients are also affected by whether there are comorbid conditions. Clinical outcomes of choosing different levels of hospitals were different.