Natural gas distributed energy is recognized as a pivotal means to enhance energy efficiency and mitigate carbon dioxide emissions through localized energy cascading. Positioned as a key option for advancing the Sustainable Development Goals, this system optimizes energy utilization near end-users. While maximizing energy efficiency, it is imperative to address potential environmental challenges. A thorough, comprehensive environmental assessment, facilitated by the life cycle assessment method, proves instrumental in meeting this standard. Employing this method enables an intuitive grasp of the environmental strengths and weaknesses inherent in natural gas distributed energy within the power structure. This insight serves as a foundation for informed project decision-making, fostering the growth of the industry. We selected six environmental impact assessment categories based on the CML 2001 method, and conducted the life cycle analysis across four stages. China's inaugural natural gas distributed energy demonstration project was chosen as a model case, and an environmental impact assessment inventory was established, utilizing survey data and literature for comprehensive data collection and analysis. Results from case testing yield environmental impact assessment outcomes, with a specific sensitivity analysis for stages with notable environmental impact factors. The study underscores that the operation phase has the highest environmental impact, comprising 78.37% of the total combined environmental impact, followed by the fuel production phase. Comparative analyses with coal-fired and conventional natural gas power generation, based on dimensionless literature data, reveal that abiotic resources depletion potential is the primary contributor to the environmental impact of 1 kWh of electricity product, constituting 52.76% of the total impact value, followed by global warming potential. Concrete strategies have been outlined for decision-making in both the operational and planning phases of natural gas distributed energy projects. The strengthening of policies is pinpointed towards grid connection and scale expansion.

Urban flood risk assessment requires attention in inland areas with intensifying climate change and an increasing probability of extreme precipitation. This study describes the developments and testing of a sub-catchment-based multi-index fuzzy evaluation approach that can provide adaptation guidance for municipal decision-makers at a local level. We first built a comprehensive flood risk assessment system considering three categories: hazard, urban system, and social environment. The proposed evaluation system includes hybrid uncertain information that involves random indicator sources and hesitant fuzzy judgments from experts. The storm weather management model combined with geographic information system tools was then applied to obtain random indicators. Subsequently, hesitant fuzzy linguistic sets and the Euclidean distance method were adopted to solve the problem of uncertainty and vagueness from subjective hesitant information. Therefore, the aggregation method provides a beneficial way to assess flood risk in a hybrid uncertain environment. In addition, the proposed approach was applied to the Jinjiang district in an inland city in the P. R. of China. This supports efforts to prioritize locally tailored policies and practical measures for higher-risk sub-catchments within large urban systems.

This paper proposes a new life cycle assessment (LCA) statistics method to calculate the energy consumption of Chinese buildings from the perspective of LCA under the sustainable supply chain system. We divide the life cycle of buildings into the materialization stage, the construction stage, and the operation stage. Based on the new LCA statistics method, we obtain the following findings. First, the growth of total building energy consumption has slowed down since 2014, and its share of the Chinese total energy consumption levels off, remaining at about 40%. In 2018, the stages of materialization, construction, and operation account for about 34.02%, 4.65%, and 61.33% in total building energy consumption, respectively. Second, the materialization and operation stages are the main sources of energy consumption in the whole supply chain. Energy consumption in the materialization stage has been declining year by year since 2014, due to the impact of energy-saving policy. Moreover, we find that energy consumption in the operation and construction stages has been increasing year by year. Finally, in the life cycle of Chinese buildings, energy consumption in the operation stage plays a dominant role. This paper puts forward some managerial suggestions to relevant departments and provides some measures to optimize energy consumption in the Chinese building industry.

The growing demand for electricity in residential sectors and the global need to decarbonize power systems are accelerating the transformation toward smart and sustainable energy networks. Smart homes and smart grids, integrating renewable generation, energy storage, and intelligent control systems, represent a crucial step toward achieving energy efficiency and carbon neutrality. However, ensuring real-time optimization, interoperability, and sustainability across these distributed energy resources (DERs) remains a key challenge. This paper presents a comprehensive review of artificial intelligence (AI) applications for sustainable energy management and low-carbon technology integration in smart grids and smart homes. The review explores how AI-driven techniques include machine learning, deep learning, and bio-inspired optimization algorithms such as particle swarm optimization (PSO), whale optimization algorithm (WOA), and cuckoo optimization algorithm (COA) enhance forecasting, adaptive scheduling, and real-time energy optimization. These techniques have shown significant potential in improving demand-side management, dynamic load balancing, and renewable energy utilization efficiency. Moreover, AI-based home energy management systems (HEMSs) enable predictive control and seamless coordination between grid operations and distributed generation. This review also discusses current barriers, including data heterogeneity, computational overhead, and the lack of standardized integration frameworks. Future directions highlight the need for lightweight, scalable, and explainable AI models that support decentralized decision-making in cyber-physical energy systems. Overall, this paper emphasizes the transformative role of AI in enabling sustainable, flexible, and intelligent power management across smart residential and grid-level systems, supporting global energy transition goals and contributing to the realization of carbon-neutral communities.

Urban metro network is highly vulnerable to flooding, yet most existing risk assessments rely on deterministic weighting methods that inadequately address uncertainty. To address this limitation, this study develops a probabilistic GIS-based flood risk assessment framework that integrates the Analytic Hierarchy Process (AHP) with its Monte Carlo extension (MC-AHP). Unlike conventional studies, the framework (i) explicitly quantifies uncertainty in expert judgments through β-PERT simulations, (ii) incorporates a comprehensive indicator system covering hazard, exposure, and vulnerability-including passenger flow and under-construction lines often overlooked in prior work, and (iii) provides spatially explicit risk mapping for both operational and planned infrastructure. Application to the Chengdu metro network reveals a concentric flood risk distribution, with the central urban core exhibiting the highest vulnerability despite moderate hazard levels. Compared to AHP, MC-AHP identifies a greater proportion of high-risk areas and provides probability distributions of indicator weights, thereby reducing the influence of subjective bias. Validation against historical flood events confirms the framework's predictive reliability. By explicitly incorporating uncertainty and extending analysis to planned infrastructure, this study advances the methodological rigor of urban flood risk assessment and provides generalizable insights for strengthening metro network resilience worldwide in the face of accelerating climate change and rapid urbanization.