China’s coal-based energy structure and its large proportion of the manufacturing industry have resulted in China having the highest CO₂ emissions in the world, accounting for about one-third of the world’s total emissions. Achieving the carbon peak by 2030 and carbon neutrality by 2060, while maintaining economic development, presents a significant challenge. Industrial parks, as core units of industrial clusters, are major sites for energy consumption and CO₂ emissions. Thus, industry parks with great carbon reduction potential are crucial for China’s carbon reduction efforts. This review systematically examines the current state of the carbon emissions of industrial parks in China and analyzes the situation and challenges faced in the low-carbon transition of these parks. Then, the major pathways, technologies, and key policies for the low-carbon transition are summarized based on the current situation. This paper assists in the low-carbon development of industrial parks and promotes the proposal and implementation of carbon emission reduction strategies.

Social platforms serve as central hubs for information exchange, where user behaviors and platform interventions jointly shape opinions. However, intervention policies like recommendation and content filtering, can unintentionally amplify echo chambers and polarization, posing significant societal risks. Proactively evaluating the impact of such policies is therefore crucial. Existing approaches primarily rely on reactive online A/B testing, where risks are identified only after deployment, making risk identification delayed and costly. LLM-based social simulations offer a promising pre-deployment alternative, but current methods fall short in realistically modeling platform interventions and incorporating feedback from the platform. Bridging these gaps is essential for building actionable frameworks to assess and optimize platform policies. To this end, we propose PolicySim, an LLM-based social simulation sandbox for the proactive assessment and optimization of intervention policies. PolicySim models the bidirectional dynamics between user behavior and platform interventions through two key components: (1) a user agent module refined via supervised fine-tuning (SFT) and direct preference optimization (DPO) to achieve platform-specific behavioral realism; and (2) an adaptive intervention module that employs a contextual bandit with message passing to capture dynamic network structures. Experiments show that PolicySim can accurately simulate platform ecosystems at both micro and macro levels and support effective intervention policy.

In recent years, China’s rapid economic growth and urbanization have heightened the conflict between economic development and resource sustainability, leading to severe urban water challenges, including scarcity and environmental degradation. This study proposes a quantitative model that integrates the “Human-Water-City” (HWC) feedback mechanisms to assess and measure urban comprehensive water resources and environmental carrying capacity (CWRECC), aimed at addressing urban water sustainability challenges. The CWRECC integrates water quantity and quality dimensions following the principles of the “Cannikin Law”—selecting the lower envelope between water resources and water environment carrying capacities, which emphasizes the importance of weaknesses in enhancing the overall system. The maximum sustainable population and Gross Domestic Product (GDP) under the CWRECC constraints can be obtained using this quantitative method. A case study was conducted in Wuhan City. The results show that Wuhan has abundant water resources. From 2013 to 2020, if only considering the water quantity aspect, the water resources carrying capacity could support a population ranging from 22.63 to 61.17 million and a GDP between 1946.6 and 7988.9 billion yuan, maintaining a sustainable state throughout the period. However, when considering both water quantity and quality, the CWRECC revealed an overloaded state in 2013, 2014, 2018, and 2019, primarily attributable to significant water environmental issues. In 2013, 2014, 2018, and 2019, the quantified CWRECC could sustain populations of 9.88 million, 10.01 million, 10.33 million, and 10.57 million people, and support a GDP of 849.5 billion, 976.5 billion, 1402.9 billion, and 1538.9 billion yuan, respectively. Both the population and GDP capacities fell short of the actual recorded values for those years. The findings demonstrate that Wuhan needs to make greater efforts in water environmental protection to sustain the harmonious development within the HWC. This empirical study highlights the model’s potential to provide a scientific foundation for urban water resources management and environmental protection strategies.

The graphical abstract presents a three-stage workflow for dynamic urban waterlogging forecasting using real-time SWMM model calibration. Step 1: Multi-source Data Fusion integrates rainfall forecasts, urban drainage monitoring data, and crowdsourced waterlogging information from social media platforms such as Weibo.Step 2: Real-time Dynamic Forecasting Framework runs the SWMM model, judges forecast errors (triggering calibration when the water depth error exceeds 5 cm), identifies sensitive parameters, performs real-time calibration, and updates forecasts iteratively. Step 3: The results show two key improvements: (1) Accuracy improvement: average Nash-Sutcliffe Efficiency (NSE) increased by 0.29, with all nodes reaching Class B accuracy; (2) Timeliness superiority: the timeliness coefficient (TC) is ? 0.9 (Class A), with each node’s calibration completed in 40 seconds to 1 minute. The framework balances accuracy and timeliness for real-time urban waterlogging early warning. Under the climate change paradigm, urban waterlogging triggered by extreme rainfall is intensifying. Precise, dynamic waterlogging forecasting is vital for urban management and residents to cope with disasters. Current studies often use near-real-time rainfall forecasts to drive hydro-hydraulic models for short-term urban waterlogging prediction, with static inputs and parameters, termed static forecasting. Absent a correction mechanism, static forecasts' accuracy often falls short for effective waterlogging control. This study takes the SWMM model as an example, introduces a real-time calibration mechanism for model parameters, and proposes a method for dynamic forecasting of urban waterlogging processes. A case application was carried out in Zhengzhou, China. The results show that compared with static forecasting, dynamic forecasting can improve the forecasting accuracy in stages during the process, with the average Nash–Sutcliffe Efficiency (NSE) coefficient increased by 0.29, and all nodes achieving a forecasting level above Class B. This method balances accuracy and timeliness, and can be effectively applied to urban waterlogging emergency management.

Runoff generation is a key process of the hydrologic cycle. In studies, most hydrological models can’t explain how changes in the runoff generation mechanism caused by rainfall and soil conditions. Accounting for this issue, this article proposed a runoff generation mechanism index (RGMI) to realize intelligent adaptation to runoff generation mechanism. The posteriori RGMI was constructed with runoff coefficient (C) and curve number (CN) by weighting, and the priori RGMI was calculated based on rainfall and antecedent soil moisture through nonlinear fitting. Further, Xin’anjiang model (XAJ), Green-Ampt model (GA), and Vertically Mixed Runoff Model (VMM) were adopted to compose a novel adaptive runoff generation module. In the Xun River basin, the performances of the proposed runoff generation method and three fixed runoff generation methods were compared. The forecasting results of flood events were evaluated using relative peak error (Q_p), error between simulated and observed peak times (T_p), Nash-Sutcliffe efficiency (E_NS), and relative flood volume error (W_p). The results showed that the adaptive runoff generation module can simulate the flood peak flow and peak time with higher accuracy than fixed runoff generation module. In addition, the dominant runoff generation processes in the Xun River basin mostly are saturation-excess and hybrid runoff generation processes.