Water shortage and water pollution have become major problems hindering socio-economic development. Due to the scarcity of water resources, the conflict between water supply and demand is becoming more and more prominent, especially in urban areas. In order to ensure the safety of urban water supply, many cities have begun to build reservoirs. However, few previous studies have focused on the optimal allocation of water resources considering storage reservoirs. In this study, a multi-water resources and multiple users chance-constrained dynamic programming (MMCDP) model has been developed for water resources allocation in Beijing, China, which introduces reservoir and chance-constrained programming into the dynamic programming decision-making framework. The proposed model can distribute water to different departments according to their respective demands in different periods. Specifically, under the objective of maximal benefits, the water allocation planning and the amount of water stored in a reservoir for each season under different feasibility degrees (violating constraints or available water resources situations) can be obtained. At the same time, the model can be helpful for decision-makers to identify the uncertainty of water-allocation schemes and make a desired compromise between the satisfaction degree of the economic benefits and the feasibility degree of constraints.

In this paper, a stochastic multi-objective chance-constrained programming model (SMOCCP) was developed for tackling the water supply management problem. Two objectives were included in this model, which are the minimization of leakage loss amounts and total system cost, respectively. The traditional SCCP model required the random variables to be expressed in the normal distributions, although their statistical characteristics were suitably reflected by other forms. The SMOCCP model allows the random variables to be expressed in log-normal distributions, rather than general normal form. Possible solution deviation caused by irrational parameter assumption was avoided and the feasibility and accuracy of generated solutions were ensured. The water supply system in the Xiaoqing River watershed was used as a study case for demonstration. Under the context of various weight combinations and probabilistic levels, many types of solutions are obtained, which are expressed as a series of transferred amounts from water sources to treated plants, from treated plants to reservoirs, as well as from reservoirs to tributaries. It is concluded that the SMOCCP model could reflect the sketch of the studied region and generate desired water supply schemes under complex uncertainties. The successful application of the proposed model is expected to be a good example for water resource management in other watersheds.

Water scarcity and aquatic environment deterioration have become increasingly serious problems in Urumqi, China. To alleviate the conflicts arising from competitive water users and retard the environment degradation, an optimization model was developed for regional water resources management under considering the dual total amount of water quantity and pollutant emissions control. Water allocation schemes and guarantee rates that combine diverse water sources and economic activities were obtained through the solutions of the proposed model. The interactions among water allocation schemes, various activities benefits, and pollutant emissions were analyzed synchronously. Policies for industrial structure adjustment and pollutants emission reduction were acquired through the analysis of the results. The modeling results can be valuable for water resources planning and justification of the existing industrial structure.

To ensure a smooth transition towards peak carbon emissions and carbon neutrality, one key strategy is to promote a low-carbon transition in the energy sector by facilitating the coordinated development of the electricity market, carbon market, and other markets. Currently, China’s national carbon market primarily focuses on the power generation industry. High-energy-consuming industries such as the steel industry not only participate in the electricity market but also play a significant role in China’s future carbon market. Despite existing research on market mechanisms, there remains a significant research gap in understanding how steel enterprises adjust their trading behaviors to optimize costs in multi-market coupling contexts. This study employs a system dynamics approach to model the trading interconnection between electricity trading (ET), carbon emission trading (CET), and tradable green certificates (TGC). Within this multi-market system, thermal power enterprises and renewable generators serve as suppliers of carbon allowances and green certificates, respectively, while steel companies must meet both carbon emission constraints and renewable energy consumption obligations. The results show that companies can reduce future market transaction costs by increasing the proportion of medium to long-term electricity contracts and the purchase ratio of green electricity. Additionally, a lower proportion of free quotas leads to increased costs in the carbon market transactions in later stages. Therefore, it is beneficial for steel companies to conduct cost analyses of their participation in multivariate market transactions in the long run and adapt to market changes in advance and formulate rational market trading strategies.

In the context of China’s transition from “dual control of energy consumption” to “dual control of carbon emissions,” understanding the synergistic mechanisms among carbon emission trading (CET), energy use rights trading (EURT), and electricity markets is critical for achieving the nation’s dual carbon goals. This study develops a system dynamics (SD) model to examine the coupled interactions within this “carbon–electricity–energy” ternary market system, focusing on thermal power enterprises as the primary analytical subject. The model reveals that the ternary market framework drives energy conservation and emission reduction through three key mechanisms: price signal transmission, dual regulatory constraints, and mutual quota recognition. These mechanisms propagate low-carbon incentives throughout the industrial chain by transmitting cost signals to end-users via electricity prices. Compared to binary market structures, the ternary framework achieves superior outcomes, it facilitates higher renewable energy consumption, maintains more stable price levels, enhances market liquidity for both carbon and energy rights, and improves resource allocation efficiency alongside environmental–economic performance. However, the simulation also exposes critical inefficiencies under the current “dual control of energy consumption” regime. The parallel operation of EURT and CET markets creates functional overlap and duplicated compliance burdens. This redundancy increases enterprise costs without commensurate environmental gains, validating the necessity of transitioning to carbon-focused dual control. Further analysis demonstrates that a mutual recognition mechanism between carbon and energy rights effectively alleviates dual compliance pressures and improves enterprise profitability. Optimal market performance emerges when the recognition ratio is appropriately calibrated. Additionally, gradually increasing the share of auctioned quotas while maintaining appropriate levels of free allowances can drive emission reductions without compromising enterprise profitability. This research provides both theoretical foundations and practical policy recommendations for building an efficient multi-market coordination mechanism, facilitating the policy transition, and advancing low-carbon transformation in China’s power sector.