Recently, the Chinese government has implemented stringent water requirements based on the concept of ‘Basing four aspects on water resources’. However, existing research has inadequately addressed the constraints of water resources on population, city boundaries, land, and production, failing to adequately analyze the interplay between water resource limitations and urban development. Recognizing the interconnectedness between urban water use and economic development, a multi-objective model becomes crucial for optimizing urban water resources. This study establishes a nonlinear multi-objective water resources joint optimization model, aligning with the “Basing four aspects on water resources” requirement to maximize urban GDP and minimize total water use. A genetic algorithm (NSGA-II Algorithm) is applied to solve this complex nonlinear multi-objective model and obtain the Pareto solution set, addressing information loss inherent in the traditional water quota method. The model was tested in Wujiang District, an area located in China’s Jiangsu Province that has been rapidly urbanizing over the past few decades, and yielded 50 non-inferior water resource optimization schemes. The results reveal that the Pareto solution set visually illustrates the competition among objectives and comprehensively displays the interplay between water and urban development. The model takes a holistic approach to consider the relationships between water resources and urban population, land use, and industries, clearly presenting their intricate interdependencies. This study serves as a valuable reference for the rational optimization of water resources in urban development.

As China attaches increasing importance to its ecological environment, ecology-related industries have become essential to China’s national economy. However, in the current national economic accounting practice, the ecological industry (eco-industry) is not independent, and the ecological service value ecology creates is currently not within the scope of national economic accounting. To clarify the impact of the development of the eco-industry on the whole regional economy, this paper takes Beijing as the study area. For the first time, the input–output analysis method is adopted to differentiate the eco-industry as an independent sector. Moreover, the ecosystem services value is integrated into the eco-industry, and each coefficient is quantitatively analyzed from an industrial-chain perspective. The results show that the eco-industry exerts a good pulling effect on the regional economy. The inputs and outputs of the eco-industry clearly tend to focus on eco-environmental and public-service-related industries, followed by industries for which ecological development can create value. Judging from the entire regional economy, ecological investment significantly impacts both the education and financial industries. Ecological investment can promote socio-economic development, achieving a 1.318 increase in regional GDP per unit of eco-investment. The results imply that the development of the eco-industry in China should be boosted further and social capital investment should be attracted. Finally, this paper provides a scientific basis for policymakers to better understand the overall situation of both the eco-industry and industry linkages and guide them to develop relevant ecological investment strategies.

The current water supply pricing management policy for hydraulic projects introduces new challenges and opportunities in water pricing design, particularly under the ‘permitted cost-plus-reasonable-return’ approach. This study develops a computable general equilibrium (CGE) model tailored to the unique characteristics of a water diversion project. The model incorporates multi-source water substitution and user preferences to evaluate the socio-economic impacts of water price adjustments. Using Beijing as a case study, the research examines key indicators such as water use, regional GDP, and CPI under various pricing scenarios, including phased adjustments. The results show that recalculated water prices under the new policy are 71% higher than initial estimates, mainly due to including investment profits in the pricing mechanism. Simulations reveal that a 2–5% increase in water prices decreases regional water use by 0.028–0.070%, increases the CPI by 0.0028–0.0071%, and decreases the GDP by 0.0001–0.001%. Moreover, phased pricing adjustments are shown to minimize short-term economic disruptions. These findings offer valuable insights into optimizing water pricing mechanisms and their implications for regional economic stability and resource management.