Dynamic simulation for comprehensive water resources policies to improve water-use efficiency in coastal city

An, Ziyao; Yan, Jingjing; Sha, Jinghua; Ma, Yufang; Mou, Siyu

doi:10.1007/s11356-020-12191-z

Cited by 12 publications

(5 citation statements)

References 69 publications

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“…Different types of water demand require diverse prediction methods. Common approaches for predicting water demand include gray theory and machine learning methods like neural networks, regression analysis, and quota analysis (Al-Zahrani and Abo-Monasar, 2015;Villarín, 2019;Fan et al, 2020;Meng and Wu, 2021). To account for the impact of multiple social indicators on water demand and policy adjustments, this study employs a wellestablished and precise technique-quota analysis.…”

Section: Water Demand Predictionmentioning

confidence: 99%

How can we achieve more sustainable water allocation in water-deficient areas? A case study from China’s Fen River Basin

Li,

2023

Front. Environ. Sci.

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Achieving the goal of water resource allocation improvement is significant for the ecological conservation of basins. Considering the water supply and demand contradiction and the competition among stakeholders within water-deficient regions, we developed a multi-objective optimization model and proposed the most effective method for allocating water resources based on the study of supply and demand. This study reveals the following findings: 1) the total demand for the Fen River Basin in 2030 will be 6.68 billion m3. Compared with previous years, the actual consumption has increased, but the growth rate has slowed down. 2) The basin’s total supply of water in 2030 will be 7.855 billion m3. With the tightening of the management practice, the consumption and supply in each county showed a decreasing trend year by year. 3) The results of optimal water allocation for 2030 indicate that the FRB should allocate 1.655 billion m3 for domestic water, 4.57 billion m3 for production consumption, and 57 million m3 for ecological use.

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Section: Water Demand Predictionmentioning

confidence: 99%

How can we achieve more sustainable water allocation in water-deficient areas? A case study from China’s Fen River Basin

Li,

2023

Front. Environ. Sci.

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“…In the indicator system, the attribute "+" indicates a higher value that is more favorable, while the attribute "-" indicates a lower value that is more favorable. The entropy weight method is a widely recognized and objective approach for determining the weights of multiple indicators in multi-criteria decision-making [28,37]. This method, based on information entropy theory, ensures objectivity, comprehensiveness, and convenience in assessing the importance of each indicator.…”

Section: Evaluation Index Selectionmentioning

confidence: 99%

“…(2019) [26], Haro-Monteagudo (2020) [27], and An, Z. (2021) [28] have explored the impact of water resource utilization on aquatic ecosystems, with particular attention to the health status of rivers, lakes, wetlands, and urban ecosystems, aiming to achieve sustainable utilization of water resources and protection of the ecological environment. Fifth is the relationship between WRCC and socio-economic development.…”

Section: Introductionmentioning

confidence: 99%

Study on Spatial and Temporal Differences of Water Resource Sustainable Development and Its Influencing Factors in the Yellow River Basin, China

Ding,

Li,

Wang

et al. 2023

Sustainability

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This study conducted a comprehensive analysis of the spatial and temporal variations of water resource carrying capacity (WRCC) and its influencing factors in the significant Yellow River Basin in China. Combining the composite weighting TOPSIS method with geographic detector analysis, the water resource carrying capacity index for nine provinces within the Yellow River Basin was evaluated from 2005 to 2021. The results reveal a continuous upward trend in water resource carrying capacity in the Yellow River Basin, with significant improvements attributed to increased investment in scientific research, enhanced water use efficiency, proactive water resource management, and environmental protection measures. The study also identified differences in water resource carrying capacity among provinces, highlighting the significant impact of factors such as economic development, population density, industrial wastewater discharge, and precipitation. The findings underscore the importance of balancing economic growth with environmental protection. Specifically, Inner Mongolia and Ningxia provinces showed remarkable progress, while Qinghai and Sichuan provinces exhibited slower growth, primarily due to geographical location and industrial development status. Based on these findings, it is recommended that research investment be strengthened, economic structures be optimized, water resource management be improved, and environmental protection measures be enhanced.

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“…Meanwhile, to fully describe the interactive relationships among all agents, some modules in the dynamic CGE model are concluded as follows: (1) the module of income and expenditure of agents and (2) the module of savings and investment and (3) the dynamic block. The dynamic I-O optimization model is widely applied to water management (An et al 2021 ), carbon tax (Li et al 2019 ), pollution management (Song et al 2019 ), etc. Therefore, it is suitable for environmental policy analysis.…”

Section: Introductionmentioning

confidence: 99%

The optimal industrial carbon tax for China under carbon intensity constraints: a dynamic input–output optimization model

Guang-wei

Sun

et al. 2022

Environ Sci Pollut Res

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To reduce carbon emissions, the Chinese government is considering introducing a differentiated industrial carbon tax on enterprises outside the carbon trading market in the future. An efficient carbon tax must consider not only how carbon taxes impact the current economy but also how the size of the tax should be adjusted across time due to external changes. To calculate the optimal industrial carbon tax for China which is subject to certain constraints, this paper investigates the economic and environmental effects of four possible industrial carbon tax rate models under carbon intensity constraints from 2021 to 2030 by a dynamic input–output optimization model. The results show that the dynamic tax rate model leads to larger fluctuations in GDP growth than the other tax models, with a low initial tax rate in the beginning and a high tax rate exceeding ¥180/t in 2030. Second, a large quantity of capital stock is distributed across the energy-intensive industries, which leads the existing capital investment structure to be path-dependent. This offsets the performance of carbon taxes. Third, indirect energy-intensive industries such as construction and transport are insensitive to the industrial carbon tax. Finally, comparing the impacts of the four tax rate models, the optimal industrial carbon tax for China is found to be a fixed differentiated tax rate, in which energy-intensive sectors are taxed ¥75/t and low-carbon sectors are taxed ¥50/t. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-022-19162-6.

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Dynamic simulation for comprehensive water resources policies to improve water-use efficiency in coastal city

Cited by 12 publications

References 69 publications

How can we achieve more sustainable water allocation in water-deficient areas? A case study from China’s Fen River Basin

How can we achieve more sustainable water allocation in water-deficient areas? A case study from China’s Fen River Basin

Study on Spatial and Temporal Differences of Water Resource Sustainable Development and Its Influencing Factors in the Yellow River Basin, China

The optimal industrial carbon tax for China under carbon intensity constraints: a dynamic input–output optimization model

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