Domestic water plays a growing role with the unprecedented economic development and rising urbanization. The lack of long-term evaluation of domestic water usage trends limits our understanding of the relationship between domestic water usage and economics. Here, we present a pragmatic approach to assess the long-term relationship between domestic water usage and economics through historical data of the last 100 years from 10 typical countries to establish an evaluation method for different economics. The relationship between domestic water usage and GDP per capita was described as an expanded S-curve model and the mathematical modeling was derived to simulate this relationship for four typical countries as case studies. The simulation results show that the expanded S-curve of different countries can be calibrated with three key points: takeoff point, turning point, and zero-growth point, and four transitional sections: slow growth, accelerated growth, decelerated growth, and zero/negative growth, corresponding to the same economic development level. In addition, other factors influencing domestic water usage are also discussed in this research, including urbanization, industrial structure, and technical progress. We hope to provide a case study of an expanded S-curve as a foundation for forecasting domestic water usage in different countries or in the same economy at different developmental stages.

As the most populous country in the world, China has a great shortage pressure of water resources. With the acceleration of urbanization, China’s water usage in different sectors will change significantly in next few years. In order to investigate the main reasons behind water usage change in China, the Logarithmic Mean Divisia Index (LMDI) model was adopted in this paper from 2000 to 2020 with provincial data. Three effects, including that of technology, industrial structure, and regional scale, were analyzed. In addition, the decoupling effect between water usage and economic growth was also considered. The results show that: (1) from 2000 to 2020, the technological effect, industrial structure effect, and regional scale effect are −376.54, −89.85 and 20.66, respectively; (2) the technical effect and industrial structure effect have the greatest impact on primary industry, followed by secondary industry; (3) the technical effect is greater than the industrial structure effect in most provinces; and (4) the decoupling state gradually changes from weak decoupling to strong decoupling. In the future, the key policy recommendations for water saving are the following: (1) technological innovation has the most efficient effect on the reduction of water usage in China, and (2) the optimization of industrial structure can be helpful in water-saving in the future.

Shale gas, with its lower carbon content and pollution potential, is the most promising natural gas resource in China. When modeling the shale gas supply in a specific gas field, it is of paramount importance to determine the gas supply under economic considerations. Two common calculation methods are used in China for this purpose: Method 1 (M1) is the breakeven analysis, where the gas supply is based on the relationship between costs and revenues, while Method 2 (M2) is the Geologic Resource Supply-Demand Model, where the supply relies on demand and price scenarios. No comparisons has been made between these two methods. In this study, the Fuling shale gas field in the Sichuan Basin was chosen as a study case to forecast the shale gas supply using these two different methods. A sensitivity analysis was performed to discuss the influencing factors of each method and error measures were used to compare the different shale gas supply values calculated by each method. The results shows that M1 is more sensitive to initial production, while M2 is more sensitive to gas price. In addition, M2 is more feasible for its simplicity and accuracy at high price scenarios and M1 is considered to be reliable for low price scenarios with profit. This study can provide a quick and comprehensive assessment method for the shale gas supply in China.

Natural gas is regarded as the main transition energy under the carbon-neutral strategy and its main consumers are Organization for Economic Cooperation and Development countries, accounting for 44.5% of world consumption in 2021. In order to investigate the effects of technology, industry, and regions on natural gas consumption, 12 major Organization for Economic Cooperation and Development countries from three different country groups were selected in this paper to explore the consumption change. Firstly, the Logarithmetic Mean Divisia Index model is adopted to find out the driving factors. Then, the Tapio model is used to consider the decoupling state between natural gas consumption and economic growth. The results can be concluded as follows: (a) From 2000 to 2020, the technological progress effect has the biggest values of −148.86, followed by the industrial structure effect and the regional scale effect, with values of − 37.04 and 29.42, respectively. (b) From the perspective of industry view, these three effects have the largest impact on the secondary industry, followed by the tertiary industry and primary industry; (c) the regional scale effect has a positive effect on most countries, and the industrial structure effect and the regional scale effect have a negative effect on most countries; (d) the decoupling state vary differently in countries with different groups. Therefore, we concluded two policy recommendations for nature gas reduction: (a) Technological innovation is the most effective way for reducing natural gas consumption; (b) Industrial structure optimization can help save natural gas consumption.

Port trade is a core component of the global trade system, with approximately 80% of the volume and 70% of the value of global trade goods being transported via maritime shipping. LNG serves as both a pillar of current energy security and a practical choice for low-carbon transition, while profoundly influencing the global economic and political landscape. The import and supply of LNG involve complex trade networks and maritime transportation systems, though current research primarily focuses on static data and country-level analysis. This study applies complex network theory and utilizes the Kpler port trade statistics database to construct a global LNG port trade network. Based on this, a cascading failure model is developed to investigate the avalanche process of LNG port supply crises within the global trade network and analyze the impacts of supply disruptions at different ports. The findings indicate: (1) Over the past decade, global LNG port trade interconnections have grown increasingly close. (2) During the study period, among the top 10 LNG-exporting ports, those in countries such as Qatar, Malaysia, and Australia demonstrated relatively stable export performance, while the United States showed a notable rise in its LNG export port prominence. (3) When supply risks occur at different ports, the port with the highest export volume does not necessarily incur the most severe impact. (4) European countries are relatively more affected by disruptions at U.S. LNG export ports. These results can assist natural gas trading nations in formulating corresponding trade strategies to ensure the security of natural gas supply.