Search

The virtual water strategy (VWS) is an effective tool to balance regional water resource endowments and guarantee water supply security. However, because of self‐interested games around VWS (human decision bias), there is a need for methods to maintain reliable cooperation between governments, virtual water (VW) enterprises and research institutions. This study builds a multi‐agent evolutionary game model to analyse the relationship of players and their impacts on VWS through changing decision mechanisms and the paths to enhance their confidence in cooperation. Considering differences in initial willingness to cooperate and changing factors affecting payoffs, an evolutionary game can produce changing stable equilibriums or stable cooperations, even if some players are reluctant to cooperate. Therefore, to promote the development of VWS, a multistep support mechanism can be built for the VW industry, which fosters model enterprises and optimizes the cooperation framework to stimulate research innovations at scientific institutions. Highlights: Traditional virtual water strategy (VWS) studies ignored the specific implementation and stakeholder game issues.A multi‐agent evolutionary game model is built to analyse the stable states and decision mechanisms for VWS.The payoffs of each game player will affect its strategy choice and the speed of model evolution.It is clearly possible for the stakeholders to form a stable equilibrium of long‐term cooperation around the VWS.Changing the factors of one game player may indirectly lead to changes in the strategies of other players. [ABSTRACT FROM AUTHOR]

Forecasting and analyzing corporate financial performance are of significant value to investors, managers, and regulators. In this paper, we constructed the one‐dimensional convolutional neural networks (1D‐CNN) and long short‐term memory (LSTM) deep learning models to investigate the feasibility of forecasting corporate financial performance with deep learning models, using the corporate financial features and environment, social and governance (ESG) rating index of Chinese A‐share listed corporation data from 2015 to 2021. Five evaluation metrics were employed to measure models' forecasting effects, and four competing machine learning models were built to verify the improvement in forecasting accuracy brought by the deep learning models. Furthermore, we also introduced the Accumulated Local Effects method to explore the forecasting processes of the deep learning models. The empirical results show the following: (1) Deep learning models can effectively extract the time‐series information in corporate data, thereby solving the task of predicting corporate financial performance with high accuracy. (2) The introduction of ESG information significantly contributes to the forecasting accuracy of corporate financial performance. For both 1D‐CNN and LSTM models, the ESG rating index can provide additional useful information for forecasting. (3) The interpretable results reveal the preference and emphasis of the two deep learning models for the different features. This further proves the robustness and reliability of deep learning models in forecasting corporate financial performance. [ABSTRACT FROM AUTHOR]

The implementation of the virtual water strategy (VWS) transporting invisible water resources through product scheduling faces resistance due to limited reporting and understanding and the lack of motivation analysis for stakeholders. This study builds a semi‐quantitative Hotelling game model under different scenarios to analyse the influence of preference and material benefits on potential acceptance of VWS with policymakers and stakeholders. Equilibrium analyses of the game show that human preference can be as important as real benefits. With preference differences, it is hard to make all stakeholders accept or reject a VWS approach in achieving optimal results for environment and social welfare. To implement a sustainable VWS mode, modifying preferences through propaganda and education can be effective. The natural play of the game with modified preferences will ultimately favour a holistic VWS approach to responsible management. This model supports the effectiveness of game theory in the implementation of a VWS. [ABSTRACT FROM AUTHOR]

Analysing intermediate products within a water footprint (WF) across different economic sectors can show the root causes of water usage and is helpful for water resource management and policy making. However, conventional methods and data for a WF rarely assess the input and output of intermediate products directly and comprehensively. Therefore, this study proposes an approach to access the WF of intermediate products as well as final products in each sector of an economy's water sustainability profile. An Economic Input‐output‐based Life‐Cycle Assessment (EIO‐LCA) framework is designed for the accounting, which describes the intermediate WF products of each sector in a material‐product network. This method is implemented into a 2016 case study for a comprehensive Chinese WF. The results showed that the total WF of Chinese inhabitants (consumers) in 2016 was 5.76 × 1011 m3, and the top three sectors with the largest WF were agriculture (1.78 × 1011 m3), food (1.05 × 1011 m3) and machinery manufacturing (5.68 × 1010 m3); agriculture provided the largest quantity of virtual water contained in its intermediate product for the other sectors. From the perspective of producers, the total WF of the Chinese economic sectors in 2016 was 5.84 × 1011 m3. The sectors with the largest direct water use were agriculture (2.20 × 1011 m3), electricity (7.64 × 1010 m3) and chemical Industry (2.35 × 1010 m3); and large parts of their direct water usage were consumed to prepare intermediate products for other sectors. The results of this study show that a more inclusive approach provides an enhanced qualitative and resource‐ethical view for water accounting and management. [ABSTRACT FROM AUTHOR]

Lithium niobate (LiNbO3 or LN) and lithium tantalate (LiTaO3 or LT) crystals, which have been widely applied in many fields such as electro-optics, birefringence, nonlinear optics, photorefraction, piezoelectricity and other areas, are reviewed. The studies of the properties and growth techniques are reviewed to give a brief idea of the growth of LN and LT crystals with high quality. The anti-site defects of these crystal materials have been accepted as one of the key factors constraining the improvement of their properties. This review shows the simulated calculation and structure of anti-site defects in LN/LT crystals and their effect on the physical properties and summarizes the recent progress in theoretical and technical processes, including the improved applications. We reviewed two main methods for the elimination of anti-site defects: doping of metal ions and growth of stoichiometric crystals. Finally, the prospects for future studies are outlined. [ABSTRACT FROM AUTHOR]