Can water allocation in the Yellow River basin be improved?: Insights from a multi-agent system model

In 1999, the Government of China enforced a cross-provincial, quota-based Water Allocation Agreement that was developed in 1987 and titled Unified Water Flow Regulation (UWFR) to ensure that flow to the Yellow River mouth would not be cut off. This policy was in line with the refocus of the Government, over the last decade, on sustainable water use and keeping the Yellow River healthy. The policy enforcement ended more than two decades of flow-cutoffs, that is, periods when the Yellow River did not reach the Bohai Sea at its mouth, during an increasing number of days every year.Water allocation, river basin management, multi-agent system,

Are Firms’ Disclosed Diversity Targets Credible?

Amid growing pressures to comply with ESG standards, firms increasingly disclose their ESG targets. However, given the difficulty in verifiability, it is unclear whether this public commitment to ESG goals is credible or only cheap talk. In this paper, we answer the question of how stakeholders should interpret firms’ disclosure of ESG goals and what they could expect in terms of firms’ future ESG performance. Specifically, we examine whether firms that publicly disclose diversity targets truly increase their diversity levels after the target disclosure. Exploiting a novel dataset of detailed firm employee records, we find that firms that disclosed a diversity target have indeed improved their diversity, but the diversity level already increased substantially prior to the target disclosure. To further explore how certain target characteristics are associated with disclosure credibility, we hand collected and coded firms’ diversity goals from their sustainability reports. We show that numerical, forward-looking, and all-employee targeted goals are more credible than others. We also find that firms that are historically more compliant, with greater institutional pressure, and with greater innovation demand tend to disclose more credible goals, suggesting the importance of examining firms’ incentives rather than the act of disclosure itself. Overall, our results generate practical implications for two groups: investors adjusting their decisions based on ESG disclosure and regulators assessing the necessity and content of ESG disclosure regulations

Low Carbon Consumer Lending Fintech Product Design Report

In short, green growth will play a central role on the road to a more resilient recovery, and financial technology will be a key driver. Fintech, as an essential support in building a green financial system, will play a key role in supporting green finance to serve the real economy more efficiently.This article focuses on the design of low-carbon consumer credit as a financial technology product, given the high costs of green transformation for businesses and the financing and credit risks they face. Through big data, cloud computing, machine learning and other technologies, the product will help businesses to go green, create value for users and bring positive energy to society. In response to the national “dual carbon” target, low-carbon consumer credit will become an important driver to foster the development of emerging industries. This design differs from generic financial technology products in that the design focuses on green, economic benefits, while taking into account corporate social responsibility, and the use of machine learning to create an anti-fraud system throughout credit risk management, loan withdrawal, and detection of all aspects to ensure maximum security for users

High-efficient deep learning-based DTI reconstruction with flexible diffusion gradient encoding scheme

Purpose: To develop and evaluate a novel dynamic-convolution-based method called FlexDTI for high-efficient diffusion tensor reconstruction with flexible diffusion encoding gradient schemes. Methods: FlexDTI was developed to achieve high-quality DTI parametric mapping with flexible number and directions of diffusion encoding gradients. The proposed method used dynamic convolution kernels to embed diffusion gradient direction information into feature maps of the corresponding diffusion signal. Besides, our method realized the generalization of a flexible number of diffusion gradient directions by setting the maximum number of input channels of the network. The network was trained and tested using data sets from the Human Connectome Project and a local hospital. Results from FlexDTI and other advanced tensor parameter estimation methods were compared. Results: Compared to other methods, FlexDTI successfully achieves high-quality diffusion tensor-derived variables even if the number and directions of diffusion encoding gradients are variable. It increases peak signal-to-noise ratio (PSNR) by about 10 dB on Fractional Anisotropy (FA) and Mean Diffusivity (MD), compared with the state-of-the-art deep learning method with flexible diffusion encoding gradient schemes. Conclusion: FlexDTI can well learn diffusion gradient direction information to achieve generalized DTI reconstruction with flexible diffusion gradient schemes. Both flexibility and reconstruction quality can be taken into account in this network.Comment: 11 pages,6 figures,3 table