A Multi-model Framework for Streamflow Forecasting Based on Stochastic Models: an Application to the State Of Ceará, Brazil

Rolim, Larissa Zaira Rafael; Filho, Francisco de Assis de Souza; Brown, Casey

doi:10.1007/s41101-023-00184-1

Cited by 5 publications

(1 citation statement)

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“…At present, the practices related to financial institutions and ecological compensation are mainly divided into the following categories: policy‐based financial institution compensation—mainly based on policy loans (Choi, 2022; Krahnen & Wilde, 2022; Lane et al, 2022); compensation for commercial, financial institutions—multi‐channel asset financing methods (Rolim et al, 2023); Asset securitization financing (Gao et al, 2023; Wengerek et al, 2022); agricultural green microcredit (Chen et al, 2022; Lee & Huruta, 2022; Zhou, Liu, & Luo, 2021); and ecological and environmental entrepreneurship investment fund (Speckemeier & Tsivrikos, 2022; Tenner & Horisch, 2021). This article covers two themes: “asset securitization” and “ecological compensation.” The classification and related content of specific types are shown in Table 1.…”

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confidence: 99%

The way to induce asset‐backed securities participation in eco‐compensation

Zhang,

2023

Manage Decis Econ

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By establishing an ecology compensation asset securitization project, market mechanisms can be used to regulate the interests of stakeholders in ecological conservation. Previous studies have shown that combining asset securitization with environmental protection can effectively promote the sustainable development of both economy and environment. However, there is still a lack of research on the stable mechanisms and impact of integrating ecology compensation and asset securitization. In this study, we propose a comprehensive operating model for ecology compensation asset securitization and a specific valuation framework supported by a multi‐period, multi‐state Markov model for ecology compensation asset securitization. If the project's net present value is positive, it can effectively reduce the risks of ecology compensation projects and increase the return rate of ecology compensation asset securitization projects. By employing the case study of ZhongHang ShouGang Green Energy (SZ:180801) and implementing the asset pool valuation using the multi‐state Markov model, the deviation error is 0.89%, validating the effectiveness of the model. Additionally, sensitivity analysis from simulation results indicates that changes in interest rates and default probabilities are the main factors influencing the valuation of the asset pool. After summarizing the research conclusions, relevant policy recommendations and future prospects are presented. This research aims to provide reference for the development and application of ecology compensation asset securitization.

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