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Article
Publication date: 2 January 2024

Yi-Hsin Lin, Ruixue Zheng, Fan Wu, Ningshuang Zeng, Jiajia Li and Xingyu Tao

This study aimed to improve the financing credit evaluation for small and medium-sized real estate enterprises (SMREEs). A financing credit evaluation model was proposed, and a…

Abstract

Purpose

This study aimed to improve the financing credit evaluation for small and medium-sized real estate enterprises (SMREEs). A financing credit evaluation model was proposed, and a blockchain-driven financing credit evaluation framework was designed to improve the transparency, credibility and applicability of the financing credit evaluation process.

Design/methodology/approach

The design science research methodology was adopted to identify the main steps in constructing the financing credit model and blockchain-driven framework. The fuzzy analytic hierarchy process (FAHP)–entropy weighting method (EWM)–set pair analysis (SPA) method was used to design a financing credit evaluation model. Moreover, the proposed framework was validated using data acquired from actual cases.

Findings

The results indicate that: (1) the proposed blockchain-driven financing credit evaluation framework can effectively realize a transparent evaluation process compared to the traditional financing credit evaluation system. (2) The proposed model has high effectiveness and can achieve efficient credit ranking, reflect SMREEs' credit status and help improve credit rating.

Originality/value

This study proposes a financing credit evaluation model of SMREEs based on the FAHP–EWM–SPA method. All credit rating data and evaluation process data are immediately stored in the proposed blockchain framework, and the immutable and traceable nature of blockchain enhances trust between nodes, improving the reliability of the financing credit evaluation process and results. In addition, this study partially fulfills the lack of investigations on blockchain adoption for SMREEs' financing credit.

Article
Publication date: 30 April 2024

Yanwen Tan, Ruixue Yue, Liru Chen, Congxi Li and Kevin Z. Chen

This paper aims to examine whether China's grain price support policy has distorted the grain market price.

Abstract

Purpose

This paper aims to examine whether China's grain price support policy has distorted the grain market price.

Design/methodology/approach

The time-varying differences-in-differences (DID) model is used to study the impact of support policies on grain prices, and it is combined with the event study method to explore the dynamic effects of price support policy. Panel data model is used to study the effect of the price support policy on price formation for national grain market prices. In addition, we apply the smooth transformation (STR) model to verify whether there is a distortion in the transmission of grain prices among different markets in China and from the international market to China’s market.

Findings

China’s grain price support policy plays a significant role in rising grain market prices, weakens the decisive role of the market mechanism in the formation of grain prices, hinders the spatial transmission of market price signals and decreases the effect of price transmission from the world market to China’s market.

Research limitations/implications

In order to ensure both the stability of grain production as well as the market stability, and also to ensure that intervention policies do not distort the food market, the minimum purchase price of grain and market regulation policies should be adjusted as follows: (1) price support policy should be shifted to an income support policy and (2) reasonably determine the scale of reserves and implement a grain minimum purchase price policy in limited areas.

Originality/value

Our findings are relevant for understanding the effect of China's grain price support policies on the implementation regions and the price transmission effect, which provide reference experience for developing countries to implement food price policies.

Details

China Agricultural Economic Review, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1756-137X

Keywords

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