The first-hand house price in Beijing, the capital of China, has skyrocketed with 43 percent annual growth from 2005 to 2017, exerting tremendous adverse effects on people’s livelihood and the development of real estate. Thus, exploring the behavioral mechanism and accurate forecasts of house prices is a critical element in making decisions under uncertain conditions and is of great practical significance for both participants and policymakers in real estate. According to the complex features of house price, including nonlinear, nonstationary, and multiscale, and considering the remarkable time and frequency discrimination capability of multiscale analysis in dealing with house price problems, we develop an ensemble empirical mode decomposition- (EEMD-) based multiscale analysis paradigm to investigate the behavioral mechanism and then obtain accurate forecasts of house prices. Specifically, the monthly house price in Beijing over the period January 2005 to November 2018 is first decomposed into several different time-scale intrinsic-mode functions (IMFs) and a residual via EEMD, revealing some interesting characteristics in house price volatility. Then, we compose the IMFs and residual into three components caused by normal market disequilibrium, extreme events, and the economic environment using the fine-to-coarse reconstruction algorithm. Finally, we propose an improved hybrid prediction model for forecasting house prices. Our experimental results show that the proposed multiscale analysis paradigm is able to clearly reveal the behavioral mechanism hidden in the original house price. More importantly, the mean absolute percentage errors (MAPEs) of the proposed EEMD-based hybrid approach are 5.62%, 7.24%, and 8.63% for one-, three-, and six-step-ahead prediction, respectively, consistently lower than the MAPE of the three competitors.
In this study, we investigated the impact of three different perceived risk and environmental attitude on the fertilizer reduction behavior in vegetable production and the interplay between perceived risk and environmental attitude. We found that perceived economic risk can exert a significant and negative effect on farmers’ fertilizer reduction behavior (−0.39) and perceived social and psychological risks has a relatively weak negative impact with coefficients of −0.25 and −0.23, respectively. A more friendly environmental attitude can significantly and positively affect farmers’ fertilizer reduction behavior. Furthermore, environmental attitude has a moderating effect on the association between perceived risk and farmer’s fertilizer reduction behavior, but just significant for economic and social risk. In other words, a better environmental attitude could reduce the negative effect of perceived risk. This study promoted our new understanding of the risk perception’s impact on farmers’ behavior.
Abstract:The paper analyzes the constraint factors and main problems of Chinese horticultural industry. The horticultural industry is a major agricultural pillar industry in China which is highly market-oriented. It is important for national economy and the people's livelihood and can guarantee urban and rural residents' nutrition health and increase peaeants' income. The study proposes that the government should carry out five strategies, i.e., layout optimization, deepening market-oriented development, going out, improving quality and effectiveness and extending industrial chain, to ensure the sustainable development of Chinese horticultural industry.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.