Influence of Loss Aversion and Income Effect on Consumer Food Choice for Food Safety and Quality Labels

Food safety and food quality are two closely related aspects of the food management system. The difference between the two is that one keeps consumers safe while the other keeps consumers satisfied. This study examined the differences in how consumers value food safety and food quality with a focus on the influence of loss aversion on one’s psychological level and of income effect on one’s socio-demographic level. Our findings indicate that loss aversion and income effect significantly influence the way consumers value food safety vs. quality labels when considering potential health risks and food price. High risk-averse and low-income consumers with strong loss aversion and a weak income effect show a higher demand for food safety labels as a way to ensure easy access to safety indications. Low risk-averse and high-income consumers with weak loss aversion and a strong income effect show a higher demand for food quality labels because they hope to gain more health benefits from high-quality food at good prices. This study provides insights that will assist public authorities and food industry in balancing food safety control and food quality improvement in order to meet the heterogeneous market demand changing alongside the transition of China’s food consumption and production.

Driving forces of temporal-spatial differences in CO2 emissions at the city level for China’s transport sector

The paper aims to investigate the influencing factors that drive the temporal and spatial differences of CO2 emissions for the transportation sector in China. For this purpose, this study adopts a Logistic Mean Division Index (LMDI) model to explore the driving forces of the changes for the transport sector’s CO2 emissions from a temporal perspective during 2000–2017 and identifies the key factors of differences in the transport sector’s CO2 emissions of China’s 15 cities in four key years (i.e., 2000, 2005, 2010, and 2017) using a multi-regional spatial decomposition model (M-R). Based on the empirical results, it was found that the main forces for affecting CO2 emissions of the transport sector are not the same as those from temporal and spatial perspectives. Temporal decomposition results show that the income effect is the dominant factor inducing the increase of CO2 emissions in the transport sector, while the transportation intensity effect is the main factor for curbing the CO2 emissions. Spatial decomposition results demonstrate that income effect, energy intensity effect, transportation intensity effect, and transportation structure effect are important factors which result in enlarging the differences in city-level CO2 emissions. In addition, the less-developed cities and lower energy efficiency cities have greater potential to reduce CO2 emissions of the transport sector. Understanding the feature of CO2 emissions and the influencing factors of cities is critical for formulating city-level mitigation strategies of the transport sector in China. Overall, it is expected that the level of economic development is the main factor leading to the differences in CO2 emissions from a spatial-temporal perspective.