Qiyou Luo

Bio: Qiyou Luo is an academic researcher. The author has contributed to research in topics: Income elasticity of demand & Per capita. The author has an hindex of 1, co-authored 1 publications receiving 3 citations.

A Study on the Relationship between Income Change and the Water Footprint of Food Consumption in Urban China

Abstract: We use a threshold model to analyze the relationship between per capita income and the per capita water footprint of food consumption in the urban Guangdong Province of China, and further simulate the effect of changes in income distribution on the per capita water footprint of food consumption. The income growth of urban residents has a significant positive effect on the per capita water footprint of food consumption, where the effect varies by income stratum. The income elasticity of the per capita water footprint of food consumption for the total sample is 0.45, where the income elasticity of the low-income group (0.75) is greater than that of the high-income group (0.23), indicating that a change of income in the low-income group has a greater effect on water resources. The simulation results show that increasing the income of residents, especially that of the low-income group, significantly increases the water footprint due to food consumption for the whole society. At present, China is in a period of rapid economic growth and urbanization, comprising a period of profound change and sensitive response to the income level of urban and rural residents. Therefore, in order to reduce the effect of food consumption on the environment, sustainable food consumption management strategies should consider group differences. We should correctly guide all kinds of groups to carry out sustainable consumption, advocate healthy and reasonable diet models, reduce animal food consumption, avoid the excessive consumption of food, and strengthen the management of food waste.

Sustainable Consumption in Consumer Behavior in the Time of COVID-19: Topic Modeling on Twitter Data Using LDA

Abstract: By using text mining techniques, this study identifies the topics of sustainable consumption that are important during the COVID-19 pandemic. An Application Programming Interface (API) streaming method was used to extract the data from Twitter. A total of 14,591 tweets were collected using Twitter streaming API. However, after data cleaning, 13,635 tweets were considered for analysis. The objectives of the study are to identify (1) the topics users tweet about sustainable consumption and (2) to detect the emotion-based sentiments in the tweets. The study used Latent Dirichlet Allocation (LDA) algorithm for topic modeling and the Louvain algorithm for semantic network clustering. NRC emotion lexicon was used for sentiment analysis. The LDA model discovers six topics: organic food consumption, food waste, vegan food, sustainable tourism, sustainable transport, and sustainable energy consumption. While the Louvain algorithm detects four clusters—lifestyle and climate change, responsible consumption, energy consumption, and renewable energy, sentiment analysis results show more positive emotions among the users than the negative ones. The study contributes to existing literature by providing a fresh perspective on various interconnected topics of sustainable consumption that bring global consumption to a sustainable level.

Spatial–Temporal Heterogeneity and Driving Factors of Rural Residents’ Food Consumption Carbon Emissions in China—Based on an ESDA-GWR Model

Abstract: The increase in income among Chinese residents has been accompanied by dramatic changes in dietary structure, promoting a growth in carbon emissions. Therefore, in the context of building a beautiful countryside, it is of great significance to study the carbon emissions of rural residents’ food consumption to realize the goal of low-carbon food consumption. In this paper, the calculation of food consumption carbon emissions of Chinese rural residents is based on the carbon conversion coefficient method, and the spatial heterogeneity of influencing factors is analyzed with the aid of the ESDA-GWR model. The results indicate that the per capita food consumption carbon emissions of rural residents have increased by 1.68% annually, reaching 336.73 kg CO2-eq in 2020, which is 1.32 times that of 2002. Carbon emissions generated from rural residents’ food consumption have significant spatial agglomeration characteristics, showing the spatial distribution characteristics of a north–south confrontation, with a central area collapse. The influencing factors of food consumption carbon emissions have significant spatial heterogeneity, among which, as the main force to restrain the growth of food consumption carbon emissions, the price factor has a regression coefficient between −0.1 and −0.3, and its influence has weakened from northwest to southeast in 2020. The education–social factor is the main driving force for the growth of food consumption carbon emissions, with a regression coefficient between 0.58 and 0.99, and its influence has increased from east to west. In the future, formulating food consumption optimization policies should be based on the actual situation of food consumption carbon emissions in various regions to promote the realization of low-carbon food consumption.

Determinants of Food Consumption Water Footprint in the MENA Region: The Case of Tunisia

Abstract: Tunisia, like most countries in the Middle East and North Africa (MENA) region, has limited renewable water resources and is classified as a water stress country. The effects of climate change are exacerbating the situation. The agricultural sector is the main consumer (80%) of blue water reserves. In this study, to better understand the factors that influence the food water footprint of Tunisian consumers, we used a multiple linear regression model (MLR) to analyze data from 4853 households. The innovation in this paper consists of integrating effects of socio-economic, demographic, and geographic trends on the food consumption water footprint into the assessment of water and food security. The model results showed that regional variations in food choices meant large differences in water footprints, as hypothesized. Residents of big cities are more likely to have a large water footprint. Significant variability in water footprints, due to different food consumption patterns and socio-demographic characteristics, was also noted. Food waste is also one of the determining factors of households with a high water footprint. This study provides a new perspective on the water footprint of food consumption using “household” level data. These dietary water footprint estimates can be used to assess potential water demand scenarios as food consumption patterns change. Analysis at the geographic and socio-demographic levels helps to inform policy makers by identifying realistic dietary changes.

How Does Income Heterogeneity Affect Future Perspectives on Food Consumption? Empirical Evidence from Urban China

Abstract: China is undergoing a rapid dietary transition as well as a changing income distribution. In this paper, we examine the impacts of income heterogeneity on the prediction of food consumption using a dataset that covered 22,210 urban households in China’s 6 provinces. The two-stage Exact Affine Stone Index Implicit Marshallian Demand System (EASI demand system) model, which deals with the problem of censoring and endogeneity, is applied to estimate demand elasticity across income strata. Additionally, a dynamic simulation method considering income heterogeneity is conducted to predict future food consumption trends. The results reveal that income elasticity follows a decreasing trend with income growth. Furthermore, the results show that the consumption of major food items in the 15th period will increase by 7.9% to 42.0% over the base period. The growth potential of low-income groups is significantly higher than that of middle- and high-income groups. However, the prediction results may be overestimated if the differences in consumer behavior across income groups and the dynamic simulation procedure are not taken into account. Our study indicates that the consumption features of different income groups need to be included in food consumption forecasts. Moreover, the government should formulate food policies for different income groups to promote a sustainable food system transformation.