From Attitude to Behavior: The Effect of Residents' Food Waste Attitudes on Their Food Waste Behaviors in Shanghai.

Preventing food waste is important. Analyzing the effects of food waste attitudes on urban residents' waste production behaviors is necessary to reduce food waste. As Shanghai is a mega-city with a population of 24 million people, once food is adequately supplied, more attention is paid to the safety of food in terms of quantity, quality, and nutrition. COVID-19 gave a shock to the food supply in Shanghai, which in turn resulted in food waste behavior. The moderating effect of pandemic during the COVID-19 is one that has rarely been mentioned in previous studies. An investigation of urban residents was conducted in Shanghai. A total of 1030 valid questionnaires were collected in October 2020. This study examined the influence of food waste attitudes on food waste behavior under the moderating effects of pandemic expectations, quantity safety, quality safety, and price stability using a multivariate ordered probit model. The results show that food waste attitudes had a significant negative effect on food waste behavior at a p < 0.01 level of significance, which means that the more people feel shame about food waste, the less food waste they will have. The interaction coefficient between food waste attitudes and pandemic expectations was positive at a p < 0.1 level of significance. This paper concludes with strategies for dealing with food waste in the future.

The Association of Socio-Economic Factors and Indigenous Crops on the Food Security Status of Farming Households in KwaZulu-Natal Province.

Indigenous crops have been proposed as part of a solution for household food security and sustainable farming systems. However, they have been overlooked and underutilised by households and farmers despite their potential contribution to household food security. The objective of this paper was to determine the association of socio-economic factors and indigenous crops with the household food security of farming households. About 260 farming households were selected using a simple random sampling procedure. The food security status was measured through the use of the Household Food Insecurity Access Scale (HFIAS). The Chi-square test and extended ordered probit regression model assessed the relationship of socio-economic factors and indigenous crops with household food security status. The results from the HFIAS showed that farming households were largely in the mildly and moderately food-insecure categories, with 34.2% and 36.2% of the sampled farmers found in these categories, respectively. The Chi-square test showed a statistically significant relationship between food security status and socio-economic factors. Young men and everyone were perceived to be the ones likely to consume indigenous crops. Consumption of indigenous crops was perceived to be associated with food security. The results also showed that farming experience is likely to positively contribute to the food security status of the farming households. Selling indigenous crops in a formal market is perceived to be associated with food security compared to selling in an informal market. This study concludes that consuming indigenous crops is likely associated with improved food security. Identifying an appropriate market for sales of indigenous crops is imperative. Government, extension officers, and nutritionists must conduct training workshops to encourage households to grow, market and buy indigenous crops. Government and policymakers need to include indigenous crops in the national food and nutrition security policy and create formal markets for indigenous crops.

Preferences for index-based crop insurance in South Africa.

The main reason cited for the low participation rate in indexed insurance in Africa is the lack of farmers' involvement in the initial conception. The main aim of this article was, therefore, to design an improved participatory methodology that would help farmers in South Africa gather information on the value they place in new crop insurance initiatives based on weather indices. The main objective of this study was to determine their willingness to pay (WTP) crop insurance in the Eastern Cape. The study adopted a cross-sectional design, and the respondents were selected by using a probability sampling procedure. A sample of 150 respondents was selected. The results from descriptive statistics showed that 62% of the smallholder irrigation scheme farmers were between 52 and above years of age, male and female: 87% and 13%, respectively. The results from the probit model revealed that out of nine variables included in the model, only four namely, logAge square, gender, marital status and age were significantly influencing the WTP index-based crop insurance at 1%, 5% and 10%, respectively. However, age was found to reduce the chances of participating in crop-based insurance. A large proportion of those who were willing to pay insurance had not previously experienced losses because of natural hazards. Therefore, it recommended that the government should use existing extension officers to educate the farmers and promote the importance and benefits of insurance products. Contribution: The results are expected to contribute to the local government to improve disaster resilience through strengthening regional financial funding.

Gen Z's Willingness to Adopt Plant-Based Diets: Empirical Evidence from Greece, India, and the UK.

Comprising the largest population cohort on this planet, Gen Z presents a future-oriented consumer segment driven by climate change and food. This study sought to investigate Gen Z's perceptions toward plant-based foods and diets and explore the relationship that attitude components, meal preparation involvement, personal and lifestyle factors, and perceived barriers in adopting a plant-based diet have with willingness to adopt green-eating practices. Using cross-sectional data from university students in Greece, India, and the UK, various tools were employed to determine the factors influencing youths' consumer behavior toward animal-protein substitutes. PCA indicated the underlying dimensions of students' viewpoints on plant-based foods, whereas hierarchical and k-means clustering provided the cluster structure. An ordered probit model was estimated to delineate Gen Z's willingness to adopt plant-based diets and distinguish among mostly unwilling, somewhat willing, and mostly willing youths. Our findings identified two consumer segments, namely proponents and opponents of plant-based foods and diets, with statistically significant differences in the perceived health benefits of plant-based diets, attachment to animal-based proteins, perceived exclusion of animal-based foods, dissatisfaction with plant-based foods' attributes, and demand for ensuring adequate protein intake. The ordered probit model estimates showed that there is a "homogeneity" in the factors influencing youths' intention to adopt plant-based diets, with attitude components, meal preparation indicators, perceived barriers to eating "green", and personal factors, such as self-assessed knowledge of healthy eating and physical activity, being strongly associated with students' willingness to switch to plant-based diets in all three countries. Mapping potential obstacles and enablers in terms of shifting to more green-eating behaviors, our findings could add information to better understand the factors affecting food choice and youths' transition to a more sustainable lifestyle.

Bayesian inference for multivariate probit model with latent envelope.

The response envelope model proposed by Cook et al. (2010) is an efficient method to estimate the regression coefficient under the context of the multivariate linear regression model. It improves estimation efficiency by identifying material and immaterial parts of responses and removing the immaterial variation. The response envelope model has been investigated only for continuous response variables. In this paper, we propose the multivariate probit model with latent envelope, in short, the probit envelope model, as a response envelope model for multivariate binary response variables. The probit envelope model takes into account relations between Gaussian latent variables of the multivariate probit model by using the idea of the response envelope model. We address the identifiability of the probit envelope model by employing the essential identifiability concept and suggest a Bayesian method for the parameter estimation. We illustrate the probit envelope model via simulation studies and real-data analysis. The simulation studies show that the probit envelope model has the potential to gain efficiency in estimation compared to the multivariate probit model. The real data analysis shows that the probit envelope model is useful for multi-label classification.

Probability and severity of climate change threats to natural world heritage sites vary across site specifics and over time.

This study contributes a first comparison of current and potential threats to Natural World Heritage Sites from climate change, as assessed by experts, when site and location characteristics (size, year of inscription to the World Heritage list, continent, climate zone and kind of site) are controlled for. The probability of a threat as well as its intensity is analysed. Another novelty lies in the use of data from the IUCN Conservation Outlook Assessment, covering all 245 Natural and Mixed World Heritage Sites across the world for three points in time: 2014, 2017 and 2020. The threat of climate change is broadly defined and includes temperature extremes, rising temperatures, disappearing glaciers, coral bleaching, droughts, desertification, and rising sea levels. Results based on a simultaneous Probit model with random effects show that the probability of actual and potential climate change threats increases over time, but with differences for size, kind of site and location. The probability that a threat is identified is highest for marine and coastal sites, and for those in Latin America, while it is significantly lower for sites on the African continent. Larger sites have a higher probability of being assessed as at risk and the severity of threats is found to be lower for recently inscribed sites. The rate at which the likelihood of a threat assessment increases is consistent for both current and future situations, while the probability of the most severe threat is larger for the current than the future period. A serious threat from climate change is assessed as highest for locations in the tropical monsoon (current period) or the tropical savannah climate (future period). Estimations also show that pure descriptive statistics or bivariate correlations may not correctly identify the risk or the dignity of a threat.

Bivariate ordered probit modelling of motorcycle riders and pillion passengers' injury severities relationship and associated risk factors.

This study simultaneously modelled the injury severity of motorcycle riders and their pillion passengers and determine the associated risk factors. The analysis is based on motorcycle crashes data in Ashanti region of Ghana spanning from 2017 to 2019. The study implemented bivariate ordered probit model to identify the possible risk factors under the premise that the injury severity of pillion passenger is endogenously related to that of the rider in the event of crash. The model provides more efficient estimates by considered the common unobserved factors shared between rider and pillion passenger. The result shows a significant positive relationship between the two injury severities with a correlation coefficient of 0.63. Thus, the unobservable factors that increase the probability of the rider to sustain more severe injury in the event of crash also increase that of their corresponding pillion passenger. The rider and their pillion passenger injury severities have different propensity to some of the risk factors including passengers' gender, day of week, road width and light condition. In addition, the study found that time of day, weather condition, collision type, and number of vehicles involved in the crash jointly influence the injury severity of both rider and pillion passenger significantly.

Innovation and entrepreneurship of Chinese returning migrant workers in their home region.

This study aims to investigate the influence of pre-entrepreneurial career mobility frequency and job changes among returning migrant workers (RMWs) on the innovation level of enterprises, with a particular focus on the mediating role of urban social capital accumulated by RMWs. Using data from the "current entrepreneurial status of RMWs", we applied an ordered probit model to validate our hypotheses. To address endogeneity, a recursive bivariate probit model was used, and stepwise regression combined with bootstrap methods was employed to ascertain the mediating influence of urban social capital. Our results demonstrate a significant positive correlation (P < 0.01) between pre-entrepreneurial career mobility frequency and the innovation level of enterprises among RMWs. However, there's a notable negative correlation (P < 0.01) between experiences as elementary workers and enterprise innovation level, while technical and marketing roles correlate positively (P < 0.01) with enterprise innovation level. Urban social capital mediates the relationship between career mobility and enterprise innovation level, suggesting that career mobility enhance urban social capital accumulation, influencing RMWs' enterprise innovation levels. This empirical evidence holds even after robustness tests. We further observed that RMWs with fewer than three career moves lean towards low level of enterprise innovation, while those with three or more gravitate towards high level of enterprise innovation. This study advances the theory of career mobility and enriches the understanding of enterprise innovation levels. Additionally, it provides critical theoretical insights for prospective RMWs entrepreneurs in strategizing their career mobility and job changes. Policy implications suggest that, in addition to providing entrepreneurial support, the government should facilitate pre-entrepreneurial career mobility channels for migrant workers and consider the transition from rural to urban employment as a socialized ritual for RMWs engaging in entrepreneurship.

A threat to the natural World Heritage site rarely happens alone.

This study contributes an empirical investigation of the likelihood that different external threats to a UNESCO Natural World Heritage Site occur in combination with each other when site characteristics and location are controlled for. For the purpose of the analysis, the World Heritage database and the UNESCO State of Conservation Reports are used and the nine most frequently appearing external threats are identified. These databases include 6852 site-year observations and 3316 threats over the period 1979-2023. The most commonly identified external threats are illegal activities, with eleven percent of all observations and mining with six percent. Transport infrastructure, tourism and visitor pressure are also common threats. Estimation results based on the multivariate Probit (equation system) model demonstrate that there are strong positive correlations between many pairs of the nine external threats. Most apparent are the links between illegal activities and loss of identity, social cohesion, changes in local population and community, water infrastructure (dams) and farming, as well as illegal activities and land conversion. There are also clear links between tourism and infrastructure. This emphasises that the various threats seldom appear in isolation from each other. Results also highlight that the threats have different drivers. Among the determinants, site characteristics and location are the most important ones. The likelihood of threats is highest for Natural Heritage Sites covered by forests or those in marine and coastal areas, Africa as well as the Arab region. It is also possible to identify a general increase in threats over time, although with a diminishing rate of growth towards the period 2015-2019. Contrary to this development and the general downturn in threats during the Covid-19 pandemic period of 2020-2023, pressure from tourism continues to grow. Methodologically, the results emphasize the need for multivariate Probit models when research goes beyond analyses of descriptive statistics and single equation approaches.

Heterogeneity in crash data: A cross comparison between ordered probability model and its variant using crashes at suburban type arterial.

Transportation researchers have long been using the statistical analysis of traffic crash data to create a proactive awareness of traffic safety, make important decisions about the design of vehicles and highways, and develop and implement safe preventive strategies to improve safety. Despite significant progress toward maintaining and analyzing traffic crash data, researchers still encounter several challenges and methodological barriers when conducting statistical analysis. One of these challenges is dealing with the issue of unobserved heterogeneity in crash data. This study uses state-of-the-art methodologies to model the injury severity of traffic crashes that occurred on a specific road segment, namely, a suburban-type road (STR), simultaneously addressing issues related to unobserved heterogeneity in data. Multiple heterogeneity ordered probit models are evaluated against Ohio crash data from the Highway Safety Information System (HSIS). The findings reveal the heterogeneous nature of some variables, such as the nighttime indicator, and demonstrate the distinctive feature of each model to capture the effect of unobserved heterogeneity in analyzing data with such variables. Furthermore, the result helps comprehend the contextual scenarios of crashes at STRs and formulate practical plans to lower the severity of such crashes.

Gender, rainfall endowment, and farmers' heterogeneity in wheat trait preferences in Ethiopia.

Wheat is a vital cereal crop for smallholders in Ethiopia. Despite over fifty years of research on wheat varietal development, consideration of gendered trait preferences in developing target product profiles for wheat breeding is limited. To address this gap, our study used sex-disaggregated survey data and historical rainfall trends from the major wheat-growing regions in Ethiopia. The findings indicated heterogeneity in trait preferences based on gender and rainfall endowment. Men respondents tended to prefer wheat traits with high straw yield and disease-resistance potential, while women showed a greater appreciation for wheat traits related to good taste and cooking quality. Farmers in high rainfall areas seemed to prioritize high straw yield and disease resistance traits, while those in low rainfall areas valued good adaptation traits more highly. Most of the correlation coefficients among the preferred traits were positive, indicating that farmers seek wheat varieties with traits that serve multiple purposes. Understanding men's and women's preferences and incorporating them in breeding and seed systems could contribute to the development of more targeted and effective wheat varieties that meet the diverse needs of men and women farmers in Ethiopia.

Data on the factors driving the decision of rural people to move into the city.

The data set explores the driving factors of migration of rural people to the cities. Primary data were collected purposively from 172 farm households from three upazilas of kishoreganj district in Bangladesh. Among 172 households, 89 households had at least one migrant member and 83 households were without any migrant member. Probit model was used to analyze factors of migration decision at the household level. Data set reveals that various factors motivate the decision of the farm households for their member to move into the city. Among which household head age, number of active male member in the family and value of the household asset holding significantly influence migration decision. Beside econometric analysis, household's perception on different motivating factors of migration was also assessed. Most of the households perceived that too many family members, poor living condition, migrant's family influence and job availability in the city mostly motivate the people for migration into the city along with other driving factors.

Does food safety perception matter to farmers' happiness？ evidence from China.

Although the determinants of individuals' happiness have been widely examined in the literature, little is known regarding whether and how food safety perception affects farmers' happiness. To fill this research gap, this paper examines the impact of food safety perception on happiness among Chinese farmers, utilizing open-access data collected through the Chinese Social Survey project in 2013, 2017 and 2021. This study focuses on Chinese farmers as the research subject, attempting to analyze the "happiness code" from the perspective of food safety, which supplements the literature on happiness and provides reference for protecting the rights of low-income groups and promoting food safety strategies in developing countries. To address sample selection bias, this paper employs the recursive bivariate ordered probit (RBOP) model and conditional mixed process (CMP) method. The results reveal that the perception of food safety exerts a positive and statistically significant impact on farmers' happiness in China. In addition, food safety perception is more important among middle-aged and elderly farmers and among those with higher education. Thus, the policy makers should continue to make up for the shortcomings of rural food safety work and extend regulatory measures to rural areas. They also need to take efforts to strengthen food safety promotion, enhance farmers' safety awareness, and safeguard farmers' "safety on the tongue".

Understanding the characteristics of the household energy transition in a developing country.

Understanding the energy transition process in developing countries is crucial to implement policies to reduce the use of traditional fossil fuel-based energy sources, which increase pollution, worsen global climate change, and negatively impact people's health. Existing literature indicates that households account for approximately 25% of total energy consumption for consumption and production activities. However, understanding the energy consumption patterns of choices of energy sources and their determinants has largely been neglected in the existing literature. As such, this study uses the multivariate probit model to identify the factors influencing household energy choices in Vietnam, using data from 2016 to 2020 from the Vietnamese Households Living Standard Surveys (VHLSS). Our study focuses on how households' multiple occupations, particularly in rural areas, affect energy sources choices. We find that households participate in different agricultural sub-sectors simultaneously, resulting in differential access to multiple energy sources. Households engaging in cultivation, forestry, and animal husbandry activities are more likely to choose coal, fuelwood, and biomass as the primary energy sources for production and daily uses. In contrast, higher household income and household head with higher education level also increases the likelihood of choosing cleaner energy choices for daily uses and production activities. In addition, our empirical findings confirm the validity of the "energy stacking" hypothesis rather than the "energy ladders" hypothesis - two contrasting hypotheses concerning household energy transition in the literature. Policy implications have emerged based on the findings of this paper to enhance the clean energy transition in Vietnam in achieving sustainable economic development and social transformation and her commitment to combating global climate change issues.

Investigating injury severity interaction between urban and urban-rural fringe areas: a grouped random parameters seemingly unrelated bivariate probit approach.

Urban-rural integration is the critical process of urbanization, while during this process there are various corresponding problems generated, safety is the most important one, especially at urban-rural fringe areas due to mixed traffic flow and complicated land use. This study intended to investigate the injury severity interaction between urban area and urban-rural fringe area, and explore the correlation within injury severity levels and heterogeneity attributed to unobserved factors. To address the correlation and heterogeneity issues, a grouped random parameters seemingly unrelated bivariate (SUB) probit model was proposed, in which the SUB probit model addressed the correlation of residuals, while the random parameters model accommodated the heterogeneity due to unobserved factors. By comparing the pooled with random parameters models, the results showed that random parameters SUB probit model performed better than the pooled one, and the dataset collected from 2013 to 2017 in Chengdu, China was adopted to illustrate the proposed model. It is found that crash location, speed limit and age of person injured are significant for injury severity in urban and urban-rural fringe areas, but crash form plays a significant role in urban area while number of persons involved should be paid more attention due to injury severity in urban-rural fringe area. Some empirical suggestions are presented to improve the safety in urban and urban-rural fringe areas.

The public perspective on renewable energy versus nuclear power for carbon neutrality in South Korea.

South Korea has legislated "2050 carbon neutrality" in 2021 and is currently implementing it, and debate is brewing over which to focus on as the main means of achieving it in the power generation sector: renewable energy (RE) or nuclear power (NP). This article aims to collect and analyze data on the public preference for RE versus NP. In a national survey of 1000 people, respondents were first asked which was preferred, RE or NP, and then asked to indicate the preference intensity along a 5-point scale. Of all the respondents, 60.3% preferred RE and 27.7% preferred NP. The preference for the former was about 2.2 times more than that for the latter. However, the intensity of the preference for NP was 1.3 times more than that for RE. Both the two-limit Tobit model and ordered probit model have been applied to analyzing the factors influencing the preference. The effects of some variables on the preference for RE over NP are explained, and implications from this are discussed. The findings can be used as a reference to determine the main means of implementation of carbon neutrality or to increase the public acceptance of the specified means.

Drivers and intensity of adoption of digital agricultural services by smallholder farmers in Ghana.

The penetration of digital technologies to enhance market participation by farmers, and intensify farmers' access to support services such as finance, farm inputs, and agricultural production information is on the rise in developing countries. However, the drivers and intensity of the adoption of these technologies by Ghanaian farmers have received little attention in policy and academic circles. This study analyzed the factors that drive the adoption and intensity of adoption of digital agricultural solutions by smallholder farmers in the Bono East Region of Ghana. The study used a survey questionnaire to collect data from 1199 randomly selected smallholder farmers in 2023. The multivariate probit model and the Heckpoisson regression model were used to analyze the drivers of different digital agricultural solutions and the intensity of adoption of these solutions, respectively. The results show that there is a joint demand for technologies that enhance access to extension services and those that accelerate access to inputs. Market-oriented solutions and agricultural extension solutions exhibited a complementary relationship. In addition to selected socio-demographic factors, the study found that membership in farmer-based organizations, access to credit, and participation in agronomic training increased farmers' propensity to adopt different digital agricultural solutions and increased the number of solutions adopted by farmers. Receiving visits from extension officers reduced the likelihood and intensity of adopting digital agricultural solutions. The results suggest that government and development partners should enhance access to credit and promote capacity development programmes among farmers. This will capacitate them to adopt digital agricultural solutions.

Is the happiness of Chinese truly the highest in the world? The impact of basic public services on happiness.

Based on the survey report by the United Nations Sustainable Development Solutions Network (SDSN) and Ipsos Group, the world ranking of Chinese people's happiness shows a significant gap. This study attempts to analyze the subjective well-being of Chinese residents through public database from the China Household Finance Survey Center in 2017. An ordered Probit model is constructed to investigate the impact of non-monetary factors, specifically basic public services, on the subjective well-being of Chinese people. The results indicate that: (1) The subjective well-being of Chinese residents is found to be lower than what the survey report indicated. (2) Basic public services have a significant positive impact on residents' happiness. (3) Social trust played a moderating role, positively influencing the relationship between basic public services and residents' happiness. (4) The impact of basic public services on happiness varied significantly depending on factors such as age, registered residence, and places of residence. To enhance the happiness of Chinese residents, it is recommended to focus on improving the equalization of basic public services and establishing a robust basic public service system. These measures can effectively contribute to the overall well-being and happiness of the population.

An Exploratory Assessment of Self-Reported Satisfaction with Infrastructure and Out-of-Home Activities for People with Vision Impairments.

PURPOSE: The purpose of this study is to assess the satisfaction levels of people with VI with regard to infrastructure and outdoor activities. Furthermore, this study aims to develop an assessment model for the levels of difficulty in using public transport. METHODS: Participants in a standardized survey questionnaire included 74 participants with VI. Three assessment-ordered probit models were estimated based on self-reported responses. RESULTS: Estimation results revealed that the use of public transport is extremely difficult for 83.47% of older participants. In addition, 84.2% of people with albinism have extreme difficulty using public transport. Furthermore, 53.98% of people with restricted horizontal and vertical fields face extreme difficulty using public transport. There was dissatisfaction with outdoor activities among 97.40% of people with macular disease. The results show that 51.70% of people with normal or near-normal horizontal visual fields and restricted vertical planes are satisfied with their level of outdoor activity while 72.65% of people with retinal diseases expressed dissatisfaction with the existing infrastructure. CONCLUSION: This study revealed that the experiences of people with VI are heterogeneous and depend on their eye condition, access to assistive technology, and socioeconomic characteristics. Results clearly show evidence of heterogeneity among individuals with VI. The combination of horizontal and vertical restrictions yields random parameters, underscoring the heterogeneous experiences of people with VI, influenced by their eye condition and access to assistive devices. Our results have important implications for developing targeted interventions to enhance the mobility of people with VI.

Comparative analysis of pedestrian crash severity at United Kingdom rural road intersections and Non-Intersections using latent class clustering and ordered probit model.

Pedestrian safety is a critical issue in the United Kingdom (UK) as pedestrians are the most vulnerable road users. Despite numerous studies on pedestrian-vehicle crashes globally, limited research has been conducted to explore the factors contributing to such incidents in the UK, especially on rural roads. Therefore, this study aimed to investigate the severity of pedestrian injuries sustained on rural roads in the UK, including crashes at intersections and non-intersections. We utilized the STATS19 dataset, which provided comprehensive road safety data from 2015 to 2019. To overcome the challenges posed by heterogeneity in the data, we employed a Latent Class Analysis to identify homogeneous clusters of crashes. Additionally, we utilized the Ordered Probit model to identify contributing factors within each cluster. Our findings revealed that various factors had distinct effects on the severity of pedestrian injuries at intersections and non-intersections. Several parameters like the pedestrian location in footway and one-way roads are only statistically significant in the intersection section. Certain factors such as the day of the week, the pedestrian's location in a refuge, and minor roads (class B roads) were found to be significant only in the non-intersection section.Parameters includingpedestrians aged over 65 years and under 15 years, drivers under 25 years, male drivers and pedestrians, darkness, heavy vehicles, speed limits exceeding 96 km/h (60 mph), major roads (class A roads), and single carriageway roadsare significant in both sections. The study proposes various measures to mitigate the severity of pedestrian-vehicle crashes, such as improving lighting conditions, enhancing pedestrian infrastructure, reducing speed limits in crash-prone areas, and promoting education and awareness among pedestrians and drivers. The findings and suggested measures could help policymakers and practitioners develop effective strategies and interventions to reduce the severity of these incidents and enhance pedestrian safety.