The Chinese Communist Party and regulatory transparency in China's food industry.

While it is widely accepted that the Chinese Communist Party (CCP) occupies a dominant position in the Chinese political system, few studies have demonstrated CCP's dominant position based on rigorous statistical analysis. Our paper presents the first such analysis using an innovative measure of regulatory transparency in the food industry across nearly 300 prefectures in China over 10 years. We show that actions by the CCP, while broadly scoped and not targeting the food industry, significantly improved regulatory transparency in the industry. In sharp contrast, food-industry-specific interventions by the State Council, which exercises direct regulatory supervision of the industry, had no impact on regulatory transparency. These results hold in various specifications and robustness checks. Our research contributes to research in China's political system by empirically and explicitly demonstrating the dominating power of the CCP.

Leveraging machine learning to assess market-level food safety and zoonotic disease risks in China.

While many have advocated for widespread closure of Chinese wet and wholesale markets due to numerous zoonotic disease outbreaks (e.g., SARS) and food safety risks, this is impractical due to their central role in China's food system. This first-of-its-kind work offers a data science enabled approach to identify market-level risks. Using a massive, self-constructed dataset of food safety tests, market-level adulteration risk scores are created through machine learning techniques. Analysis shows that provinces with more high-risk markets also have more human cases of zoonotic flu, and specific markets associated with zoonotic disease have higher risk scores. Furthermore, it is shown that high-risk markets have management deficiencies (e.g., illegal wild animal sales), potentially indicating that increased and integrated regulation targeting high-risk markets could mitigate these risks.

Drivers of Farmers' Intention to Use the Digital Agricultural Management System: Integrating Theory of Planned Behavior and Behavioral Economics.

China's fishery industry has national and international relevance whose aquaculture production accounts for more than 60 percent of the world's total aquaculture production. But the average amount of pesticides used per hectare in China is roughly five times of the world average. The abuse of chemical fertilizers and drugs has brought chronic, long-term, and cumulative harm to both human beings and environment. The digital agricultural management system should be adopted to reduce non-negligible environment pollution and the quality and safety risks of aquatic products. So, it is essential to understand the factors that may influence the adopting intention of this digital management approaches. The present study aimed to examine the adopting intention of farmers toward the digital agricultural management system using two theories-the theory of planned behavior (TPB) and the behavioral economics-as the research framework. The population was composed of farmers in the provinces of Guangdong province in south China of whom 219 farmers were sampled with stratified random sampling technique. Structural equation modeling was used to analyze the data, and it was revealed that this research framework could potentially predict intention. And we observed that the two biased belief of availability bias and loss aversion bias can be the main predictive influence factors of responsible behaviors in adopting the digital agriculture management system, which highlights the importance of framing recommendations in terms of losses rather than gain may be more effective to increase farmers' intention to adopt the digital system on their farms.