Environmental regulation is essential to promote green and sustainable development in dairy farming. Nevertheless, limited studies have focused on the impact of environmental regulation on the green total factor productivity (GTFP) of dairy farming. This study measures the GTFP of dairy farming in 27 provinces in China during 2009–2020 using the Slack Based Measure (SBM) model and the Malmquist–Luenberger (ML) productivity index. In addition, random effects and threshold regression models are used to measure the impact of environmental regulations on the GTFP of dairy farming. The results demonstrate the fluctuating growth of the GTFP of dairy farming and that technical efficiency is the primary driver of the GTFP growth. The annual growth rate of GTFP is the highest in large-scale dairy farming (3.27%), followed by medium-scale dairy farming (2.73%) and small-scale dairy farming (1.98%). Furthermore, environmental regulation positively affects the GTFP and has a threshold effect on the GTFP, with the urban–rural income gap as the threshold variable in medium-scale dairy farming and small-scale dairy farming. The impact on the GTFP can be significantly negative if the urban–rural income gap crosses the threshold value. Overall, this study provides some policy recommendations for attaining green and sustainable dairy farming development in China.

A comprehensive understanding of current Chinese public attitudes toward farm animal welfare and the relevant influencing factors is essential for improving farm animal welfare and promoting further development of animal husbandry. The attitudes of 3,726 respondents in China were investigated using paper and online questionnaires. Three components (affective, cognitive, and behavioral) of attitudes toward farm animal welfare were assessed using 18 items designed based on the literature review. Influential factors of attitudes toward farm animal welfare were explored via tobit regression. The results revealed that the Chinese public not only considers farm animals to be emotional and sentient but are also sympathetic toward farm animals that suffer inhumane treatment. Although they have limited knowledge about farm animal welfare, the public believes improving farm animal welfare is beneficial, especially for food safety and human health. The Chinese public prefers regulation policies to incentive policies for improving farm animal welfare. The main factors influencing attitudes toward farm animal welfare included gender, age, education, monthly household income, area of residence, farm animal raising experience, and attention to farm animal welfare events. The effect of these influencing factors on attitudes varied. These findings provide a basis for improving Chinese public attitudes toward farm animal welfare. The implications of formulating and implementing effective policies to improve the Chinese public attitudes toward farm animal welfare were discussed.

Precision livestock farming (PLF) utilises information technology to continuously monitor and manage livestock in real-time, which can improve individual animal health, welfare, productivity and the environmental impact of animal husbandry, contributing to the economic, social and environmental sustainability of livestock farming. PLF has emerged as a pivotal area of multidisciplinary interest. In order to clarify the knowledge evolution and hotspot replacement of PLF research, based on the relevant data from the Web of Science database from 1973 to 2023, this study analyzed the main characteristics, research cores and hot topics of PLF research via CiteSpace. The results point to a significant increase in studies on PLF, with countries having advanced livestock farming systems in Europe and America publishing frequently and collaborating closely across borders. Universities in various countries have been leading the research, with Daniel Berckmans serving as the academic leader. Research primarily focuses on animal science, veterinary science, computer science, agricultural engineering, and environmental science. Current research hotspots center around precision dairy and cattle technology, intelligent systems, and animal behavior, with deep learning, accelerometer, automatic milking systems, lameness, estrus detection, and electronic identification being the main research directions, and deep learning and machine learning represent the forefront of current research. Research hot topics mainly include social science in PLF, the environmental impact of PLF, information technology in PLF, and animal welfare in PLF. Future research in PLF should prioritize inter-institutional and inter-scholar communication and cooperation, integration of multidisciplinary and multimethod research approaches, and utilization of deep learning and machine learning. Furthermore, social science issues should be given due attention in PLF, and the integration of intelligent technologies in animal management should be strengthened, with a focus on animal welfare and the environmental impact of animal husbandry, to promote its sustainable development.

Despite the current rapid growth of organic agriculture, the problem of low demand for organic agricultural products persists in China, and the consumption space warrants improvement. Exploring consumers’ preferences for organic agricultural products and increasing their purchase intentions are of utmost significance to promote organic agricultural production. Thus, this study takes organic milk, which accounts for 58% of China’s organic processed agricultural products in sales, as the research object, and uses a choice experiment to investigate the influence of consumers on the purchase intention of organic milk under the intervention of environmental protection information and quality and safety information. The main research results revealed that both environmental protection information and quality and safety information have significantly increased consumers’ willingness to purchase and that quality and safety information has increased more than environmental protection information.

There is a large bias between consumers’ perception of food safety risks and the actual state of food safety. Accurate measurements of consumers’ perceived bias of food safety risk provide a scientific basis for the government to improve food safety risk communication measures. Based on the random sample of 559 consumers obtained by the scenario simulation experiment on domestic infant formula, consumers’ perceived bias of the safety risk of domestic infant formula was accurately measured with a principal component analysis and a multidimensional model. The results show that consumers’ perceived bias of the safety risk of domestic infant formula includes physical-performance risk, financial-time risk, and psychological risk. The physical-performance risk perception bias is the highest, followed by psychological risk perception bias and financial-time risk perception bias. There are significant differences in the perception bias of the safety risk of domestic infant formula among consumers with different demographic characteristics. The Chinese government could adjust consumers’ perceived bias of the food safety risk by establishing a food safety risk communication mechanism, strengthening the popularization of food safety knowledge, and preventing and managing food safety rumors.