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Climatic changes caused by greenhouse gases (GHGs) emissions are an urgent challenge for all regions around the globe while the livestock sector is an important source of GHGs emissions. The adoption of low-carbon manure treatment technology (LMTT) by farmers is emerging as an effective remedy to neutralize the carbon emissions of livestock. This paper aims to incorporate environmental literacy and social norms into the analysis framework, with the aim of exploring the impact of environmental literacy and social norms on farmers' adoption of LMTT and finally reduce GHGs emission and climate effects.

This research survey is conducted in Hebei, Henan and Hubei provinces of China. First, this research measures environmental literacy from environmental cognition, skill and responsibility and describes social norms from descriptive and imperative social norms. Second, this paper explores the influence of environmental literacy and social norms on the adoption of LMTT by farmers using the logit model. Third, Logit model's instrumental approach, i.e. IV-Logit, is applied to address the simultaneous biases between environmental skill and farmers’ LMTT adoption. Finally, the research used a moderating model to analyze feasible paths of environmental literacy and social norms that impact the adoption of LMTT by farmers.

The results showed that environmental literacy and social norms significantly and positively affect the adoption of LMTT by farmers. In particular, the effects of environmental literacy on the adoption of LMTT by farmers are mainly contributed by environmental skill and responsibility. The enhancement of social norms on the adoption of LMTT by farmers is mainly due to the leading role of imperative social norms. Meanwhile, if the endogeneity caused by the reverse effect between environmental skill and farmers’ LMTT adoption is dealt with, the role of environmental skill will be weakened. Additionally, LMTT technologies consist of energy and resource technologies. Compared to energy technology, social norms have a more substantial moderating effect on environmental literacy, affecting the adoption of farmer resource technology.

To the best of the authors’ knowledge, a novel attempt is made to examine the effects of environmental literacy and social norms on the adoption of LMTT by farmers, with the objective of identifying more effective factors to increase the intensity of LMTT adoption by farmers.

Internet use has become particularly prevalent among adolescents, prompting much thought and concern about both its potential benefits and adverse effects on adolescent learning outcomes. Much of the empirical literature on the impact of Internet use on adolescent learning outcomes is mixed, and few studies examine the causal relationship between the two in rural China. In order to bridge these gaps, we use empirical analysis to investigate the effect of Internet use on the learning outcomes of adolescents in rural China.

We use fixed effect models with samples drawn from a large nationally representative dataset (the China Family Panel Studies—CFPS) to identify the causal impacts of Internet use on the learning outcomes of three cohorts (Cohort A (N = 540), Cohort B (N = 287) and Cohort C (N = 827)) of adolescents in rural China.

The results of the descriptive analysis show a continued increase in the number of adolescents accessing the Internet and the amount of time they spend online. The results of the fixed effect models show that Internet use has positive (in many of the analyses), but mostly insignificant impacts, on the learning outcomes of adolescents. In the sets of results that find significant associations between Internet use and learning outcomes, the measured effects are moderate.

This study investigates the causal relationship between Internet use and adolescent learning outcomes in rural China. The findings claim that there is not a great need to worry about adverse effects of Internet use on adolescent learning development. Attention, however, should focus on seeking ways to improve the positive effects of the Internet use on adolescent learning outcomes. The study will provide a reference and experience for the development of education and the Internet in rural areas and promote the integrated development of urban and rural areas in China.

This paper presents a comprehensive pallet-picking approach for forklift robots, comprising a pallet identification and localization algorithm (PILA) to detect and locate the pallet and a vehicle alignment algorithm (VAA) to align the vehicle fork arms with the targeted pallet.

Opposing vision-based methods or point cloud data strategies, we utilize a low-cost RGB-D camera, and thus PILA exploits both RGB and depth data to quickly and precisely recognize and localize the pallet. The developed method guarantees a high identification rate from RGB images and more precise 3D localization information than a depth camera. Additionally, a deep neural network (DNN) method is applied to detect and locate the pallet in the RGB images. Specifically, the point cloud data is correlated with the labeled region of interest (RoI) in the RGB images, and the pallet's front-face plane is extracted from the point cloud. Furthermore, PILA introduces a universal geometrical rule to identify the pallet's center as a “T-shape” without depending on specific pallet types. Finally, VAA is proposed to implement the vehicle approaching and pallet picking operations as a “proof-of-concept” to test PILA’s performance.

Experimentally, the orientation angle and centric location of the two kinds of pallets are investigated without any artificial marking. The results show that the pallet could be located with a three-dimensional localization accuracy of 1 cm and an angle resolution of 0.4 degrees at a distance of 3 m with the vehicle control algorithm.

PILA’s performance is limited by the current depth camera’s range (< = 3 m), and this is expected to be improved by using a better depth measurement device in the future.

The results demonstrate that the pallets can be located with an accuracy of 1cm along the x, y, and z directions and affording an angular resolution of 0.4 degrees at a distance of 3m in 700ms.

Farm income in the agricultural sector is susceptible to natural and market risks. A large body of literature has studied the effects of cooperative membership on household welfare, technical efficiency, productivity and production behavior, yet little has been known about the impact of cooperative membership on farm income volatility. This paper aims to fill this research gap by investigating the relationship between cooperative membership and farm income volatility of Chinese pig farmers and drawing policy implications.

This paper examines the effect of cooperative membership on farm income volatility, using data from a two-round survey of pig farmers in China. The authors employ an endogenous switching regression model to address the selection bias issues associated with unobserved factors simultaneously affecting farmers' participation in agricultural cooperatives and income earning activities.

Using household panel from a two-round survey of 193 pig farmers in China, this analysis highlights two key findings: (1) agricultural cooperative membership has significant and positive effect on farm income stability and (2) the impact of cooperative membership on farm income stability varies with production scale.

This research makes two contributions to the literature. First, this study contributes to the scant literature exploring the relationship between agricultural cooperatives and farm income stability. Second, to the best of the authors' knowledge, this is the first study that explores such relationship in a livestock sector. The pig sector in China and around the developing world has been increasingly challenged by multifaceted risks (e.g. price fluctuations, epidemic diseases, environmental regulations), and understanding the role of agricultural cooperatives in farm income stability of pig farmers is of great practical and policy significance.

This paper explores the effect of financial support on farmer cooperative development in the Chinese context, aiming to evaluate the effectiveness of public inputs and draw implications for the sustainable development of cooperatives. The variance of the effect in different sectors, i.e. crop, forestry, husbandry, fishery and services, is investigated.

Provincial-level panel data from 2007 to 2017 are used for this study. A linear dynamic panel regression model is estimated using multiple estimation methods, i.e. the generalized method of moments (GMMs), fixed-effect model and ordinary least squares (OLS) are applied.

The empirical analyses indicate that the role of the government is important for the development of farmer cooperatives but limited in some specific aspects. First, the coverage of financial support is positively associated with the growth of cooperative population and membership size, but the strength of financial support, measured by the total amount of financial support divided by local agricultural gross domestic product (GDP), has no statistically significant effects on the development of cooperatives. Second, financial support does not exhibit significant influence on the revenue of cooperatives. Third, the magnitude of the effect of government support on cooperative development is heterogeneous across different sectors.

The research study adds to the institutional economics literature on the association between institutional environment and organization development by focusing on a particular and an important type of organization, i.e. farmer cooperatives. It is one of the attempts and a most extensive study to empirically investigate the role of financial support in the development of farmer cooperatives.

The purpose of this paper is to study the effects of introducing a health insurance program in rural China between 2004 and 2006, the New Cooperative Medical Scheme (NCMS).

The authors apply difference in difference and propensity score matching methods (PSM-DID) to a widely used panel dataset, the China Health and Nutrition Survey (CHNS). Findings are robust across several treatment and comparison groups used in previous NCMS studies.

Households who participated in NCMS increased the use of preventive services and western medicine, while lowering the use of traditional Chinese medicine. NCMS also reduced hospital use, out of pocket payments, travel time to healthcare facilities and waiting time to see doctors. The authors estimate that reductions in travel and waiting time saved roughly 52m U.S. dollars in 2006.

Previously divergent findings on health insurance effects may be due to researchers studying health insurance across different healthcare delivery systems. In addition, in estimating how health insurance access affects healthcare costs, the authors should consider economic costs related to the time needed to access health services.

The authors study how health insurance access affects patients' choice of providers and economic costs to accessing health care services, outcomes that have not received much attention previously. The authors depart from previous NCMS studies by comparing several different approaches to identifying treatment and control groups when applying PSM-DID.