Water scarcity, land pollution, and global warming are serious challenges and crises facing the development of sustainable or green agriculture and need to be addressed using efficient and environmentally friendly management strategies. This paper proposed an integrated framework appropriate for agricultural green total factor productivity (AGTFP) assessment coupled with microscopic and mesoscopic perspectives under water-energy-food (WEF) nexus, which generated scientific and reasonable strategies for green and low-carbon agriculture from internal core factors and peripheral environmental impacts to improve green agricultural production sustainability. Taking the Lianshui irrigation district (LID) with three sub-areas as the object, internal core factors were explored by partial least squares regression (PLSR) and the external impact path through partial least squares structural equation modeling (PLS-SEM). Results indicated that AGTFP in LID was the smallest (0.818) compared to the three sub-areas and was in a fluctuating state. Meanwhile, AGTFP which was calculated considering undesirable outputs, was closer to tangible productivity. Resource endowments and technical facilities will promote agricultural production, desirable outputs will stimulate green production, and undesirable outputs can inhibit green production. The external influence pathway was shown to be primary environment - > secondary environment - > economic aspects - > social aspects - > AGTFP. The innovative perspectives presented in this study can facilitate preferable decisions and avoid unintended consequences for human-natural systems.

Exploring the effect and mechanism of farmland transfer on agricultural green total factor productivity (AGTFP) in China is of great significance for exerting the effectiveness of China\'s farmland transfer policy and promoting green agricultural development. Based on panel data from 30 provinces from 2005 to 2020, this paper applies a two-way fixed effects model to analyze the impact of farmland transfer on AGTFP, and the mechanism of farmland transfer on AGTFP is also investigated. We find that farmland transfer has a significant and sound promoting effect on AGTFP, with respect to multiple robustness checks; there is heterogeneity regarding the impact of farmland transfer on AGTFP in terms of food functions, and farmland transfer can promote regional AGTFP through nonagricultural labor transfer and agricultural technology utilization. When considering the fact that farmland transfer has increased China\'s AGTFP, the Chinese government should continue to adhere to the farmland transfer policy, accelerate nonagricultural labor transfer, improve the level of agricultural technology utilization, and ultimately promote green agricultural development.

The Chinese government has proposed a \"double carbon\" target as a response to climate change and has been incorporated in the vision 2035. Agriculture\'s green development is intrinsically related to financial support, but conventional financial mechanisms fall short in their efforts to foster economic growth and curb environmental degradation. The current research examines the theoretical effects of green finance on agricultural green total factor productivity in the context of \"double carbon.\" The mediating effect and moderating effect models are used to empirically examine this mechanism based on the panel data (2015-2019) of 30 provinces in China. The estimated results suggest that green finance development can significantly increase agricultural green total factor productivity, with an inverted U effect and an apparent regional heterogeneity. The eastern region has a stronger promotional effect than the central and western regions. The advanced industrial structure plays a mediating effect on the impact of green finance on agricultural green total factor productivity. Rural human capital can effectively mediate the mediating effect of advanced industrial structure on the impact of green finance on agricultural green total factor productivity. The aforementioned results offer fresh perspectives and empirical evidence for China\'s green finance policy improvement, harmonizing regional green finance development, promotion of industrial structure improvement, and rural human capital optimization.

Large-scale agricultural operations number among the ways to promote the green development of the agricultural sector, which can not only encourage farmers to adopt green innovative technology, reduce the input of chemical fertilizers and pesticides, and achieve environmental protection, but it also enables production with a high efficiency through an economy of scale and an improvement in farmers\' income. Based on the agricultural panel data of 30 provincial administrative regions in China from 2000 to 2019, the panel autoregressive distribution lag model was used to explore the dynamic relationship between a business\' scale, financial support, and agricultural green total factor productivity (AGTFP). The empirical outcomes indicate that there is a significant cross-sectional dependence, cointegration relationship, and long-run relationship between the scale of agricultural operations, financial support for agriculture, and AGTFP. Strengthening the intensity of financial support for agriculture is not conducive to improving AGTFP. On the contrary, increasing the scale of agricultural operations could promote AGTFP. In addition, the panel Granger causality test results indicate that financial support for agriculture has a unidirectional causal relationship with the scale of agricultural operations and AGTFP. The impulse response results demonstrate that reducing part of the financial support for agriculture or increasing the scale of operation can promote AGTFP. These conclusions have a long-term practical significance for agricultural departments and decision-making regarding financial distribution.

As a favorite means to promote the development of green agriculture, agricultural insurance can not only encourage farmers to adopt green production technology and improve production efficiency, but also achieve the purpose of reducing the input of chemicals to protect the environment. This article aims to study the dynamic relationship between agricultural insurance, air pollution, and agricultural green total factor productivity using the panel vector auto-regressive method (PVAR) and panel data of 30 provinces in China from 2005 to 2018. The empirical results show that there is a significant cross-sectional dependence and the co-integration relationship between agricultural insurance, air pollution, and agricultural green total factor productivity. The increase in agricultural insurance can improve agricultural green total factor productivity and aggravate air pollution to a certain extent. However, serious air pollution does not improve agricultural green total factor productivity. Panel Granger causality test results show that agricultural insurance has a one-way causal relationship with green total factor productivity and air pollution, and so does air pollution with agricultural green total factor productivity. In addition, impulse response results show that increasing agricultural insurance or reducing air pollution can improve agricultural green total factor productivity. These conclusions have long-term practical implications for both agricultural policymakers and environmental managers.

Agricultural sector is the basic industry providing food for human and supporting the development of national economy. The agricultural green total factor productivity (AGTFP) plays a significant role in coordinating agriculture sustainable development and pollution abatement. We employ a nonseparable undesirable output modified three-stage data envelopment analysis to evaluate the AGTFP of China\'s 30 provinces from 2000- 2018. We construct a more comprehensive AGTFP measurement indicator system, including seven separable inputs and three non-separable inputs and one separable good output and two non-separable bad outputs. The empirical results demonstrate that it is necessary to run a stochastic frontier analysis to eliminate the influence of random error and external environment. We get a more scientific and accurate results. The real AGTFP experiences an increase trend during the sample. From the spatial perspective, there is an obvious regional difference across the country. Efficiency decomposition indicates that the source of inefficiency is mainly from two undesirable outputs. Therefore, policy implications are put forward.