The improvement of agricultural green total factor productivity (AGTFP) is crucial to achieve sustainable agricultural development. By matching China’s provincial digital financial inclusion index and agricultural production data from 2011 to 2020, and on the basis of using the DEA–Malmquist productivity index to measure AGTFP, the fixed effect model and Mesomeric effect model are used to empirically test the impact and mechanism of digital financial development on China’s AGTFP. Our research found that from 2011 to 2020, China experienced consistent improvement in AGTFP, which was largely attributed to advancements in technology. Interestingly, the AGTFP in non-major grain-producing areas surpassed that in major grain producing areas. Additionally, digital finance has proven to be an effective tool in boosting China’s AGTFP, the coverage subdimension, the depth of use subdimension and the digitization subdimension all significantly promote the AGTFP. Digital finance can significantly promote the efficiency and progress of agricultural green technology, which shows that digital finance promotes AGTFP in a “dual wheel” driven manner. However, the impact of digital finance on agricultural green technology efficiency and progress is more pronounced in major grain-producing areas than in non-major areas. The impact mechanism demonstrates that digital finance has the potential to stimulate AGTFP in two key ways. First, it can improve the mismatch of agricultural production resources; second, it can promote agricultural technology innovation. Therefore, it is necessary to further promote the rapid development of digital finance, optimize the rational allocation of financial resources, and formulate tailored digital finance development strategies to promote green development of agriculture.

Sustainable production is considered as an important approach to solve the dilemma of food insecurity. Green technologies have made contributions to improving food production and reducing environmental pollution. Studying the effects of green technologies on sustainable food production has great significance. The paper started with the influence mechanism of green technology application on the green total factor productivity of grain (GTFPG). With the GTFPG, green technology efficiency change of grain (GECG) and green technical progress change of grain (GTCG) measured, threshold models were constructed to explore the nonlinear impacts of various green technologies on GTFPG and the influence paths. Results indicated that the differences of GTFPG among provinces in China were decreased mainly due to the changes of GTCG, while the regional differences of GECG remained small. The impacts of green technologies had threshold effects that depended on the ecological effects of green technologies in different application stages, and were significantly different in the major and non-major grain producing areas. Meanwhile, significant differences existed in the influence paths of green technologies. In the major grain producing areas, green technologies were more likely to improve GTFPG through the GTCG path; while in the non-major grain producing areas, the GECG path and the GTCG path were both important to improve GTFPG. The differences of green technologies’ threshold effects and influence paths in the major and non-major grain producing areas were caused by regional technology preference, resource endowment and technology compatibility. This study emphasizes that the development of green technologies should fully consider the resource endowment and economic development of different regions, as well as the applicability and adoption rate of green technologies.

Accurate measurement of agricultural total factor productivity (AGTFP) is crucial to measure the level of sustainable agricultural development, and agricultural carbon sink is an important element to leverage the development of green transformation. Few studies have incorporated agricultural carbon sink into the measurement framework of AGTFP, and the evolutionary dynamics and related spatial effects of Chinese AGTFP from the perspective of carbon sinks are unclear. On this basis, the paper used a provincial-level agricultural panel data set of China from 2000 to 2019 to measure the provincial indicators of agricultural carbon sinks, CO₂ emissions and agricultural non-point source pollution. Then, we incorporated these environmental factors into the measurement framework of AGTFP and used the SBM-DEA model to calculate the Chinese AGTFP from the perspective of carbon sinks. We further analyzed the spatial and temporal divergence and convergence of AGTFP in China using Moran’I and spatial econometric models. We found that after measuring AGTFP, including agricultural carbon sinks, 28 out of 30 Chinese provinces showed an increased trend, but the development gap between regions was obvious. The spatial econometric model showed a significantly positive spatial correlation between the AGTFP of each province and did not have absolute α-convergence and absolute β-convergence characteristics. After adding the control variables of resource endowment of each province, it showed conditional β-convergence characteristics, and the spatial spillover effect of China’s AGTFP was increasing. Finally, the paper proposed policy recommendations for the sustainable and coordinated development of China’s agricultural regions in response to the research findings.

The carbon trading system affects all aspects of the economy and society profoundly. Agriculture, as a high-carbon-emitting industry, has been hard-hit. China’s agricultural activities will emit about 820 million tons of carbon dioxide equivalents, accounting for 7% of the country’s total carbon emissions. In order to develop a green and low-carbon economy and control greenhouse gas emissions, China officially launched the pilot carbon emissions trading policy in 2013. The effects and mechanism of this on agricultural carbon emissions are still unclear. Herein, this paper uses China’s provincial panel data from 2000 to 2019 to measure agricultural green total factor productivity regarding the implementation of China’s carbon emissions trading pilot policy in 2013 as a quasi-natural experiment, and uses PSM-DID robustness analysis to evaluate the effect of China’s carbon emission rights trading pilot policy on agricultural green total factor productivity in pilot areas. The propensity score method is a type of statistical method that uses nonexperimental or observational data for intervention-effect analysis, which reduces the effects of bias and allows for more reasonable comparisons between treatment and control groups. “Difference in difference” is an approach to policy-effect evaluation based on a counterfactual framework to assess the change in the observed factors in both cases of policy occurrence and nonoccurrence. PSM-DID is a combination of PSM and DID using the PSM method to match each treatment group sample to a specific control group sample, which can solve the problem of self-selection bias in the DID method and assess the policy implementation effect more accurately. This study found that China’s carbon emissions trading pilot policy has significantly improved China’s agricultural green total factor productivity. Further impact mechanism tests show that China’s carbon emissions trading pilot policy will improve agricultural green total factor productivity through environmental protection policies and technological innovation. Finally, this paper puts forward corresponding countermeasures and suggestions based on the research results.

Leveraging the transformative potential of digital technologies to support the farmers’ green production transformation (FGPT) is a critical catalyst for facilitating the accelerated green transformation and upgrading of agricultural systems, thereby achieving high-quality agricultural development. Using survey data from major watermelon- and muskmelon-producing regions in Henan, Shandong, and Xinjiang, and employing Ordinary Least Squares (OLS), Propensity Score Matching (PSM), and the Logit model, this study examines the mechanisms through which digital technology influences FGPT. The findings reveal that the adoption of digital technology significantly reduces farmers’ use of pesticides and fertilizers, with a particularly pronounced effect on pesticide reduction. Heterogeneity analysis indicates that the impact of digital technology on reducing pesticide and fertilizer usage varies across regions: while its adoption leads to significant reductions in Henan and Shandong, the positive effects in Xinjiang remain statistically insignificant. Mechanism analysis shows that digital technology enhances FGPT by reducing information asymmetry, improving market access, and enhancing precision management practices. Based on these findings, this study recommends expanding awareness campaigns to accelerate the adoption of digital technology, enhancing digital infrastructure to bridge the urban–rural digital divide, and optimizing digital technology promotion systems. These actions can be taken alongside the implementation of economic incentives and compensation mechanisms. The insights and policy recommendations from this research provide valuable guidance for China and other countries seeking to leverage digital technology for sustainable agricultural development.

The China–Tanzania Village-based Learning Center for Poverty Reduction project aims to demonstrate China’s experience in poverty reduction through developing smallholder agriculture at the village level, and through the promotion of improved technologies with the purpose of increasing agricultural productivity and improving village-level agricultural production. However, to promote technology application effectively, a better understanding of farmers’ behavioral intention toward improved maize technology is needed. This study uses microdata from 282 Tanzanian maize farmers. Compared with previous research, the innovation of our study is that the methods of structural equation model (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) are applied to study the influencing factors of farmers’ intention to adopt improved maize technology and the combination paths that affect farmers’ intention. The analysis results show that farmers’ behavior perceptions and compatibility perceptions positively impact their intention to choose agricultural technology. Three modes can trigger farmers’ intention to adopt technology: “behavior perception, compatibility perception, non-self-efficacy, facilitation conditions,” “behavior perception, social impact, non-self-efficacy, facilitation conditions,” and “compatibility perception, social impact, self-efficacy, facilitation conditions.” To increase maize yield and promote the extension of improved agricultural technology through the China–Tanzania Village-based Learning Center for Poverty Reduction project, it is necessary to choose an effective combination path to influence farmers’ intention to adopt the proposed changes. If farmers’ intention to adopt improved maize technology can be increased to a greater extent, it can accelerate the improvement of agricultural technology in Tanzania, thereby increasing agricultural productivity, improving agricultural production at the village level, and reducing poverty.