With the development of agricultural modernization, the carbon emissions caused by the agricultural sector have attracted academic and practitioners’ circles’ attention. This research selected the typical agricultural development province in China, Fujian, as the research object. Based on the carbon emission sources of five main aspects in agricultural production, this paper applied the latest carbon emission coefficients released by Intergovernmental Panel on Climate Change of the UN (IPCC) and World Resources Institute (WRI), then used the ordered weighted aggregation (OWA) operator to remeasure agricultural carbon emissions in Fujian from 2008–2017. The results showed that the amount of agricultural carbon emissions in Fujian was 5541.95 × 10³ tonnes by 2017, which means the average amount of agricultural carbon emissions in 2017 was 615.78 × 10³ tonnes, with a decrease of 13.13% compared with that in 2008. In terms of spatial distribution, agricultural carbon emissions in the eastern coastal areas were less than those in the inland regions. Among them, the highest agricultural carbon emissions were in Zhangzhou, Nanping, and Sanming, while the lowest were in Xiamen, Putian, and Ningde. In addition, this paper selected six influencing variables, the research and development intensity, the proportion of agricultural labor force, the added value of agriculture, the agricultural industrial structure, the per capita disposable income of rural residents, and per capita arable land area, to clarify further the impacts on agricultural carbon emissions. Finally, geographically- and temporally-weighted regression (GTWR) was used to measure the direction and degree of the influences of factors on agricultural carbon emission. The conclusion showed that the regression coefficients of each selected factor in cities were positive or negative, which indicated that the impacts on agricultural carbon emission had the characteristics of geospatial nonstationarity.

Agriculture is increasingly facing major challenges such as climate change, scarcity of natural resources, and changing societal demands. To tackle these challenges, there is a pressing need to evolve towards more sustainable agricultural practices. As a result, sustainability stands among the most relevant topics in agricultural research worldwide, and countries along the Belt and Road (B&R) route are no exception. This paper selected 25 indicators from the five subsystems of population, society, economy, environment, and resources in order to build an evaluation index system of agricultural sustainable development capability, and then it used an improved entropy weight method, technique for ordering preference by similarity to an ideal solution (TOPSIS), and coordination degree method to measure the comprehensive capability and coordination of agricultural sustainable development of all countries along the B&R route from 2006 to 2015. First, according to the time dimension, the comprehensive score of sustainable development capability of agriculture along the B&R route: This had an average annual score of 0.3195 which initially decreased, then increased in a fluctuating manner, before finally falling again. Second, according to the spatial dimension, the average comprehensive score of agricultural sustainable development capability showed an evolutionary trend of ‘high–low–high–low–high’ from west to east, which showed an obvious basic spatial pattern of the ‘W’ type. Third, from the perspective of the subsystems of agriculture, although the coordination degree among subsystems in the main grain-producing areas increased continually from 2006 to 2015, the overall level of development needed to be further improved. In order to further clarify the main factors affecting the capability of agricultural sustainable development, this paper selected six explanatory variables: The level of economic development, financial expenditure for agriculture, agricultural foreign direct investment, agricultural labor force, the intensity of agricultural R&D investment, and the level of agricultural informatization. Then, geographically and temporally weighted regression was applied to evaluate the direction and degree of influences of selected factors on sustainability development capability of agriculture. The results showed that the regression coefficients of each variable in 53 countries were positive or negative, which indicated that the influencing factors of agricultural sustainable development capacity had the characteristics of geospatial nonstationarity.

Agriculture is becoming a substantial contributor to carbon emissions in China, and the efficient reduction of agricultural carbon emissions (ACEs) has become a universal concern. Currently, the digital transformation of agriculture (DTA) provides a new way to reduce carbon emissions. However, questions regarding whether and how DTA suppresses ACEs remain the focus of both academic and practical attention; thus, they serve as the objectives of this study. Based on theoretical analyzes, the carbon emission reduction effect of DTA is empirically analyzed using panel data from 30 Chinese provinces between 2015 and 2021 with a benchmark regression model, mediating effect model and threshold effect model. The results show the following: (1) DTA significantly reduces ACEs. (2) DTA reduces ACEs through three paths: expanding the agricultural production scale, optimizing the agricultural industrial structure, and promoting agricultural technological progress. The agricultural industrial structure has the most considerable mediating effect, while the agricultural production scale expansion has the smallest effect. (3) The inhibitory effect of DTA on ACEs shows very distinct heterogeneity. DTA primarily reduces ACEs in eastern and nongrain production areas yet has an insignificant impact on other areas. In addition, the DTA has a significant inhibitory effect on ACEs from both fertilizers and diesel oil. (4) The inhibitory effect of DTA on ACEs is nonlinear rather than linear. There is a single threshold effect for the effect of DTA on ACEs, which is insignificant below the threshold and significantly negative above the threshold. Therefore, China’s central and local governments need to increase the level of DTA, encourage farmers to moderately expand the scale of their operations, emphasize the agricultural service industry, and enhance agricultural technological innovation to ultimately reduce ACEs.